Bhargav_Solanki_assignment_04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please fill in your name and that of your teammate.\n",
    "\n",
    "You: Bhargav Solanki\n",
    "\n",
    "Teammate: Carmen Prieto Sangros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the fourth lab. This week the math load is lower on purpose to give you a chance to catch up on the first 3 labs before we start to _use_ all the foundations you learned so far. You may want to go back and fix your submissions with the help of the solutions, you will need the material for the exam.\n",
    "\n",
    "This week we introduce the major data analysis library in Python: [`pandas`](https://pandas.pydata.org/docs/). You can think of it as providing **feature-rich data containers**: load your data in a `pandas` object and you will have fast access, manipulation, statistics, even math with `numpy` and plotting with `matplotlib`, all well integrated.  \n",
    "Unfortunately it also has a fame of being frustrating, unintuitive and stubborn for the newcomers, so I suggest you spend some time practicing and download the documentation as [offline pdf](https://pandas.pydata.org/docs/pandas.pdf) for the exam.\n",
    "\n",
    "The two main classes are `Series`, for one-dimensional data, and `DataFrame` for _tensors_. You may have heard this word before: you can think of a tensor as a generic structure for feature-based data. A zero-dimensional tensor is a scalar; a 1D tensor is a vector; a 2D tensor is a matrix; you can imagine a 3D tensor as a cube, or as a list of matrices. Higher dimensions are of course hypercubes.  \n",
    "You will start mostly using `DataFrame`s with 2D tables/matrices, but some methods will return (kind-of) higher-dimensional tensors (e.g. be careful with [`groupby()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html)! Takes time to grasp).\n",
    "\n",
    "This library is best learned hands-on, but before starting it is important to understand how the data is stored and accessed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The feature's perspective\n",
    "\n",
    "Let's look again at our dataset of snakes. It has three fields: `head size`, `length` (in cm) and whether it is `poisonous` or not. It looks like this:\n",
    "\n",
    "```python\n",
    "snakes = [['small', 38, False],\n",
    "          ['small', 62, True],\n",
    "          ['medium', 55, True]]\n",
    "```\n",
    "\n",
    "This form puts the emphasis on the data points (the rows), which is an intuitive approach at first for humans to manually write down the data. We can scroll to the middle of a dataset in a CSV or text file and read one line -- in principle, if the columns are few and we remember their order.  \n",
    "Accessing the data by indices can also be confusing: if you want to know whether the second snake is poisonous you should use `snakes[1][2]`, which is prone to the type of misunderstandings that lead to bugs (e.g. writing instead `[2][1]`).  \n",
    "From the machine perspective this is not an issue, but this way of storing has the distinct (performance) disadvantage of having **multiple types** in the same data structure: here we have lists with strings, floats and booleans.\n",
    "\n",
    "An alternative form to represent the data which is also very common (you already encountered it e.g. for data plotting) gives instead priority to the columns or *features*. It contains the same data, but rows and columns are *transposed*:\n",
    "\n",
    "```python\n",
    "snakes = [['small', 'small', 'medium'],\n",
    "          [38, 62, 55],\n",
    "          [False, True, True]]\n",
    "```\n",
    "\n",
    "As you can see each feature is not in its own list, and all types are the same, meaning we could store it in a specialized array casted to the correct data type, and enjoy a big performance boost.  \n",
    "Moreover, we can now switch to a hash map (a `dict` in Python), allowing us to include the feature name and use them for indexing, making the data and its description self-contained:\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "snakes = {'head_size' : np.array(['small', 'small', 'medium'], dtype='str'),\n",
    "          'length'    : np.array([38, 62, 55], dtype='float'),\n",
    "          'poisonous' : np.array([False, True, True], dtype='bool')}\n",
    "```\n",
    "This has the added advantage that you can now call the vast library of `numpy` methods on your features, such as: `snakes['length'].mean()`. Create a new cell below, copy+paste the code and give it a try!\n",
    "\n",
    "Data access readability is also improved by using explicit feature names: `snakes['poisonous'][1]` does not leave room for misunderstandings, it is the `poisonous` field of the first data point. Writing `snakes[1]['poisonous']` will now raise an error (remember: errors are your friends when debugging, what is dangerous is a silent bug).  \n",
    "One last feature we could add is to add an explicit extra feature called `index` with incremental numbers for the data points, which allows us to explicitly sort the data or access by index as we did before:\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "snakes = {'index'     : np.array([1, 2, 3], dtype='int'),\n",
    "          'head_size' : np.array(['small', 'small', 'medium'], dtype='str'),\n",
    "          'length'    : np.array([38, 62, 55], dtype='float'),\n",
    "          'poisonous' : np.array([False, True, True], dtype='bool')}\n",
    "```\n",
    "Isn't that neat? Congratulations, you just derived a naïve implementation of a Pandas `DataFrame`. Get used to this name because you will be using it all the time over the next weeks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas objects\n",
    "\n",
    "Let's actually convert our data to a `DataFrame` so we can play with it:\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(snakes) # the constructor accept most sensible inputs\n",
    "df.head() # this prints the first few lines of your (potentially large) dataframe\n",
    "```\n",
    "\n",
    "A few tips before we start:\n",
    "- DataFrames are composed of `Series` to hold the features: Series wrap the Numpy array to extend with more methods, so anything you can do with a Numpy array can be done with most Pandas structures, and much more.\n",
    "- Data always has an `index` (actually of class `Index`, a special Series), and if you took a database course you should know why (hint: primary key). You can provide an index explicitly, or it will automatically generate one on creation from a counter.\n",
    "- Accessing data with `[]` (e.g. `df['length']`) does not give direct pointer access at the stored data, but returns a [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write) handler. This means that creation is free, read access is fast, but trying to write or edit the data first generates a _copy_ of the data, and then edits that. The original is unchanged by `[]`. The full truth is actually even more complex, e.g. try creating a new column `df['new_col'] = 5`, this requires no copy)\n",
    "- To access the data directly by column name and index, you should use `df.loc[]` (by index and column) and `df.iloc[]` (by indices). Yes they are both methods but they take square parenthesis (ah, python...). Data access is even more confusing than this, and you will need some serious practice to reliably get Pandas to do what you want. Do not skip on this! I suggest you follow [this tutorial](https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-6fcd0170be9c).\n",
    "- Sometimes you need to access ranges of values. Ranges in Python are also [weird](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html): keep in mind that you can use the explicit `range()` method, or more commonly the `:` format: `start:end:step`. Funny part, all three are optional: `:k` means from the first to the $k^{th}$ element (excluded: the `end` is never included in a Python range, careful); `p:` means from element number `p` until the end; `::2` means (from beginning to end but only) every second element; and most importantly `:` means all elements. This is necessary as `iloc` always requires rows as first argument, so if you want the third column (implicitly: *values of all rows* for the third column) you need to write `df.iloc[:,2]` (remember indices start from 0). Again, complex to grasp, but super easy and obvious once you get it: practice it!\n",
    "- You can access the data by \"conditions\", by generating a boolean array that will hard-select the rows or columns you want. Yeah even more confusing :) bear with me. Try writing `df['length']<60`: this will return a *boolean numpy array*, with values `[True, False, True]`. This is the answer to the boolean question you asked. Now to get the lines of the dataframe that answered True, you need to write `df[df['length']<60]`. Read it and try it until it makes sense before moving forward. Also do yourself a favor and check also the second part of the tutorial above ([boolean indexing](https://www.dunderdata.com/blog/selecting-subsets-of-data-in-pandas-part-2)): all the time you invest now in Pandas will pay dividends later in the harder assignments.\n",
    "- You can plot the dataframe data directly using the function `df.plot()` (takes `type=` as a parameter), which is sometimes useful for a quick peek. In almost every practical case though it is easier (and produces better results) to simply use `seaborn`, because it integrates DataFrames natively! Finally a good news! try `sns.barplot(data=df, y='length', x=df.index, hue='poisonous')`\n",
    "- Most DataFrame methods also return the result of a computation as a \"copy\" or \"view\" on the Pandas object. If you want your method calls to have persistent consequences, which means you want to modify the original data, you should either (i) capture the output in a variable and use that, or (ii) use the optional argument `inplace=True` which is available for most methods and forces modifying the original data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to pass the lab?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below you find the exercise questions. Each question awarding points is numbered and states the number of points like this: **[0pt]**. To answer a question, fill the cell below with your answer (markdown for text, code for implementation). Incorrect or incomplete answers are in principle worth 0 points: to assign partial reward is only up to teacher discretion. Over-complete answers do not award extra points (though they are appreciated and will be kept under consideration). Save your work frequently! (`ctrl+s`)\n",
    "\n",
    "**You need at least 18 points (out of 27 available) to pass** (66%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Fundamentals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now you should be familiar with this. We start easy:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 **[2pt]** Write a small DataFrame with at least 3 of the common problems you can find when dealing with unknown data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you need inspiration you can reuse and expand your cats and dogs dataset from lab. Loading into a dataframe should be straightforward: here is the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html), pass the table as `data=` and the labels as `columns=` to the constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Test\n",
      "0   1.0\n",
      "1   2.0\n",
      "2   NaN\n",
      "3   4.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Barks</th>\n",
       "      <th>Retractable Claws</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height  Barks  Retractable Claws  Weight\n",
       "0      90   True              False      30\n",
       "1      95  False               True      35\n",
       "2      45   True              False      18\n",
       "3      70  False               True      22\n",
       "4      22  False               True       7"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "x = [1,2,None,4]\n",
    "df = pd.DataFrame(data=x, columns=['Test'])\n",
    "print(df)\n",
    "labels = ['Dog', 'Cat', 'Dog', 'Cat', 'Cat']\n",
    "feature_names = ['Height', 'Barks', 'Retractable Claws', 'Weight']\n",
    "pets = [\n",
    "            [90, True, False, 30],\n",
    "            [95, False, True, 35],\n",
    "            [45, True, False, 18],\n",
    "            [70, False, True, 22],\n",
    "            [22, False, True, 7]\n",
    "       ]\n",
    "df = pd.DataFrame(data = pets, columns = feature_names)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 **[1pt]** Explain with your own word why data defects mitigation is important and why it cannot ever be perfect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here \"own word\" means that copying and pasting from the slides will actually fail the question. Feel free to discuss around the concepts if the words come hard, just prove that you got the meaning and that you can talk about it. Write as long as you need to show your skills, but no longer (brevity is also a skill that takes practice)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Why is data defect mitigation important?\n",
    "In simple words, garbage in garbage out. ML learns from the data that it is provided. You build a model with defective data, you will get defective model. So the best bet is to mitigate the defective data. Although, if you put in enough effort talking to experts you may land up with slightly less defective data, but more often the effort put in to clense the defect, is not worth it for the gain. Hence, we prefer to mitigate it.\n",
    "\n",
    "#### Why it can never be perfect?\n",
    "\n",
    "So many things can go wrong. The snesors might not work, there might be time delays, defective instruments. Even in a perfect system, things arent perfect. We will always get some noise. All we can aim for is almost good data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 **[1pt]** Give two reasons why to drop lines with defects rather than fixing them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " *** \n",
    "* [i) Fixed data, may or may not be accurate. So we might feed in the wrong data which is way worse than simply deleting it]\n",
    " * [ii) The time and effort involved in order to fix some data, may not be worth the reward we get in return.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4 **[1pt]** Why does data quality impact learning more than data quantity? Mention one of the algorithms seen so far as an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the professor gave gave the example of baking a cake with 100 litres milk, 100 kg flour but only 5 usable eggs, we will be able to make only 5 eggs worth of cake. Even if we have large quantity of data, but only small quantity of it is usable ( worth 5 usable eggs) then we will only use that data. For eg, in perceptron, if our data was not correctly recorded, we would have not been able to use it to draw a linear seperator correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.5 **[1pt]** Convert (by hand!) a categorical feature from your hand-written dataset into a numerical one using binary encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that you need to rewrite the hand-written dataset, but a previously categorical feature needs to be written in binary encoding.  \n",
    "If your dataset did not have a categorical feature yet, go ahead and simply add one.  \n",
    "IMPORTANT: you may want to go ahead and fix the defects that you introduced earlier, or you may encounter problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[90, 2, False, 30],\n",
       " [95, 1, True, 35],\n",
       " [45, 2, False, 18],\n",
       " [70, 1, True, 22],\n",
       " [22, 1, True, 7]]"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ['Dog', 'Cat', 'Dog', 'Cat', 'Cat']\n",
    "feature_names = ['Height', 'Sound', 'Retractable Claws', 'Weight']\n",
    "pets = [\n",
    "            [90, \"barks\", False, 30],\n",
    "            [95, \"meows\", True, 35],\n",
    "            [45, \"barks\", False, 18],\n",
    "            [70, \"meows\", True, 22],\n",
    "            [22, \"meows\", True, 7]\n",
    "       ]\n",
    "# 10 --> barks\n",
    "# 01 -- meows\n",
    "sounds = {\"barks\": \"10\", \"meows\": \"01\"}\n",
    "for pet in pets:\n",
    "    pet[1] = int(sounds.get(pet[1]),2) # converting Sound here to binary\n",
    "pets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.6 **[1pt]** Consider 5-fold cross-validation: how many models does it train? Now take the first fold: how many of the models that were trained did in fact use this particular fold as part of their training set?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It trains 5 models. 4 models used this fold as training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To simplify the process, here's a CSV string rather than a separate file. You are free to copy this string into a file if you like, the call below simply emulates an Input Output object (such a file) from a String using a `StringIO` object. It is often a useful trick to have on your tool belt.\n",
    "\n",
    "**IMPORTANT**: do not edit the string! The defects and problems are on purpose! It simulates problematic data that was passed to you unclean, potentially too big to read for a human, and you need to use only Pandas to load and clean the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded\n"
     ]
    }
   ],
   "source": [
    "from io import StringIO\n",
    "csv_str = StringIO('sepal_length$sepal_width$petal_length$petal_width$species$is_flower\\n0$5.1$-3.5$1.4$0.2$setosa$True\\n1$-3.0$1.4$setosa$True\\n2$5.0$-3.6$1.4$0.2$setosa$True\\n3$4.6$-3.1$1.5$0.2$setosa$I guess\\n4$4.7$-3.2$1.3$0.2$zetosa$True\\n5$5.4$-3.9$1.7$0.4$setosa$True\\n6$4.6$-3.4$1.4$0.3$setosa$True\\n7$5.0$-3.4$1.5$0.2$setosa$Fals\\n8$4.4$-2.9$1.4$0.2$setosa$True\\n9$4.9$-3.1$1.5$0.1$setosa$True\\n10$5.4$-3.7$1.5$0.2$setosa$I guess\\n11$4.8$-3.4$1.6$0.2$setosa$True\\n12$4.8$-3.0$1.4$0.1$setosa$True\\n13$4.3$-3.0$1.1$0.1$setosa$True\\n14$5.8$-4.0$1.2$0.2$setosa$True\\n15$-4.4$1.5$setosa$True\\n16$5.4$-3.9$1.3$0.4$setosa$True\\n17$5.1$-3.5$1.4$0.3$setosa$True\\n18$5.7$-3.8$1.7$0.3$setosa$True\\n19$5.1$-3.8$1.5$0.3$setosa$True\\n20$5.4$-3.4$1.7$0.2$setosa$True\\n21$5.1$-3.7$1.5$0.4$setosa$True\\n22$4.6$-3.6$1.0$0.2$setosa$True\\n23$5.1$-3.3$1.7$0.5$setosa$Fals\\n24$4.8$-3.4$1.9$0.2$setosa$True\\n25$5.0$-3.0$1.6$0.2$setosa$True\\n26$5.0$-3.4$1.6$0.4$setosa$True\\n27$5.2$-3.5$1.5$0.2$setosa$True\\n28$5.2$-3.4$1.4$0.2$setosa$True\\n29$4.7$-3.2$1.6$0.2$setosa$True\\n30$4.8$-3.1$1.6$0.2$setosa$True\\n31$5.4$-3.4$1.5$0.4$setosa$True\\n32$5.2$-4.1$1.5$0.1$setosa$Fals\\n33$5.5$-4.2$1.4$0.2$setosa$True\\n34$4.9$-3.1$1.5$0.2$setosa$True\\n35$5.0$-3.2$1.2$0.2$setosa$True\\n36$5.5$-3.5$1.3$0.2$setosa$True\\n37$4.9$-3.6$1.4$0.1$setosa$True\\n38$4.4$-3.0$1.3$0.2$setosa$True\\n39$5.1$-3.4$1.5$0.2$setosa$True\\n40$5.0$-3.5$1.3$0.3$setosa$True\\n41$4.5$-2.3$1.3$0.3$setosa$True\\n42$4.4$-3.2$1.3$0.2$setosa$True\\n43$5.0$-3.5$1.6$0.6$setosa$True\\n44$5.1$-3.8$1.9$0.4$setosa$True\\n45$4.8$-3.0$1.4$0.3$setosa$True\\n46$5.1$-3.8$1.6$0.2$setosa$True\\n47$4.6$-3.2$1.4$0.2$setosa$True\\n48$5.3$-3.7$1.5$0.2$setosa$True\\n49$5.0$-3.3$1.4$0.2$setosa$True\\n50$7.0$-3.2$4.7$1.4$versicolor$True\\n51$6.4$-3.2$4.5$1.5$versicolor$True\\n52$6.9$-3.1$4.9$1.5$versicolor$I guess\\n53$5.5$-2.3$4.0$1.3$versicolor$True\\n54$6.5$-2.8$4.6$1.5$versicolor$True\\n55$5.7$-2.8$4.5$1.3$versicolor$True\\n56$6.3$-3.3$4.7$1.6$versicolor$True\\n57$4.9$-2.4$3.3$1.0$versicolor$True\\n58$6.6$-2.9$4.6$1.3$versicolor$True\\n59$5.2$-2.7$3.9$1.4$versicolor$True\\n60$5.0$-2.0$3.5$1.0$versicolor$True\\n61$5.9$-3.0$4.2$1.5$versicolor$True\\n62$6.0$-2.2$4.0$1.0$versicolor$True\\n63$6.1$-2.9$4.7$1.4$versicolor$True\\n64$5.6$-2.9$3.6$1.3$versicolor$True\\n65$6.7$-3.1$4.4$1.4$versicolor$True\\n66$5.6$-3.0$4.5$1.5$versicolor$True\\n67$5.8$-2.7$4.1$1.0$versicolor$True\\n68$6.2$-2.2$4.5$1.5$versicolor$True\\n69$5.6$-2.5$3.9$1.1$versicolor$True\\n70$5.9$-3.2$4.8$1.8$versicolor$True\\n71$6.1$-2.8$4.0$1.3$versicolor$True\\n72$6.3$-2.5$4.9$1.5$versicolor$True\\n73$6.1$-2.8$4.7$1.2$versicolor$True\\n74$6.4$-2.9$4.3$1.3$😉$True\\n75$6.6$-3.0$4.4$1.4$versicolor$True\\n76$6.8$-2.8$4.8$1.4$versicolor$True\\n77$6.7$-3.0$5.0$1.7$versicolor$True\\n78$6.0$-2.9$4.5$1.5$versicolor$True\\n79$5.7$-2.6$3.5$1.0$$$$versicolor$True\\n80$5.5$-2.4$3.8$1.1$versicolor$True\\n81$5.5$-2.4$3.7$1.0$versicolor$True\\n82$5.8$-2.7$3.9$1.2$versicolor$True\\n83$6.0$-2.7$5.1$1.6$versicolor$True\\n84$5.4$-3.0$4.5$1.5$versicolor$True\\n85$6.0$-3.4$4.5$1.6$versicolor$True\\n86$6.7$-3.1$4.7$1.5$versicolor$True\\n87$6.3$-2.3$4.4$1.3$versicolor$True\\n88$-3.0$4.1$versicolor$True\\n89$5.5$-2.5$4.0$1.3$😉$True\\n90$5.5$-2.6$4.4$1.2$versicolor$True\\n91$6.1$-3.0$4.6$1.4$versicolor$True\\n92$5.8$-2.6$4.0$1.2$versicolor$True\\n93$5.0$-2.3$3.3$1.0$versicolor$True\\n94$5.6$-2.7$4.2$1.3$versicolor$True\\n95$5.7$-3.0$4.2$1.2$versicolor$True\\n96$5.7$-2.9$4.2$1.3$versicolor$True\\n97$6.2$-2.9$4.3$1.3$versicolor$True\\n98$5.1$-2.5$3.0$1.1$versicolor$True\\n99$5.7$-2.8$4.1$1.3$versicolor$True\\n100$6.3$-3.3$6.0$2.5$virginica$True\\n101$5.8$-2.7$5.1$1.9$virginica$True\\n102$7.1$-3.0$5.9$2.1$virginica$True\\n103$6.3$-2.9$5.6$1.8$virginica$True\\n104$6.5$-3.0$5.8$2.2$virginica$True\\n105$7.6$-3.0$6.6$2.1$virginica$True\\n106$4.9$-2.5$4.5$1.7$virginica$True\\n107$7.3$-2.9$6.3$1.8$virginica$True\\n108$6.7$-2.5$5.8$1.8$virginica$True\\n109$7.2$-3.6$6.1$2.5$virginica$True\\n110$6.5$-3.2$5.1$2.0$virginica$True\\n111$6.4$-2.7$5.3$1.9$virginica$True\\n112$6.8$-3.0$5.5$2.1$virginica$True\\n113$5.7$-2.5$5.0$2.0$virginica$True\\n114$5.8$-2.8$5.1$2.4$virginica$True\\n115$6.4$-3.2$5.3$2.3$virginica$True\\n116$6.5$-3.0$5.5$1.8$virginica$True\\n117$7.7$-3.8$6.7$2.2$virginica$True\\n118$7.7$-2.6$6.9$2.3$virginica$True\\n119$6.0$-2.2$5.0$1.5$virginica$True\\n120$6.9$-3.2$5.7$2.3$virginica$True\\n121$5.6$-2.8$4.9$2.0$virginica$True\\n122$7.7$-2.8$6.7$2.0$virginica$True\\n123$6.3$-2.7$4.9$1.8$virginica$True\\n124$6.7$-3.3$5.7$2.1$virginica$True\\n125$7.2$-3.2$6.0$1.8$virginica$True\\n126$6.2$-2.8$4.8$1.8$virginica$True\\n127$6.1$-3.0$4.9$1.8$virginica$True\\n128$6.4$-2.8$5.6$2.1$virginica$True\\n129$7.2$-3.0$5.8$1.6$virginica$True\\n130$7.4$$$$-2.8$6.1$1.9$virginica$True\\n131$7.9$-3.8$6.4$2.0$virginica$True\\n132$6.4$-2.8$5.6$2.2$virginica$True\\n133$6.3$-2.8$5.1$1.5$virginica$True\\n134$6.1$-2.6$5.6$1.4$virginica$True\\n135$7.7$-3.0$6.1$2.3$virginica$True\\n136$6.3$-3.4$5.6$2.4$virginica$True\\n137$6.4$-3.1$5.5$1.8$virginica$True\\n138$6.0$-3.0$4.8$1.8$virginica$True\\n139$6.9$-3.1$5.4$2.1$virginica$True\\n140$6.7$-3.1$5.6$2.4$virginica$True\\n141$6.9$-3.1$5.1$2.3$virginica$True\\n142$-2.7$5.1$virginica$True\\n143$6.8$-3.2$5.9$2.3$virginica$True\\n144$6.7$-3.3$5.7$2.5$virginica$True\\n145$6.7$-3.0$5.2$2.3$virginica$True\\n146$6.3$-2.5$5.0$1.9$virginica$True\\n147$6.5$-3.0$5.2$2.0$virginica$True\\n148$6.2$-3.4$5.4$2.3$virginica$True\\n149$5.9$-3.0$5.1$1.8$virginica$True\\n')\n",
    "print('Data loaded')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 **[2pt]** Load the data despite the errors.\n",
    "\n",
    "- Be patient\n",
    "- Peruse the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n",
    "- If Python raises an error, read carefully the message at the end of the output cell\n",
    "- You should not need a `try... except`: make Pandas get the format until there are no errors\n",
    "- You will need to skip the lines with errors that impede loading (it will still generate warnings, but that is fine for now)\n",
    "- To print the DataFrame, a simple `df` often looks better in Jupyter notebooks than an explicit `print(df)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>is_flower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>I guess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.7</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>zetosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>Fals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>I guess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>Fals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>Fals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>I guess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5.7</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5.2</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6.1</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5.6</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6.7</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5.6</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6.2</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.1</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.4</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>😉</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.5</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.5</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.8</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>😉</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5.5</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>-2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width petal_length petal_width     species is_flower\n",
       "0             5.1         -3.5          1.4         0.2      setosa      True\n",
       "1            -3.0          1.4       setosa        True         NaN       NaN\n",
       "2             5.0         -3.6          1.4         0.2      setosa      True\n",
       "3             4.6         -3.1          1.5         0.2      setosa   I guess\n",
       "4             4.7         -3.2          1.3         0.2      zetosa      True\n",
       "5             5.4         -3.9          1.7         0.4      setosa      True\n",
       "6             4.6         -3.4          1.4         0.3      setosa      True\n",
       "7             5.0         -3.4          1.5         0.2      setosa      Fals\n",
       "8             4.4         -2.9          1.4         0.2      setosa      True\n",
       "9             4.9         -3.1          1.5         0.1      setosa      True\n",
       "10            5.4         -3.7          1.5         0.2      setosa   I guess\n",
       "11            4.8         -3.4          1.6         0.2      setosa      True\n",
       "12            4.8         -3.0          1.4         0.1      setosa      True\n",
       "13            4.3         -3.0          1.1         0.1      setosa      True\n",
       "14            5.8         -4.0          1.2         0.2      setosa      True\n",
       "15           -4.4          1.5       setosa        True         NaN       NaN\n",
       "16            5.4         -3.9          1.3         0.4      setosa      True\n",
       "17            5.1         -3.5          1.4         0.3      setosa      True\n",
       "18            5.7         -3.8          1.7         0.3      setosa      True\n",
       "19            5.1         -3.8          1.5         0.3      setosa      True\n",
       "20            5.4         -3.4          1.7         0.2      setosa      True\n",
       "21            5.1         -3.7          1.5         0.4      setosa      True\n",
       "22            4.6         -3.6          1.0         0.2      setosa      True\n",
       "23            5.1         -3.3          1.7         0.5      setosa      Fals\n",
       "24            4.8         -3.4          1.9         0.2      setosa      True\n",
       "25            5.0         -3.0          1.6         0.2      setosa      True\n",
       "26            5.0         -3.4          1.6         0.4      setosa      True\n",
       "27            5.2         -3.5          1.5         0.2      setosa      True\n",
       "28            5.2         -3.4          1.4         0.2      setosa      True\n",
       "29            4.7         -3.2          1.6         0.2      setosa      True\n",
       "30            4.8         -3.1          1.6         0.2      setosa      True\n",
       "31            5.4         -3.4          1.5         0.4      setosa      True\n",
       "32            5.2         -4.1          1.5         0.1      setosa      Fals\n",
       "33            5.5         -4.2          1.4         0.2      setosa      True\n",
       "34            4.9         -3.1          1.5         0.2      setosa      True\n",
       "35            5.0         -3.2          1.2         0.2      setosa      True\n",
       "36            5.5         -3.5          1.3         0.2      setosa      True\n",
       "37            4.9         -3.6          1.4         0.1      setosa      True\n",
       "38            4.4         -3.0          1.3         0.2      setosa      True\n",
       "39            5.1         -3.4          1.5         0.2      setosa      True\n",
       "40            5.0         -3.5          1.3         0.3      setosa      True\n",
       "41            4.5         -2.3          1.3         0.3      setosa      True\n",
       "42            4.4         -3.2          1.3         0.2      setosa      True\n",
       "43            5.0         -3.5          1.6         0.6      setosa      True\n",
       "44            5.1         -3.8          1.9         0.4      setosa      True\n",
       "45            4.8         -3.0          1.4         0.3      setosa      True\n",
       "46            5.1         -3.8          1.6         0.2      setosa      True\n",
       "47            4.6         -3.2          1.4         0.2      setosa      True\n",
       "48            5.3         -3.7          1.5         0.2      setosa      True\n",
       "49            5.0         -3.3          1.4         0.2      setosa      True\n",
       "50            7.0         -3.2          4.7         1.4  versicolor      True\n",
       "51            6.4         -3.2          4.5         1.5  versicolor      True\n",
       "52            6.9         -3.1          4.9         1.5  versicolor   I guess\n",
       "53            5.5         -2.3          4.0         1.3  versicolor      True\n",
       "54            6.5         -2.8          4.6         1.5  versicolor      True\n",
       "55            5.7         -2.8          4.5         1.3  versicolor      True\n",
       "56            6.3         -3.3          4.7         1.6  versicolor      True\n",
       "57            4.9         -2.4          3.3         1.0  versicolor      True\n",
       "58            6.6         -2.9          4.6         1.3  versicolor      True\n",
       "59            5.2         -2.7          3.9         1.4  versicolor      True\n",
       "60            5.0         -2.0          3.5         1.0  versicolor      True\n",
       "61            5.9         -3.0          4.2         1.5  versicolor      True\n",
       "62            6.0         -2.2          4.0         1.0  versicolor      True\n",
       "63            6.1         -2.9          4.7         1.4  versicolor      True\n",
       "64            5.6         -2.9          3.6         1.3  versicolor      True\n",
       "65            6.7         -3.1          4.4         1.4  versicolor      True\n",
       "66            5.6         -3.0          4.5         1.5  versicolor      True\n",
       "67            5.8         -2.7          4.1         1.0  versicolor      True\n",
       "68            6.2         -2.2          4.5         1.5  versicolor      True\n",
       "69            5.6         -2.5          3.9         1.1  versicolor      True\n",
       "70            5.9         -3.2          4.8         1.8  versicolor      True\n",
       "71            6.1         -2.8          4.0         1.3  versicolor      True\n",
       "72            6.3         -2.5          4.9         1.5  versicolor      True\n",
       "73            6.1         -2.8          4.7         1.2  versicolor      True\n",
       "74            6.4         -2.9          4.3         1.3           😉      True\n",
       "75            6.6         -3.0          4.4         1.4  versicolor      True\n",
       "76            6.8         -2.8          4.8         1.4  versicolor      True\n",
       "77            6.7         -3.0          5.0         1.7  versicolor      True\n",
       "78            6.0         -2.9          4.5         1.5  versicolor      True\n",
       "80            5.5         -2.4          3.8         1.1  versicolor      True\n",
       "81            5.5         -2.4          3.7         1.0  versicolor      True\n",
       "82            5.8         -2.7          3.9         1.2  versicolor      True\n",
       "83            6.0         -2.7          5.1         1.6  versicolor      True\n",
       "84            5.4         -3.0          4.5         1.5  versicolor      True\n",
       "85            6.0         -3.4          4.5         1.6  versicolor      True\n",
       "86            6.7         -3.1          4.7         1.5  versicolor      True\n",
       "87            6.3         -2.3          4.4         1.3  versicolor      True\n",
       "88           -3.0          4.1   versicolor        True         NaN       NaN\n",
       "89            5.5         -2.5          4.0         1.3           😉      True\n",
       "90            5.5         -2.6          4.4         1.2  versicolor      True\n",
       "91            6.1         -3.0          4.6         1.4  versicolor      True\n",
       "92            5.8         -2.6          4.0         1.2  versicolor      True\n",
       "93            5.0         -2.3          3.3         1.0  versicolor      True\n",
       "94            5.6         -2.7          4.2         1.3  versicolor      True\n",
       "95            5.7         -3.0          4.2         1.2  versicolor      True\n",
       "96            5.7         -2.9          4.2         1.3  versicolor      True\n",
       "97            6.2         -2.9          4.3         1.3  versicolor      True\n",
       "98            5.1         -2.5          3.0         1.1  versicolor      True\n",
       "99            5.7         -2.8          4.1         1.3  versicolor      True\n",
       "100           6.3         -3.3          6.0         2.5   virginica      True\n",
       "101           5.8         -2.7          5.1         1.9   virginica      True\n",
       "102           7.1         -3.0          5.9         2.1   virginica      True\n",
       "103           6.3         -2.9          5.6         1.8   virginica      True\n",
       "104           6.5         -3.0          5.8         2.2   virginica      True\n",
       "105           7.6         -3.0          6.6         2.1   virginica      True\n",
       "106           4.9         -2.5          4.5         1.7   virginica      True\n",
       "107           7.3         -2.9          6.3         1.8   virginica      True\n",
       "108           6.7         -2.5          5.8         1.8   virginica      True\n",
       "109           7.2         -3.6          6.1         2.5   virginica      True\n",
       "110           6.5         -3.2          5.1         2.0   virginica      True\n",
       "111           6.4         -2.7          5.3         1.9   virginica      True\n",
       "112           6.8         -3.0          5.5         2.1   virginica      True\n",
       "113           5.7         -2.5          5.0         2.0   virginica      True\n",
       "114           5.8         -2.8          5.1         2.4   virginica      True\n",
       "115           6.4         -3.2          5.3         2.3   virginica      True\n",
       "116           6.5         -3.0          5.5         1.8   virginica      True\n",
       "117           7.7         -3.8          6.7         2.2   virginica      True\n",
       "118           7.7         -2.6          6.9         2.3   virginica      True\n",
       "119           6.0         -2.2          5.0         1.5   virginica      True\n",
       "120           6.9         -3.2          5.7         2.3   virginica      True\n",
       "121           5.6         -2.8          4.9         2.0   virginica      True\n",
       "122           7.7         -2.8          6.7         2.0   virginica      True\n",
       "123           6.3         -2.7          4.9         1.8   virginica      True\n",
       "124           6.7         -3.3          5.7         2.1   virginica      True\n",
       "125           7.2         -3.2          6.0         1.8   virginica      True\n",
       "126           6.2         -2.8          4.8         1.8   virginica      True\n",
       "127           6.1         -3.0          4.9         1.8   virginica      True\n",
       "128           6.4         -2.8          5.6         2.1   virginica      True\n",
       "129           7.2         -3.0          5.8         1.6   virginica      True\n",
       "131           7.9         -3.8          6.4         2.0   virginica      True\n",
       "132           6.4         -2.8          5.6         2.2   virginica      True\n",
       "133           6.3         -2.8          5.1         1.5   virginica      True\n",
       "134           6.1         -2.6          5.6         1.4   virginica      True\n",
       "135           7.7         -3.0          6.1         2.3   virginica      True\n",
       "136           6.3         -3.4          5.6         2.4   virginica      True\n",
       "137           6.4         -3.1          5.5         1.8   virginica      True\n",
       "138           6.0         -3.0          4.8         1.8   virginica      True\n",
       "139           6.9         -3.1          5.4         2.1   virginica      True\n",
       "140           6.7         -3.1          5.6         2.4   virginica      True\n",
       "141           6.9         -3.1          5.1         2.3   virginica      True\n",
       "142          -2.7          5.1    virginica        True         NaN       NaN\n",
       "143           6.8         -3.2          5.9         2.3   virginica      True\n",
       "144           6.7         -3.3          5.7         2.5   virginica      True\n",
       "145           6.7         -3.0          5.2         2.3   virginica      True\n",
       "146           6.3         -2.5          5.0         1.9   virginica      True\n",
       "147           6.5         -3.0          5.2         2.0   virginica      True\n",
       "148           6.2         -3.4          5.4         2.3   virginica      True\n",
       "149           5.9         -3.0          5.1         1.8   virginica      True"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_df = pd.read_csv(csv_str, lineterminator='\\n', sep=\"$\", on_bad_lines='skip')\n",
    "err_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 **[1pt]** Explore the DataFrame, and try to spot the defects. Mitigate two by hand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may want to learn about the method `head()`. Also calling `unique()` on a Series will give you the set of different values available. Remember the goal of the question is to fix only two errors for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['True', nan, 'I guess', 'False'], dtype=object)"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_df.head()\n",
    "#err_df[\"is_flower\"].unique()\n",
    "err_df.is_flower.unique()\n",
    "err_df['is_flower'] = err_df['is_flower'].replace('Fals', 'False') # fixing Fals --> False\n",
    "err_df.is_flower.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "err_df.head()\n",
    "err_df.species.unique()\n",
    "err_df[err_df.species=='zetosa']\n",
    "err_df.species = err_df.species.replace('zetosa', 'setosa')\n",
    "err_df.species.unique()\n",
    "err_df.sepal_width = err_df.sepal_width.apply(abs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Cleaning the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following questions require you to either write a Python function or some automated Python code: manually selecting (like last question) for example rows with missing values is not accepted here, you need to provide code that will work with any (unknown) dataset and rows.  \n",
    "NOTE: no two students ever followed the same process in this exercises, but for those who copy from last year's solutions. As per University policy on plagiarism, students have been expelled from their course of study for something as cheap as an ungraded assignment. Unbelievable. Please not again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 **[1pt]** Remove all rows with missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[This](https://www.geeksforgeeks.org/working-with-missing-data-in-pandas/) can help (and [geeksforgeeks.org](https://www.geeksforgeeks.org) is a great resource to keep in mind). Also consider customizing the Pandas display options, for example: `pd.set_option(\"display.max_rows\", 200)` to see more than just a few lines (see more options in the method's documentation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>is_flower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>I guess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>I guess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>I guess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>😉</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>😉</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width petal_length petal_width     species is_flower\n",
       "0             5.1          3.5          1.4         0.2      setosa      True\n",
       "2             5.0          3.6          1.4         0.2      setosa      True\n",
       "3             4.6          3.1          1.5         0.2      setosa   I guess\n",
       "4             4.7          3.2          1.3         0.2      setosa      True\n",
       "5             5.4          3.9          1.7         0.4      setosa      True\n",
       "6             4.6          3.4          1.4         0.3      setosa      True\n",
       "7             5.0          3.4          1.5         0.2      setosa     False\n",
       "8             4.4          2.9          1.4         0.2      setosa      True\n",
       "9             4.9          3.1          1.5         0.1      setosa      True\n",
       "10            5.4          3.7          1.5         0.2      setosa   I guess\n",
       "11            4.8          3.4          1.6         0.2      setosa      True\n",
       "12            4.8          3.0          1.4         0.1      setosa      True\n",
       "13            4.3          3.0          1.1         0.1      setosa      True\n",
       "14            5.8          4.0          1.2         0.2      setosa      True\n",
       "16            5.4          3.9          1.3         0.4      setosa      True\n",
       "17            5.1          3.5          1.4         0.3      setosa      True\n",
       "18            5.7          3.8          1.7         0.3      setosa      True\n",
       "19            5.1          3.8          1.5         0.3      setosa      True\n",
       "20            5.4          3.4          1.7         0.2      setosa      True\n",
       "21            5.1          3.7          1.5         0.4      setosa      True\n",
       "22            4.6          3.6          1.0         0.2      setosa      True\n",
       "23            5.1          3.3          1.7         0.5      setosa     False\n",
       "24            4.8          3.4          1.9         0.2      setosa      True\n",
       "25            5.0          3.0          1.6         0.2      setosa      True\n",
       "26            5.0          3.4          1.6         0.4      setosa      True\n",
       "27            5.2          3.5          1.5         0.2      setosa      True\n",
       "28            5.2          3.4          1.4         0.2      setosa      True\n",
       "29            4.7          3.2          1.6         0.2      setosa      True\n",
       "30            4.8          3.1          1.6         0.2      setosa      True\n",
       "31            5.4          3.4          1.5         0.4      setosa      True\n",
       "32            5.2          4.1          1.5         0.1      setosa     False\n",
       "33            5.5          4.2          1.4         0.2      setosa      True\n",
       "34            4.9          3.1          1.5         0.2      setosa      True\n",
       "35            5.0          3.2          1.2         0.2      setosa      True\n",
       "36            5.5          3.5          1.3         0.2      setosa      True\n",
       "37            4.9          3.6          1.4         0.1      setosa      True\n",
       "38            4.4          3.0          1.3         0.2      setosa      True\n",
       "39            5.1          3.4          1.5         0.2      setosa      True\n",
       "40            5.0          3.5          1.3         0.3      setosa      True\n",
       "41            4.5          2.3          1.3         0.3      setosa      True\n",
       "42            4.4          3.2          1.3         0.2      setosa      True\n",
       "43            5.0          3.5          1.6         0.6      setosa      True\n",
       "44            5.1          3.8          1.9         0.4      setosa      True\n",
       "45            4.8          3.0          1.4         0.3      setosa      True\n",
       "46            5.1          3.8          1.6         0.2      setosa      True\n",
       "47            4.6          3.2          1.4         0.2      setosa      True\n",
       "48            5.3          3.7          1.5         0.2      setosa      True\n",
       "49            5.0          3.3          1.4         0.2      setosa      True\n",
       "50            7.0          3.2          4.7         1.4  versicolor      True\n",
       "51            6.4          3.2          4.5         1.5  versicolor      True\n",
       "52            6.9          3.1          4.9         1.5  versicolor   I guess\n",
       "53            5.5          2.3          4.0         1.3  versicolor      True\n",
       "54            6.5          2.8          4.6         1.5  versicolor      True\n",
       "55            5.7          2.8          4.5         1.3  versicolor      True\n",
       "56            6.3          3.3          4.7         1.6  versicolor      True\n",
       "57            4.9          2.4          3.3         1.0  versicolor      True\n",
       "58            6.6          2.9          4.6         1.3  versicolor      True\n",
       "59            5.2          2.7          3.9         1.4  versicolor      True\n",
       "60            5.0          2.0          3.5         1.0  versicolor      True\n",
       "61            5.9          3.0          4.2         1.5  versicolor      True\n",
       "62            6.0          2.2          4.0         1.0  versicolor      True\n",
       "63            6.1          2.9          4.7         1.4  versicolor      True\n",
       "64            5.6          2.9          3.6         1.3  versicolor      True\n",
       "65            6.7          3.1          4.4         1.4  versicolor      True\n",
       "66            5.6          3.0          4.5         1.5  versicolor      True\n",
       "67            5.8          2.7          4.1         1.0  versicolor      True\n",
       "68            6.2          2.2          4.5         1.5  versicolor      True\n",
       "69            5.6          2.5          3.9         1.1  versicolor      True\n",
       "70            5.9          3.2          4.8         1.8  versicolor      True\n",
       "71            6.1          2.8          4.0         1.3  versicolor      True\n",
       "72            6.3          2.5          4.9         1.5  versicolor      True\n",
       "73            6.1          2.8          4.7         1.2  versicolor      True\n",
       "74            6.4          2.9          4.3         1.3           😉      True\n",
       "75            6.6          3.0          4.4         1.4  versicolor      True\n",
       "76            6.8          2.8          4.8         1.4  versicolor      True\n",
       "77            6.7          3.0          5.0         1.7  versicolor      True\n",
       "78            6.0          2.9          4.5         1.5  versicolor      True\n",
       "80            5.5          2.4          3.8         1.1  versicolor      True\n",
       "81            5.5          2.4          3.7         1.0  versicolor      True\n",
       "82            5.8          2.7          3.9         1.2  versicolor      True\n",
       "83            6.0          2.7          5.1         1.6  versicolor      True\n",
       "84            5.4          3.0          4.5         1.5  versicolor      True\n",
       "85            6.0          3.4          4.5         1.6  versicolor      True\n",
       "86            6.7          3.1          4.7         1.5  versicolor      True\n",
       "87            6.3          2.3          4.4         1.3  versicolor      True\n",
       "89            5.5          2.5          4.0         1.3           😉      True\n",
       "90            5.5          2.6          4.4         1.2  versicolor      True\n",
       "91            6.1          3.0          4.6         1.4  versicolor      True\n",
       "92            5.8          2.6          4.0         1.2  versicolor      True\n",
       "93            5.0          2.3          3.3         1.0  versicolor      True\n",
       "94            5.6          2.7          4.2         1.3  versicolor      True\n",
       "95            5.7          3.0          4.2         1.2  versicolor      True\n",
       "96            5.7          2.9          4.2         1.3  versicolor      True\n",
       "97            6.2          2.9          4.3         1.3  versicolor      True\n",
       "98            5.1          2.5          3.0         1.1  versicolor      True\n",
       "99            5.7          2.8          4.1         1.3  versicolor      True\n",
       "100           6.3          3.3          6.0         2.5   virginica      True\n",
       "101           5.8          2.7          5.1         1.9   virginica      True\n",
       "102           7.1          3.0          5.9         2.1   virginica      True\n",
       "103           6.3          2.9          5.6         1.8   virginica      True\n",
       "104           6.5          3.0          5.8         2.2   virginica      True\n",
       "105           7.6          3.0          6.6         2.1   virginica      True\n",
       "106           4.9          2.5          4.5         1.7   virginica      True\n",
       "107           7.3          2.9          6.3         1.8   virginica      True\n",
       "108           6.7          2.5          5.8         1.8   virginica      True\n",
       "109           7.2          3.6          6.1         2.5   virginica      True\n",
       "110           6.5          3.2          5.1         2.0   virginica      True\n",
       "111           6.4          2.7          5.3         1.9   virginica      True\n",
       "112           6.8          3.0          5.5         2.1   virginica      True\n",
       "113           5.7          2.5          5.0         2.0   virginica      True\n",
       "114           5.8          2.8          5.1         2.4   virginica      True\n",
       "115           6.4          3.2          5.3         2.3   virginica      True\n",
       "116           6.5          3.0          5.5         1.8   virginica      True\n",
       "117           7.7          3.8          6.7         2.2   virginica      True\n",
       "118           7.7          2.6          6.9         2.3   virginica      True\n",
       "119           6.0          2.2          5.0         1.5   virginica      True\n",
       "120           6.9          3.2          5.7         2.3   virginica      True\n",
       "121           5.6          2.8          4.9         2.0   virginica      True\n",
       "122           7.7          2.8          6.7         2.0   virginica      True\n",
       "123           6.3          2.7          4.9         1.8   virginica      True\n",
       "124           6.7          3.3          5.7         2.1   virginica      True\n",
       "125           7.2          3.2          6.0         1.8   virginica      True\n",
       "126           6.2          2.8          4.8         1.8   virginica      True\n",
       "127           6.1          3.0          4.9         1.8   virginica      True\n",
       "128           6.4          2.8          5.6         2.1   virginica      True\n",
       "129           7.2          3.0          5.8         1.6   virginica      True\n",
       "131           7.9          3.8          6.4         2.0   virginica      True\n",
       "132           6.4          2.8          5.6         2.2   virginica      True\n",
       "133           6.3          2.8          5.1         1.5   virginica      True\n",
       "134           6.1          2.6          5.6         1.4   virginica      True\n",
       "135           7.7          3.0          6.1         2.3   virginica      True\n",
       "136           6.3          3.4          5.6         2.4   virginica      True\n",
       "137           6.4          3.1          5.5         1.8   virginica      True\n",
       "138           6.0          3.0          4.8         1.8   virginica      True\n",
       "139           6.9          3.1          5.4         2.1   virginica      True\n",
       "140           6.7          3.1          5.6         2.4   virginica      True\n",
       "141           6.9          3.1          5.1         2.3   virginica      True\n",
       "143           6.8          3.2          5.9         2.3   virginica      True\n",
       "144           6.7          3.3          5.7         2.5   virginica      True\n",
       "145           6.7          3.0          5.2         2.3   virginica      True\n",
       "146           6.3          2.5          5.0         1.9   virginica      True\n",
       "147           6.5          3.0          5.2         2.0   virginica      True\n",
       "148           6.2          3.4          5.4         2.3   virginica      True\n",
       "149           5.9          3.0          5.1         1.8   virginica      True"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_df = err_df.dropna(axis=0)\n",
    "err_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the next question, here are the correct types for the columns:\n",
    "corr_types = {'sepal_length' : 'float',\n",
    "              'sepal_width' : 'float',\n",
    "              'petal_length' : 'float',\n",
    "              'petal_width' : 'float',\n",
    "              'species' : 'string',\n",
    "              'is_flower' : 'bool'\n",
    "             }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 **[1pt]** Check the column types. Find the reason for the columns having wrong type, and write it. Mitigate the defects and cast the column to the correct type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    float64\n",
       "sepal_width     float64\n",
       "petal_length    float64\n",
       "petal_width     float64\n",
       "species          object\n",
       "is_flower          bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "err_df[['petal_length', 'petal_width']] = err_df[['petal_length', 'petal_width']].apply(pd.to_numeric, errors='coerce')\n",
    "err_df\n",
    "err_df[['petal_length', 'petal_width', 'sepal_length', 'sepal_width']] = err_df[['petal_length', 'petal_width', 'sepal_length', 'sepal_width']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "err_df[['species']] = err_df[['species']].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>is_flower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>😉</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>😉</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species  \\\n",
       "0             5.1          3.5           1.4          0.2      setosa   \n",
       "2             5.0          3.6           1.4          0.2      setosa   \n",
       "3             4.6          3.1           1.5          0.2      setosa   \n",
       "4             4.7          3.2           1.3          0.2      setosa   \n",
       "5             5.4          3.9           1.7          0.4      setosa   \n",
       "6             4.6          3.4           1.4          0.3      setosa   \n",
       "7             5.0          3.4           1.5          0.2      setosa   \n",
       "8             4.4          2.9           1.4          0.2      setosa   \n",
       "9             4.9          3.1           1.5          0.1      setosa   \n",
       "10            5.4          3.7           1.5          0.2      setosa   \n",
       "11            4.8          3.4           1.6          0.2      setosa   \n",
       "12            4.8          3.0           1.4          0.1      setosa   \n",
       "13            4.3          3.0           1.1          0.1      setosa   \n",
       "14            5.8          4.0           1.2          0.2      setosa   \n",
       "16            5.4          3.9           1.3          0.4      setosa   \n",
       "17            5.1          3.5           1.4          0.3      setosa   \n",
       "18            5.7          3.8           1.7          0.3      setosa   \n",
       "19            5.1          3.8           1.5          0.3      setosa   \n",
       "20            5.4          3.4           1.7          0.2      setosa   \n",
       "21            5.1          3.7           1.5          0.4      setosa   \n",
       "22            4.6          3.6           1.0          0.2      setosa   \n",
       "23            5.1          3.3           1.7          0.5      setosa   \n",
       "24            4.8          3.4           1.9          0.2      setosa   \n",
       "25            5.0          3.0           1.6          0.2      setosa   \n",
       "26            5.0          3.4           1.6          0.4      setosa   \n",
       "27            5.2          3.5           1.5          0.2      setosa   \n",
       "28            5.2          3.4           1.4          0.2      setosa   \n",
       "29            4.7          3.2           1.6          0.2      setosa   \n",
       "30            4.8          3.1           1.6          0.2      setosa   \n",
       "31            5.4          3.4           1.5          0.4      setosa   \n",
       "32            5.2          4.1           1.5          0.1      setosa   \n",
       "33            5.5          4.2           1.4          0.2      setosa   \n",
       "34            4.9          3.1           1.5          0.2      setosa   \n",
       "35            5.0          3.2           1.2          0.2      setosa   \n",
       "36            5.5          3.5           1.3          0.2      setosa   \n",
       "37            4.9          3.6           1.4          0.1      setosa   \n",
       "38            4.4          3.0           1.3          0.2      setosa   \n",
       "39            5.1          3.4           1.5          0.2      setosa   \n",
       "40            5.0          3.5           1.3          0.3      setosa   \n",
       "41            4.5          2.3           1.3          0.3      setosa   \n",
       "42            4.4          3.2           1.3          0.2      setosa   \n",
       "43            5.0          3.5           1.6          0.6      setosa   \n",
       "44            5.1          3.8           1.9          0.4      setosa   \n",
       "45            4.8          3.0           1.4          0.3      setosa   \n",
       "46            5.1          3.8           1.6          0.2      setosa   \n",
       "47            4.6          3.2           1.4          0.2      setosa   \n",
       "48            5.3          3.7           1.5          0.2      setosa   \n",
       "49            5.0          3.3           1.4          0.2      setosa   \n",
       "50            7.0          3.2           4.7          1.4  versicolor   \n",
       "51            6.4          3.2           4.5          1.5  versicolor   \n",
       "52            6.9          3.1           4.9          1.5  versicolor   \n",
       "53            5.5          2.3           4.0          1.3  versicolor   \n",
       "54            6.5          2.8           4.6          1.5  versicolor   \n",
       "55            5.7          2.8           4.5          1.3  versicolor   \n",
       "56            6.3          3.3           4.7          1.6  versicolor   \n",
       "57            4.9          2.4           3.3          1.0  versicolor   \n",
       "58            6.6          2.9           4.6          1.3  versicolor   \n",
       "59            5.2          2.7           3.9          1.4  versicolor   \n",
       "60            5.0          2.0           3.5          1.0  versicolor   \n",
       "61            5.9          3.0           4.2          1.5  versicolor   \n",
       "62            6.0          2.2           4.0          1.0  versicolor   \n",
       "63            6.1          2.9           4.7          1.4  versicolor   \n",
       "64            5.6          2.9           3.6          1.3  versicolor   \n",
       "65            6.7          3.1           4.4          1.4  versicolor   \n",
       "66            5.6          3.0           4.5          1.5  versicolor   \n",
       "67            5.8          2.7           4.1          1.0  versicolor   \n",
       "68            6.2          2.2           4.5          1.5  versicolor   \n",
       "69            5.6          2.5           3.9          1.1  versicolor   \n",
       "70            5.9          3.2           4.8          1.8  versicolor   \n",
       "71            6.1          2.8           4.0          1.3  versicolor   \n",
       "72            6.3          2.5           4.9          1.5  versicolor   \n",
       "73            6.1          2.8           4.7          1.2  versicolor   \n",
       "74            6.4          2.9           4.3          1.3           😉   \n",
       "75            6.6          3.0           4.4          1.4  versicolor   \n",
       "76            6.8          2.8           4.8          1.4  versicolor   \n",
       "77            6.7          3.0           5.0          1.7  versicolor   \n",
       "78            6.0          2.9           4.5          1.5  versicolor   \n",
       "80            5.5          2.4           3.8          1.1  versicolor   \n",
       "81            5.5          2.4           3.7          1.0  versicolor   \n",
       "82            5.8          2.7           3.9          1.2  versicolor   \n",
       "83            6.0          2.7           5.1          1.6  versicolor   \n",
       "84            5.4          3.0           4.5          1.5  versicolor   \n",
       "85            6.0          3.4           4.5          1.6  versicolor   \n",
       "86            6.7          3.1           4.7          1.5  versicolor   \n",
       "87            6.3          2.3           4.4          1.3  versicolor   \n",
       "89            5.5          2.5           4.0          1.3           😉   \n",
       "90            5.5          2.6           4.4          1.2  versicolor   \n",
       "91            6.1          3.0           4.6          1.4  versicolor   \n",
       "92            5.8          2.6           4.0          1.2  versicolor   \n",
       "93            5.0          2.3           3.3          1.0  versicolor   \n",
       "94            5.6          2.7           4.2          1.3  versicolor   \n",
       "95            5.7          3.0           4.2          1.2  versicolor   \n",
       "96            5.7          2.9           4.2          1.3  versicolor   \n",
       "97            6.2          2.9           4.3          1.3  versicolor   \n",
       "98            5.1          2.5           3.0          1.1  versicolor   \n",
       "99            5.7          2.8           4.1          1.3  versicolor   \n",
       "100           6.3          3.3           6.0          2.5   virginica   \n",
       "101           5.8          2.7           5.1          1.9   virginica   \n",
       "102           7.1          3.0           5.9          2.1   virginica   \n",
       "103           6.3          2.9           5.6          1.8   virginica   \n",
       "104           6.5          3.0           5.8          2.2   virginica   \n",
       "105           7.6          3.0           6.6          2.1   virginica   \n",
       "106           4.9          2.5           4.5          1.7   virginica   \n",
       "107           7.3          2.9           6.3          1.8   virginica   \n",
       "108           6.7          2.5           5.8          1.8   virginica   \n",
       "109           7.2          3.6           6.1          2.5   virginica   \n",
       "110           6.5          3.2           5.1          2.0   virginica   \n",
       "111           6.4          2.7           5.3          1.9   virginica   \n",
       "112           6.8          3.0           5.5          2.1   virginica   \n",
       "113           5.7          2.5           5.0          2.0   virginica   \n",
       "114           5.8          2.8           5.1          2.4   virginica   \n",
       "115           6.4          3.2           5.3          2.3   virginica   \n",
       "116           6.5          3.0           5.5          1.8   virginica   \n",
       "117           7.7          3.8           6.7          2.2   virginica   \n",
       "118           7.7          2.6           6.9          2.3   virginica   \n",
       "119           6.0          2.2           5.0          1.5   virginica   \n",
       "120           6.9          3.2           5.7          2.3   virginica   \n",
       "121           5.6          2.8           4.9          2.0   virginica   \n",
       "122           7.7          2.8           6.7          2.0   virginica   \n",
       "123           6.3          2.7           4.9          1.8   virginica   \n",
       "124           6.7          3.3           5.7          2.1   virginica   \n",
       "125           7.2          3.2           6.0          1.8   virginica   \n",
       "126           6.2          2.8           4.8          1.8   virginica   \n",
       "127           6.1          3.0           4.9          1.8   virginica   \n",
       "128           6.4          2.8           5.6          2.1   virginica   \n",
       "129           7.2          3.0           5.8          1.6   virginica   \n",
       "131           7.9          3.8           6.4          2.0   virginica   \n",
       "132           6.4          2.8           5.6          2.2   virginica   \n",
       "133           6.3          2.8           5.1          1.5   virginica   \n",
       "134           6.1          2.6           5.6          1.4   virginica   \n",
       "135           7.7          3.0           6.1          2.3   virginica   \n",
       "136           6.3          3.4           5.6          2.4   virginica   \n",
       "137           6.4          3.1           5.5          1.8   virginica   \n",
       "138           6.0          3.0           4.8          1.8   virginica   \n",
       "139           6.9          3.1           5.4          2.1   virginica   \n",
       "140           6.7          3.1           5.6          2.4   virginica   \n",
       "141           6.9          3.1           5.1          2.3   virginica   \n",
       "143           6.8          3.2           5.9          2.3   virginica   \n",
       "144           6.7          3.3           5.7          2.5   virginica   \n",
       "145           6.7          3.0           5.2          2.3   virginica   \n",
       "146           6.3          2.5           5.0          1.9   virginica   \n",
       "147           6.5          3.0           5.2          2.0   virginica   \n",
       "148           6.2          3.4           5.4          2.3   virginica   \n",
       "149           5.9          3.0           5.1          1.8   virginica   \n",
       "\n",
       "     is_flower  \n",
       "0         True  \n",
       "2         True  \n",
       "3         True  \n",
       "4         True  \n",
       "5         True  \n",
       "6         True  \n",
       "7        False  \n",
       "8         True  \n",
       "9         True  \n",
       "10        True  \n",
       "11        True  \n",
       "12        True  \n",
       "13        True  \n",
       "14        True  \n",
       "16        True  \n",
       "17        True  \n",
       "18        True  \n",
       "19        True  \n",
       "20        True  \n",
       "21        True  \n",
       "22        True  \n",
       "23       False  \n",
       "24        True  \n",
       "25        True  \n",
       "26        True  \n",
       "27        True  \n",
       "28        True  \n",
       "29        True  \n",
       "30        True  \n",
       "31        True  \n",
       "32       False  \n",
       "33        True  \n",
       "34        True  \n",
       "35        True  \n",
       "36        True  \n",
       "37        True  \n",
       "38        True  \n",
       "39        True  \n",
       "40        True  \n",
       "41        True  \n",
       "42        True  \n",
       "43        True  \n",
       "44        True  \n",
       "45        True  \n",
       "46        True  \n",
       "47        True  \n",
       "48        True  \n",
       "49        True  \n",
       "50        True  \n",
       "51        True  \n",
       "52        True  \n",
       "53        True  \n",
       "54        True  \n",
       "55        True  \n",
       "56        True  \n",
       "57        True  \n",
       "58        True  \n",
       "59        True  \n",
       "60        True  \n",
       "61        True  \n",
       "62        True  \n",
       "63        True  \n",
       "64        True  \n",
       "65        True  \n",
       "66        True  \n",
       "67        True  \n",
       "68        True  \n",
       "69        True  \n",
       "70        True  \n",
       "71        True  \n",
       "72        True  \n",
       "73        True  \n",
       "74        True  \n",
       "75        True  \n",
       "76        True  \n",
       "77        True  \n",
       "78        True  \n",
       "80        True  \n",
       "81        True  \n",
       "82        True  \n",
       "83        True  \n",
       "84        True  \n",
       "85        True  \n",
       "86        True  \n",
       "87        True  \n",
       "89        True  \n",
       "90        True  \n",
       "91        True  \n",
       "92        True  \n",
       "93        True  \n",
       "94        True  \n",
       "95        True  \n",
       "96        True  \n",
       "97        True  \n",
       "98        True  \n",
       "99        True  \n",
       "100       True  \n",
       "101       True  \n",
       "102       True  \n",
       "103       True  \n",
       "104       True  \n",
       "105       True  \n",
       "106       True  \n",
       "107       True  \n",
       "108       True  \n",
       "109       True  \n",
       "110       True  \n",
       "111       True  \n",
       "112       True  \n",
       "113       True  \n",
       "114       True  \n",
       "115       True  \n",
       "116       True  \n",
       "117       True  \n",
       "118       True  \n",
       "119       True  \n",
       "120       True  \n",
       "121       True  \n",
       "122       True  \n",
       "123       True  \n",
       "124       True  \n",
       "125       True  \n",
       "126       True  \n",
       "127       True  \n",
       "128       True  \n",
       "129       True  \n",
       "131       True  \n",
       "132       True  \n",
       "133       True  \n",
       "134       True  \n",
       "135       True  \n",
       "136       True  \n",
       "137       True  \n",
       "138       True  \n",
       "139       True  \n",
       "140       True  \n",
       "141       True  \n",
       "143       True  \n",
       "144       True  \n",
       "145       True  \n",
       "146       True  \n",
       "147       True  \n",
       "148       True  \n",
       "149       True  "
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "my_map = {\"True\":True, \"False\": False, \"I guess\": np.nan}\n",
    "err_df['is_flower'] = err_df['is_flower'].replace(my_map)\n",
    "\n",
    "err_df['is_flower']=err_df.is_flower.astype('bool')\n",
    "err_df.dtypes\n",
    "err_df = err_df.dropna(axis=0)\n",
    "err_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 **[1pt]** Remove all columns with constant values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used `loc` and a boolean array with `nunique`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>is_flower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>😉</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>😉</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species  \\\n",
       "0             5.1          3.5           1.4          0.2      setosa   \n",
       "2             5.0          3.6           1.4          0.2      setosa   \n",
       "3             4.6          3.1           1.5          0.2      setosa   \n",
       "4             4.7          3.2           1.3          0.2      setosa   \n",
       "5             5.4          3.9           1.7          0.4      setosa   \n",
       "6             4.6          3.4           1.4          0.3      setosa   \n",
       "7             5.0          3.4           1.5          0.2      setosa   \n",
       "8             4.4          2.9           1.4          0.2      setosa   \n",
       "9             4.9          3.1           1.5          0.1      setosa   \n",
       "10            5.4          3.7           1.5          0.2      setosa   \n",
       "11            4.8          3.4           1.6          0.2      setosa   \n",
       "12            4.8          3.0           1.4          0.1      setosa   \n",
       "13            4.3          3.0           1.1          0.1      setosa   \n",
       "14            5.8          4.0           1.2          0.2      setosa   \n",
       "16            5.4          3.9           1.3          0.4      setosa   \n",
       "17            5.1          3.5           1.4          0.3      setosa   \n",
       "18            5.7          3.8           1.7          0.3      setosa   \n",
       "19            5.1          3.8           1.5          0.3      setosa   \n",
       "20            5.4          3.4           1.7          0.2      setosa   \n",
       "21            5.1          3.7           1.5          0.4      setosa   \n",
       "22            4.6          3.6           1.0          0.2      setosa   \n",
       "23            5.1          3.3           1.7          0.5      setosa   \n",
       "24            4.8          3.4           1.9          0.2      setosa   \n",
       "25            5.0          3.0           1.6          0.2      setosa   \n",
       "26            5.0          3.4           1.6          0.4      setosa   \n",
       "27            5.2          3.5           1.5          0.2      setosa   \n",
       "28            5.2          3.4           1.4          0.2      setosa   \n",
       "29            4.7          3.2           1.6          0.2      setosa   \n",
       "30            4.8          3.1           1.6          0.2      setosa   \n",
       "31            5.4          3.4           1.5          0.4      setosa   \n",
       "32            5.2          4.1           1.5          0.1      setosa   \n",
       "33            5.5          4.2           1.4          0.2      setosa   \n",
       "34            4.9          3.1           1.5          0.2      setosa   \n",
       "35            5.0          3.2           1.2          0.2      setosa   \n",
       "36            5.5          3.5           1.3          0.2      setosa   \n",
       "37            4.9          3.6           1.4          0.1      setosa   \n",
       "38            4.4          3.0           1.3          0.2      setosa   \n",
       "39            5.1          3.4           1.5          0.2      setosa   \n",
       "40            5.0          3.5           1.3          0.3      setosa   \n",
       "41            4.5          2.3           1.3          0.3      setosa   \n",
       "42            4.4          3.2           1.3          0.2      setosa   \n",
       "43            5.0          3.5           1.6          0.6      setosa   \n",
       "44            5.1          3.8           1.9          0.4      setosa   \n",
       "45            4.8          3.0           1.4          0.3      setosa   \n",
       "46            5.1          3.8           1.6          0.2      setosa   \n",
       "47            4.6          3.2           1.4          0.2      setosa   \n",
       "48            5.3          3.7           1.5          0.2      setosa   \n",
       "49            5.0          3.3           1.4          0.2      setosa   \n",
       "50            7.0          3.2           4.7          1.4  versicolor   \n",
       "51            6.4          3.2           4.5          1.5  versicolor   \n",
       "52            6.9          3.1           4.9          1.5  versicolor   \n",
       "53            5.5          2.3           4.0          1.3  versicolor   \n",
       "54            6.5          2.8           4.6          1.5  versicolor   \n",
       "55            5.7          2.8           4.5          1.3  versicolor   \n",
       "56            6.3          3.3           4.7          1.6  versicolor   \n",
       "57            4.9          2.4           3.3          1.0  versicolor   \n",
       "58            6.6          2.9           4.6          1.3  versicolor   \n",
       "59            5.2          2.7           3.9          1.4  versicolor   \n",
       "60            5.0          2.0           3.5          1.0  versicolor   \n",
       "61            5.9          3.0           4.2          1.5  versicolor   \n",
       "62            6.0          2.2           4.0          1.0  versicolor   \n",
       "63            6.1          2.9           4.7          1.4  versicolor   \n",
       "64            5.6          2.9           3.6          1.3  versicolor   \n",
       "65            6.7          3.1           4.4          1.4  versicolor   \n",
       "66            5.6          3.0           4.5          1.5  versicolor   \n",
       "67            5.8          2.7           4.1          1.0  versicolor   \n",
       "68            6.2          2.2           4.5          1.5  versicolor   \n",
       "69            5.6          2.5           3.9          1.1  versicolor   \n",
       "70            5.9          3.2           4.8          1.8  versicolor   \n",
       "71            6.1          2.8           4.0          1.3  versicolor   \n",
       "72            6.3          2.5           4.9          1.5  versicolor   \n",
       "73            6.1          2.8           4.7          1.2  versicolor   \n",
       "74            6.4          2.9           4.3          1.3           😉   \n",
       "75            6.6          3.0           4.4          1.4  versicolor   \n",
       "76            6.8          2.8           4.8          1.4  versicolor   \n",
       "77            6.7          3.0           5.0          1.7  versicolor   \n",
       "78            6.0          2.9           4.5          1.5  versicolor   \n",
       "80            5.5          2.4           3.8          1.1  versicolor   \n",
       "81            5.5          2.4           3.7          1.0  versicolor   \n",
       "82            5.8          2.7           3.9          1.2  versicolor   \n",
       "83            6.0          2.7           5.1          1.6  versicolor   \n",
       "84            5.4          3.0           4.5          1.5  versicolor   \n",
       "85            6.0          3.4           4.5          1.6  versicolor   \n",
       "86            6.7          3.1           4.7          1.5  versicolor   \n",
       "87            6.3          2.3           4.4          1.3  versicolor   \n",
       "89            5.5          2.5           4.0          1.3           😉   \n",
       "90            5.5          2.6           4.4          1.2  versicolor   \n",
       "91            6.1          3.0           4.6          1.4  versicolor   \n",
       "92            5.8          2.6           4.0          1.2  versicolor   \n",
       "93            5.0          2.3           3.3          1.0  versicolor   \n",
       "94            5.6          2.7           4.2          1.3  versicolor   \n",
       "95            5.7          3.0           4.2          1.2  versicolor   \n",
       "96            5.7          2.9           4.2          1.3  versicolor   \n",
       "97            6.2          2.9           4.3          1.3  versicolor   \n",
       "98            5.1          2.5           3.0          1.1  versicolor   \n",
       "99            5.7          2.8           4.1          1.3  versicolor   \n",
       "100           6.3          3.3           6.0          2.5   virginica   \n",
       "101           5.8          2.7           5.1          1.9   virginica   \n",
       "102           7.1          3.0           5.9          2.1   virginica   \n",
       "103           6.3          2.9           5.6          1.8   virginica   \n",
       "104           6.5          3.0           5.8          2.2   virginica   \n",
       "105           7.6          3.0           6.6          2.1   virginica   \n",
       "106           4.9          2.5           4.5          1.7   virginica   \n",
       "107           7.3          2.9           6.3          1.8   virginica   \n",
       "108           6.7          2.5           5.8          1.8   virginica   \n",
       "109           7.2          3.6           6.1          2.5   virginica   \n",
       "110           6.5          3.2           5.1          2.0   virginica   \n",
       "111           6.4          2.7           5.3          1.9   virginica   \n",
       "112           6.8          3.0           5.5          2.1   virginica   \n",
       "113           5.7          2.5           5.0          2.0   virginica   \n",
       "114           5.8          2.8           5.1          2.4   virginica   \n",
       "115           6.4          3.2           5.3          2.3   virginica   \n",
       "116           6.5          3.0           5.5          1.8   virginica   \n",
       "117           7.7          3.8           6.7          2.2   virginica   \n",
       "118           7.7          2.6           6.9          2.3   virginica   \n",
       "119           6.0          2.2           5.0          1.5   virginica   \n",
       "120           6.9          3.2           5.7          2.3   virginica   \n",
       "121           5.6          2.8           4.9          2.0   virginica   \n",
       "122           7.7          2.8           6.7          2.0   virginica   \n",
       "123           6.3          2.7           4.9          1.8   virginica   \n",
       "124           6.7          3.3           5.7          2.1   virginica   \n",
       "125           7.2          3.2           6.0          1.8   virginica   \n",
       "126           6.2          2.8           4.8          1.8   virginica   \n",
       "127           6.1          3.0           4.9          1.8   virginica   \n",
       "128           6.4          2.8           5.6          2.1   virginica   \n",
       "129           7.2          3.0           5.8          1.6   virginica   \n",
       "131           7.9          3.8           6.4          2.0   virginica   \n",
       "132           6.4          2.8           5.6          2.2   virginica   \n",
       "133           6.3          2.8           5.1          1.5   virginica   \n",
       "134           6.1          2.6           5.6          1.4   virginica   \n",
       "135           7.7          3.0           6.1          2.3   virginica   \n",
       "136           6.3          3.4           5.6          2.4   virginica   \n",
       "137           6.4          3.1           5.5          1.8   virginica   \n",
       "138           6.0          3.0           4.8          1.8   virginica   \n",
       "139           6.9          3.1           5.4          2.1   virginica   \n",
       "140           6.7          3.1           5.6          2.4   virginica   \n",
       "141           6.9          3.1           5.1          2.3   virginica   \n",
       "143           6.8          3.2           5.9          2.3   virginica   \n",
       "144           6.7          3.3           5.7          2.5   virginica   \n",
       "145           6.7          3.0           5.2          2.3   virginica   \n",
       "146           6.3          2.5           5.0          1.9   virginica   \n",
       "147           6.5          3.0           5.2          2.0   virginica   \n",
       "148           6.2          3.4           5.4          2.3   virginica   \n",
       "149           5.9          3.0           5.1          1.8   virginica   \n",
       "\n",
       "     is_flower  \n",
       "0         True  \n",
       "2         True  \n",
       "3         True  \n",
       "4         True  \n",
       "5         True  \n",
       "6         True  \n",
       "7        False  \n",
       "8         True  \n",
       "9         True  \n",
       "10        True  \n",
       "11        True  \n",
       "12        True  \n",
       "13        True  \n",
       "14        True  \n",
       "16        True  \n",
       "17        True  \n",
       "18        True  \n",
       "19        True  \n",
       "20        True  \n",
       "21        True  \n",
       "22        True  \n",
       "23       False  \n",
       "24        True  \n",
       "25        True  \n",
       "26        True  \n",
       "27        True  \n",
       "28        True  \n",
       "29        True  \n",
       "30        True  \n",
       "31        True  \n",
       "32       False  \n",
       "33        True  \n",
       "34        True  \n",
       "35        True  \n",
       "36        True  \n",
       "37        True  \n",
       "38        True  \n",
       "39        True  \n",
       "40        True  \n",
       "41        True  \n",
       "42        True  \n",
       "43        True  \n",
       "44        True  \n",
       "45        True  \n",
       "46        True  \n",
       "47        True  \n",
       "48        True  \n",
       "49        True  \n",
       "50        True  \n",
       "51        True  \n",
       "52        True  \n",
       "53        True  \n",
       "54        True  \n",
       "55        True  \n",
       "56        True  \n",
       "57        True  \n",
       "58        True  \n",
       "59        True  \n",
       "60        True  \n",
       "61        True  \n",
       "62        True  \n",
       "63        True  \n",
       "64        True  \n",
       "65        True  \n",
       "66        True  \n",
       "67        True  \n",
       "68        True  \n",
       "69        True  \n",
       "70        True  \n",
       "71        True  \n",
       "72        True  \n",
       "73        True  \n",
       "74        True  \n",
       "75        True  \n",
       "76        True  \n",
       "77        True  \n",
       "78        True  \n",
       "80        True  \n",
       "81        True  \n",
       "82        True  \n",
       "83        True  \n",
       "84        True  \n",
       "85        True  \n",
       "86        True  \n",
       "87        True  \n",
       "89        True  \n",
       "90        True  \n",
       "91        True  \n",
       "92        True  \n",
       "93        True  \n",
       "94        True  \n",
       "95        True  \n",
       "96        True  \n",
       "97        True  \n",
       "98        True  \n",
       "99        True  \n",
       "100       True  \n",
       "101       True  \n",
       "102       True  \n",
       "103       True  \n",
       "104       True  \n",
       "105       True  \n",
       "106       True  \n",
       "107       True  \n",
       "108       True  \n",
       "109       True  \n",
       "110       True  \n",
       "111       True  \n",
       "112       True  \n",
       "113       True  \n",
       "114       True  \n",
       "115       True  \n",
       "116       True  \n",
       "117       True  \n",
       "118       True  \n",
       "119       True  \n",
       "120       True  \n",
       "121       True  \n",
       "122       True  \n",
       "123       True  \n",
       "124       True  \n",
       "125       True  \n",
       "126       True  \n",
       "127       True  \n",
       "128       True  \n",
       "129       True  \n",
       "131       True  \n",
       "132       True  \n",
       "133       True  \n",
       "134       True  \n",
       "135       True  \n",
       "136       True  \n",
       "137       True  \n",
       "138       True  \n",
       "139       True  \n",
       "140       True  \n",
       "141       True  \n",
       "143       True  \n",
       "144       True  \n",
       "145       True  \n",
       "146       True  \n",
       "147       True  \n",
       "148       True  \n",
       "149       True  "
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_df[err_df.species!=True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    34\n",
       "sepal_width     22\n",
       "petal_length    43\n",
       "petal_width     22\n",
       "species          4\n",
       "is_flower        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have no columns with constant values. However, I know the column is_flower is useless. I know its a flower. So dropping it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "del err_df['is_flower']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 **[3pt]** There are still errors in the data: fix them all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species\n",
       "0             5.1          3.5           1.4          0.2      setosa\n",
       "2             5.0          3.6           1.4          0.2      setosa\n",
       "3             4.6          3.1           1.5          0.2      setosa\n",
       "4             4.7          3.2           1.3          0.2      setosa\n",
       "5             5.4          3.9           1.7          0.4      setosa\n",
       "6             4.6          3.4           1.4          0.3      setosa\n",
       "7             5.0          3.4           1.5          0.2      setosa\n",
       "8             4.4          2.9           1.4          0.2      setosa\n",
       "9             4.9          3.1           1.5          0.1      setosa\n",
       "10            5.4          3.7           1.5          0.2      setosa\n",
       "11            4.8          3.4           1.6          0.2      setosa\n",
       "12            4.8          3.0           1.4          0.1      setosa\n",
       "13            4.3          3.0           1.1          0.1      setosa\n",
       "14            5.8          4.0           1.2          0.2      setosa\n",
       "16            5.4          3.9           1.3          0.4      setosa\n",
       "17            5.1          3.5           1.4          0.3      setosa\n",
       "18            5.7          3.8           1.7          0.3      setosa\n",
       "19            5.1          3.8           1.5          0.3      setosa\n",
       "20            5.4          3.4           1.7          0.2      setosa\n",
       "21            5.1          3.7           1.5          0.4      setosa\n",
       "22            4.6          3.6           1.0          0.2      setosa\n",
       "23            5.1          3.3           1.7          0.5      setosa\n",
       "24            4.8          3.4           1.9          0.2      setosa\n",
       "25            5.0          3.0           1.6          0.2      setosa\n",
       "26            5.0          3.4           1.6          0.4      setosa\n",
       "27            5.2          3.5           1.5          0.2      setosa\n",
       "28            5.2          3.4           1.4          0.2      setosa\n",
       "29            4.7          3.2           1.6          0.2      setosa\n",
       "30            4.8          3.1           1.6          0.2      setosa\n",
       "31            5.4          3.4           1.5          0.4      setosa\n",
       "32            5.2          4.1           1.5          0.1      setosa\n",
       "33            5.5          4.2           1.4          0.2      setosa\n",
       "34            4.9          3.1           1.5          0.2      setosa\n",
       "35            5.0          3.2           1.2          0.2      setosa\n",
       "36            5.5          3.5           1.3          0.2      setosa\n",
       "37            4.9          3.6           1.4          0.1      setosa\n",
       "38            4.4          3.0           1.3          0.2      setosa\n",
       "39            5.1          3.4           1.5          0.2      setosa\n",
       "40            5.0          3.5           1.3          0.3      setosa\n",
       "41            4.5          2.3           1.3          0.3      setosa\n",
       "42            4.4          3.2           1.3          0.2      setosa\n",
       "43            5.0          3.5           1.6          0.6      setosa\n",
       "44            5.1          3.8           1.9          0.4      setosa\n",
       "45            4.8          3.0           1.4          0.3      setosa\n",
       "46            5.1          3.8           1.6          0.2      setosa\n",
       "47            4.6          3.2           1.4          0.2      setosa\n",
       "48            5.3          3.7           1.5          0.2      setosa\n",
       "49            5.0          3.3           1.4          0.2      setosa\n",
       "50            7.0          3.2           4.7          1.4  versicolor\n",
       "51            6.4          3.2           4.5          1.5  versicolor\n",
       "52            6.9          3.1           4.9          1.5  versicolor\n",
       "53            5.5          2.3           4.0          1.3  versicolor\n",
       "54            6.5          2.8           4.6          1.5  versicolor\n",
       "55            5.7          2.8           4.5          1.3  versicolor\n",
       "56            6.3          3.3           4.7          1.6  versicolor\n",
       "57            4.9          2.4           3.3          1.0  versicolor\n",
       "58            6.6          2.9           4.6          1.3  versicolor\n",
       "59            5.2          2.7           3.9          1.4  versicolor\n",
       "60            5.0          2.0           3.5          1.0  versicolor\n",
       "61            5.9          3.0           4.2          1.5  versicolor\n",
       "62            6.0          2.2           4.0          1.0  versicolor\n",
       "63            6.1          2.9           4.7          1.4  versicolor\n",
       "64            5.6          2.9           3.6          1.3  versicolor\n",
       "65            6.7          3.1           4.4          1.4  versicolor\n",
       "66            5.6          3.0           4.5          1.5  versicolor\n",
       "67            5.8          2.7           4.1          1.0  versicolor\n",
       "68            6.2          2.2           4.5          1.5  versicolor\n",
       "69            5.6          2.5           3.9          1.1  versicolor\n",
       "70            5.9          3.2           4.8          1.8  versicolor\n",
       "71            6.1          2.8           4.0          1.3  versicolor\n",
       "72            6.3          2.5           4.9          1.5  versicolor\n",
       "73            6.1          2.8           4.7          1.2  versicolor\n",
       "75            6.6          3.0           4.4          1.4  versicolor\n",
       "76            6.8          2.8           4.8          1.4  versicolor\n",
       "77            6.7          3.0           5.0          1.7  versicolor\n",
       "78            6.0          2.9           4.5          1.5  versicolor\n",
       "80            5.5          2.4           3.8          1.1  versicolor\n",
       "81            5.5          2.4           3.7          1.0  versicolor\n",
       "82            5.8          2.7           3.9          1.2  versicolor\n",
       "83            6.0          2.7           5.1          1.6  versicolor\n",
       "84            5.4          3.0           4.5          1.5  versicolor\n",
       "85            6.0          3.4           4.5          1.6  versicolor\n",
       "86            6.7          3.1           4.7          1.5  versicolor\n",
       "87            6.3          2.3           4.4          1.3  versicolor\n",
       "90            5.5          2.6           4.4          1.2  versicolor\n",
       "91            6.1          3.0           4.6          1.4  versicolor\n",
       "92            5.8          2.6           4.0          1.2  versicolor\n",
       "93            5.0          2.3           3.3          1.0  versicolor\n",
       "94            5.6          2.7           4.2          1.3  versicolor\n",
       "95            5.7          3.0           4.2          1.2  versicolor\n",
       "96            5.7          2.9           4.2          1.3  versicolor\n",
       "97            6.2          2.9           4.3          1.3  versicolor\n",
       "98            5.1          2.5           3.0          1.1  versicolor\n",
       "99            5.7          2.8           4.1          1.3  versicolor\n",
       "100           6.3          3.3           6.0          2.5   virginica\n",
       "101           5.8          2.7           5.1          1.9   virginica\n",
       "102           7.1          3.0           5.9          2.1   virginica\n",
       "103           6.3          2.9           5.6          1.8   virginica\n",
       "104           6.5          3.0           5.8          2.2   virginica\n",
       "105           7.6          3.0           6.6          2.1   virginica\n",
       "106           4.9          2.5           4.5          1.7   virginica\n",
       "107           7.3          2.9           6.3          1.8   virginica\n",
       "108           6.7          2.5           5.8          1.8   virginica\n",
       "109           7.2          3.6           6.1          2.5   virginica\n",
       "110           6.5          3.2           5.1          2.0   virginica\n",
       "111           6.4          2.7           5.3          1.9   virginica\n",
       "112           6.8          3.0           5.5          2.1   virginica\n",
       "113           5.7          2.5           5.0          2.0   virginica\n",
       "114           5.8          2.8           5.1          2.4   virginica\n",
       "115           6.4          3.2           5.3          2.3   virginica\n",
       "116           6.5          3.0           5.5          1.8   virginica\n",
       "117           7.7          3.8           6.7          2.2   virginica\n",
       "118           7.7          2.6           6.9          2.3   virginica\n",
       "119           6.0          2.2           5.0          1.5   virginica\n",
       "120           6.9          3.2           5.7          2.3   virginica\n",
       "121           5.6          2.8           4.9          2.0   virginica\n",
       "122           7.7          2.8           6.7          2.0   virginica\n",
       "123           6.3          2.7           4.9          1.8   virginica\n",
       "124           6.7          3.3           5.7          2.1   virginica\n",
       "125           7.2          3.2           6.0          1.8   virginica\n",
       "126           6.2          2.8           4.8          1.8   virginica\n",
       "127           6.1          3.0           4.9          1.8   virginica\n",
       "128           6.4          2.8           5.6          2.1   virginica\n",
       "129           7.2          3.0           5.8          1.6   virginica\n",
       "131           7.9          3.8           6.4          2.0   virginica\n",
       "132           6.4          2.8           5.6          2.2   virginica\n",
       "133           6.3          2.8           5.1          1.5   virginica\n",
       "134           6.1          2.6           5.6          1.4   virginica\n",
       "135           7.7          3.0           6.1          2.3   virginica\n",
       "136           6.3          3.4           5.6          2.4   virginica\n",
       "137           6.4          3.1           5.5          1.8   virginica\n",
       "138           6.0          3.0           4.8          1.8   virginica\n",
       "139           6.9          3.1           5.4          2.1   virginica\n",
       "140           6.7          3.1           5.6          2.4   virginica\n",
       "141           6.9          3.1           5.1          2.3   virginica\n",
       "143           6.8          3.2           5.9          2.3   virginica\n",
       "144           6.7          3.3           5.7          2.5   virginica\n",
       "145           6.7          3.0           5.2          2.3   virginica\n",
       "146           6.3          2.5           5.0          1.9   virginica\n",
       "147           6.5          3.0           5.2          2.0   virginica\n",
       "148           6.2          3.4           5.4          2.3   virginica\n",
       "149           5.9          3.0           5.1          1.8   virginica"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", 200)\n",
    "err_df = err_df[err_df.species != '😉']\n",
    "err_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time, I suggest you do not just \"answer\" this question in order. It is easier to move to the next questions, see what errors you get, come back and write code that fixes them (remember: data analysis is an iterative process). If you can answer all questions then there should be no errors left. Also keep in mind that Python 3 strings handle UTF-8 characters if you have not noticed already 😉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Visualizing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are still stuck with some error and need to give up on question 3.4, you can use the correct dataframe from `df = sns.load_dataset('iris')` for the following parts. But it would be really great if you were able to clean the data at the previous question and use that instead. As I mentioned: never underestimate the data cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f88ef5054c0>"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iWeLda7Fiib+1+uKo2N9YmsXK/XjvjkO4sa0Om3YeRmmBA/m5ObjvucPB8v9ybSPu/OhibPyfQ6aIIyX0RxarW+vK1yQv5KrFlXj20EDKY0pdaYGqPBJZedl5pdpOTWKKGWPvAeAQx2hwznl9ygcJgWKQCIOwTHyclnFYatapl2lKPvz5S3CzQI/457dcimV1pVH1H+g5q5c2ZaZCMcUGEe9eixVLXJSXGxX7G0+zWIktVu6PW69YiIdeii7/HzcswaH+YSydNwfzywqNiiO1jM+0OrJY3U2fWCr0pWrzQrauW66qnljt1CJPRdYeFe3UL6aYc34+ADDGnJzzMPE4xhi9ryGINKNlHJaadeplmpIDEj3igWGvsP5Y2pSt81Q1nyB0Jd69FiuWWK1msaI9G7ofY+Lyw94pbHruCB5dd6kpYkkJfZHF6sp8qdoYXrX1xGqnnu1JtZ1axxS/DGBZAtsIgtCRdGhTCvWIXfnC41aW5Em2y7UphdqrOmlTEkSyyGIeK4oCscI2xmLei5G/xdMsVo4ZWkaoYeywUxxxhiLTIxbZocyXxorhFZVXW4+MZPJUxO3Rp52aBOcxxqoYY38FIJ8xtpQxtmz289cASHCUINKMEnvozA3c4lrHFCpxZ9ds2oVP/mg3rtm0Czu6+rGooggb1jaFHTegR1ws3N5S7RLW3+guwcaO8PIbO5rQ6BaXJwijUGIeH3rpKDbvPIIf7zqK29sb0H02sLzul7e+gfUrG4T3oug+VTSLY927ofv9+rXuqPrvXt2Ih18+SnHEGYjM99a68oV2uLiyWNVYIBs7Gt0lmowpascmeXvE90mq7dQqpvgzAD4LoA1AaHDQCICfcs41XbqZYpAIg7BUfJyaWGC1xIrz+vb2Ltx8WT0mfNPId9jx8MtH8Z3rlgh1ih2OHOkxFPWJfo8XVS4nGt0uSrKTQzHFBiG7F9Z9uD4on+Z2OXF9W60wvld0nwKxNYsj96ssdmLEO4VejxflRXmw24CSfIcZVrGzlM+0AsnE/KrVKZaNHVqNKWrrUdueBOvXNab4ZwB+xhj7OOf811rUSRBEaqiJBVZLrDivPcc92HP89bDfZDrFsbDbbWidV0oxxISpkd0L/pDnTX0erzS+V3afxrt3Rfu10L2S8SQb86tmLJDZpFZjitp61LYnlXZqHVM8nzF2R8Q2D4DXOOdvaHwsgiDSgCx+TRbPddXF5fi75efjzNgUzivMxc9ffS9mPJcW2pcEYRSyeyHShOPFNYbeB26XEzP+wB+TkfdErPuF7qXMJ5mYX63sQm09arXmzWC/Wk+K22Y/T89+/yiAvwD4ImPscc75dzQ+HkEQOhJLs1KkL3xheSGuXFyNLzzy2jk94jVNqJZMBrTSUyYIoxBpeX//+iXwc39wkhJPizX0PigtcODmD84P0x1W7gkA0vsl1m90L2UOMu14JZY2cntdaYEmdqHWV09P+1VpzZtlLNAkpjhYGWO/B/Bxzvno7PciAE8A+FsEnhZfrMVxMjkGqWZeHXq7Tya9f05uHmamJuMXlFBdOw89J08kvX+Gk3XxcbE0JUVxantPnMGnHtot1CMWhU7ouK59tkExxQYSGcNoY8BnfhKtGfuTz14SV4tVpjv8TOcKAJDeL7F+M/BeyjqfmQ7UxNIeGxrTxC7U+mqZZr1Ma96AsUC/mOIQ6gD4Qr5PAZjPOZ9gjCU/U8siertP4sYfvpz0/lu/cFnK+xOEQjxNyci4rX5J+YHhMPnyhOsnCCsQGcP4yrunhZqxiWixynSHT414wWNoGMf6je6lzEJNLK1WPlZtPWq15s0yFmg9Kf4lgFcZY0/Ofr8WwK8YY4UA3tT4WARBaIgsdrhtviugJjE5jYI8O3728lFpnJpbEu9WWSIuH2tde4IwOyKbBoCCWY3gZLVYRfvabTaU5ucG75fiPDsWVxfj7LgPuTYbyosdumuTE+ZATeytVpr1sXy1Gs36Kpf6sSCdscaaToo5599mjP0OwIcQeDT9Rc658l7k77Q8FkEQ2iGL52pvqMANH5iPf3pi37kY4bWBGGFZ+Q1rm3DXkwfDyjdVlUjL33pFQ1T5Wle+0V1CEDGR3TMOO8O3t7+JzvYGbNoZHhccT4v1jsfeCOoOh8YUd7Y34KuPv4FvXrMYt13RgG+F3C+d7Q34wR/ewe3tDfjhp5fhC4/sTeiYhDVRG3tbV1qAjR1NUbG9svh2GYoed2Q9ta58YXuuXDRXeNzFleKx4MpFc1XVr1essaYxxQDAGMsBUImQCTfnXNMg1UyOQWKMGR4+obVNZBAZGx8ni+f6+S2XSmOERduf6Vwh1CPu9kyoql8Wd0ZIoZjiNBNPn9jtcuJjy2qRYwNWXjQXzTVzEtZirSpx4vSoDy8eHsSMH/jN3m70ebzoXLkQW16Mjje+5fJ6PPTSUWz9P8tRnJ+rizZ5kmSszzQKtbG3RwdH8bmfJh7frva4sfSR60oLorTmT5wZV1VPrPpTDKvQP6aYMXY7gLsBDACYmT0oB9Ci5XEIgtAWWTyXLEZYtl2mR6y6fkncGUGYhXj6xH0ebzCm+LILyuJOTiPjQd/q7wsu/qHgl8QNK3HI/cNetNaVUgxxBqM29nZg2Ksqvl3tcePpI0dqzautJ179WqN1TPF6AIs450Ma10sQWY+eGpGyeC63ZHuVylhgWVxblWy7yyltvxm0LAki1KZDnwpfWFkMt8uJPk8guVSx86ODo0GbrXXl49DAMPo8XtSW5sNpz8Hg6CQKHHb4ZmZQVpgX2B5xb+QwcbzxospizC/LR5Ur+v6j+yWzUJuHoTYvRG1sstulzVggi0F2u+Qx0bL2p2LzWk+KTyKwWAdBEBqit0akLF7s4spiYcxvY2WxsLwsTk2mrdlS7RLGnS2qKBa2f01zNf749inDtSwJQrHpe3ccwo1tdWHxw+tXNuDhV47jzLgPm29aijf7RoI22zbfhRs+MB93PXlQqEnc2d6ArXtO4J/+ZjG+e10LvvbE/uBvC+cW4fvXL8FXH38jrPz3nn0Lt/51AxZVFIe10Szar4R2yHy1zPfWuvJV5YXIbKPWlS/MF7mwvEiTsUCms9zodqnSX75qcSWePTSQtM1rrVP8EIBFAP4HQFCCjXP+A80OgsyOQaKYYlNjWHycURqRWsZ5ybQ1lSfCoXFnXX0eYf2xYpmz/JUxxRQbgN/PcaDnrNBWf/a5S1BRnAfOgY/ef+7e2vTJpcEJikyTWIkR3n7b5RjzTYfdGzYbw94TZ6LijUX3twl0wCmmWGO0GgvU+tJ9J8+g89HXo2KTv3/9EtU+WY3Oslr9ZRVjU1p0ik/MfhyzH4IgNMAojUgt47xk2pp2uy0q7kzafpNoWRIEELDpcd+M0CY5OOorivDKu6fDfp+YnA5+l2kSK9tPj01ieX151D17enQyKt5YdH+bRfuV0A7NxoIk6hHFJifjk9XoLMu2JxvjHA+tJdnuAQDGWCHnfEzLugkiW5BpOKrRmoylESlCyzgvLZAel3SNCRPh93MUOHLQuXIh/Bz49WvnntoyMBwdHA27V9wuJ6rmOIPl83NtQjvnPPp+CvULFUV5mF+Wj+NDE2H7Rd7fWmnUEuZBbUyxdCxIYkyR5Z3obWNqxsRUxyZx1k2SMMY+yBh7E8Ch2e+tjLH/1PIYBJHJKDGA12zahU/+aDeu2bQLO7r6UVdagB/csATO3MAtG0+DtNFdgo0dTWHlN3Y0odHtEpZX4rwi619cWSysZ1GFeLta7UsZ0vZXleD29gY89NJRbN55BD/edRS3tzdodlyCSBTlXr1xy6vY9FzAFj+9fD7ml+Vj/coGfHnrG7hm0y682TeCzTctxfyyfHx6+Xx84ZHXguWLHHbc8ZELw+y8s70B2/f3hN3fkX7h7x7ajVuvaMD8svzgfqL7W3Zfk3axdVFiihP1gYsri7FhbbgvVbTj1djG4tn8ktDj3npFAxpV1qMWtWOiEoOcbHu0jineDeA6AE9xzpfObjvIOW/S7CDI7Bgkiik2NbrHx8WKF1PiqBLVIBXF6srUJwBxnNexoTGhxuWmTywVxpep1b6Mhaj9Mo1LiimmmOJ0I7tXv3ddK/71mUNh6hP/c/sKjE5OCWMdH/0/y1GUZ59Vn8jB1Iwf5xXmhd3fsXTEh0YnY97fsjjNNEExxRqTjE7xPz3xRkB9wjeNfIcdD798FN+5bomqMUXLsUmr85UdN0GbT0tMMTjnJxkLO9aM1scgiEwlXryYKN5KhihWNxayuC1RHJksvkzLWEVR+ylGkjALMlt8a2AkOCFWtg2OesElGsPe6RksqSvFwspw5YhEjjXt9+Nvmtwx2ymL0ySsSTI6xXuOe7Dn+Oth29WOKVqOTWpI5rip2LzmkmyMscsAcMaYA0AnZkMpCIKITzpiALXSppS1U40+slooRpJIJ7HuFZktKrdSqHZxQa4d035/WGyx8lt+rh1+P4/5ZE12rPzcHLzy7mnSH85Q1OaX+Hwz2N/rQf+wF+4SJ5qrXZr5TLWxzFqRbp+vaUwxgC8CuBVADYBuAEtmvxMEkQB6xwDK4rP8fnHIjKJNGRmPdmF5kTDmt7rYiW37enDjllfxxZ/vxY1bXsG2fT2YnvYL61cLxUgS6SLevSKzxZZaVzB++KGXjmLTc0dww5ZXcLDHg69ceWHUbzdueSXmPSg71saOJnQ++npC9zFhPWT2V+vKF/ped1Eetu3vxace2o3bfvk6/u6h3di2vxfVxU5NfKbaWGatSLfP1zSmOF1kcgwSxRSbmrTEx+kZA5iMxqVMm/Krj7+RsGalTB85GQyOkTQrFFOsMYncKyJbBCDVLl6/sgE1pQVBnWJZvSJCj5Wfm4POR1+PUp8wYWw9xRQnSSzteJFP/t51rfj0f/9ZGHe+rK40ZZ9ppOa1Tj5fv5hixtj9AKQzKc55pxbHSQc18+rQ230y6f2ra+eh5+QJDVtEZBt6xgAmo3Ep06ZUo1kp00dOBoqRJNJBIveKzBZl2sVjvhm8MzCSVFx86LFeefd02IQ40ToI6xBLh1fseyeF5QeGvZr4TCPzOdLp87WKKU7pzzzG2BwAPwbQhMDk+vOc81c0aJdqertPpvyklSBSwaiYXFH8WiyNS5lmpWi7TB85GVJZ154gEiXZWMbpaT8cOWL94UJHDmpLC6S6xvtOnoVvZgZlEeoTWrWNMBY98zmqSvKE2ytL5HkeasYao2KK040mk2LO+c8SKccYu59zfrvgp/sA7OCcXzeboEeio0RWMj3tx7Z9PVHryHe01mgyMZatOy9bR/7KRXOxsaMpqj1Ns3rBkdsvntWyvOvJc9s3rG3C4soSDXrnXJxdsuvaE0Si1JUWCG0/Vgylcv/ev/MwOtsbsGnn4eC+37z6IkxM+fG12dAJRZN4654T+MQH6vDlrW/gzLgvuO3rqxZL7Vp2H1NsvXlR67tk11jR4Y3c3ux2YcPapijf21hZLBxTVje6sb2rL+GxRokpVnM/WJG0xhQzxvZyzpdFbCsBsA9APU+wMXrGIGkR05tKn1JMsanRPT5Otk693jG5snXkn+lcgbrSgoT1glWsO58URsa1mRyKKdaYo4OjQo3uWFrcofdvqMLEhxsqwBjwdz+OjrcX6Rrfcnk9HnrpaEy7tkhsPcUUz5KM75JdY9l2RX1CeRLdUu3CoYFhoU9+5POXCGOQZWNNBvre9OgUJ0E9gEEAP2GMtQJ4DcD6yGWiGWPrAKwDgLq6urQ3kiBkaGmb0nXqdY7JjRcvlqhecKrrzseDdIoTh3xmasg0umPZWqj993m8wX0b3SUoyc9NWNeYsfh2beXY+my0zWR8l+way7Y7HDloW3Be2DbpmCKJQZaNNdnie80wKbYDWAbgds75bsbYfQD+L4BvhRbinG8BsAUI/GWZ9lYShAQtbVMaw6thTK4ItTGK8ni3fNVxZ1rE2WVaXJsWkM9MDZmtVRQ5cXRwVGivse7fYmduwrrGDXOLMb8sP2PtOhtt0yjfJc8LEccgy8aabIkp1lqnOB6ika4bQDfnfPfs9ycQmCQTRNbR6C4RalA2ul26HletFqSs/KKKItx6RbiW5a1XNKDWlS+sR61uMukUE+lCZmvvDY1K7TXW/avEKEf+JtI1/toT+9KiAUukD6N81+LKYqHWfFOVurGm1pWvyrdblXQ/Kb4vcgPnvJ8xdpIxtohz/jaAlQDeTHO7CMIU2O02dLTWoGFuUVgMr1bqEzJsNoZVjVW4qHNFQjGKsvIHes4GEz2AwOu1u548iEWVRcI4tWNDY8GEEaX8HY+9gYskcWpq20kQySKyNRsDVt23S2qvse7fo4OjuH/nYdxyeX0wRvn+nYfxs89dgk2fWBoW9+md8uPObQexrK40o15NZzNG+a5uzwQe+8txfOe6Vkz4ppHvsOPhl4+ibX6pqrHm0MCwKt9uVbTSKX4asXWK18z++1NJkdsB/GJWeeIogM9p0S6CsCJ2uy0qhjcdqI1RFJVXGxOtZZwdQWhNpK298u7puPYqu39lMcr9w15wjqyI18x2jPBdA8Ne7DnuwZ7jr4dtl+WLyEhHvosZ0OpJ8fdS2Zlz/gaANm2aQhBELGQxvF7vNA70edA/PImqkjw0u11wOu2qYn7VxkRbPUY40zSTM+18kkXph6GxSThybBj3zWBusRPFTrtQYzgRe41n61a+D9KF1e1Tbfu10KzXysfG8u16auvHQg970Eqn+H+1qCcjsNnBmHVuUiK7kGllXrGwHE8f7MddT4VoXK5pwrVNVXj+yOmEtTWVmMpILUtZnJqV9VYzTTM5084nWZR+uHfHIdzYVhemNbx+ZQMe39MdpSeciL3GsnWfbwYb1jRF3X/VNCkOYnX7VNt+rTTrtfKxMt++qEKsg6yVtr4MvexBU51ixlgDgH8HcDGA4N3MOa/X7CAwv06x1fcnnWIpltfclGlNPvz5S3CzQLNStj2WNqXy1CDRmGiL6K1GYTLdzpR1ik12Poah9IOiFRzZH7dcXo8Hnj8S1HRtrpmTsL3KbH3Psffx1cffiNJE/v71S6IktiyGZj7T6vaptv1aatZr5WNFvr2rz6O7tr4IDewhLTrFPwFwN4D/AHAFArHB5h/dCCJLkMXwDkg0K2XbY8U6qo2JtmqMcKbpdmba+SSL0g+KVnAoynbl/xNTM6omFzJb75fEGw8Me0EEsLp9qm2/ljG8WvlYkW83KtZYL3vQelKczzl/jjHGOOfHAfwLY2wXAhNlgiDSiCjeShZfVinRrJRtn1vslMZzWTnuz2yayensy0TOR9QeAJa53on0p9IPQHicr9vlxPVttVhQVoifff4DODvuQ67Nhulpf9ibELXXzO/nqCyW3X8UPqEQyz6t4IvU6vzKNN+rXM7gynX9w164S5xornbB4chJ05lEt1MWa6xn/+vlf7WeFHsZYzYAhxljtwHoATBX42MQBBEHWbzVlYvmCuPCmqqKhTGNjVVF2LC2KSjFo2hcVhc7hfVftbgSzx4asGTcn9oYNb3jodMdQxnvfETt2XzTUvimuSWud6L9qfTDvTsOobO9AZt2HkZpgQM3f3A+7nvuXHxxZ3sDfvCHd3B7e0MwflLtNVPK//dL7+Lu1Y24Z3tX2H3WUq2vPrmVkNlnXWmBJXxRXWkBbm9viPK9Mi3qxZXFuPWKhijf21BeiG37e6O2d7RUGzIxlsUaL64s0dV/6eV/tY4p/gCAQwDmAPg2ABeA73DOX9XsIKCYYr33p5hiKZaJKZbFW21dtxydj74eFbv4k89egupiJw70eTAwPInKWfWJtwdHhOW/f/0SfOqh3cL6RfFlVoj7SyZGTc94aJXtSTmmGIh9PqL2dK5ciC0vRsfdmvF6q+lPpR/eH5tEbk7gadRnfhIdW6/EHSvxk2ptKLR8S00J/v7DF8Drm8b55YVorZlj2NM/DdHUZ4rs89jQmNTXmckXpWIboeUf+fwl+LQgz+Pnt1xqWPy5KNb4xJlx3WPAU/S/+scUc87/AgCzT4s7OecjWtZPEERiyOKt+jzi2EUlDusD55eFbZeV749Rv1Xj/symmWxEDGWs8xG1x28hfV01/ZmoPrESd6zET6q9ZqHl9/cMo/NXAS3ZR9ddmgkTYs0R2WcsX2cm20zFNkLL90vzP4yLPxfFGqfDf+nhfzWdFDPG2hBItiue/e4B8HnO+WtaHocgiNjI4q3cLnVxedK4tiTqNztm00y2QntymHX0dWX9ycBwdHA07ClT5L0wt1i8L+fhOtyJxo0q9U9MzWD9yoV4bE9A81ip14z9Z1aS8XVGoDYmWla+Spr/IT8vI2Kr1cZQmwWtReT+G8CXOOcLOOcLANyKwCSZIIg0UldaIFzXfnFlCX5ww5Kw7aFxedds2oVP/mg3rtm0Czu6+rGooki43n1jZbGw/gvLAzHIods3rG1CrSvfmI5QgRKjFtk3RmkmW6E9zbUuU7UxFqL2r1/ZgC9vfSNo734/D8b5ht4L3WfHouy9s70B2/f3YMPawH0FnIsbDb1fbm9vCIsbDa3/8z/dgx++eBQ3f3B+cBJn1v4zK7L7pNFtLtuUtVPme+tKC4TlL64qwoY1ET52TRMurpSHeInq9/v1DZNM5F4wI1rHFP+Jc/6heNtShWKK9d2fYoqlWCqm+HM//bMwdliJw0slLk8Wm/z965cI9VY3fWKprpqVWmE2zWQV7dEkpjiZ9gAwVZ/FQmn/8aExvH7yLB6PeEL7TOcKABDGTj/5Rg++etVF6PdMYFFVMc6O+8CYDQ+/fBTfuW4J6iuKEooblZX52ecuQUVxnqn7LwnS4jNl94kV7meZ732mc4XQV+89cQb/v9+9iZsvq8eEbxr5Djsefvko/u/VFwtjio3Sd7aArnRadIr/zBj7IYBfAeAAbgTwAmNsGQBwzvdqfDzCZNTMq0Nv98mk96+unYeekyc0bFF2MiDRPVXiuVKNy4sVayzcrrNmpVaYTTPZKu0xUxtjobR/YNiLTc+F26gS78gFcdJ+DhwfmsA7AyPYvDN8P+DcfZVIHKWsDAe3RB+aEZldWuH+iWczkeX7h73Yc9yDPcdfD9tHFlNslL6zVXWltZ4UL5n9N1KX+DIEJsntGh+PMBm93SdTflJNiJHFhYnWnY8Vz6Umfk2mQSmL13PL4uBc+seRmUmTVIYV2qgQ2tYChx2+mRmUFWbGk8xQe3e7nPjYslrk2IAChx02Fngy7OfAi2+fwooL56KuNB/rVy6Ew24T2ndFkRNHB0cTihE2W6y4lRD5Oj2XEtYame9V46tlPlYWU2yUvVnVzrVWn7hCy/oIgggQS3f4qQO9URqRqxvdQk3MWle+VNNTpPnY6C6RbHcJtzdXu4SalY1uffVW063pmwxWaKOCqK2d7Q3YuucEvr5qsSnbrIZQLeIb2+qwaWdAf3jLi0exfmUDHt/TDYed4YsfXhimHfzNqy/CV668EP/xx3fCruF7Q6O47ZevB7etX9mAh185jjPjvqg4Vr31rTOV6Wk/tu3rifItika02Ynlw9X46r9eWCbWlK8sFh7XKHuzqp1rHVNcCeDfAFRzzq9mjF0M4IOc84c0Owgopljv/VOxCS36z8QxzYbFFMvis35+y6VCvWDZ9ljanaL4tVhxebLtIs1KvQctC8Sv6d1GTWOKZW1VdHnN1K/J4vdzHOg5K7wfbrm8HgDw0EvRGsw/+/wH4LTnYNw3g8oSJ2wMWHWfuhhhs8W66ohmPnPfyTPS/AYr5CvE0o6XnZdo+89vuVR13oZR9mZyO09LTPFPEVCb+OfZ7+8A2ApA00kxQWQbcs1KddvjaXeqicuLtb3YmYtx3wyKnbmaO0HRK9Rk4tfUhDJo8drWSjF2srYqurxv9g1jxDuNRndAdcGKr7RtNoZx34z0PJX/R/52dmwKf9NUHtwm0zA+M+5DRXGe9NhminW1AjLfFStfwUzLISeTtyHz7cnmbaT7eZMV7VzrSXE55/wxxtg3AIBzPs0Ym9H4GASRdUhjftVul8QIaxXnpXeIgOwV6tLaOarOS007tXpta6UYu3i6vIf6RvCPj+/Dxo4m5Nlt+NoT+y35Slt2TTgHmESDOTI+XlbHgZ5hfHnrG6YNkbEaMt8li6X1+WZMtRyyWj1l2fnKNOJleRtWCtsyA1p7rTHGWBkCSXVgjC0H4NH4GASRdcg0LpUY3tDtGzua0Fwt1uhUYoT10u48NjQWdL5A4MnGHY+9gWNDY5rU39XnCU5Olfrv3HYQE9Mzqs5LTTtlx+zqU+fazKY7HIscG7B+ZYNQl7ezvQG/2dsd7IfDp0ZT7hujkGkX/2ZvN57e1xOluS2KjxfVEdpHWtp/NuOwA3df2xjWz3df24i8XPHEbn+vJzghBgK2edeTB7G/1xjbVKunLPPVLRKfL8vb0NsnZxpaPym+A8BTAC5gjP0JQAWA6zQ+BkFkHTYbw6rGKlzUuSIqPqujtQYNc4uiYnhl5WXbtUDvEAHZK8WeMxOqzktNO5N5bStC777Xkj6PFw+/chy3XF6PPLsNC8oLwbkfq1tq8Mirx4PKCt4pPyLXAEimb4wi8ppUFDmRYwOW1s3B3GInal35WFQZfW/J6nhnYAQHeoaj+siMITJW4/jQBH61+zi+c11rUJ/3xy++i/MKFmKxe05UeVkImVHLIce6/9Vul/l8EVYK2zIDWk+KLwBwNYB5AD4O4FIdjkEQWYksPku07nys8nrGeekdIiB9pehyqjovNUuQxjqmWqwSY1dZ4sSZcV9Y3OL6lQuxfX8PPrasNqzPIuf06ZLg0wrRNVlQfu7/ontLVgcAfHnrG7rZfzbjduXD453C2/0jQfvzeKektqZWuiwdJJOfocbni7BS2JYZ0Dp84luc82EApQCuBLAFwH9pfAyCIEyK3iECje4SVa8OZahZglSrY1oJ0XVcNn+OeMnv6pKs6ptYWClExmosriwW2p+yxHYkzdUu4ZLzLdXZZZtkk+rQ+imuklT3UQAPcs6fZIz9i8bHIAjCpOgdImC321S9OpRx4sy4ME54WV2p8KmMFse0EqLryDnwhUf2RsVobr/tcmxdtzxr+iYWVgqRsRrdnglhjHDb/Oh7FgAcjhx0tFSjvrwwqDDTYqD6hFGQTapD60lxz+wyz1cCuJcxlgftn0YTemKzgzG6WYjk0TtEQM2rQxlq4+y0OKbViLyOMumx02OTWF5fnlV9EwurhMhYjWRiYx2OHLQtOC8dzTM1ZJOJo/Wk+AYAqwB8j3N+ljHmBvA1jY9B6Il/mpZpzlKssgSxrJ1q2q93nJ1V+jIRlHOxMZY1sYmZdP2siJql6DPR/pKF7DZ1tF7meRzAb0K+9wHo0/IYBEFoj1W0LGXtvGpxJZ49NJBw+/VcgtQqfZkIoedy4dwi3H1tI+55+tyyxxvWBpajzSQy6fpZEVn/tzdUYMPapijd4Uyzv2Qhu9UGCm0gCMIyWpaydnb1eVS1X4mze6ZzBR5ddyme6Vyh2eBhlb5MhNBzWXHhXDz4v0dwy+X1uK19IW65vB4PPH8YhwaGjW6mpmTS9bMisv4/2D+MB54/nPH2lyxkt9pAcmkEQVhGy1LtUqmx2q9XnJ1V+jIRQs+FMSS9vKyVyKTrZ0ViLWmfDfaXLGS32kCTYoLIUMwUY5sMsrhCkb6wbKnUWO0X1T897cf+Xg/6h71wlzjRnES2uhn7Mlkiz0Xp+2JnDmrmFOD40BjKi/Jw7PQo+jyBfqwrLcCJM+NpiWvUI4Yyk66fFZH1f1WJE23zXbj5snpMTE6jIM+On718FFUuJ6an/ejq86DP44XblY9Gdwnsdpt0u9lib7VoD9mtNtCkmCAyELXxZXrG2CaDrP1XLpqL29sbgnJqii7uheVFquINRfX/8NPLMDDsi6qjo6Va1cTYbH2ZCrWu/GC/vvj2KXzx/1uIB//3CG5sq8PXntgXPL/1Kxvw8CvH4bCzqOujV1yjXjGUmXT9rIis/y+uLMINbfPxTyF2t2FNExaWF2Dbvp4on7C60Y3tXX1R29c0V+OPb58yTeytVnZMdqsNNCkmiAxEFl92UecK4as0s2lZytq/dd1yob7wgrLCYLwhYwDnwAPPH8aiyiK0zitNqP6RiRmhDmp9eaEqWSez9WUqHBo4F8d5yfml+MIjr+GWy+uxaefhsH6677lAGQBR1yeW3aWCWhtPlEy6flZE1v97T5zBXU9F3J9PHcSC8kuEPmH+eQVSX6GH3SSLVnZMdqsNNCkmiAwkmfgyM2lZqo0dVhtvKKp/bHJaWPfAsFd1+83Ul6nQ5znXrw1zl8A75QdjEPaTEs6SrrhGPWMoM+X6WRVR//dLrvfA8KTEJ8i2myv2Vks7JrtNHdNMihljxwCMILAq3jTnvM3YFhGENmgVv2aVGGG156sudjhfdbxhopqnhU67sO7KkuyJyQvtK7fLiYriPHzz6kVwzylAbg7D+pULke/IEfYT54FkvHTZXTwbF113AKaKJSUSxy3xCZUlecLtVSV5Ul8hsxu9Y41FMc4UC2wuTDMpnuUKzvlpoxtBEFqhVbyYVWKE1bZTfexwoTB2eHFloTDe8MLyIqmucWT/FDtzhHW3VLt07TOzEHotSgscuPmD8/HoX05ExQ9/7W8W4d//thnf+O2BkL5uxKvvnsZrJ89iY0dTVEyxHnYXy8ZlduWwM9z2y9dNEUtKqGNRZQFu/euGYAiFco8vqirErVc0RN23jVXFwvu5qapEaDd1pQW66vxOT/uFsc9rmqspFthEmG1STBAZhVbxYlaJEVbbTq1ih88vKxDGG9ZXiOMHFV3iyP6Znvajvrww+KSoJQn1CasSei0+tqw2GCccGT/83d+/jfUrG8KvwQtH8N2Pt+K2lReirrQAy+pKdbe7WDZ+dHBUeN3XfbjeNLGkhDre7B+TxhSLcgEWVRYJfUXb/FKh3egVo67Q1ecR+rSGuUUUC2wizDQp5gCeZYxxAD/knG8J/ZExtg7AOgCoq6szoHkEISaWbWoVL2aVGGG17dQsdjjJ+MHI/nE4clQl1ZkZtT4zUpM4VvzwmG8m6hoMjk7ikvoyAEib3clsXGZXfo6obaTjmn6SGc9lscOy7aHx8KHI7n29dX6lPs3jRes8igU2C2aaFH+Ic97LGJsL4A+Msbc45y8qP85OkrcAQFtbG5dVQhDpJpZtahUvFqseWRycEVqcsdqpJp4uVuyweLs8flAUb5gN8XpqfWbktXDm2oL/RvZrpBmZLfZaZleidmttC2bTwDUj8WxT7CvE97hsu1rtcr1je6U+zWWe+0Yhm23YNMs8c857Z/89BeC3AC4xtkUEkTpK3GPoBCOZeDFZPUoc3DWbduGTP9qNazbtwo6ufkxP+4Xb/ZGPyjSmrrQAGzuawtq5saMJ1cVObNvXgxu3vIov/nwvbtzyCrbt60GtK194Xo3uEuH2lmqXsP5mt3h7U1UJbm9vwEMvHcXmnUfw411HcXt7A+pKC3TtBysSamO/fq0b61c24Ol9Pehsbwjr13vWNKKiKC9sm9lir2V22FLrSvlejIUSy5zu+y6TUGJvI33F4qpCbFgTfk03rGnCxVVi39jodqnyvVr5ahmLKwMxzpH3zeLKEk3q14pst2FTPClmjBUCsHHOR2b/fxWADQY3iyBSRqvYXlk9sWJyjdDiPHFmHPfvDI/ju3/nYSwoK1QdTyfb3tFag4a5Rej3eFHlcqLR7YLdbhNuP3FmXHjcZXWl9Koygsg+rypx4pIF5+G14+/jO9e14tjpMUxO+/GfLxzBJz5Qh1sur0eODbh8YTmW1MwxVey1zA5/9rlL8IyOsZt6x6VmA7LY2/nnXYLH9hzHd65rxYRvGvkOOx5++SgWVan3ISL0zsPo9kzgsb9Et79tvrl8UbbbsCkmxQAqAfyWBd5v2gH8knO+w9gmEYQ2aBXbK6pHbUyu3vGTA9KYX/XxdLJ+s9ttaJ1XGqU/LNqud5xgphHZ52/19+Hffvd2VLnQmOJGd4mpJsRAbDtcXl+u27Une0sdeT7BJPYc92DP8dfDfkvGh8jQMw9jYNgrbL/ZbCPbbdgUk2LO+VEArVrUVTOvDr3dJ7WoiiBMj9qYXL1jaSslesFuWSxwEvF0onhDu10cCUYaoKnhduULY7L57JtUZ64N5xU6MD3tD14DM8QjGnXdyd5SR55PIMkbkGiRmy0G1iq2YZV26oUpJsVa0tt9Ejf+8OWk99/6hcs0bA1B6ItMq1WJyU239mWtKx83fCBCL3htExZXFkn0hdXF08m0Pjtaa4QTY6P0mjOFRRVFUdqwd1/biF/tPg5nrg2d7Q34xyf24fb2BnS01sBmY7pqvSaKUded7C11lNjbKF8xG1McqVPcUF5oCpuLh1Vswyrt1IuMmxQTRDYRKw7OCO3LQwPDQs3Q+nKxvrDaeLpYWp+t80qjyhvVD5nC24MjUdqw9zzdhe9d14pD/SN45NXj6PN4g9eg2JlrinhEo6472VvqdHsmpFrkD7wQsf2Fwzi/vMAUNhcPq9iGVdqpFzQpJgiLI4uDM0KnWK2+sNo4tdhan+J9jOiHTEHW328NjIRdS+UajPtmTBOPaNR1J3tLDXk8+KQqjXIzxsBaxTas0k49MI0kG0EQ1keJBwwlVF84cruiX7zv5BnsONiHfSfPYno6fIBLqH4Tan1aHb+fh0mvKcj0fqtczmA8YuRv2RKPSKSOzIaUmOJEt5PNEclAk2KCIDSj0V0i1oetFmuG1rryhZqksomxrP5Gt3k0cjMBRav033/3Ju5e3RjW3//6t81YOLdIqLeqt9YrkfnIbKixqlioU9xYVUw2R2gGhU8QBKEZMr1gu90mjFM70HNWVYxwrPoJ7QjVKvVNB7RVvb5p1FcUoazQgc/+9M/S+PBsjkckUkcW03qg56wwpjiWTjFBqIUmxQRBaIpMR1gUp5ZMjLCsfkI7QrVK9/cMo/NXAW3VR9ddGjc+PJvjEQltkPkKYUxxDJ1iglALTYoJgkgLIi1RmQ5urBhhtZqkVtAwNQpZ34RqlbbUlODvP3wBJnzTyM2xwZlry2odU8IYYvkKuscJraBJMUEQuqPEqEZqX7Y3VODWKxoS1i+W1SPTJFVbPpuI1TdKXOd/v/QuPr6sLkx3+p41jfjnaxbjX585lJU6poQxXFheGKWZbSWdYsIaUCAekVHUzKsDYyzpT828OqNPISMJjVEFzmmJvj04ItQ17vZMqKrn2NCYJuWziVh9o8R1/t+rL8Y927vCytz9VBfKi/Jwy+X16Fy5EFvXLacJCKE7XQPRmtl3PXUQXf0jdI8TmkFPiomMglY0NCehMaoK3im/NKZYpjEqq0er8tlEvL6x2RhOj4o1YIe9U8HYzssuKKMJMaE7/RJ7tZJOMWF+aFJMEBbBynFzoTGqCs5cW1B3ONH4VFk9WpXPJuL1TahOcWSZfIc9qjxB6IlbYq9VJXnCWGOySyIZKHyCMBc2e0rhD5mKEv95zaZd+OSPduOaTbuwo6sffj83umkJIdUedZeo0hhVq4NLurlyYvVNMN748TfQ2d4QVubuaxvx4xffpb4k0kpztQsb1kboFK9tQlNVCW5vb8BDLx3F5p1H8ONdR3F7ewPqSgsMbjFhRehJMWEu/NMU/iBAFv95UecKS7wilGmPxtquth4tymcTsfrm6OBo0N4eefU4brm8Hjk24MMNFagoduD88gLqSyKtOBw56GipRn15YfBtWUu1C92eCaHW+bK6Ukv4RsJc0KSYICxAJsTGyvRr1era6l0+m5D1Tai99Xm8YfHD88uKML+M+pJIPw5HDtoWnBe2LRN8I2EeaFJMEBbAjLGxshhn0hG2PpUlTmGcZn5uDvx+TteHMASfbwb7ez3oH/bCXeJEc7XLlL6RsC40KSYIC6DEf0ZqcRoVzynTuL1qcSWePTRAOsIWp9aVH6Uffc+aRjzw/DvoWFpH14dIOz7fDLbt743SNF/T5DaVbySsDU2KCcICmC02VhbjvHXdclWxz1aPlc5UDg0MR+lH3/1UF75zXStdH8IQ9vd6hJrm9eWFpvKNhLWhSTFBWAQzxcYapTtMpAfZdZzwTdP1IQxBplM8MOw1lW8krA1NigmCUI1RusOEPkTGdcuuY77DTteH0B1RnoFMp7iyhGyR0A7SKSYIQjUyjdvFlcXY2BGuJbqxo0mqGUo6wsYj0sA+OzEZdR3vXt2Ih18+SteH0BWZJntjVYlQp7il2mVwi4lMgp4UEwShGlmM87GhMdy/8zBuubwejAGcA/fvPCzVDDVbrHQ2Iorr/sIje/H7L6/A1nXL0efxorwoD3Yb8J3rltD1IXRFlmfwTOcKoU6xw5FjcIuJTIImxQRBJIUojm9g2IvjQxNBXVuFWDGoFA9oLLHiw5fXl6N1nkENI7KSeHkGkTrFBKElFD5BEIRmKDHCoVAMqrmha0aYCbJHwkhoUkwQhGZQjLD1oGtGmAmyR8JIKHyCIAjNoBhh60HXjDATZI+EkdCkmCAITaEYYetB14wwE2SPhFFQ+ARBEARBEASR9dCkmCAIgiAIgsh6GOfc6DaohjE2COC40e2YpRzAaaMbEQNqX2qEtu8053xVrMJxbNPs56o12XS+Rp9rTNs0gc80un9SxertB4w5h0R95his379qyAR7UoMZz1dom5acFJsJxtgeznmb0e2QQe1LDS3bZ/Zz1ZpsOt9sOtdksHr/WL39gLnPwcxt0wM6X/NC4RMEQRAEQRBE1kOTYoIgCIIgCCLroUlx6mwxugFxoPalhpbtM/u5ak02nW82nWsyWL1/rN5+wNznYOa26QGdr0mhmGKCIAiCIAgi66EnxQRBEARBEETWQ5NigiAIgiAIIuuhSTFBEARBEASR9dCkmCAIgiAIgsh60jIpZozlMMZeZ4xtF/z214wxD2PsjdnPXeloE0EQBEEQBEEo2NN0nPUADgEokfy+i3O+Ok1tIQiCIAiCIIgwdH9SzBirBfBRAD/Wqs5Vq1ZxAPShT7o/cSHbpI9Bn5iQXdLHoE9cyDbpY9BHSDqeFP8/AP8EoDhGmQ8yxvYB6AXwj5zzrsgCjLF1ANYBQF1dnQ7NJIjkINskzAjZJWFWyDYJs6Lrk2LG2GoApzjnr8UothfAfM55K4D7AWwTFeKcb+Gct3HO2yoqKrRvLEEkCdkmYUbILgmzQrZJmBW9wyc+BGANY+wYgEcBtDPGfh5agHM+zDkfnf3/MwByGWPlOreLIAiCIAiCIILoOinmnH+Dc17LOV8A4BMAdnLOPxVahjFWxRhjs/+/ZLZNQ3q2y4r4/RxHB0fxyruncXRwFH6/NCSGIAhCF8gPEVaC7JVQS7rUJ8JgjH0RADjnDwK4DsA/MMamAUwA+ATnnCw3BL+fY0dXP+547A14p/xw5trwgxuWYFVjFWw2ZnTzCILIAsgPEVaC7JVIhrQt3sE5f0GRXeOcPzg7IQbnfDPnvJFz3so5X845fzldbbIKx4bGgjc2AHin/LjjsTdwbGjM4JYRBJEtkB8irATZK5EMtKKdBRgY9gZvbAXvlB+nRrwGtYggiGyD/BBhJcheiWSgSbEFqCxxwpkbfqmcuTbMLXYa1CKCILIN8kOElSB7JZKBJsUWYEFZIX5ww5LgDa7ERi0oKzS4ZQRBZAvkhwgrQfZKJIMhiXaEOmw2hlWNVbiocwVOjXgxt9iJBWWFlCxAEETaID9EWAmyVyIZaFJsEWw2hvqKItRXFBndFIIgshTyQ4SVIHsl1ELhEwRBEARBEETWQ5NigiAIgiAIIuuhSTFBEARBEASR9dCkmCAIgiAIgsh6KNHOJPj9HMeGxjAw7EVlCWXJEgRhDsg3EWaBbJHQG5oUmwBao50gCDNCvokwC2SLRDqg8AkTQGu0EwRhRsg3EWaBbJFIBzQpNgG0RjtBEGaEfBNhFsgWiXRAk2ITQGu0EwRhRsg3EWaBbJFIBzQpNgG0RjtBEGaEfBNhFsgWiXRAiXY6oSZLltZoJwjCjET6pooiJ3JswO73hij7n9AN2fhJ4yShNzQp1oFksmRpjXaCIMyI4psWlBVS9j+hO/HGTxonCT2h8AkdoCxZgiAyDfJrRDogOyOMhCbFOkBZsgRBZBrk14h0QHZGGAlNinWAsmQJgsg0yK8R6YDsjDASmhTrAGXJEgSRaZBfI9IB2RlhJGlJtGOM5QDYA6CHc7464jcG4D4A1wAYB/BZzvnedLRLL9KRJUtrwBMEoTWx/Apl/xPpIJad0bhH6E261CfWAzgEoETw29UAGmY/lwL4r9l/LY2eWbK0BjxBEFqTiF+h7H8iHYjsjMY9Ih3oHj7BGKsF8FEAP5YUWQvgYR7gVQBzGGNuvdtlZSg7lyAIrSG/QpgZsk8iHaQjpvj/AfgnAH7J7zUAToZ8757dFgZjbB1jbA9jbM/g4KDmjbQSlJ1rLsg2gXkLzkeO3Z7QZ96C841ublag1i7JrxDpIhmfSfZJpANdwycYY6sBnOKcv8YY+2tZMcE2HrWB8y0AtgBAW1tb1O/ZhJKdG+ogKDvXOMg2gd7uk7j+P3clVPbxL63QuTUEoN4uya8Q6SIZn0n2SaQDvZ8UfwjAGsbYMQCPAmhnjP08okw3gHkh32sB9OrcLktD2bkEQWgN+RXCzJB9EulA1yfFnPNvAPgGAMw+Kf5HzvmnIoo9BeA2xtijCCTYeTjnfXq2ywxMT/vR1edBn8cLtysfje4S2O2J/Y1CWeAEQWhNpF+pKnFixg/sfm8oZqY/KQIQ6SCZcS+VcZbITtKlPhEGY+yLAMA5fxDAMwjIsR1BQJLtc0a0KZ1MT/uxbV8P7tx2MJhFu7GjCR2tNaomxpQFThCElih+ZUFZYUKZ/qQIQKQTNeOeFuMskX2kzTI45y8oGsWc8wdnJ8SYVZ24lXN+Aee8mXO+J11tMoquPk/wRgUCyQJ3bjuIrj6PwS0jCIJIPNOfFAEIs0LjLJEM9OeSAfR5xFm0/R7KoiUIwngSzfQnRQDCrNA4SyQDTYoNwO3KF67tXuWiLFqCIIxHyfQPRZTpn2g5gkg3NM4SyUCTYgNodJdgY0dTWBbtxo4mNLpdBreMIAgi8Ux/UgQgzAqNs0QyGJJol4n4fDPY3+tB/7AX7hInmqtdcDhyhGXtdhs6WmvQMLcI/R4vqlxONLpdMYP/KcM7NeL1H/UvQZxDlukPAEcHRzE0NglHjg3jvhksmluEJ774QXSfmYDblY/FlcV0L1mQWD7QLP5R1g6ZysSa5mosKCsMG5cpyS59qLUbM9gZTYo1wOebwbb9vbjryXNZrhvWNqGjpTrmxLh1Xila5wl/DoMyvFMjXv9R/xJENJGZ/sp9cu+OQ7ixrQ6bdh4O3i/rVzbg4VeOw2FnuL29ISzjn+4l8xPLBwIwhX+UtfHKRXPx1IHeKJWJNc3V+OPbpwxvd7aidlw1yzhMfzJpwP5eT3BCDASC+e968iD292qT5UoZ3qkRr/+ofwkiPsp9srqlJjghBgL3y33PHcbHltVidUtNVMY/3UvmJ5YPNIt/lLVjf69YZWJ/r8cU7c5W1NqNWeyMJsUa0C/JwB4Y1ibLlTK8UyNe/1H/EkR8lPuEMQjvF8Yg/Y3uJXMTyweaxT/K2iEbf2XbyRbTg1q7MYud0aRYA9ySDOzKEm2yXCnDOzXi9R/1L0HEJ/Q+Ed0vnEP6G91L5iaWDzSLf5S1o0qyXTYuky2mB7V2YxY7o0mxBjRXu7BhbXiW64a1TWipPpfl6vdzHB0cxSvvnsbRwVFMT/vDvvv9XFo/ZXinRrz+o/4liPgsKCvEDz+9DE3uEvz7x5qxfuVCuF3OYEzxb/Z24+l9PVEZ/3QvmZ9YPtAs/nFBWSE237QUnSsX4rb2hVi/ciE237QULdUuocpEc7XLFO3OVtTajVnsjBLtNMBut6GyxIHvXdeKMd80Ch12FOfnBLNcRQHkGzuacP/Owzg+NBE3oDyZNd+Jc8TrP+pfgojP9LQfA8O+sITib69twrK6OcixMSytm4O5xU7UlRZgWV0p3UsWIp4PNIN/9Ps5zo5PYcuLR8PGUZuNCdWcbDYGh51h3Yfr4eeAjQEOO9lhulA7rpplHGacy59QmpW2tja+Z495VoM+OjiKazbtCouHceba8EznCtRXFEl/v+Xyejzw/JGo8oRpiXt3ms0200WO3Y7r/3NXQmUf/9IKzExP69yirCOmbWphl3uOvY9PPbQ7yo/9/JZL0bbgvJTqJjIWzXzmvpNncOOWV6Psb+u65WidVxpVPt64TGQ9Qtuk8AkNSDaRizFxeYIgCLOhd0IxQcRC7bLNZkncIqwFTYo1INlErtCH9JQAQBCEmdE7oZggYqF22WazJG4R1oImxRqQTCLXxo4mbN/fIyxPEARhNhJJKCYIvVC7bLNZErcIa5G1iXapLj9YV1qAE2fGg9+vWlyJZ1QkclEySmzMsNwjQWQ6idxnoUvoNlUX4xe3XIr+2fItMZazJ8yHVfyqqJ12uw2rG92Yf14B+ocnUVWSh2a3fNlmsyRuEeknFTtXNSlmjF0GYEHofpzzh9XUYQa0WH5Qph4hC+CPXDIVQNR3IoBZlnskiEwmkftsetqPbft6opbQ7WitkU5GCHNiFb8aaznn7V19qmxRNO4SmU2qdp6wV2OMPQLgewAuB/CB2U9bsg03Ei2WH7xz20GsbqlJaH9CHWZZ7pEgMplE7rOuPvESul192ixhT6QPq/hVtcs5ky0SoaRq52r+1G8D8CHO+Zc457fPfjrVN9l4tFp+kNQj9IGyhglCfxK5z9Rm/BPmxSp+VfVyzmSLRAip2rmaSfFBAFUqypsWrZYfJPUIfaCsYYLQn0TuM7UZ/4R5sYpfVbucM9kiEUqqdh53UswYe5ox9hSAcgBvMsZ+zxh7Svkk02ij0WL5QVKP0A/KGiYI/UnkPlOb8U+YF6v4VVk7Zcs5ky0SoaRq53FXtGOM/X+xfuec/2+MfZ0AXgSQh0By3hOc87sjyvw1gCcBvDe76Tec8w2xjqnF6kxKdmKiWamR5auLnTjYP4z+YS/cJU5cXFmMd06Pos/jhduVj8WVxej2TCStbpHtWbJqr0+aoBXtJNCKdoaT1Ip2se4zRXWi3+NFaaEDnokplBY4UFqQi/fHfXDk2DDumyF/ZSEM8KtJ+UxZOycmpnCgfxgDw5OoLMlDc1UJ8vNzwxRS3K58NLpLkkoEpXE4M0jQzoUXNq76hDLpZYzdyzn/eliNjN0LQDopBjAJoJ1zPsoYywXwEmPsd5zzVyPK7eKcr47XFi1Rm5UaWl6Ukb1hTRMe23Mce457ML8sH7de0YC7njz3u1p1CzNmBacTyhomCP2R3WciH3f3tY04MzaJB154Fze21WHTzsPkryyGVfyqqJ0+3wye7uoPG1c3rG3CtY1V+J83+1NWSKFxOHNIxc7V/Cn1EcG2q2PtwAOMzn7Nnf3EfjRtAUQZ2Xc9dRA3X1YPAFjdUhO8cZXf1apbmDErmCCI7EDk4+55ugsFjlysbqkJToiV38hfEXqzv9cTNa7e9eRBHOgf1kSVgsZhAkgspvgfGGMHACxijO0P+bwHYH8C++cwxt4AcArAHzjnuwXFPsgY28cY+x1jrFFSzzrG2B7G2J7BwcF4h9UVWUb2hC/wSpgxaKJuYbasYEKMmWyTIBRSsUuZjxvzTav2bwQRSTK2KVOfGBie1ESVgsZhAkjsSfEvAVwL4KnZf5XPX3HOPxVvZ875DOd8CYBaAJcwxpoiiuwFMJ9z3grgfgDbJPVs4Zy3cc7bKioqEmi2fsgysvMd9rDvkb+rVbcwW1YwIcZMtkkQCqnYpczHFc76OPJXRCokY5tuyThZWZKniSoFjcMEkNikOAfAMIBbAYyEfMAYOy/RA3HOzwJ4AcCqiO3DSogF5/wZALmMsfJE6zUCUUb2hjVNePjlowCAp/f1YMPapoSzH62SFUwQRHYg8nF3X9uIcd8Unt7Xg872BvJXRFpprnZFjasb1jahuUobhRQahwkgsWWeX0MgDpgBqANwZvb/cwCcAHC+bEfGWAWAKc75WcZYPoArAdwbUaYKwADnnDPGLkFgoj6k/lTUES/LNFY2q2gN9ourilBfURimRrGosihs/1j1X7loLp7RcI32eOcX+XtdaQFOnBmnrFuCIGC329DRWoOGuQEfdl6hA2O+aVQU5uF717Xi/fEpPPz5S3BmzIfyojyUFTmw+70h8iXQT8Eg2322w5GDqxsrsKDskqD6xOKqQuTn52JNczUWlAXG36oSJ1qqXbDbbapUKWw2hlWNVbhIw3HY7FhFbUPWTj3an4j6xPkAwBh7EMBTs09zwRi7GoFJbizcAH7GGMtBYLL7GOd8O2Psi7N1PwjgOgD/wBibBjAB4BM8nk5cisTLMhVlXodms05P+8PWYBepTWzsaML9Ow/j+NCEqvq1yAqOd36i32O1lyCI7MNut6HR7cLhU6P48tY3cOHcInzy0vm45+muc4oUqxvxo13vYuXiKjz8ynE47Ay3tzeE+bZs8iV6KRiQzwbGJ3z43cFB3PVUuOrTR5vm4n+PvB/V51cumounDvSqUqWwijqHFlhFbUPWzqsWV+LZQwOat1+N+sQHlAkxAHDOfwcgnobxfs75Us55C+e8SdEf5pw/ODshBud8M+e8kXPeyjlfzjl/OZkTUUO8LFNR5nVoNmvk7yK1iTu3HcTqlpqk6tf7/ES/x2ovQRDZSaiv+vsPXxCcEAOzihTbu3DzZfW477nD+NiyWqxuqYnybdnkS/RSMCCfDRzsHwlOiIFzqk9d/aPCPt/fq+84a3WsorYha2dXn0eX9quZFJ9mjN3JGFvAGJvPGPtnpCHMQQ/iZZnKMq+VbNbI32XZ2IyFf0+0/lSJd36y32XtJQgiOwn1VROT01LVHcV/ZLsyhV4KBuSzIVWZkKpPSPpMq3HW6lhFbUPWTtk8KtX2q5kUfxJABYDfIqAQMXd2m+WIl2Uqy7xWslllv0d+Dw0CUVN/qsQ7P9nvsvYSBJGdhPqqgjy7VHUn1H9kcwa/XgoG5LMhVZmQbZepVWg1zlodq6htyNrpdunT/oQnxZzz9znn62fDIZbO/v/9lI5uEPGyTEWZ16HZrJG/i9QmNnY0Yfv+nqTq1/v8RL/Hai9BENlJqK/60Yvv4u5rG8MVKVY34uGXj2L9ygb8Zm83nt7XE+XbssmX6KVgQD4baKoqxoY10apPjVVFwj5vrnbpOs5aHauobcja2eh26dJ+Fi+njTH2/zjnX2aMPQ3BanSc8zUptSAJRGulqyXe2thK1mq/x4sqlxONbldYcL7PN4P9vZ4wtYl3To/OZrk6sbiyBN2eiaTrT5V45xf5e3WxEwf7h8Oydx2OnISPp9Xa8yYnbvS+FrZpRXLsdlz/n7sSKvv4l1ZgZnpa5xZlHTFtMxW7DPV1NXOcYGDoOTuBiuI8TE5Pw5XvgA0M3Wcn4HblY3FlcUzfl+mIfC+AlLPkI+tV1CdOjXhRWezEiHcKvQL/a7DCgKY+c2xiEl39o0H1icaqIhTm50nHO9k4axXVBb0JH7e1n4fIUNv/susb77rHmY8ID5jIpPivOOevMcaESXWc8/+NffraY/TEI9MygeOpbei9v4WgSbEEmhQbji6T4mSUbKzk+9KB3n0Uy//abMzo66OZz9SqH8lmAxjVD0beDxHzEeHB4s5YOOevzf43B8BfOOf/G/pJ+QwsSKZlAqeqhqG3mgZBEMaQjJKNlXxfOtC7j2L530y6PlqdSyb1SSoY1Q9G3g+JoOYx3mcBvMEYe4Ux9h3G2LWMsVLVLc4AMi0TOFU1DL3VNAiCMIZklWys4vvSgd59FMv/ZtL10epcMqlPUsGofjDyfkgENYl2N3POLwTwcQDdAB4AMJh4UzOHTMsETlUNQ281DYIgjCFZJRur+L50oHcfxfK/mXR9tDqXTOqTVDCqH4y8HxIh4UkxY+xTjLEfAngCgZXsNgNYkXhTM4dMywROVQ1DbzUNgiCMIRklGyv5vnSgdx/F8r+ZdH20OpdM6pNUMKofjLwfEiFuol2wIGOnAbwL4EEAz3POjyXVYg1IJDA/XnZj5O+1rnwcGhhOWD0hMmszntqE3qSaTRuZpbuoohhvD44E+0PJKJfVH7m/0h9KeSVTWvY93vUxSXZwViXazVtwPnq7TyZU1j/jx40//FNCZSnRThc0SbQLve+qSgKKBh6vD067HUNjvmCGus3GguXmFjuRYwP6h7NTbSISke/y+zm6+jwYGptEidMB37QfVa7wvhLtBySmWhGqEKKoB9ntNhwbGkO/x4s8uw0erw9lhc50KwNp6jPHJ3w42D8SVJ9oqipGQb5DdaPiqTNZFdm4GW97ov2gVmVKq+NGqn01x1HHSlDdS3hAu7TWCDjn5YyxRgAfBvCvjLEGAG9zzj+daB3pIpmM6Q1rm/DA8+fUI2KpJ/j9XLrmthFrpmuRzWm329A6rxSt88TZm5H9E1l/6P6JqHPEUuug7GBz0Nt9MmFFia1f+JDOrSH0JvS+Ky1w4OYPzsejfzmBG9vqsGnn4bB70WFnuO2Xr9P9GYHMdznsDN/e/qawL1c1VgFA1H6bb1oK3zSP6wf9fo4/vn0q7r6d7Q3YuudNfH3VYkteq4mJKWw/OBBc6lnRKb62qQr5+bmq6rLZGOorigwZr/VCZntXLa6UzlfU9INalal443iix/X5ZrBtfy/uejJ8PtLRUi2dGIfOR9SiJnyiBEAdgPkAFgBwAfDH2scoksmYvuvJcPWIWNmKZste1bo9ouzNyP6JVX8i6hyx1DrM1r8EkQ2E3ncfW1aL+547jNUtNcFJHHDuXtzf7aH7U4DMd+3v9kj78tjQmHC//d2ehPxgovtu2hm4nla9Vgf6h4MTYmB2XHrqIA70DxvcMnMgs72uvsTsKB5qVR20Gsf393qCE2KlnruePIj9vfqoW6l5h/ISgGsB7AdwI+d8Eef8M7q0KkWSzZiOVI+QZSuaLXtV6/bIsjcTVddItH9l9ZmtfwkiGwi97xg7d4+K7kV/RNQd3Z8BZL7LzyHty1MjYpUIP5eXj3dM2b5KG6x4rQaGJ4XnNDA8aVCLzIXM9mTjuVobUKvqoNU43i+pZ2BYHxtWoz7Rwjn/Euf8l5zz7sjfGWP3a9u05Ek2YzpSPUKWrWi27FWt2yPL3kxUXSPR/pXVZ7b+JYhsIPK+C01UCcWZa0Pkm3e6PwPIfJfSXzK/Jtovh8nLxzumbF/OrXutKkvyhOdUWZJnUIvMhcz2ZOO5WhtQq+qg1TjultRTWaKPDWsZbW+aoMJkMqY3rA1Xj4iVrWi27FWt2yPK3ozsn1j1J6LOEUutw2z9SxDZQOh99+vXurF+ZQOe3teDzvaGqHuxpdZF96cAme9qqXVJ+3JBWaFwv+ZaV0J+MNF9O9sbsH1/j2WvVXNVCTasiRiX1jShuarE4JaZA5ntNbpLNBlP1ao6aDWON1e7sGFt9HykpVofdauE1SfiVsTYXs75Mk0qi4Ma9QlZdmOkeoSitiBTT4hUp1DUGMySvap1Nm1ktmdjVQl6R7zS+uOVdxfl4WD/MPqHJ1FVkoemqhL0jU5K6zNpdnBWqU+oWbp56xc+ROoTxqKp+sSpES8qiwPqE0Njkyh2OjA140d5UR7GJqfR55lAWVEeRienjFA0MB2hWfYiNQ5FfWJg2Iuywjz4weHIscE340dZYV5YmdDMfkXl49RIQFVixg+cGvGiwGGHn/thYwzjvhnhMYFzyhUFjhxMzfhx3uyx0uhLk/KZMtWC0Qkv3uwfC6pPXFxViKJ86z311guZOoRa9QYZauuRjeNq1aW83mkc6PME5w/NbhecTnuqKlWpqU9YjVjZjbHUI1rnRasfzC/Lx61XNIRlPypZl2bJXtUym1aUzRxLXSNWdmh9RVHc3/U+H4IgEiPWfSe6j62uaKAFiagdRY43keo7sZQm6iuKsKCsMGpM+uKHF+Ke7V0xlSms6ENl/dneUIFnugZVqRBkE7J5zZWL5uKpA70Jq0bEql82L5Dd9yJ/olZdyu/n2Hl4ULWqRrJo+ae9ZbyhWnWK1S01UdmPatbSthpqs0bjZYemO3uUIAjtEd3HVlc00IJk1I4i1XfiKU2IxiRlQiwqb2Vk/Xmgj8aRWEiVT3rVqUaorV+tzamtR29VjUi0nBTfp2FduqJWnUKWNZzoWtpWQ23WaLzs0HRnjxIEoT2y+9jKigZaoIXaUTyliUTHpEy4BrL+6peqT1j/nLVA3m/qVCPU1q/W5tTWo7eqRiRxwycYY08DkAYec87XzP7705RakkaUrMjQDhWpH0T+Hvk90bW0rUa8/onELSmvZIfG+50gCPMju4+trGigBcmOJ6HpPIpaRKpjUiZcA9m5Vs2qT9A4IkbWb7L7Vu38Re28QKt6pOfl0qY9kSTypPh7AL4f42M51KpTPL2vJyr7Uc1a2lZDbdZovOzQdGePEgShPaL72OqKBlqQjNpRpPpOPKUJ0Zh09+rGjFQAkfVns5vGkVhI+63apUo1Qm39am1ObT1yVY3E1FnUopn6hLByxpwAXgSQh8BT6Sc453dHlGEIhF5cA2AcwGc553tj1ZtItmpdaQFOnBmXZiXGUzeIzLJcXFmENwdGg2vLN82qKySZ9Rg3azIyi1RRu0i2fKR6RqJrliv9U13snFWPCFeXUOqvKnJErUnfP+oL/q7sr3xvichaVXv91PZvqvXNQuoTEkh9wnBSUp8Q+SMgEM93enQSDMDg6CTcLidsYOgbnsB5hYr6RB4a3a6MV59Q+mhobBIMDO+PTaKyJOBLAQT977zz8jE9wzE0NokSpwO+aT8qSwLqEKdHJ5GbY8OYdwZ5uTZ4vD6UFzlRnGfH2QkfZvyBMjVz8sEAdJ+diFKi6Pd4kZvDMOabRqHDjqkZjipXuMqF4ufnFNjRezb6mibqB1PM7geS9JkylYPxCV/UOFOQ79CinaZEdl6y7bJ+m5iYwoH+4WC/NVeVID8/V3X9snrUtl+tmoTsvFJUqUpNfYIx1gDg3wFcDCD4fJpzXh9jt0kA7ZzzUcZYLoCXGGO/45y/GlLmagANs59LAfzX7L8JI1KLuL29ISzbMjIrMVaW9fS0Pypbc8PaJjz2l+PYc9yTUP1q2hu5f+Qa4yL1i1jllfY+8Py57ObI7/GyT0P7R6gesaYJfzzUi2ffPI22+S7c8IH5UVnBoceLl12q9vppbQ8Eka2I/JGihnDvjkO4sa0uuDyxM9eGb69twpyCXNz833/OmvtJ6SNRf2zsCPTHbb98HaUFDtz8wfl49C8nosqJFCYU9Y5bPnQ+Jqb8+I8/vhP8bf3KBjz8ynGcGfcF/XVdaQH2njgTpSRwyYLz4PfzqHHg7msb8avdx/HOqdGYCheJ+OV0XWfR+LuxowkfvbgK27sGosaZNU1uoTqB1e1R1v8y1QWZysRHL67C0139CfebrP4rFpbj6YP9waW2lXnAmmY3nM7oqWQsFZGnDvYl3J546hlaK6yo+dP+JwhMWKcBXAHgYQCPxNqBBxid/Zo7+4l8NL0WwMOzZV8FMIcx5lbRLmFmbmS2pZqsRNEa33c9eRA3X1avSf3xsi8jjy9Sv4hVXmlvaHZz5Hc12adC9YinDuLvlp8PALj5snphVnDo8dRkl2rdv6nWRxCZjMgfKWoIq1tqghM75bdvPXkQB3o8WXU/KX0k6o87tx3E/u5Af3xsWS3ue+6wsJxIYUJR7zg95gtOiJXf7nvuMD62rDbMX4t8fazf7nm6C3//4Qukx08m61/v6yw7xwP9w8Jx5oBOKgRGo1Z1QaYyobbfZPUf6B8OToiD9TwVqEdN+2UqIrL2aKWekShqJsX5nPPnEAi5OM45/xcA7fF2YozlMMbeAHAKwB8457sjitQAOBnyvXt2W2Q96xhjexhjewYHB8N+0zozV5bVOOGb1qT+eNmXkcePdzxZe0Ozm0XfE80+lWWvnh2fAgBMTE4ndPxEs0u17l+9M7Vj2SZBGEWidinyR4oaguze8Uc82tDyfjIjSh/F6w/ld1E5mcIEY7F/U/7f7/FKfX2s35RxK57CheycEy2vhli2KTuPAYn6hEyVwur2qFZ1Qa7ypK7f1Pb/wPCkqvbLjivfro16RqKomRR7GWM2AIcZY7cxxv4WwNx4O3HOZzjnSwDUAriEMdYUUUT0fiMq0JlzvoVz3sY5b6uoqAj7TbbGduT3RLMSZWt85zvsYd+TrT/emuCy46stHxouLvqeaPapbO3xOQWBWKKCPHtCx4+XXRq5f+T3VPs32friEcs2CcIoErVL0f2iqCEA4nsn8q10pigfyAjto3j9ISsX2qehZTmP/Zvy/yqXU+rrY/2mjFuyY6j1y1pc51i2KTuPyln1icjtVZLtVrdHWf8rqgtR2yXl1fabrH5ZPZUlearaLzuutD2yenRS/1IzKf4ygAIAnQD+CsCnAXwm0Z0552cBvABgVcRP3QDmhXyvBdCrol3CzNzIbEs1WYmiNb43rG3Cwy8f1aT+eNmXkccXqV/EKq+0NzS7OfK7muxToXrEmib84tX3AAA/e/moMCs49Hhqsku17t9U6yOITEbkjxQ1hKf39aCzvSHst2+vbUJzjSur7ielj0T9sbGjCS21gf749WvdWL+yQVhOpDChqHeUFTrwlSsvDPtt/coG/GZvd5i/Fvn6WL/dfW0jfvziu9LjJ5P1r/d1lp1jc1WJcJxp1kmFwGjUqi7IVCbU9pu0/qoSbFgTPQ9olswj1KqIyNqjlXpGoqhWn2CMlSAQLjySQNkKAFOc87OMsXwAzwK4l3O+PaTMRwHchoD6xKUANnHOL4lVbyz1CSULMVxtwak6O1pRc+j3eFHlcmJxZQm6PRPB+hU1gySzHlWpX1SVONFYWYx3To9K1SMi27uoohhvD44Ez//C8iJ0DYxI1TMi1Rkiv0eqR4S359zxZP2VaJazrH/VqkfEq4/UJ+JD6hOWQhP1idD7AzinPgEAgyOTmFscyBR3OHJSyfo2BWoVC5Tyngkfpmc4To1MonZOPhgLhJiVFebBD46KojwMjfowNO7DnPxcDE9MYW5xHoqduTg9NglHjg3jvhkUOHIwNeNHaUFemDLFuG8GFUV58E7PoOfMBKoixq9QpaHyosC+rnxHmPpEvyfg5+cU5qLPE31NE/WrKWb3A0n6zMjxTDn/eKoFVrZHEbLzkqlwRM4bFJUntf2m9rhq26+0M1KNSlZeZg8pIjSQhCfFjLE2BJLtimc3eQB8nnP+Wox9WgD8DEAOAk+lH+Ocb2CMfREAOOcPzkqybUbgCfI4gM9xzmPOKhJx8EZkzWpFquoJovPf2NGE+3cG1CAi6xPVH1o+8njp7l8TXU+aFEugSbHhpDQplmGie09Tkj2v0P0UpYn7nhMrUSjbvnLlhcjPteHffveWZn2oxXVJ07XVzGdmqi2qZWJiSqgCsbqpEi8cGUpYTUJtv4lUruKpWJkc4cmrOZP/BvAlzvkCzvkCALciMEmWwjnfzzlfyjlv4Zw3cc43zG5/kHP+4Oz/Oef8Vs75BZzz5ngT4kQwKmtWK1JVTxCd/53bzqlBRNYnqj+0fOTx0t2/Vr+eBGFVMvXeS/a8QvdTlCYi/aaiRKFs+48/voPTYz5N+1CL62K1a2u19uqFTAXiYP+IKjUJtf0WS/kkk1AzKR7hnAcfG3HOXwIQN4TCCPTMmk0HqaonyM5fyWSOrE9Wv0w9It39a/XrSSTPvAXnI8duT+gzb8H5Rjc348jUey/Z8wrdT40yh9ZqHVpcF6tdW6u1Vy9iqUCoUZNQ22+xlE8yiYQX7wDwZ8bYDwH8CgF1iBsBvMAYWwYA8VahSydardFtFKmucy/bP1INIl79MvWIdPev1a8nkTy93ScTDuF4/EsrdG5N9pGp916y5xW5n6gOkTKH1modWlwXq11bq7VXLxQViMh+kG1X1DxS7TdZPXqpQBiFmifFSwBcCOBuAP8CYDGAywB8H8D3tG5YKhiVNasVqaoniM5/Y8c5NYjI+kT1h5aPPF66+9fq15MgrEqm3nvJnlfoforShEyJQtn2lSsvRHmhQ9M+1OK6WO3aWq29eiFTgWiqKpaoSZRo0m+xlE8yCdXqE2YgkcB8q2ejhmYYu135uLC8MKge4S5xojFCPSJe1rCiHqHsf3GEmsWiiqIQtYr4x4un5qD1WvQyNQm1meMptocS7STolWinpg1ZnsCnS6IdYH1fKiOR8wr1G26XEzN+4NSIFwUOO6ZmZlBWmIcR7xT6QrLigUD85cDwJEoLcjHmm0ZlSR4mpzhGJn1w2u14f9yH8lm1irLCvDDlCJnCUCShSgPuEieaZzP4EznnRP24bD8VNqCpz8xUW5Qh6/fRCS/e7B8LqkBcXFWIonxn1LxBsaFkbEWErB5ZO2XtkZVPVhEmyXFdWDDh8AnGWCWAfwNQzTm/mjF2MYAPcs4fSrSOdGKzMc3XxE4Xfj+PyhbdsLYJDzx/Tj3i1isawtYOj8wmDT3/yKzRyP1F6hOhx1O+P/aX49hz3BN2PFH/6pElHHo+auunrGWCSB4r+9JYxDuveEoTP7hhCZbVnQebjaFlXvQ+SrlvrLoI7wyM4tmuPnx8WR3u2b43+FtnewO27jmBb62+GGfHpxLO7J+e9uOpA72qlABi+cFY19ZM/jNTbVGErN/bGyrwTNdg2Pi/YW0T1jS5sfPwYFT5KxfNVW0rsvb88e1TCatbyI67prlaVT3pHtfVhE/8FMDvAVTPfn8HgQU9CI0RZdne9WS4ekTk2uGxskkjs0Yj9xepT4QeT/l+82X1CR1P7yxhtfVT1jJBEGqJpzQh8iEiXzM07sMP/vAObr6sHvds7wr7bdPOw1jdUoP93eoy+5NRAtBCcUPNfkRqyPr9QJ8navy/68mDOCBRmdjfq41qhKw9MnUL2XH394rLq1XJ0Msu1UyKyznnjwHwAwDnfBrATEpHJ4SoVY9Qfpdlk0ZmjSarPjHhmw77rlb9QqssYbX1U9YyQRBqSURpItKHiHyNn8/6z8lpqZ9VykT+JsvsT0YJQAvFDTX7Eakh6/d+icqEfLs2qhGy9khtUdp+dfWke1xXMykeY4yVIaA8AcbYcgQW8CA0RrZmeKQaROTvsmxS2Vry8b5HHi/fYQ/7Hk/9ItH2qUVt/Xq3hyCIzCPSbyTiQ0S+JocFyhbk2aV+VikT+Zsss1/m02MpASTrB8l/GoOs36tmVSYS3e6W1aNSNULWHqktysqrrCfd47qaSfEdAJ4CcAFj7E8AHgZwe0pHJ4SIsmw3rA1Xj4hcOzxWNmlk1mjk/iL1idDjKd8ffvloQsfTO0tYbf2UtUwQhFriKU2IfIjI15xX4MAdH7kQP3v5KO5e3Rj2W2d7A7bv70FzrUtVZn8ySgBaKG6o2Y9IDVm/N7tdUeP/hrVNaHa7xOWr1dmW2vbI1C1aJMdtrha3U61Khl52qWaZ5+sRiCmeB+DjAC4F8C0j9ImtkOGfqtpBeNamE4sqivH24Ehw7e+F5QXo6h8NZp82V5UgPz9XevxaVz4ODQwH919cWYJuz0Qwi1f5XTneheVFQfWJqhInmmbVJxLN+tU7S1ht/Rq1h9QnJJD6hOHopj6RjSj+d2DYi7JZlYjyWaWJ/uFJnFfoAA9RjpAp7xQ4cjA140d5UR5m/MCI14dpP3BmzIc5BbkYmZxCWaETje4STE3N4ED/cJhPz8uzS8cRpY39IcoX8RKnkvWDKfjPrPKZ8VA7L5CpN3i90zjQ50H/8CSqSvLQ7HbB6bRHzRsUm5DZSmrtOVdPvPbIjhtpT2ke11NTn0BgAvw4Y6wUwJUI6BP/FwKTYyKEVLMiReoTyv6t8xi83mk8daAvau3zNc1uOJ32mMdvnXfu+LHUHDZ2NOH+nefUJxLJUg5F7yxhtfVnU9YyQRDJE6nWo/hDz8QUvr39TdzYVodNOw9LfXuiqhaf/u8/B+u4/5NLcXZ8Ct8KURT49tomzCnIxe2/el14LLvdhtZ5pWidl/i5JesHyX+mTjKqSaJ5wFWLK4UqE7HUG0S2olV72hsq8NTBvig1jI6WajgcOUIbldmTGcZ1NeETSlLdRwE8yDl/EoBDs5ZkEKlmRcbb/0CfR7j2+YHZbFIt1Bnu3BauPkHZxgRBZAMyZYf93R6sbqkJToiV39T6RpG/PdDjCU6IlW3fevIgDvR4SPUhQ9BKNUmm0qC3eoNaNYz9vdZMOVMzKe6ZXeb5BgDPMMbyVO6fNaSaFRlvf1mW6cDwZFLHj6d2obb9BEEQVkWWBe/n6pV/RMRSqBAdM5VjEeZBq3FZZp96qzeoVcMYGLamnaqZ1N6AQEzxKs75WQDnAfiaHo2yOqlmRcbbX5ZlWlmSl9TxE1W7oGxj6zNvwfnIsdsT+vhn/PErJIgMQ5YFr7xRTjXjPZZCheyYyR6LMA9ajcsy+3S79FVlUquGUVliTTtNeFLMOR/nnP+Gc3549nsf5/xZ/ZpmXVLNioy3f7PbJVz7vHk2m1QLdYaNHeHqE5RtnBn0dp/E9f+5K6EPQWQjMmWHlloXnt7Xg872+CoUsRD526YaF74doSjw7bVNaK5xkepDhqCVapJMpaFRoj6hlXqDWjWMlmp16hZmIWH1CTNhhWzVZLMoZWvSK9+V36uKHDjYPxJXfUK2vyxjOrJ8pqwxn6oayCyWz6TWS1GC1CcMh9QnEiBRP6Bkzb8/NolipwO+aT8qS5zIsQGnRyeRm2PDuG8mWAcAVf5FND5MT/uxv9cTrKOlOpCpLxtHNPJpSZPg8S3vM7VEpiYhw+ebwf5eD/qHvXCXONFc7YLDkSOdX6itXyu1B6WdobbrcOSk1Fei42ps6ymrTxAqUJMVGW9N+sjf55fl4/b2hrDs6FgZ0IlkmYramynZxnqtkU4QhHVQ4wfsdhuaa+ZgR1c//uEXu2OWT8a/iPytw5GDtgXnRZUV+WGjfZrRx7cisVSlZH+YPXWgN0oFpaO1Bna7Lcou1NYPaKf2ILNdLUi3rVGinAmIlwUa+fvqlpqo7Ggj1gi3Ctl+/mbDD0ZxzUTa0SrbPrK8Ef7FaJ9m9PGtiNo+k6mgdPWJVR0y9Zqk+7zoSbEJiJUFWl9RFPV7rAxo0V988erPdLL9/E3HzAyuVxGWQRBaoNYPJFreCP9itE8z+vhWRG2fydQk+j1eoTZ1pl6TdJ8XPSk2AfGyQGW/y8qrrT/TyfbzJwhCu2z7yPJG+BejfZrRx7ciavtMpjJR5cqucT7d50WTYhMQLws08ven9/VEZUcbsUa4Vcj28ycIQrts+8jyRvgXo32a0ce3Imr7TKaC0ugWqzpk6jVJ93npGj7BGJsH4GEAVQD8ALZwzu+LKPPXAJ4E8N7spt9wzjfo2a5kiJf9mEp2pM3GcNXiSmxdtzy4pvjiypKw+q5cNDfk93wsrizGsrrShLJGbTaGVY1VuKhzhbS82qxVtf1jJImcP0EQ1kON31HjB5R63a48/PyWS3F6dBJulxONbldU+ch6q0qcmPEDu98bkrYptN3Vc5w4OzaFvuHEfa/sXADg6OCo7n6YfKp6YvWZbPztaK1Bw9wi9Hu8qJq1P7vdJrV7Wf1qx2dZea22a9VveqB3TPE0gK9yzvcyxooBvMYY+wPn/M2Icrs456t1bkvSxMt+TDU7UpQ1urGjCffvPIzjQxMx1Sa0WCN8etqPbft6pFmuqfaPGdBjjXSCIIxDK9UHWb337jiEG9vqgss6x6pfqXdBWWHcNoW2+8K5RfjkpfNxz9Ndqn1v5LmkPUuffKpqRH0Wb/xtnVcaFkMc7zqLVCnU2IWs/FWLK4XqFmq3J2OP6bQ1XcMnZhf42Dv7/xEAhwDU6HlMPVCrDqE2O1K0/53bDmJ1S6Cr1KpNqEVtlmsi7c+ErFerQKvUEdmIXn5HqXd1S01wQpxo/Ym0KbTM33/4guCEWCmvxveqPTZhPvRWmdCqfFefR5PtZrfHtKlPMMYWAFgKYLfg5w8yxvYB6AXwj5zzLsH+6wCsA4C6ujodWxqNWnWIyN+TrZ/N/jGlVm1CLWqzXCPJ1KzXRDHSNoFzq9QlAqk5ZA9G26Xe6OV3lHqT8buJtCm0zMTkdEq+V+2xzUKm26Ya9FaZ0Kq8rJ1qt5vRHkNJS6IdY6wIwK8BfJlzPhzx814A8znnrQDuB7BNVAfnfAvnvI1z3lZRUaFreyNJVh0i0exI2f6hiw3qmX2pNss1kkzNek0UI22TIGRkul3q5XdC61VbfyJtCi1TkGdPyfeqPbZZyHTbVIPeKhNalZe1U77dOvYYiu6TYsZYLgIT4l9wzn8T+TvnfJhzPjr7/2cA5DLGyvVulxrUqkOozY4U7b+xownb9/cAUK82oRa1Wa6JtD8Tsl4JgjAvevkdpd6n9/Wgs71BVf2JtCm0zI9efBd3X9uYtO9Ve2zCfOitMqFV+UZ3icrtLkvaI+OhjyO1rpwxBuBnAN7nnH9ZUqYKwADnnDPGLgHwBAJPjqUNM2Kt9PDs0HNZoApq1xCPJHSN86oSJ5qqStA74g3WV1dagBNnxnXLvlTOLzLLNVFSPX+LEPeEjLDNHLtdVfjEjSoWzrBU2S99GDYk5s+qa+fh5LH34he0DjFt0wi7TAex/E6ime+h5eYWO5FjC7zSLnDYwbkfjDGM+2YSzp5PxBeGlnG7AuoT/cPJ+V61x04zpvSZRiFTmYgc/1uqXXA4cqT1qL3OWpXXartJEDZE75jiDwH4NIADjLE3Zrd9E0AdAHDOHwRwHYB/YIxNA5gA8IlYE2IjSGRN8VSyI2OtcR5an57Zl6IsVzVQJjJhOCpWynv8Syt0bgyRDmR+J9GMe1G59Ssb8PArx3Fm3JdUtnwivjCyzPwyoFXluSd7bMIYZCoTa5qr8ce3T2muoqJHea22mxm91Sde4pwzznkL53zJ7OcZzvmDsxNicM43c84bOeetnPPlnPOX9WxTMuid1Zuq+gNBEARxjkR9tqjcfc8dxseW1VomW56wBrJxfn+vNVUaMhVa0S4BYmVvakGs7FOCILTHD5awjN28Becb3VxCJYn67HjKP1r6eSK7kY7zOs8vCHWkTZLNyijZmKGGq4f6Q2T9yWQgEwSRABRqkdEk6rNl5ZQAPitkyxPWQDrO6zy/INRBT4oTQO+s3lTVHwiCIIhzJOqzReXWr2zAb/Z2WyZbnrAGsnG+pdqaKg2ZStY8KU5lDW7R2tt1pQUpr+mtYLfbsKa5GgvKCsOyT5PNQE4GLdYoJwgic7CyT7DZGK5aXImt65aHZfqLlmcO9e0VRQH1iaV1c1T5eSv3VSZghf6PHOfdJU40z6pMRM4v4rVfpmJBpE5WTIq1WBM+NItS6zXm/X6uOvtUS7Q+H4IgrI3VfUIiikEKogz5BeWJ+3mr95XVsUr/xxvnE1VpkKlYdLTW0MRYA7KiB7VWjzB7fVY7PkEQ5sLqPkGL9qeiYGGlvrI6Vul/rdpJalX6khWTYq3VI8xen9WOTxCEubC6T9Ci/akqWFilr6yOVfpfq3aSWpW+ZMWkWOs14c1en9WOT0Qzb8H5CUuG+Wf88SskCBVY3Sdo0f5E67B6X1kdq/S/Vu1UVCwi6yG1Km3Iikmx1uoRZq/PascnountPonr/3NXQh+C0Bqr+wQt2p+KgoWV+srqWKX/tWonqVXpS1Yk2onUI1LJTjV7fUD87NTIbN2rFlfiGQ2PTxCEddHDJ6UTLdqfqoLF7veGElJCsIJygpmxiq1G25MTjW6X6nba7TZ0tNagYW4R+j1eVM3WEyvJjtQqEicrJsWA9mtwm7m+eNmpsbJ1rbRGOUEQ+qG1j0s3qbY/GQWLBWWFqpQQrKKcYHasYKtq7CkedrsNrfNK0TovfllSq1AH9UgGEi871SrZugRBEEaRjJ9Uuw/54uzBqGtNahXqoElxBhIvO9Uq2boEQRBGkYyfVLsP+eLswahrTWoV6qBJcQYSLzvVKtm6BEEQRpGMn1S7D/ni7MGoa01qFeqgSXEGEi871SrZugRBEEaRjJ9Uuw/54uzBqGtNahXqyJpEu2wiXnaqVbJ1CYIgjCIZP6l2H/LF2YNR1zoZtYpshibFGUq87FQrZOsSBEEYSTJ+Uu0+5IuzB6OutRq1imyH/lQgCIIgCIIgsh6aFBMEQRAEQRBZD02KCYIgCIIgiKyHJsUEQRAEQRBE1qPrpJgxNo8x9jxj7BBjrIsxtl5QhjHGNjHGjjDG9jPGlunZJq3w+zmODo7ilXdP4+jgKPx+bnSTCILQAT8Ycuz2hD7zFpxvdHOJGJDfJoyE7M/86K0+MQ3gq5zzvYyxYgCvMcb+wDl/M6TM1QAaZj+XAviv2X9NC61XTxBZxMwMrv/hnxIq+viXVujcGCJZyG8TRkL2Zw10fVLMOe/jnO+d/f8IgEMAaiKKrQXwMA/wKoA5jDG3nu1KFVqvniAIwlqQ3yaMhOzPGqQtppgxtgDAUgC7I36qAXAy5Hs3oifOYIytY4ztYYztGRwc1K2diUDr1ROhmMk2CWMxU6gF2WU45LfNQzbaJtmfNUjL4h2MsSIAvwbwZc75cOTPgl2iAm0451sAbAGAtrY2QwNxlDXMQw2c1qvPXsxkm4TBmCjUguwyHPLb5iEbbZPszxro/qSYMZaLwIT4F5zz3wiKdAMIXWelFkCv3u1KBVqvniAIwlqQ3yaMhOzPGuj6pJgxxgA8BOAQ5/wHkmJPAbiNMfYoAgl2Hs55n57tShVar55IhnkLzkdv98n4BQH4Z/zxCxGWRgm1SITq2nk4eew9nVuU2ZDfJoyE7M8a6B0+8SEAnwZwgDH2xuy2bwKoAwDO+YMAngFwDYAjAMYBfE7nNmkCrVdPqKW3+ySu/89dCZXd+oUP6dwawnBMFGqRLZDfJoyE7M/86Dop5py/BHHMcGgZDuBWPdtBEARBEARBELGgFe0IgiAIgiCIrIcFHtRaC8bYIIDjRrdjlnIAp41uRAyofakR2r7TnPNVsQrHsU2zn6vWZNP5Gn2uMW3TBD7T6P5JFau3HzDmHBL1mWOwfv+qIRPsSQ1mPF+hbVpyUmwmGGN7OOdtRrdDBrUvNbRsn9nPVWuy6Xyz6VyTwer9Y/X2A+Y+BzO3TQ/ofM0LhU8QBEEQBEEQWQ9NigmCIAiCIIishybFqbPF6AbEgdqXGlq2z+znqjXZdL7ZdK7JYPX+sXr7AXOfg5nbpgd0viaFYooJgiAIgiCIrIeeFBMEQRAEQRBZD02KCYIgCIIgiKyHJsUEQRAEQRBE1kOTYoIgCIIgCCLroUkxQRAEQRAEkfVYclK8atUqDoA+9En3Jy5km/Qx6BMTskv6GPSJC9kmfQz6CLHkpPj0abMtoU0QAcg2CTNCdkmYFbJNwkyYYlLMGFvEGHsj5DPMGPuy0e0iCIIgCIIgsgO70Q0AAM752wCWAABjLAdAD4DfGtkmgiAIgiAIInswxaQ4gpUA3uWcHze6IUTm4PdzHBsaw8CwF5UlTiwoK4TNxoxuliFQXxAEQRBWIZ1jlhknxZ8A8KvIjYyxdQDWAUBdXV2620RYGL+fY0dXP+547A14p/xw5trwgxuWYFVjlSY3lpVsU+++IMyDleySyC7INolESfeYZYqYYgXGmAPAGgCPR/7GOd/COW/jnLdVVFSkv3GEZTk2NBa8oQDAO+XHHY+9gWNDY5rUbyXb1LsvCPNgJbsksguyTSJR0j1mmWpSDOBqAHs55wNGN4TIHAaGvcEbSsE75cepEa9BLTIO6guCIAjCKqR7zDLbpPiTEIROEEQqVJY44cwNN3Vnrg1zi50Gtcg4qC8IETXz6sAYS+pTM49efxMEoQ/pHrNME1PMGCsA8BEAXzC6LURmsaCsEJtvWor93R74OZDDgOZaFxaUFRrdtLSzoKwQP7hhSVR8Vqy+yKbEvGw611B6u0/ixh++nNS+W79wmcatIQgiE1DrT0XlkxmzUsE0k2LO+TiAMqPbQWQefj/H2fEpbHnxaPCm2tjRBL+fZ8WEJxSbjWFVYxUu6lyBUyNezC2O7aiyKTEvm86VIAhCT9T601jl1YxZqWK28AmC0JyuPg/u3HYwLFD/zm0H0dXnMbhlxmCzMdRXFGF5fTnqK4piOpdsSszLpnMlCILQE7X+NFZ5NWNWqtCkmMhI/H6Oo4OjeOXd0+g5OyEM1O/3UHJZPJJJcgjt+6ODo/D7pcvMmwpKQiQIgtCGWP5UNEaYxf+aJnyCILQi8jXM5puWwplrC7vhnLk2VLkouSweSpJDZN/JkhysHIKg9lwJgiAIMTJ/WlHkFI4RiyqLTeF/6UkxkXFEvobZ8r/v4u5rG4MZrEpMcaPbZWQzLYGS5BDad7GSHKwcgqD2XAmCIAgxMn+aY4NwjMixwRT+l54UExlH5GuY/T3DwO7jePhzl+DMuA9VLica3S7Y7fQ3YTzUJubFegVWX1GUjiYnjdpzJQiCIMTI/Onu94bE4YzDXlP4X5oUExmH6LXNO6dGUV6ch0vqxQIn2SrFlQhKkkMik9pMCUHg1giDJgiCMD2h/jTWGKFmrNELelRGZBxqX4MrcbDXbNqFT/5oN67ZtAs7uvotkyBmJqwcgkB2QBAEoQ0yf1pXWmDqMYKeFBMZh9rX4LI42Is6V5j+lb/ZsHIIAtkBQRCENsj86TOdK0w9RtCkmLAEasMb1LyGsXIcrJHIrokZXoElA9kBQRCEHDXjcDx/atYxgibFhOnRW+YrU+Jg04mVpddkkB0QBEGIUevzrepPKaaYMD16y3xZOQ7WKKwsvSaD7IAgCEKMWp9vVX9KT4oJ06P3a20rx8EaRSaGGpAdEARBiFHr863qT2lSTJiedLyGsWocrFFY9dVYPMgOCIIgoknG51vRn1L4BGFKQtdG5xzBpZqBwI24+aal4Bxha6cnWl8i5YlziPou1qsxs/W12dpDEARhNYz0+WrrT6U99KSYMB2ygP4d61egf9iLqhIn3uwbwUfv35VQwH8mJoWli1h9J3o1BsBUfU3XniAIInVk4RCAvj5frQ9P1eeb4kkxY2wOY+wJxthbjLFDjLEPGt2mbMNMT9OODY3h3h2HcMvl9bitfSH+fkU97t1xCH4OLK8vh5+L106XBfxnYlJYuojVd8qrseX15aivKILNxpLqa5ntaWGTdO0JgiC0JXSFOr19rNr6U22PWZ4U3wdgB+f8OsaYA0CB0Q3KJsz2NG1obBI3ttVh087DwfZ0tjfg/bFJ1FcUqQ74z8SksHShd1/LbO+qxZV49tBAyjZJ154gCCJ1ZL66tCBXVx+r1Rg0MJxYewx/UswYKwHwYQAPAQDn3Mc5P2too7IMsz1Nc+TYghNipT2bdh5Gbk7AXJWA/1BiBfyrLU+cQ+++ltleV59HE5uka08QBJE6Ml9d4LDr6mPV+nBZewocOQkdz/BJMYB6AIMAfsIYe50x9mPGWJSQHWNsHWNsD2Nsz+DgYPpbmcHE+kssWVIJjH9/zCdsz7hvBoB6/UO99RIz2Tb17uuBYS9KCxy49YqFuK098CktcKDPo41NWlUrUwsy2S4Ja0O2aT1k84SpmRnVPlbN/GBBWSE237QUnSsD48P6lQux+aal0vp9MzPobG8Ia09newOmZvzC8pGYIXzCDmAZgNs557sZY/cB+L8AvhVaiHO+BcAWAGhra6P0cQ3RWl4r1cD49SsXCttTWRJoj1r9Q731EjPZNpPpO4edYd2H6+HngI0Fvstwu5y4+YPzcd9z50Jl1q9sQG1pviY2aVWtTC3IZLskrA3ZpvWQzRPOK8zDsrrzEvaxyYRr+qY5trx4NKy8jLLCPGzdcwK3XF4PxgLxz1v3nMCqpqqEztMMk+JuAN2c892z359AYFJMpAnlaVqkkYb+JRZvzfPQ3wscOcLXLBd1rhDG9ES+lnlsTze+efVFOD3mg58DOQxornWFtUet/qEV9RJTRc069bFQ03fHhsbw4AtHcPNl9ZiYnEZBnh0PvnAE9eXi/Wf8CE6IgYCt3PfcYXxkcWVcm9Sj/QRBENmCmjEi1jxB7Rghmx8sKCuMak+s8qLjLSgrxNdXLU567DB8Usw572eMnWSMLeKcvw1gJYA3jW5XNhHvaVq8v+wif+9cuTDlwPiJKX/CfxkS0RiVPOmZ8OHjy+rwT0/sCx737tWNGJ7wCcufGhG/kjs9Npm1T3gJgiD0JpkxQs1bQBmyMIz3xybxVv9Iyol8qb4dNENMMQDcDuAXjLH9AJYA+Ddjm5N9iOS1FOIl4h0bGsN/v/QuvnNdK+79WDPa5pdifll+WP3OXBvyc3OEMUSRgfQfW1aL//jjO6ZJ/LMiRiVPzviBe7Z3hR33nu1dmJaEc8VKovD7OUa8Uzg7PoUR7zQtukEQBKERyUid3fbL17HpuSPYvPMINj13BLf98vWkkp/nl+WH5ZHML8tHbo5N80Q+nsSQYfiTYgDgnL8BoM3odhBi4kmiiJ4O3rOmEf/5whEcH5qAM9eGjR1N6Hz09eD30L9II1/L5NhAMlopYpQU2enRSfGT39FJYfm60gJs7GjCndsOBm1nY0cTqoud2LavJ2p7R2sN7Haz/C1PEARhTYySNq0rLcDt7Q1Rvt034xfWz7lfOEbUlYqVe1N9S2qKSTFhbuIl4omeDt79VBce+fwlmOEc+bk5wQmx8ntoTFDk6478XHswdEJ0PCI+WidPJorbJU6Qc7vExz1xZhz37zwclhRx/87DWFBWGHSCQMBm7tx2EA1zi9A6r1TXcyAIgsh01I4RWo0pJ86MC3371nXLhfUzxoRjxLK60oRylOLFIEdCk+IsIV5A/fS0H119HvR5vHC78rGooghvD44Ev//w08vwhUf2CgPXZU8Hh8Z8WNXkxivvng5OiEN/D/0LMzRQ3+/nmiVZZSuxkiLUJuBF2kajuwR2u01YT6O7RPhXfaPbJSw/MOzF8aEJPPD8kbBj9kueSvR7vGiu0SaBkCAIIpPQKnFOi/IyFBnOjy2rBZtt2q9f68a4bwabb1qK/d2esAT7cd+McIw4NeJFXWlB1NiU6hNtmhRnAfFeJ0xP+6NeVW9Y04QHXjgcFv7w+y+vQJ8nOnC9eo746WD17NNBtX9hZrOMllZotU69yDY2djRhTXM1/vj2KWE9Ha01aJhbhH6PF1UuJxrdLthsTHjci93F4ifLEptxu5ymWn2RIAjCDKgNG9BbblOGTIazeo4TXb0jUQn2CyvkY4FobFpaOyelJ9oUnJcFxAuo7+rzRL3OeOCFw/jqVRfhtvaF+PsV9bh/52EMjviEgevFeXasXxkulr1+ZQNybAyvvHsaNgbV4t6xEv+IxBD1odrkCpFt3LntIPb3xl9xLmAqgesmO+6MX2wbzdUubOxoCtu+saMJxc5cU62+SBAEYQaSSa5WM84qcpsL5xZj3px8NMwtxoMvHFHte2UynGfHplSNEWfHpoRj08S0+sVEQqEnxVlAvNcJkauHuV1O3NgWnjjX2d6A3rPj6Hx0X9RfoP3DXjz8yvGwmJ+HXzmOiakZbHruCJy5Nmy+aSn+5/YVGBylJ79GovbVkmxlOVl4w/CED9tOnIlOnCvJlx5X9rRC9MT5L8ffpyRMgiCICPROrlYrtylDJsPZJ2n/4Kh4jHj2zX5h+Z4zExkhyUboSLy1w5XkKIWPLavFpp3hf8lt2nkYrgJH8HvoX6CVJU6cGffhgecDUi0PPH8EZ8Z9UFZV9E75cdsvXwdjoCe/BqN2HflI21DKV0nqmfZD+Nd7rp1J16OXPa2w221onVeKv2lyo3VeKex2m+r2EwRBZAN6+0a1cpsyZBJrFUV50vaLxgjp2OQSl08UmhRnAUqAfOjrhM03LQXnwCvvnkaRIwffva4l+LtMEu1Q30jY91MjXmn9ne0N+M3e7mD50gIHBkcmE1rrnNAP0bWK9WpJSZyLDGNoqXYJ65ElXZ6ZmMJXrrwwrPxXrrww4fXoQ9u/+aal6FwZ0Ldcv3IhNt+0lJIwCYLIatT6drWolduU4ZuZwTdWXRTmw7+x6iLk2ORhln4/x9HB0bD5g2xsanS7UjpPCp/IAiID6qtKnHizbwQfvX9X8DXI969fgie++EH0nJlAeVGeUBJtxDsd9l35CzRaUi0gwdbnCUyalcD6z/zkz5QcZTBqkyvsdpswjMFutwnrOdDjESY5VBblof/sRFiSRn6uDeVFearPwTfNabVDgiCIEPROUFcrtymjoigPkzPhK9be8ZELMafAgZbaUlXJ4bKxKRUYT2bJD4Npa2vje/bsMboZluXo4Ciu2bQryrifmdXx8/s5fnewH199/JwRfnttEzY/f1i4+EYkomWfRZPsZxLUDTQRcb2LlW1TrVSbCJlaRWvtHFy7+aUoG/if21fg/PLo9e5lx41nu1lMzAsVzy4ZY7jxhy8ndeCtX7gMVhxHiLSQ0T4zm5D59o7WmmASdyI+/N1To8EHcgrKWHDB3GgfrqPPFzaQnhRnIfEC8v1+Dj/3hz3VK8m346GbP4DTY5Nx/wKN/It13DdDyVEmJ9VVgBRkT5ZjJci9PRC93r3suEat1EcQBJHNyHy7TG5T5sNliXaDo17hpFjm8weG9fH5NCnOQuLpBnf1efC1J/aH/T6/LB/fv36JdC1x0VNGZTGOo4OjhqyuRiROqqsAiQiVZJPZXIEjB5/76V8SPq5RK/URBEFkO0ryc+u8c9uODo5Kx44FZdFvAdX6cCUxTzR26AEl2mUh8QLyZRJtn3poNz75o924ZtMu7OjqDybLKU8Zr9m0S/i73gkAROrEegKrBuUV241bXsUXf74XN255Bdv29aDWlS+0Adl697Ljki0RBEGYB9nY8f7YpHBeUFdaoMqH+7kfd69uDCt/9+pG3UK26ElxFhIvID8yoF4k0Rb6NC/eU0Zaoc78aPUEVrbYR8PcIqENHBsao9UOCYIgLIps7MjNsQnnBc90rlDlw22M4cEXj4Stg/Dgi0ew6RNLdTkfmhRbFJ9vBvt7Pegf9sJd4kRjVQl6R7zB1xR1pQU4cWZcGviu6Pgpr6gVyZOBYS/mFuXhu9e1BEMoZBJtShynmjhPyscxJwvKCnH/J5fiQM+5deebalxYUFaI6Wl/1PrysgzfPo94Xft+jxfNNYHvoTagPPmNjEdLZLVDiiEmCIKIjyyJWm1ytai8IpO5v/vc2NFc64qbS5SoDx/3zeD40AQeeP5I1HYtksMjoUmxBfH5ZrBtfy/uejLwRG5+WT5u/esG3PVUeFbo/TuTU4tIVKKtoijwNC/eU0atkrgI/Zie9uPs+FSYTM631zZhcnIa//NmvzDjWDQxri3NF65rXzMnX2gDVy2uhMPOwpI6HXayCYIgCC2Qjb9XLa7Es4cGEh6XZfVcuWhu1NixsaMJ888rFM4LlHlDoswtFs8vKorydJlXmCammDF2jDF2gDH2BmOM9FkiCBWv3tdzNjghBoDVLTXBCTFw7pX1V6+6CLe1L8Tfr6jHvTsOSdcoPzY0hnt3HMItl9cHy3/n94dQ4LDjb5rcKCtyYP3KhrCYnvUrG5Azaz3x4jyTWZM9mxEJlSfD9LQf+06ewY6Dfdh38iymYyw9tL/Xg289GW5D33ryIA70DwvDIbr6PMJ6nPYc4br2fnChDXT1eXDbL1/HpucCqyFueu4Ibvvl62QbBEEQGiAbf7v6PNJxWTQGyerZ3+vB/TsPh80f7t95GGOT07jjI+ELNt3xkQuD84ZEybFBOP/wTs/oMq/Q5UkxY+wyAAtC6+ecP5zArldwzk/r0SYrE/kX2r0fbw77q4kxcXjDkVMj2LzzSHCFuffHJoWvK4bGJnFjW10wbjiyfJ/Hi4dfOR4W0/PwK8extG4OFpQH6ov1tI9ktBJHq6fqsTQlRU94+6WyN+JVjPo93rAMZIVByapHvWcl6917yDYIgiD0Qi5pJvbVA8NevNUfLZNZWpAr9dWi+cPpsUnk5djC5gV5OTacHp0MzhsSQTb/OL+8UJexQ/NJMWPsEQAXAHgDwMzsZg4gkUkxISDyLzSZREnkd2UFXe+UH5t2HsbWdcuF9TtybFGJdKHlK0ucODPuC4vpCQ2PODY0htt++XrU8RVxbZLRShytpNFiJby1ziuNKu+WXKPKkjzh9irJKkYy2ywvEtfjdpFtENamZl4dertPJr1/de089Jw8oWGLCOIcMp98XqFDlUzm1nUflIYx3PHYvqj5w8OfvwS37YieF8jmITJk8w+9xg49nhS3AbiYq9fL4ACeZYxxAD/knG8J/ZExtg7AOgCoq6vTpKFmJjSA3MYYSgscwWWTf/Tiu7h7dSPu2d4F75QfT+/rwYY1TWExxZ3tDXjk1ePB+koLHPBO+fHKu6ejAtJlAfHjvsDfNPGSoeI9CU4mmcpKaGmbWj1Vlz2BlT3hba524dtrm4IhFEpMcXNVYH35yCfOsvXlfTMz+MqVF+I//vhOsPxXrrwQ9tl17SNtoNHtymjbMJJs85lG0dt9MunVAIHAioDZBtlm+vDNzKCzvSHqSS4HF/pemUzm1MwMvn/9krCVbr9//RJwiN9Unx2fEiZdK/OKRJHNH/QaO/SYFB8EUAWgT+V+H+Kc9zLG5gL4A2PsLc75i8qPs5PkLUBgWUjNWmtCRK/Q169swMOvHEefx4v9PcNw7D2BRz5/CU6NTKKyxImmqhK0LSjFqREv8nNz0Pno68FJtNvlxM0fnI/P/OTPwlfycyVP8SqK8gDEl8FK5ElwJidTaWmbWj1Vl61TX1Uirsdut2FOQW7YNZpTkIvc3BzhdlkoR0VRHvJzw1+Z5efa4Mp34PSYL8oGSGJNP7LJZxLWgmwzfZyX78DWPSfCwg+27jmBqy6uxF/VnZewTGZFcR76PN4wH+7nfpQViJ84V5XkCZOu3ZK3jDJijRF6jB2aTYoZY08j8LS3GMCbjLE/A5hUfuecr4m1P+e8d/bfU4yx3wK4BMCLsfbJVESv0O977jDWfbgem54LxAh//vIL8FfzzwsagJKMxTngys/Ft1ZfHJRIuaiqGN/9/VtRr0Nq1i3HuG8GBbk5uOMjF+IHf3gnzHi90+f+ooslgyX7S87GgFfePY0Chx3f3v4mjg9NBPfRaO3yjEOrp+pzCuy4+9pG3PN0V7Ceu69txJzCXGH5Y0NjuP1X4ldd397+Jla31IAxwM+Bb29/E/XlYluY8QMP/em9sPIP/ek9LJk3Rxpio5wbyfURBEEkj0iibGJ6Bp/4QF3U5NQ7PSMc12Vj0NmxKXzv2bfDfPv3nn0bD9y0TFi+2JkrTLq+6uIq1eclm3/oIc+p5ZPi7yW7I2OsEICNcz4y+/+rAGzQrGUWQ/YKfem8OXh03aVRfxFFPlmeX5aP29sbwiRSlHAK5emxd8qP5946FZxkf2PVRbjtioXwTvuDgez15YVoqpkTt72Rf7FVFDnx3tAoVt23K+bxKZkqGq3++u0968Wvdh/Hd65rxYRvGvkOO3784rs4v7wA88sSX19eloQ5JEnafH9cXP70mDip4/2xSWFSB8n1EQRBJI4sSduZy4SJarLxXTYGvXh4QOzbRyeF5Xe/NyT0+YOjXlww17zjvmaSbJzz/+Wc/y+Aa5T/h26Ls3slgJcYY/sA/BnA/3DOd2jVNquhvEIPxZlrw/yyQiyvLw+uEqcQ+WR5dUtNVJLVpp2H8bFltWH1hSbi/fuOtzAx5cfmnUfwwPNHcGbcJ02mEqH8xba8vhyMIeypoOz4lEwlJrQvI691olSWOPHOqVF0/up1fP3XB9D5q9fxzqlRaZ/LbK44L1eYhMkgbpMsabPE6RDWL1v1iCTZCIIgEkeWpF3idAQT1RId30VjUIFDPBYUOOzC8rIxxezjvh4xxR8B8PWIbVcLtgXhnB8F0KpDWyyJ2lfokU/5ZBJtij6gKBEv8vdYyVQiYiUGiuoPDa/QaiUa4hxqbUj6ymx8SvKE1yd8VSdL2pya8ate9UiJb9NytSKCIAiroGbFNtnbvqkZP757XQsOnxoN+t6Fc4vQ6Hapqv+0RG7z9OiksLxVE+y1jCn+BwBfAlDPGNsf8lMxgD9pdZxsQO0rdFlyVuT3C+cW47b2hbioshjfffatsEmrM9eGtvmluPfjzSh02FGcn5PwBCReYqBS/8qL5uKyC8qE4RX0ylxb1NqQrPyBHo8k8U+8mtCiymJpouDbAyNhIT0/uGEJLigX2255oT6rFREEQVgBtZr1soT5yuI8vD8WvuLc969fAgCq6q+UrCwne/Jr1SRqLVe0+yWAawE8Nfuv8vkrzvmnNDxOVqDmFXrkinJP7+vBxo6mqJVkTp4ZBwB0nx3H3106P2qFmK//+gC+/usDuO1Xr+MLj+wNe4UducLN9LQ/+P1Az1lhYuD1bbXB+n9wwxI018yRhlfQK3PtURuGISqvJOyF2srd1zYiL5cJX9XlzEqvhZb/wQ1LkGODsPyIdyqtqxURBEFYgVgrwYpWnJuYnsE3r74InSsX4rb2hVi/ciG+efVFODMxFZRRU+r56uOxV7QT0Vztwoa14fOKDWub0FItf6OsRShgutHsSTHn3APAwxi7NfI3xlgu53xKq2MR4Yj+IqsrLcCyuoBE24yf48jAKP79D28F/yL8xqqL8NBn2pBjY2Bg+PLWN6LCHZREOFkinxK33LlyoarEQFrhzjrIEvZKC8TXfHB0Uii/J13pLs2rFREEQVgB+Up04hXnChwME1P+sCfCX7nyQrw/5hPWo3Y1UYcjBx0t1agvLwyGW7RUu+Bw5Gh/8gaiR0zxXgDzAJwBwADMAdDHGDsF4P9wzl/T4Zi6oyb2Rov6In+vKy3AiTPj0vKR0iTT036MeKdwdnwK5xU68O87wiXZ/n3HW3jk85dg2s+R78iJ0g125tqQn5szK6mWEzORz8/F4RrzywqFNxetcBcbrWxNi3oKHPZgwp6CM9eGskIH5pflB+V5gMAbCgYmlF7bum65dEW7UNtjs5Nomc7y3GKn5vciQRCE2ZCNk7IV5x75/CXBRZOU7f/xx3fw8OcvEYeoFeUJfXgsH+tw5KBtwXnp6wQD0GNSvAPAbznnvwcAxthVAFYBeAzAfwK4VIdj6ora2J5U6xP9vrGjCffvPIzjQxNxjz897ce2fT1xn+Tufu99fO/Zd4T1b+xoQuejr+P40ETU/pGJfL9+rTtqxZxkkrrMHoCfDrSyNc3q4f6w1ROduTbcvboRfszgix9eGLX97Lj4qcTY5Izwmi+uLAl766DY3uLKYmH5utICijUmCCLjkY2TshXnzkh874h3ChvWNOKup8756g1rGjGnwCb0vbWu/Kz2sbos88w5/6LyhXP+LGPs3zjndzDG8nQ4nu7IYnsuSnLxiXj1iX6/c9tB3HJ5PR54/kjc43f1eRJ6kjsRUf/WdcsxMTUTXBFPWWxDtr/yvc/jxdY9J4L7J5vUlQ03XDy0sjWt6rExhgdfPBIW3vDgi0fwvetagxNipf57tndJn0o47DbhNT82NBYlH3jntoNYVlcqLa/lvUgQBGFG1CY/lxWKE+2KnbnYsP3NMB/+wAtH8N3rWoW+t2FuUVb7WD0mxe8zxr4O4NHZ7zcCOMMYywHgl+9mXrSOgRXVV1rgwODIZExJMxYyZ4x1/MhYIdGT3EhJttICB7xTgYU7vFN++Ka5dP+n9/VESbwsmz8HRXm5Ca9rrsdKNJlALFtTI1GmVT3jvhkcH5rAA88fCdsueypxdmJKaGsjXl+wXOjKdfHurUgboXh0giCyjVCf6ZuZwTdWXYShcV9w/D2vwIEcG/D965cEk+oUlYkz41NCHz4wLM7zUBtrnGnoMSm+CcDdALYhEFP80uy2HAA36HA83dE6BjayPrfLiZs/OB+f+cmfg8YskjQLvTFiHT8yHlN5kvvzWy7FtN8ffBKs1B3v+JFPgqtKnOjqDZfX2tjRhLue7EoovIOQI7O1qhKnOvkcST0VRdrUU1kijvktK3DgX/ecCHsqsXXPCWz6xFLhcS92yyXc1PQPxaMTBJFJyELgGquL8fpMeELdHR+5EOcVOXDyzERYkrOf+3FegcyHi58sx8rnyAa0lGQDAHDOT3POb+ecL+WcL+Gc38Y5H+Sc+zjnR+LXYD4iJc9SjYGNrO/6tlrhGuGhkmYbO5qwfX9PQsdvdJdESbLd3t6AJbUBSbTmmjn41uqLg9It//zRxXj0Lyeijv/P1ywOSrt8a/XFQUk1P0eUxMud2w5idUtN8DvJZyWHzNZm/GJJM1kfy+qRSaOprafRXYLNNy0Nk//ZfNNSlBU58IkP1OGhl45i884jeOilo/jEB+pgt4kl3Gb8Ygm3ePHoWt2LBEEQZkQWKnZ2bAq/2B1Q7LmtfSH+fkU9frH7OE4N+/C1J/Zj03OBles2PXcEX3tiP4qdOdiwJkJKbU0TmqqKpb49m32s5k+KGWMXAvhHAAtC6+ect2t9rHShdQxsZH2yVb1CJc1CJdbiHd9ut6GjtQYNc4vQ7/GiyuVEo9sFu/3c30C+aR72l6YSTqE8PfZO+fHOqRFs3nkkeFMoyF5hJxreQciR2ZpsHXlZH+tdj9/PcXZ8KuptweDopCqJtcFRryaLjNAbCYIgMgnZODs0Pokb2+qiQtRkIW3HhyZwbVMVFpQXYGB4EpUleWiuKkF+fq7Ul2azj9UjfOJxAA8C+DGAxAJMLYDWMbCh9R0dHE1I0kzN8e12G1rnlaJ1XvRvor9AN+08HEzkU44/M9ucyEB72SvsRMM7iNiIbC2ZsAE96znQc1aYpPHzWy7FmXFfWPyaIr0mO67ae4vi0QmCyHRkvro4Lzc4IQbOjd+yJOcqlxP5+bm45PyyqGPIfGk2+1g9JsXTnPP/0qHejEUkvbL5pqXgHHjl3dOoLHGi1pWPQwPD6PN44Xblo9FdEvbkV4s10nNmqxMl4oU+TRS1V5F0U/bPptct6UArGbtY9aixIVkyxvtjPmy+aSn2d3uCSSDNtS40ul0kw0cQBJEgC8oKhb707PiU0Pd6xqewsaMpSmKt0e0ibXcV6DEpfpox9iUAvwUwqWzknL+vw7EygsjXFVUlTrzZN4KP3r8L3ik/2ua7cMMH5uOuJ8ONvaO1Bna7TbUmrewv0JUXzcVlF5RFJeIpvytPE+OtoJdtr1vSgVavtGT1AFBlQ9VzxMkYVSV5eOfUaFRYBYCsfiVHEAShlsgwxx/csATzSsW+t9KVhysWzY0Km7TZWFbrDqtFj0nxZ2b//VrINg6gXodjZQyR4RSh4Q03X1aP7z/7VjBOEwDu33kYC8oKMTXjR4HDjnt3HEpYV1D2tLC5Zk5w8ZCvr1oc86me6PVKtr5uSRdavdIS1RNpc6E2JJJwK86zY/3KhmCCqKJYwjmk2pet80rJRgiCyFrUPLGVJdo9+n+W45tXX4TTY+ck2coKHbCBCcMmY/l28sXRaD4p5pyfr3Wd2UZkeAP3+4WB9b1nx9H56D5popza5Cnl5sz2QPtsRJrUMTaJt/pHov5Aqih2CBPq6s4rENYzMDwJgiCIbEXtG12pJvvoJCamwiXZvnLlhTg16o2qI2Y9lAgvRHNJNsZYAWPsTsbYltnvDYyx1Qnsl8MYe50xtl3rNlmNAoc9KIcCAHMKHcLAeleBI+z7x5bVBvdJNHlqeX056iuKom7KeL8TmUWkzQEBG2IQS6k5cmzBhLrNO4/ggeeP4My4D3OL84T1nFfoSNu5EARBmA3Zk1+ZHKYS5hiKM9eGOfm5+I8/vhNWz3/88R0U5+WqqocS4cVoPikG8BMAPgCXzX7vBrAxgf3WAzikQ3sswfS0H/tOnsGOg32YnJnGV668MGjIb/eNiJOaRn24rT2gE1s6u6INgKhEvaODo5ie9uPo4Gjwu9/PI5tAmAS/nwuvlWy7FvX4ZmbQ2d4Qpk3Z2d6A98fEqx6N+2aEWpb2HCash4PsjSCI7CXWE1sRMk12z4Q40e7s+JSqeijJWYweMcUXcM5vZIx9EgA45xOMsZiPGRljtQA+CuBfAdyhQ5tMzfS0H9v29YRljX7z6otw2xUL4Z32wy1JanpvaCyoI7x+ZQNWNJTjsgvKohL15pfl4/b2hrD6KdDenMhesV21uBLPHhpI+NWb2noudhdjq2Aluvs/uUwqpXbp+WVRITbHhsaE9axqqkpnNxIEQZiKucXyVUZlOOwsbIU6h51hbpFM3jJPWAeFQ6pDjyfFPsZYPgLJdWCMXYAQFQoJ/w/APwHwxylnGdQ81evq80QlJ/3b796Cb8aPzTuP4HvPvhW1Qt36lQ14fE93sPx9zx1Gfq49uOJc6Gua1S01UfXTinPmRPaKravPo+rVm9p6ZvzAt1ZfHHzbYLcFvisJdZG2l2MTh9gsKCvE11ctDlvR7uurFtNTCYIgspocG6S+VDRfODY0htt++XrYCnW3/fJ1zPj9wnrA5PMOCodMHD2eFN8NYAeAeYyxXwD4EIDPygrPxhuf4py/xhj76xjl1gFYBwB1dXUaNld71AbUyzRfa+fk47b2hbAxoKLYgf+5fQUGR71gYPjy1jfCJNOU1zAXzC2Kek3DGCjQXke0tE3ZKzaZjciuYTL1iFaoOz0mXqFuad0cLChXn8RJpA8r+Uwiu8hG2+zzeIW+9AMLSvFmX3Qy85z8XKGv7pHUU3deAXrOekl6LUX0UJ/4A2NsL4DlABiA9Zzz0zF2+RCANYyxawA4AZQwxn7OOf9URL1bAGwBgLa2NlMHKMqe0skkUNwucXjE8fcnwlaYe6ZzBZbXl2PfybM4M+4Lq8OZa0OBIweAXIdYzSpmROJoaZuyaxdrRTiRzI+SOBf9qi5PuD03x6ZqhTq1K+AR6cdKPpPILrLRNitLnEJfmptjw707DoVJrt674xB+cP0SaZiE2Cfn4dP//WeSXksRzcInGGPLlA+A+QD6APQCqJvdJoRz/g3OeS3nfAGATwDYGTkhthpqA+oXVxZjw9rw8Ii7Vzdi1zunhPvLkqKmZtdljgysf3pfT1T4BQXamxNZUoSyIlzk9rrSAuzo6sc1m3bhkz/ajWs27cKOrn74uV9oIzk2COsZ9vqkK9RRkgZBEERqyHy7byYguaqEnP1411Hc2FaHkUmf0Ifn5rCo+cKGtU2w5zBV8w5CjJZPir8f4zcOoF3DY5ka2dM+2dO1bs8EHnj+cNjrkAdfPILVLTXY3zMctX9ZYV7MZCZacc66xAo/EG2XvZXYum651EZaakuj6jnQ4xGvklSSh+aaORQOQRAEkQIyH36g56xQcvUXt1wq9eEdLS7UlxcG3w62VLvQ7ZmgN8IaoNmkmHN+RSLlGGMf4Zz/QVLHCwBe0KpNRiFbMU72dG1g2Avf9Lk3SIwFlncMlVgL3V9JZqIV5zITWfiBaPvAsBelBQ58bFlt8NXbr1/rxrhvRmojonoa3SXY2NEUplCysaMpuEwo2Q5BEIQ28JCAkXHfjPAJrx/ylWVtNoa2BeeF7aN23kGI0SPRLh73AhBOijMFtclGbpcTN39wftSSuYrEGq04R8iQ2Y7bJZZMk9mI3W5DR2sNGuYWod/jRZXLiUa3C3a7HgI1BEEQ2YUsAf9id7HwCW9ZYR7+qu68hH04zQu0wYhJcVZcITVP12b8CE5qgHMSax+6oCzsL8pk6ycyF5ntXHVxlWobsdttaJ1XitZ5iR1blOBHDpggCCIaWajb/9y+QvqEV+bDZb6X5gWpY8SkOCsyTdVwakScmPfHt05h03NHSFqFkCKzncHRgDyfXqiVHSQIgshmZAn4g6NeVU94yffqC70bNQGytclnxSRosQ1CilHr2sueepCNEoRKbHYwxpL+1MzLDp1fqxPLV6tZXIN8r74Y8aT4mAHH1BwtXx2LAuQ72xvwyKvHg2VKCxwYHJmkV9UWRM8wg2SSK7RoTyzZQXp1RxAq8E/jxh++nPTuW79wmYaNIfRiQVkhvn/9Enz18XO++vvXq0+EI9+rL5pNihljH4v1O+f8N7P/xixnBbR+fREZIJ+fm4POR18PrlinJFN95id/ptclFkPvV11qkyu0ao9a2UGCIIhsxu/n8HM/1n24Hn4O2Bjg5374/Zx8r4nQMnzi2hif1Roex3D0eH0R+vqkuWYOvr5qcfBVy/VttVHJVPS6xBqk41WXEa/eZEL0JP9DEAQRTVefB197Yj82PXcEm3cewabnjuBrT+xHV59HVT3ke/VFS53iz2lVl9nR+/VF5NM/mY4hvS4xP2Z71aVVe0j+hyAIInH6PGLf2+/xJqz4A5Dv1RtdYooZYx8F0Agg+Dyfc75Bj2MZQTpeX4RKqxwdHKXXJRbFbK+6tGwPyf8QBEEkhtuVL/S9VS7yvWZC80kxY+xBAAUArgDwYwDXAfiz1sdJN6HJSW6XE5tvWor93R74OZDDgOZal26vL2ilGuui5bWTJcipSZwjWyIIgkg/je4SfPe6Fhw+NRqcNyycW4RGt8vophEh6PGk+DLOeQtjbD/n/B7G2PcB/EaH46SNyOSk+WX5uL29AVtePBo2sdALel1iXbS6drIEuasWV+LZQwMJJ86RLRFEhjEr6ZYs1bXz0HPyhIYNImT4ZnjYvOFf/7bZ6CYREegxKZ6Y/XecMVYNYAjA+TocJ21EJietbqnBndsORiUrXdS5QrfXGfS6xLpoce1kCXJb1y0Xbo9li2RLBJFBkKSbJejq8+Cff3sgzFf/828PYGFFIVrnlRrcOkJBj8U7tjPG5gD4LoC9COgSP6rDcdJGZHISY5AmKxGEHsgS5GTJG2SLBEEQ5iFWoh1hHvR4UvwdzvkkgF8zxrYjkGxn6asuS04yS/IUkfnIbNDtMlciH0EQBBGNlol2hH7o8aT4FeU/nPNJzrkndJsVidQFfHpfDzZ2NJFOIJE2ZNqUjW4XaVYSBEGYnEZ3SdS8YWNHEyXamQwtV7SrAlADIJ8xthSAEvlfgoAahWURJSfVlRZgWV0pJSsRaSFWghwlzhEEQZgbu92GjtYaNMwtQr/HiyqXE41uF+x2PZ5NEsmiZfjE3wD4LIBaAD8I2T4M4JuynRhjTgAvAsibbc8TnPO7NWyXJoiSk0K/+/0cRwdHE5LFIohkkCXIybarkWojCIIg9MVut6F1XmnUYh3kq82Dliva/QzAzxhjH+ec/1rFrpMA2jnno4yxXAAvMcZ+xzl/Vau26Y1MLksmi0UQekM2SRCJUTOvDr3dJ41uBpGlkK82F3ok2v2JMfYQgGrO+dWMsYsBfJBz/pCoMOecAxid/Zo7++E6tEs3ZHJZekq0EUQsyCYJIjF6u0+SpBlhGOSrzYUewSw/AfB7ANWz398B8OVYOzDGchhjbwA4BeAPnPPdgjLrGGN7GGN7BgcHtW1xisjkskgWKzswo22STRJmtEuCAMg2QyFfbS70mBSXc84fA+AHAM75NICZWDtwzmc450sQiEe+hDHWJCizhXPexjlvq6io0KHZyaPIZYVCsljZgxltk2ySMKNdEgRAthkK+WpzocekeIwxVobZEAjG2HIAnkR25JyfBfACgFU6tEs3ZHJZJItFGAXZJEEQhPkhX20u9IgpvgPAUwDqGWN/AlAB4DpZYcZYBYApzvlZxlg+gCsB3KtDu3SDZLEIs0E2SRAEYX7IV5sLPSbFbwL4LYBxACMAtiEQVyzDjYBqRQ4CT64f45xv16FduiKTxSIIoyCbJAiCMD/kq82DHpPihxHQJv632e+fBPAIgOtFhTnn+wEs1aEdBEEQBEEQBJEQekyKF3HOW0O+P88Y26fDcQiCIAiCIAhCE/RItHt9NrkOAMAYuxTAn3Q4DkEQBEEQBEFogh5Pii8FcDNj7MTs9zoAhxhjBxBYq6NFh2MSBEEQBEEQRNLoMSm2lJwaQRAEQRAEQWg+KeacH9e6ToIgCIIgCILQEz1iigmCIAiCIAjCUtCkmCAIgiAIgsh6aFJMEARBEARBZD00KSYIgiAIgiCyHpoUEwRBEARBEFkPTYoJgiAIgiCIrIcmxQRBEARBEETWQ5NigiAIgiAIIuuhSTFBEARBEASR9dCkmCAIgiAIgsh6DJ8UM8bmMcaeZ4wdYox1McbWG90mgiAIgiAIIruwG90AANMAvso538sYKwbwGmPsD5zzN41uGEEQBEEQBJEdGD4p5pz3Aeib/f8IY+wQgBoAhk6K/X6OY0NjGBj2orLEiQVlhbDZmGblCYJIDj3vNbqPCcK80LhM6I3hk+JQGGMLACwFsFvw2zoA6wCgrq5O13b4/Rw7uvr//+29eXgc1ZX//T2trbW2ZVmWWpJXEMZoMTAKa8gQQ4hDjO0QAiG/CS8JMyQzgElIJnlnwpjB45lMNt5he0OYhAlkIZBAjO0QsmAYQiAJxmBZwgEbB4ysxbKBlmRr7/P7oxd3V9/b3dVd3V3VfT7P049UVbdunbp1zu3bVfd8Czc9/DImpv1wl7hw2+WnYlVbozKgzJYX8ots+mahk8lYy7c4Fr8U7Eoqvinfy0I2yPmc4hBEVAXgEQCfY+YR43ZmvpeZu5i5q76+PqO2vHHkaDiQAGBi2o+bHn4Zbxw5akl5Ib/Ipm8WOpmMtXyLY/FLwa6k4pvyvSxkA1sMiomoBIEB8Y+Y+dFc2zM0MhEOpBAT034cGp2wpLwgCKmRyViTOBYE+yLfy0I2yPmgmIgIwPcA7GHm23JtDwA01LjhLoluGneJC/Or3ZaUFwQhNTIZaxLHgmBf5HtZyAY5HxQDOBfAJwGsJKKXg5+Lc2nQ4rpK3Hb5qeGACs1FWlxXaUl5AJiZ8WPXW+/giZ4B7HrrXczM+LVlgcD8qP3DY3j+9cPYPzwGv59TPDtBcAYqn08l1pIlXt0Sf4KQW8zGp8SzkAo5T7Rj5mcB2GrWu8tFWNXWiJPXn4dDoxOYX504a7W0mHDt+5bCz4CLAss6Zmb82LzrIG7e3BNOANi0rh3rVjSjuDj2d4okDAiFhs7nL1reYCrWzKKq2+9n/HrPkMSfIOQYM/Gp6ysknoV45HxQbFdcLsLS+iosra9KWPaNI0dx/Y9fipq/5C5x4fH15yn37x3whQfEQGCe082be9A6vworFtQq61clDJysqV8QnI7O5x+69ixTsWb2mKq6H7r2LIk/QcgxZuNT11dIPAvxkEFxEKOeYYunHHuGRjDgm4DXU442b03UXdypqVl09/swODKB+dVluLitAd7aSlDwh+YjL/bh0OiEMsgGfOoEgEHfBFYsiLUtXsKABLFgN3TaoGbW63x+wDeB2opSXHp6S0ysLa6rTEuTdGhEXbcuXiX+BCF7DI1M4OK2Blz2noU4PDqJ+uoy/PSFA9r41PUV8eI53T5EcD4yKEbso9pFdeW47v2t2PCYenrD1NQsNnf3h7cvqivHdeefiA1besPlb7ygFY016gn9Xk853CWumF+wjZ74CQPG8pIwINiNeNMedI84VetP8VYrfb5lTjmuOnsRbn9yb0yspTvFyOtxK+tuqVXHq8SfIGSP5lo3zjphHj79/RfC8blxTRtOqFfHZ0utuq/QxbMVfYjgfAp2UBx5d6qitCjqccrqzubwgBcI/Iq8c/teLJpbgSNHpzC3sjRq++rO5vCAOFT+9if34j2L5+LPgwPwesqxvKEafb5xDI1MYH5VGW7/+Kno7R+Bn4EiAk6cX4U2r0dpayhhwBisViQXCYKVxJv28LUn9uCa9y4N37X52hN70DzHrSz/ixvOU/q8H4wn9wzi65etwPjkDCrKinH/c/txxuK5ph+JRj7t8da4MaeiJPwFGqrj9if34gPLG0zHn1Vv0pI3cgmFyMyMH70DvqgntYO+Sdz99L6oPuTup/fhG5etwF2fOA3dfb7w92lHiwfu4iL85IUDUeV/8sIBbTzP+iHTKoTCHBQb72atv+DEqF+NRIha9nrcuKJrIT5535+SKg8Elv+w/wi++evXlHeeN65tx2MvH8SbR8bDd6LjkcnkIkGwCt20hyNHJ3FF10Lcsf34XZv1K1tx5Oiksvzw2IQy2fX3rx/CR09fiC/9bFe4nltWt2F4TF3P0Ih6ioPxaY+7xIV/W9uO2opSDPgmouoYHps0lXhrVWKsJNgKhYguEb22skTZh4xMTGNqhnHvM/uj4uTto1PK8u+MTynj+Y9/OSLTpARbSLJlHePdLD9DqWcY4tLTW8KBlUz50PLC4J0k1Z3nDY/1YHVnc3j55s096B3wae29/scv4Y4n9+Gu7ftwx5P7cP2PX5I38wi2Q6cNWlVWEhVDE9N+3LF9L6rKSrRaoqFk17OWzsPS+iq4XISy4mLcui36qcyt23oxt7JUWU9FaZHSzu5+X0xM/stjPfhYV4uyDpUtOqx6k5Zj3sjlKgYRpfxpXpDeK6ibFyxM6/gFT46vnxFdInq1pg+pcZco4wQEZXkwlPEsusYCkMd3iuM9dhwamcBJ86vwt+87AeOTM/BUlOArFy/Hvz++BxPTfmzddRBfv6wT+w6Nwc/AyY3VOGl+Fc47aT6IgKqyInzl4uUYHpuEn4Gq0iJ89SMd+Kef7476Rdr/7jEA+jvJkf1xbUUpRiZm8Pzrh5X2yi9YwW6oYmxxXSXuvPI07D54/FFme7MHRzR3ct8+Oql89BnSEjXWPzyqrufdY9NYv7I15q7Q9Kxf/ShWE1NL5lWG5xtG1mGGePFqJpHHMXHvn8EV33ku5d0f+sw5aR2+v++tnB7f8eT4+hnRJ9NOKtcPx1mvfgo1qTyuTFMUgDwdFCd67Ng0x40rz1wU9Qj21jVteOSzZ2N0cgYN1W7sfOvdqMcxt1zShnv+dx/ePDKORXXl+IfzT4za/o8fXIYbL2jF0alZMAMP7TgQvhMMQDmxn4N64aEEn797YIfSXkm0E+yGLsZWttbj3WPTUbHxb2vbcVJDlSa5pRz9vomYR586LdGWOeokmdqKEjy04/j8wVAMfrCtQfkodmnE4DeynvLiopg6VrU3mmobXbyaTeSRuBcKkQVzNQlyczSJsJrk2yaPufhJ5f0EQv6Rl9Mn3jhyFPc9+zq+ftkKfO3SDnzjshXY/NIB7D74Lp5//TCGR6dw69boR7C3BBPlmIEjR6fwleBd39D2W7f2hge5qzubw+VD27/xq1cxPj2Lu7bvw/ee3Y/P/vWJ2NZ9EACwdddB3HJJW9SbdTaubQ9v/1hXSzgh4PqVJ+Jvz1uKrz2xJ/yYNJNv8RKcg53ewqR7tL97wId/UUxLmPUzNq5tj4mByrJiZT29Az7lej9zTCwFlotw3fknYHljNRbMKcdybzWuO/8EvHNsWvkotix4fKM95WUufO/Z/eE4/vKq5abfgKWLV10ij246hMS9UIjMzLIy4XXGD+X66Vm/cj0RlDHe2aROaAfU0yqEwiIv7xT7xqdiknFuXdOG9T95CW8eGY9JlAMCgfT68Bi+9MhufO2jHXGnO5QVu5TbT/HW4PaPn4o5FSX41q9exerO5vAdpwf/+Cbu/9QZYDDqq9zoe/co1p7aDD8DJ9RXKhMC3j46GQ5M+QVb2Ngt6Ur/aF/9yHJwZALrOpuwdF5leOpAZ5MHOw68oyyv0xI96JvAg398M6A+MTWD8tJifPeZ1/H5i07CLBO+GJmAd0kb3jk2payn7+1xpT3FxYEXgUTGGQBTba+LV7OJPBL3QiEST8df3bfo+pxJNNSU4puXrcDRqRlUlhajurxI+dZYQQiRl4PiWT9iknFu2dKLa967FHc/tS+cKGd8rOIuDTRHRWlx3OkOSzSPXqdmGTf+5GVcv/JEvHZoDN0HR6K211eXYWl9FfYPj+EzP9gZ3v/OK09TJgQ8dO1Z4f2Nb9gL3bkSqabCwG5vNdQ92q+vLlOvrypDaWkRuhbPjaqnrNilLF8XTJxTTUF47dAY1j/4UtT6ytJi3Lp1Z1T73Lq1Fw98+gytJrjKHgAxb7LcPzymbXvdHGHVGzFTmQ5h5s2agpAP6HT859eo+xbd+rmVpbgqqBgVud6Kt18K+Ute/mQ6rEnqiZyEv35la/Qj2NVt+O4zrwMA/vuZ13HLasN0hzXHpzsMvHssZv/1K1txZDQg5bR118GYxzaRjz2Nd9n+cvio0t5jU7PK8wvdNbz4jt/hyv/+Iy6+43d4oncwp4/ThcwSL+kqF+ge7YOhjA1ofq+NTk4py0/Nzirr72zyYNO66NjatK4dR8bUd4TfPTatLK/TBFeha/uhkQlTcSjTIQQhMW3eGmXMHpuaiflevmV1G6ZmZ5V9iG982lZ9puAM8vJOse6XZuhO74BvAg/tOIAfXnMmjoxNoq6qDP/5y1fCd3a7D46gdOfx7Y0eN5bVV2NZYxUGfROYW1mKL/5sV0xCzjcvW4Hv/M3paPS4sbyhBl2LapWPPY13jKZm/Up7GzRvxLPbXUMh89gt6Ur3aP+NI0eVCW+6ZLXqslJl+TOWnIpVbfXKqQPrVjSjdX4gFhs9brR5PegdGNHEUBlWLpsfU97MI1Rd21eUFuFTwbdrAYnjUKZDCEJiiotdyhjf+dY7eGTn3qipUw88tx9fWrVc2Yd862On2qrPFJxB3gyKI+Wbmua4sWlde1TG+ca17bj7qb0AAoHx5VXLcfrCWrhcBL+f8en3noCe/uNzBj/93hPC20OsWFCLFQuAF944opwDTAR8sN0bLq977GmUftm662CMvfHuIDlGqkmwDDvKBake7S+uq8SXVy1P2k4/WBlLDNZOHSgudoVjMUTo7pJRZSI0ADaWN4Ou7adm/abjUKZDCEJiXC5CtbsEx6ZmUe0uCXxP8yw+1hWbK1RaxLhhZWtM7Hc2eWzXZwr2Jy8GxaokpO988nQ89HdnYXBkIuGdW7N3cOoqy0zdDTOiOt7C2gqcvlBtnxG73TUUMo+T7jKaeftiurEUQnd3yYqkmnh3xSUOBcFadEnFzXPK8eSev+A7n/wrvHtsGnMqSvCjP/wFJzeepI19p/SZgn2wxaCYiO4DsBrAIWaO/75jBarpBJ/5wU48vv48rFhYGy6XKFEt2Ts4Zu+GqVDdMTJzfPkFXHg44S5j6O2LySa3WBFLIXR3hOO9yCdZdHfFJQ4FwVp00wN/9bnzcFFbEz7zgxeTfhrkhD5TsBe2GBQD+D6AuwA8kMrOZqcTpCtvleu7drk+viDoMBuLmfblTErZSRwKgvXo+pDh0UnMqSiJego1p6JE4k2wFFsMipn5GSJanOr+ZqcTWJGolutfoLk+viCosJvsWKaTUiUOBcFadH1ISZHL1FMoQUgFx0iyEdG1RLSDiHYMDw9HbTMrdWQ3eSvB2cTzzULDbrJjhRzr4peCXUnl+/zY1GzBxrKQPWxxpzgZmPleAPcCQFdXV5QQqNnHmJKoJlhJPN8sNOw2paCQY138UrArqXyfS2KrkA0cc6c4EWbeWW63u1mCkE+YicVMI7EuCM5D1YdILAvZwDF3iq3EbnezBEHIDBLrgpAfSCwL2cAWg2IiehDA+QDmEVEfgFuY+XuZPKYkyAhCYSCxLgj5gcSykGlsMShm5itzbYMgCIIgCIJQuOTNnGJBEARBEARBSBVidl5SMhENA3gz13YEmQfgcK6NiIPYlx6R9h1m5lXxCifwTbufq9UU0vnm+lzj+qYN+sxct0+6ON1+IDfnkGyfeRTOb18z5IM/mcGO56v0TUcOiu0EEe1g5q5c26FD7EsPK+2z+7laTSGdbyGdayo4vX2cbj9g73Ows22ZQM7Xvsj0CUEQBEEQBKHgkUGxIAiCIAiCUPDIoDh97s21AQkQ+9LDSvvsfq5WU0jnW0jnmgpObx+n2w/Y+xzsbFsmkPO1KTKnWBAEQRAEQSh45E6xIAiCIAiCUPDIoFgQBEEQBEEoeGRQLAiCIAiCIBQ8MigWBEEQBEEQCh4ZFAuCIAiCIAgFjwyKBUEQBEEQhILHkYPiVatWMQD5yCfbn4SIb8onR5+4iF/KJ0efhIhvyidHHyWOHBQfPnw41yYIghLxTcGOiF8KdkV8U7ATjhwUC4IgCIIgCIKVyKBYEARBEARBKHiKM1k5ES0A8ACARgB+APcy8+2GMucDeAzAX4KrHmXmjZm0S4jF72e8ceQohkYm0FDjxuK6SrhclLX9M12f4DxmZvzoHfBhwDcBr6ccbd4aFBeb/x0vviTkO/F8PHKb1+PGrB84NCqxIAgqMjooBjAD4AvMvJOIqgG8SES/YeZXDOV+x8yrM2yLoMHvZzzRO4ibHn4ZE9N+uEtcuO3yU7GqrTGpDjPd/TNdn+A8Zmb82LzrIG7e3BP2gU3r2rFuRbOpgbH4kpDvxPNxAOFttRWluOrsRbj9yb0SC4KgIaPTJ5h5gJl3Bv8fBbAHQHMmjymY540jR8MdKgBMTPtx08Mv440jR7Oyf6brE5xH74AvPCAGAj5w8+Ye9A74TNUjviTkO/F8PHLbpae3hAfExnKCIATI2pxiIloM4DQAf1RsPpuIdhHRL4moTbP/tUS0g4h2DA8PZ9LUgmNoZCLcUYaYmPbj0OhEVvbPdH2ZRnzTegZ8ah8Y9JnzAaf5kpWIXxYG8Xw8chsRbBML4puCXcnKoJiIqgA8AuBzzDxi2LwTwCJmXgHgTgCbVXUw873M3MXMXfX19Rm1t9BoqHHDXRLtCu4SF+ZXu7Oyf6bryzTim9bj9ZQrfaDRY84HnOZLViJ+WRjE83HjNrvEgvimYFcyPigmohIEBsQ/YuZHjduZeYSZx4L/Pw6ghIjmZdou4TiL6ypx2+WnhjvM0FyzxXWVWdk/0/UJzqPNW4NN69qjfGDTuna0eT2m6hFfEvKdeD4eue2RF/tw4wWtEguCEAdi1r7YI/3KiQjA/QDeZubPaco0AhhiZiaiMwD8DIE7x1rDurq6eMeOHZkw2TFYnVGfbqZ/9P5utHk9KSkFhAid36HRCcyvtk2WdEID8tE3rfI1s/WEfGrQN4HGCJ8yW49Nfclq4p5QPvplvpFqfAz4JtBSWw53cREOH52M8fFI/2+sCahPDI9lLRYKss8UHIHSNzOtPnEugE8C2E1ELwfX/TOAhQDAzPcAuAzA3xPRDIBxAB+PNyAWMqP28Os9Q2mpT6SzvwqXi7C0vgpL66tS2l+wBqt8LZV6iotdWLGgFisWpFeP+JJgd8z6tRl1FpX/nzBfYkEQVGRafeJZZiZm7mTmU4Ofx5n5nuCAGMx8FzO3MfMKZj6LmZ/LpE35gN3UHiTDP3+x6trarR5BsBNm/doqdRZBEKKRN9o5ELupPRRyhn++Y9W1tVs9gmAnzPq1VeosgiBEI4NiB2I3tYdCzvDPd6y6tnarRxDshFm/tkqdRRCEaGRQ7EDspvYgGf75i1XX1m71CIKdMOvXVqmzCIIQTUbVJzKFZKumn1FvzHReWFuBA+8cC9cXWk4lE9rrKcfyhmr0+cbD+7d4yrFnaCRldYt0SVddI0heZFJnWr1B19a69VNTs+ju92FwZALeGjc6mjwoLS0yXX+BI+oTDkelJjE8NomK0mJMzc6ivqoMs37g0Gh0nzroC6hKzKksQf+7x2MaQEycq9almgydZD+SF32mkJfkRH1CyBDpZNTHy3ReWl9lOhNapT6xaV077ty+F28eGYe7xIWNa9tx91PHl3WZ0pnATKZ2vpNp9QZdW6/paMJvXz0Uc9yVrfXY0jOADY8dL79xbTvWdTYpB8aZUDoRhFwT6de1FaW46uxF4Vcyu0tc+KdVJ2Ny1o/bfvNajN93NANP9A7iiv/+Q3jbXZ84DVMzHBMnpcWE63/8UtqxY7UCko7mBQvR3/dWyvs3tSzAwbcOWGaPkP/IneICZP/wGC6+43dRiRruEhceX38eltZXJdyebH3XvHcp7n5qn3b5oWvPwooFtZk6zTC73noHV9z7hxj7Uji+4+96mL22ZtG19Q+vORN/870/xqz/wafPwCfv+5OyfNfiuVm338HInWIHE+nX173/RHzv2f1RPr7+ghNx7zP7lX4PICYmdOWvfd9S3PHkvpg6zMaOiThMq88kIlzxndQFqR76zDlw4hhHyApK3yys22QCgMSZzmYzoXXlieIvZytTWjK1j5Np9QZtW2uOOzgyqVw/NGLO10R9QnAykX5NhBgf93PsupDfq2JCV95vGB+mGjsSh0K+IoPiAiRRprPZTGhd+cgf6KrlbGVKS6b2cTKt3qBta81xG2vKlOsbasz5mqhPCE7G6NdGHy+i2HUhv1fFhK68cWZDqrEjcSjkKzIoLkASZTqbzYRWld+0rh3bug+GlzeujV7OZqa0ZGofJ9PqDbq27mzyKI/b4fVg49ro8hvXBsrnwn5ByAWRfv3Ii3248YLWKB+fW1GKmz5wktLvVTHR0aKOt84WjyWxI3Eo5Csyp7hASaQokEhNwljeWJ9RzSIqU9rjRpvXk9Ukt0iFg8YaNzoTKBxocPycYsC8moRZtQpdW+uOGyofqj/RtUlXeSUeZs/VRsicYocT6deNNe6w0kRFaRGmZ/2YF1SfGB6LVQiaX+1GkSswfamitAhTs/6wWkWofKT6hNnYUcVFknXJnGLBroj6hHCceIoCyahJGDONVfUZl1csqMWKBZk/NyN+PyuVDwpVscCMmkQqSiTx2lp13NLSImVSnRX2myFbGfWCoELl1yfMj/XxE+brFYJKiwmf+v4Lcf3XbOwkUisq8ARXIc+Q6RNCDG8cORruAIFAAsXNm3uwurM5vHzTwy/jjSNHc2lm0qjOx0n25xKzbefktnay7UJhofPV7j6f5f4rcSEUEjIoFmJIVk3CKZnGkimdOlYpkTihrZ1su1BY6HzVKnWJZI4lcSHkIzIoFmJIVk3CKZnGkimdOlYpkTihrZ1su1BY6HzVKnWJZI4lcSHkIzIoFmJIRk3CSZnGkimdOlYokTilrZ1su1BY6HzVKnWJZI4lcSHkI6I+ISiJVp9wY3lDDfp849pMY2N2cmRmtGo50f7pZv3r7ElTsSAv1CfMYvSFRMohExMz2D3gw+DIJBprytDh9cDtLjZ9jXOhBJFJZYsMI+oTeU5kPFSUFoOIMTXDODw2CW+NG3MqS9D/bmDb9OwsaivKwooUqcZP6JhHjk6itMiFY1OzZusS9QnBroj6hJAcKvWJyGxjVXljdnKkWsWiunLcsLIVN2/uUWZFW531nyhbWkieeL6gujZTU7PY0jOADY8dv9Yb17ZjTbsX2/cOm1KxyIUSRKaULQQhHVTxcMvqNtzzzL6wItCNF7TigeffxDvHpnDb5afi3fFpXP/jl1KOH10Mnrmkzik/FAXBNDJ9QojBCsWBSLWK1Z3N4QGxqj6rs5slW9o6zLZld78vPCAOld/wWA92D/gKRsVCEKxGFQ+3buuNUgS6/cm9uPT0FsuUKCQGhUJEBsVCDFYpDoTUKogQtz6rs5slW9o6zLbloKb84MhkwahYCILVJKsIFFq2QolCYlAoRGRQLMRgleKAUa1CV5/V2c2SLW0dZtvSqynfWFNWMCoWgmA1yfaxoWUrlCgkBoVCRAbFQgxWKA5EqlVs3XUQm9a1a+uzOrtZsqWtw2xbdjR5sHFt9LXeuLYdHV5PwahYCILVqOLhltVtUYpAN17Qikd39lmmRCExKBQiGVWfIKIFAB4A0AjAD+BeZr7dUIYA3A7gYgDHAFzNzDvj1VsImdSJ1BwynRVvzMJv8ZRjz9BIUIGgHG3emigFAmN5o9pDIvUHq7P+dfakqX7hKPUJq9QbpqZm0d3vw+DIBBpr3Ohs8qC0tEhbPpH6hPEa6+zMhRJELhQvLELUJxxEpJ/Nr3aHVSIqSosxNTuL+qoyzPqBQ6PH/RBAhPpEEZgZRIRjU7PhOgZHjsdKqHyy8aPy/VAdbx+dRImoTwj5RU7UJ2YAfIGZdxJRNYAXieg3zPxKRJkPAWgNfs4E8O3g34IlkZpDNjLxI7PwZ2b82LzrYJR6xKZ17Vi3ojk8MFZl7Sda1h3PavsTKRnkSukgk1h1TjMzfmzZ3R/32huPG09lwniNE9mZTSWIfPQDwX6o/CxSOeKfVp2MyVk/bvvNa0r1nHjxsHhe9LZk4yee7y+uq8SfB0clLoSCIKPTJ5h5IHTXl5lHAewB0GwothbAAxzgDwDmEJE3k3bZnURqDtnOAu4d8MWoR9y8uQe9A76sHD9dEmVR52OWtVXnZPbaW6Fckqu2t5MtQv6i8rNI5Ygjx6bCA+LQ9kz7YTzfl7gQComszSkmosUATgPwR8OmZgBvRSz3IXbgDCK6loh2ENGO4eHhjNlpB5LNNM5WFvCAT6Mo4HNGFnKiLOp0s6zt6JtWZY6bvfZWKZfkIsPdTrZYgR39Ukjcv/s5vlpPNm06NDqRkbgQ3xTsSlYGxURUBeARAJ9j5hHjZsUuMZOAmPleZu5i5q76+vpMmGkbks00zlYWsNdTrlYU8DgjCzlRFnW6WdZ29E2rMsfNXnurlEtykeFuJ1uswI5+KSTu34sovlpPNm2aX+3OSFyIbwp2JeODYiIqQWBA/CNmflRRpA/AgojlFgD9mbbLziRSc8h2FnCbtyZGPWLTuna0eT1ZOX66JMqizscsa6vOyey1t0K5JFdtbydbhPxF5WeRyhFzK0px0wdOyqofxvN9iQuhkMi0+gQBuB/A28z8OU2ZDwO4HgH1iTMB3MHMZ8SrtxAyqROpOSRSg0j1eDq1i9DxBn0TaPS40eb1pHW8dDGrEpBIySBJpQNHqU/MzPjRO+BL2kciVSa8NW50BFUmdOt1mFWNyIXKhBNsMYmoTziISD9rqHZjdGIagyMTmFNRipGJadRXlqGirBiHxyZRUVqEqVk/6irLLFPlUfWb8Xw/jbgQ9QnBruREfeJcAJ8EsJuIXg6u+2cACwGAme8B8DgCA+J9CEiyfSrDNjmCeGoOyahBmMGM2sWKBbkfIKSiEpBIySDbSgeZxu9n/HrPUNJtNDU1i83d/eFXNIf0hde0e+OqSagw25Z2ans72SLkLyE/W1xXGdOXrV/Zin/b8QpuWNmKORUl+NT3X7BE9SFRvxnP9yUuhEIh0+oTzzIzMXMnM58a/DzOzPcEB8QIqk5cx8wnMHMHM8vtjARYrQZhN7WLREg2dGLMtlF3vy88IA6V3/BYD3YP+KStBSFDqOL0ju17sbqzGTdv7kF3n8+y2JN+UxASI2+0cyBWq0HYTe0iEfmmEpAJzLbRoKb84MiktLUgZIh4fe/EtB9+w5P/dGJP+k1BSIwMih2I1WoQdlO7SES+qQRkArNt5NWUb6wpk7YWhAwRr+91l7hgnCWRTuxJvykIiZFBsQOxWg3CbmoXiZBs6MSYbaOOJg82ro32qY1r29Hh9UhbC0KGUMXp+pWt2NZ9EJvWtaOzxWNZ7Em/KQiJyaj6RKaQTOrjygIhNYhl9dV4dXg0rDSwvKEafb5xrZqEcbmp2o2ewREMjkygscaNtoZqvHZ4TFufTr0hWTWIRCRSwzCqcWRJJcDB6hOJFUMmJmawe8CHwZFJNNaUocPrgdtdrK1Hp26hq0fnI2ZVMgQloj7hAEJKLodGJzCvqgzHpqYxr8oNd3ER3hmfAhgYHptEQ3VA5aW42BWj+uD3czhe6qvK4HIBnvLS8ODWGGOR67weN2b9wPBYtKqRVf22AlGfEOxKTtQnhAxRXOzCigW1WLEASjWKjWvbcfdTx9UjjGoSxuXI8ovqynHd+1ujlAh0ahQuF6WkBhEPM2oYkg2txqz6hN/PSpWJi5Y3KOu5cNl8bNndH6OAcvEpDdjWM4QNWyJ8cU07LmlvxFP7DiddT6pKKoJgV1QKL+tXtuKhHa/gixctg4tc+MJPY+M1UvVB1dffsroNj+w8gM+efyKmZjgqxu76xGkx60L1ArC03xaEfEC+dfIAlRrFhsei1SOMahLG5cjyqzubY5QI4qlRWJ3V7DQ1DDti9proyvdq1Ce6+9UKKD2Do+EBcWj9hi092D04YqqeVJVUBMGuqBReQkoTew+NhQfEoW2qeFX19bdu68VV5yxFd19srKrWheoVNQpBiEUGxXmATo3CqB6R7HIo8zlRfaGsZauzmp2mhmFHzF4TXXmt0omm/JBGrUK3Xqt6kaKSiiDYFZ2vEwF+Vve5xnjVxeP41Iyyjnj1ihqFIMQig+I8QKdGYVSPMLucqL5Q1rLVWc1OU8OwI2avia68VulEU75Bo1ahW6+rJ1UlFUGwKzqFF2agiNR9rjFedfFYXlqsrCNevaJGIQixyKA4Sfx+xv7hMTz/+mHsHx6D3yggmUNUahQb10arRxjVJIzLkeW37joYo0QQT43C6qxmp6lh2BGz10RXvs1bo1zf2eRRKqC0N1Zj4xqDL65pR0ejuXpSVVIRhFyj+65QKbyElCZOmF+Fr36kI2G8qvr6W1a34YHn9qOjJVYpRrUuVK+oUQhCLKI+kQRWJ5JlAqMaxfKGGvT5xsNZy0a1BuNyi6cce4ZGkt4/MkvZ72dsf3UI3X0++BlwEdDZ4sHKZQ0pt09IqSCZ42cRx6hPpHJNjG0eamPdeqPPtXk9cLkIOw4cxuws4fDYJOZVlaGoiNG1cB4AJF2PJNmZRtQnbECi74pY9YkZlBUX4Vu//jMOvjuJj3W14KSGaixvrMGSeeo+LlKtZV5VGYpdQI1BfSIyxlTrIvtu3TaLEPUJwa4ofTPpQTERXQrgawDmBysjBN7SXGOVhcmS7Q5+//AYLr7jd1Hzr9wlLjy+/jxRP0BBtY9jBsW5uiYF5At2QwbFNsCs/xdAvMigWLArSt80czvm6wDWMLOHmWuYuToXA+JcIAkJ8ZH2sR+5uibiC0IhY1WCq8SLIOQGM4PiIWbekzFLbIwkJMRH2sd+5OqaiC8IhYxVCa4SL4KQGxIOiono0uDUiR1E9BARXRlaF1yf90hCQnykfexHrq6J+IJQyFiV4CrxIgi5IeGcYiL6nzibmZk/ba1JicnF/LgsJCRYitlX56b7mmar28fq10ZbhGPmFAPmr4lVbW7Va5t19tjUN3KNzCm2CarXortcpPXZkD+/fXQSJUUuHJuaTdmvbRgbMqdYsCupveaZmT8FAER0LjP/PqpGonOtsc3+uFwU9bpNO6N6FWi8V+daoa5hZfs4Qe3DCZi5Jla1udnXS5u1R/faafENwQ7o/L+0mHD9j19S+qzLRVhcV4k/D46m5dfSbwpC+pi5fXNnkuuEHKN6FWi8V+fa7XWfdrOnELCqzTNdj+610+Ibgh3Q+W13ny+uz1oRN9JvCkL6JDOn+Gwi+gKAeiK6KeLzrwCKMm6hYBrtq3k1r861Wwa03ewpBKxq80zXo/Nt8Q3BDuj81viuJ6PPWhE30m8KQvokc6e4FEAVAlMtqiM+IwAuy5xpQqpoX82reXWu3TKg7WZPIWBVm2e6Hp1vi28IdkDnt8bZC0aftSJupN8UhPRJOChm5v9l5lsBnMXMt0Z8bmPmvVmwUTCJ6lWg8V6da7cMaLvZUwhY1eaZrkf32mnxDcEO6Py2s8UT12etiBvpNwUhfZJRn9gKQFuImdfE2fc+AKsBHGLmdsX28wE8BuAvwVWPMvPGREbnQyZ1oixhs1nExvLeqjL0DI5gcGQSjTVl6PB64HYXa8s3VbuD5SfgrXGjrbEG/aMT4e2h1yynam+q52MztY+sqE9kWr0hUXnda5iN9ujqn5iYwe4BX4zvma3H7GunCxxRn8gSieLq2PgUegZHMTQyiYaaMtSUF6G8pBjvHp3GwEjA95fVV+HV4VEM+CbQUlsOd3ER3hmfAoHw9tFJNNSkFvc2jA1RnxDsSmrqEwC+Gfx7KYBGAD8MLl8J4I0E+34fwF0AHohT5nfMvDoJO/KGRFnCZrOIjeW7Fnlw+XsWYcNjavWJZMpvXNuOh194Ezve9GFRXTluWNkapWZhxt5UsqKdpPZhJWaVQ3RY1eY6e9Z0NOG3rx6KqX9laz229AzE+NIlbY34xSuDSdcTslPlA4XqG0LuSRRX4+PT2NYzhA1bjvv51z/agXePTeM/fvnnqJi4+6m9mJphXHX2IvzkhQO4omsh7ti+Ny3lCIkNQUiPZKdP/C+A05j5CmbeGvx8AsB7E+z7DIC3LbI1b0iUJWw2i9hY/qpzloYHJaH9I9Unkim/4bEeXHXOUgDA6s7mGDULM/ZKVnTymFUO0WFVm+vs6e5Xq0DsHvApfWn34IipesQ3BDuSKK52D46EB8Sh7fuGj4YHxKF1Gx7rwerOZlx6egtuf3IvVnc2hwfEqnoFQcgOZp7N1BPR0tACES0BUG+BDWcT0S4i+iURtekKEdG1RLSDiHYMDw9bcNjckShL2GwWsbH8+ORMXPWJZMuPT80AAIiQlr35nhVtpW+aVQ7RYVWba+3R1D84MqlcP6RZr6snX3wjl+RTn2kXEvd1sX7uZ3X/SXS8b03Ux+Yb4puCXTEzKP48gKeJ6GkiehrAUwA+l+bxdwJYxMwrENA83qwryMz3MnMXM3fV11sxFs8dibKEzWYRG8tXlBXHVZ9Itnx5aXHUcqr25ntWtJW+aVY5RIdVba61R1N/Y02Zcn2DZr2unnzxjVyST32mXUjc18X6eRGp+8/QVNfIxDhdvfmG+KZgV5IeFDPzEwBaAdwY/Cxj5l+lc3BmHmHmseD/jwMoIaJ56dTpBBJlCZvNIjaWv/+5/di4Vq8+kUz5jWvb8cBz+wEAW3cdjFGzMGOvZEUnj1nlEB1WtbnOns4mj7L+Dq9H6UsdjebqEd8Q7EiiuOporMHGNdF+fkJ9Jf75QyfHxMS27oN45MU+3HhBK7buOoj1K1slDoQomhcsBBGl/GlesDDXp+A4klGfWMnM24noUtV2Zn40wf6LAWzTqE80AhhiZiaiMwD8DIE7x3GNckImdbJqDLosYbNZxMbyLZ5y7BkawaBvAo0eN06aV4XeodEYdQld+eUNNejzjYe3h9QnjMuh8wvtH1AWOL6/Ub1Ct78NsqSTIavqE6Fr0eb1pKU+oVNv0Kk9GNdPTc2iu9+HwZEJNNa40dnkQWlpkUFN4rid4+PT2D04Es6+72isQXl5iel6BFOI+oSFxOu/VXHl93PYhxfPK8fo+CzePjaN2ooSjIxPY36NG+xnHPSNo76qDH5mFLtcGJmYQn21G+7iIrw7PoWSIheOTc2mpDhk0z5U1CfSoNDPP8OkrD7x1wC2A7hEsY0BaAfFRPQggPMBzCOiPgC3ACgBAGa+B4GXf/w9Ec0AGAfw8UQDYieQTOZ/oixhs1nEqvIrFtRixQJgamoWm7v7YxQB1nU2KcuHMNYXWlad36Z17bhz+168eWQ8rlqFbv9UMq3zleJiV8y1SAWVT+ja/qLlDfj1niHlepU6hK78hcvmm1KZ0NUjviDkikT9kzGuVAot37isEy5y4ar7/hRed9MHTkJZkQs3Pbwrqt5TvB7Tvi59qCBkhmTUJ24J/v2U4vPpBPteycxeZi5h5hZm/h4z3xMcEIOZ72LmNmZewcxnMXPqP4lshN3UFrr71YoA3f3mFA1CqM7v5s2BbGogfbUKIXPo2r53QK0CYXZ9d785tQpdPeILQq4w2z+pFFr2HhrDF34aXcdtv3kNR45NWeLr0ocKQmZI+hklEb1ORD8ios8S0SmZNMrp2E1tQZfhPzSSmj2686PgDYp01SqEzKFre53KhNn1elUKc/WILwi5wmz/pPJhneKE3/AcNFVflz5UEDKDmYl7pwD4DoA6AN8kov1E9PPMmOVs7Ka24NXY01CTmj2684uc+FLIahR2Rtf2Xo9uvVp9Qlteq0ph7rjiC0KuMNs/qWJEpzhhnNmQqq9LHyoImcHMoHgWwHTwrx/AEIBDmTDK6dhNbaGjSa0I0NlkTtEghOr8Nq0LZFMD6atVCJlD1/ZtXrUKRJu3xlT5jiaPKZUJXT3iC0KuMNs/qRRaTpxfhW99LLqOmz5wEuoqSi3xdelDBSEzJFSfCBckOgZgN4DbAPyWmY9k0rB4OCGT2qx6RHQGfjlOmlcZpRaxvKEKrwyNhZc7gpn7yTIxMYPdAz4MjkyisaYM7Y01GBibjFGH0C0b7TcqBiRSq0hXXcMmZEV9QofRR9q8NXFVGhKpTBjbXle/0Xc6vB643cU4Nj6FnsHRsMpEe2M1KspLtSoTidQwHOYLdkPUJ0ySjMKEb3wKM7OMQ6OTaKktBxg4NDqJORUlGJ2cxvxqN/zMGPJNwlNRAt/4NOZWlKK+phRHxqYw6wcOj03C63Gj2EU4NDqBancppmf9aStGOCRuRH0iDQr9/DNMyuoTIa5E4LXO/wDgb4noOQDPMPOTFhiXd5hRjzBmLy+qK8d157eGXxfatciDy7sWhZcj1SOSGRj7/Yzte4fDiRkqdYhI9QjVcmRms9/PWsUAlVpFuu0jqDPcN61rx7oVzcqBcaLsdJUqheqarmytx5aegRjlkovb6vF4z3C0T65px+r2Bjy970jSxwXEF4Tsk4zCRIunHDvefAcbHutBbUUprjp7EW5/cm+4/OcvPAl9b4+HX+HsLnFh/cpWPLTjAP7PmYtQVuTCV5/4s7J+K5C4EQTrMfPyjseY+R8BfAbA4wCuBrAtQ3YVFMbs5dWdzeHBBgBcdc7SqGWz6hHGTGWVOkSkeoRqWdQjcosqw/3mzT3oHVD7gNlrpCu/e0CtXPLK4NFYn9zSg57BUfENwfYkEx+Rqj2Xnt4SHhCHyv9/v30Nh49Gq0ncsX0vVnc2W6o0IQhC9jCjPvEIEb0O4HYAlQCuAlCbKcMKCWP2slG9YXxyJi31CGOmsk4dgij+sqhH5A6t2oNP3eZmr5Gu/ODIpMb3zK0X3xDsRDLxEamYouszVWoSobJWKU0IgpA9zCTa/SeAk5j5g8y8iZn/l5nDEU5EH7DevMJAl+EfoqKsOC31CF2msnHZqB5hXBb1iNyh85FGj7rNzV4jXfnGmjKN75lbL74h2Ilk4sOopJKsmgSztUoTgiBkDzPTJ15g5tk4Rb5mgT0FiTF7eeuug9i45vjy/c/tj1o2qx5hzFRWqUNEqkeolkU9IreoMtw3rWtHm1ftA2avka58h1etXHJKY2WsT65pR3tjtfiGYHuSiY9I1Z5HXuzDjRe0RpX//IUnYV5ltJrE+pWt2NZ90FKlCUEQskfS6hMJKyJ6iZlPs6SyBORjJnVkxn6k2kQoM3p5QyVeGRwLKwC0NVZj7+GjSSsRGJUFljdUx1WLyFP1iHSxhfrEoG8CjR432ryepNQnklWZMCqKhOrXqU+Mj09j9+BIWH2io7EG5eUlplUyBEsQ9QmTxOvDQtvePTaFGT/j0MgkmmvdKCLCodFJ1JSX4N3xacyvKoPLBQz5jitS1FW6MaeiGMOjkygpcuHY1Cy8Hjdm/cChUbWaTx4j6hNpUOjnn2HSVp9IhLR8ivj9jN++ekibCa3KlN64th13PxWtFhFPiSAVtQhRj7AXxcUurFhQixULkiuvukY6FYs1HU1KH7xoeUOUckmk75SXl+CMJXVRx4znawUyCBAcgq4PC/W3X3tiD67oWog7th9XnLjrE6dhapZx1X1/ivLv0mLCJw3r4vXfEhOCYE/k9o0NSJQJrdq+4bFYtQirlAiE/EWnYtHd71P6SO+Aer1ZFQvxNcEphHx4dWdzeEAMBHy5u08dD919PlP9t8SEINgTKwfFb1hYV0GRKBNat92oDmGVEoGQv2hVLDQ+oitvVsVCfE1wCiEfVilO+Dl5FQpR6xEE55Fw+gQRXRpvOzM/Gvwbt5ygJ5QJHdlxqtQejNuN6hCJlAh09QuFQ0jFwugLjRof8XrM+Y74muB0IpUpjL5cRLHrEilNSEwIgnNI5k7xJXE+qzNnWuGQKBNatX3j2li1CKuUCIT8Radi0dnkUfpIm1e93qyKhfia4BRCPrx110GsXxmtONHRoo6HzhaPqf5bYkIQ7Ill6hPZxI6Z1KFs5ZBahNnsYmPm//KGGvT5xsP1GdUgWjzl2DM0krYSgWCKnKpP6ND5XiKVCaPv6HzErO+Ir+WEglSfSLff1dV35OgkCISxyWlUlZVgetYfrh9AjH+r1olaDwBRn0iLQj//DJO++gQRfRhAG4Dwcx9m3pieXc4n3exiVcb+pnXtuHP7cXUJlVpEukoEgvPR+d6Fy+Zjy+7+GJWJkEKJynd0PmLWd8TXhGxgtaqDqr71K1vx0I4D+PKq5ThzSV24XpV/i1qPIDgfM695vgfAFQBuQGCE/TEAizJkl6NIN7tYtf/Nm6PVJSRbWVCh873ufrXKhE6hRBCchtWqDqr67ti+F6s7m6X/FYQCwYz6xDnMfBWAd5j5VgBnA0jyPmV+k252cbLqEpKtLBjR+Y5OTUKnUCIITsNqVYd4/bD0v4JQGJgZFI8H/x4joiYA0wCWWG+S84jMVg5hJrtYt79RXUKylQUjOt/xatbrFEoEwWmk2+8mWx+z9L+CUCiYGRRvI6I5AL4BYCcCusQ/yYBNjiPd7GLV/pvWRatLSLayoELnex1NHqXKhE6hRBCchtWqDqr61q9sxbbug9L/CkKBYCbR7uvMPAngESLahkCyXdznSUR0HwKybYeYuV2xnQDcDuBiAMcAXM3MO03YlDWMWc4h9YdQZv+Fy+bj8fXnpZRd7HIRLlregIeuPStc3/KGapy+sDZc38Laiqjjh9QodFnXVmdlC6lj9lroyuvWX7hsPn54zZkYHJmAt8aNjiYPSkuLsG5FM1rnV2lVJpKtXxDsiMtFWNXWiJM1/a5KfQVAzDqXi8J+v6yhGk/ceB4GfBOoKC3C1Kwf555Yh6lZPw68fRSzfuDQqMSHIOQrZgbFzwM4HQCCg+NJItoZWqfh+wDuAvCAZvuHALQGP2cC+Hbwr60wZiV3LfLg8vcswobHYjP7U8kuVqlPRKpNqLKideoUocGNlVnZQuqYvRa68hctb1D6iFmVCbP1i88Idkan6jAz48fmXQej4uLfP9KB0iLCP/6sO7zuG5d1wkUufOGnsX4PIBwrtRWluOrsRbj9yb0SH4KQxyScPkFEjUT0VwDKieg0Ijo9+DkfQEW8fZn5GQBvxymyFsADHOAPAOYQkTd587ODMSv5qnOWhgfEQPqZ/YmyqM2qU1idlS2kjtlroSvfO+CzRGXCbP3iM4IT6R2IjYuv/Hw39h4ai1q399BYeEAcWhfy+8hYufT0lvCA2FhOEIT8IZk5xR8E8E0ALQBuA/Ct4OfzAP45zeM3A3grYrkvuC4GIrqWiHYQ0Y7h4eE0D2sOY1by+OSMpZn9ibKozapTWJ2VLcQnnm+avRa68gM+a1QmzNYvPuNcctln5hqdP/sN7zHwM7R+HxkrIQUKVTnBPIXsm1nFVQwiSvnTvGBhrs8g6yScPsHM9wO4n4g+ysyPWHx81XMn5etXmPleAPcCgTfgWGxHXIzvrq8oK1a+yz7VzH5j/aH6QtnOuu06dYpE9QnWEs83zV4LXXmvp1ztc5ryOl/U1y8+k2/kss/MNbp4Mc50KCLE9fvIbRIf1lHIvplV/DNpvxGv0DCjPvF7IvoeEf0SAIjoFCK6Js3j9yFa67gFQH+adVqOMSv5/uf2Y+Na6zL7E2VRm1WnsDorW0gds9dCV77NW6Nc32lSZUJfv0d8Rsgb2rw1MXHx7x/pQOv8qqh1J86vwrc+pvb7yFh55MU+3HhBq8SHIOQ5lOx7sYOD4f8B8BVmXkFExQBeYuaOBPstBrBNoz7xYQDXI6A+cSaAO5j5jES2xHtXeqYwvrs+pD5hzOy3qn6dmkSkGsWBd44lXV4ypS0hYQOqfNPstdCV160PZdkn64tm6xccQdwLlYs+M9eo4gJAzLqQ+oTK7yNjorHGjVk/MDwm8WGClPrM8M5Ead/pTHaMY0esOP+09v/79wH+mZT3b2pZgINvHUh5/wyj9E0z6hPzmPlhIvonAGDmGSKajXtEogcBnA9gHhH1AbgFQElw/3sAPI7AgHgfApJsnzJhT1ZRZTkbM/utrj/RdrPlhdxg9lroyuvWq1QmrKxfEJyILi5U63R+r4qJE+ZLfAgFQgFOvzAzKD5KRHUIzvklorMAxJVbYOYrE2xnANeZsEEQBEEQBEEQLMfMoPgmAFsALCWi3wOoB3BZRqwSBEEQBEEQhCxiZlD8CoCfIzDNYRTAZgCvZcAmQRAEQRAEQcgqZjLDHgBwMoD/AHAnAm+h+0EmjBIEQRAEQRCEbGLmTvEyZl4RsfwUEe2y2iBBEARBEARByDZm7hS/FEyuAwAQ0ZkAfm+9SYIgCIIgCIKjceAb9czcKT4TwFVEFBKdWwhgDxHtRkBIotNy6wRBEARBEATn4UBJNzOD4lUZs0IQBEEQBEEQckjSg2JmfjOThgiCIAiCIAgCgPD0i1RJ5Y16Zu4UC4IgCIIgCELmycH0CzOJdoIgCIIgCIKQl8igWBAEQRAEQSh4ZFAsCIIgCIIgFDwyKBYEQRAEQRAKHhkUC4IgCIIgCAWPDIoFQRAEQRCEgkck2VLE72e8ceQohkYm0FDjxuK6SrhcqevpCYKQHhKT2UPaWhCEfEQGxSng9zOe6B3ETQ+/jIlpP9wlLtx2+alY1dYoXwyCkAMkJrOHtLUgCPmKTJ9IgTeOHA1/IQDAxLQfNz38Mt44cjTHlglCYSIxmT2krQVByFdkUJwCQyMT4S+EEBPTfhwanciRRYJQ2EhMZg9pa0EQ8hUZFKdAQ40b7pLopnOXuDC/2p0jiwShsJGYzB7S1oIg5CsyKE6BxXWVuO3yU8NfDKE5dYvrKnNsmSAUJhKT2UPaWhCEfCXjiXZEtArA7QCKAHyXmf/TsP18AI8B+Etw1aPMvDHTdqWDy0VY1daIk9efh0OjE5hf7cbC2grJxhYECzGjcKCKSYnBzGBs6/oqN4pcwB//ckT6PkGIoHnBQvT3vZVrMwQTZHRQTERFAO4G8AEAfQBeIKItzPyKoejvmHl1Jm2xGpeLsLS+CkvrqyQbWxAsJpWYioxJIbOE2npxXaX0fYKgob/vLVzxnedS3v+hz5xjoTVCMmR6+sQZAPYx835mngLwEwBrM3zMrCPZ2IJgLRJTzkCukyAI+USmB8XNACKfHfQF1xk5m4h2EdEviahNVRERXUtEO4hox/DwcCZsTRnJxi5s7OybTkViKn2y4ZdynYRUyFqf6SoGEaX8aV6wMHO2CbYk03OKVc/P2LC8E8AiZh4joosBbAbQGrMT870A7gWArq4uYx05JZSNHfnlINnYhYOdfdOpSEylTzb8Uq6TkApZ6zP9MzJ9QTBFpu8U9wFYELHcAqA/sgAzjzDzWPD/xwGUENG8DNtlKZKNLQjWIjHlDOQ6CYKQT2T6TvELAFqJaAmAgwA+DuATkQWIqBHAEDMzEZ2BwED9SIbtShtjZvzK1nr88JozMTgyAW+NGx1NnriJJlNTs+ju90WVLy0tSvp4kuEt5DPx1CRmZvzoHfBhwDcBr6ccbd4aFBeb/32viikAyjiz6phW2JituFcde2bGj+5+X1B1ogwMYNn8Kvzss2ej751xeD1uLG+okb5KEARHktFBMTPPENH1AH6FgCTbfczcS0SfDW6/B8BlAP6eiGYAjAP4ODPb+hG0MTN+UV05rnt/KzY81hPOwN60rh3rVjQrvzinpmaxubs/qvzGte1Y19mkHBiLuoVQiKjUJGZm/Ni86yBu3pxcrOlQxdRdnzgNUzMcE2cXLpuPLbv70z6mWXIZ96pjf+eTp2NoZCqq31q/shUP7TiAj79nIR54/k2UFhNuWNka1VbSVwmC4BQyfquDmR9n5pOY+QRm/vfgunuCA2Iw813M3MbMK5j5LGZOfQJQljBmXK/ubA5/UQCBRJObN/egd8Cn3L+73xdTfsNjPejuV5eXDG9BCNA74AsPuIDEsaZDFVPdfT5lnHX3W3NMs+Qy7lXHHh2fjem37ti+F6s7m3H7k3tx6ektWN3ZHNNW0lcJguAU5I12KWDMuCaCMgN70KfOwB7UZGwPjajLS4a3IAQY8KljQRdrOlQx5WdNHGviz+wxzZLLuFcd++jkjNKeUP9HpO8Lpa8SBMEJyKA4BUIZ15Golhs96gxsr2b/hhp1ed3xJMNbKDS8nnJTsaZDFVNFpIljTfyZPaZZchn3qmNXuouV9jAf/xtaZywjfZUgCE5ABsUpYMy43rrrIDaubY/KwN60rh1tXo9y/44mT0z5jWvb0dmkLi8Z3oIQoM1bg03rko81HaqY6mjxKOOss8ljyTHNksu4Vx272l0U02+tX9mKbd0HceMFrXh0Zx+27joY01bSVwmC4BQyrT5hG4yZ1AtrK3DgnWPaDOl4Wd8uF+Gi5Q146NqzwtnorfMqsKSuAoMjk2isKUOH16NNwiktLcK6ziYsnVcZrr8zjvqE6nht3hpJXCkwnKJAkkk7i4tdWNPRhMV1lRgcmUBjMHaKi12mFCJ06hYzM/6wikxjRFyuW9GM1vlVGPRNoNHjRluc+LaKeAocmcblIly4bD5+eM2ZeOfYFDzlJThydBInNVTiwb89E/2+CdRXlwEALjqlARMzs1gyrxJeTzmWN1Tj9IW1WbdZEAQhXQpiUKzKpN60rh13bt+LN4+Mx2RIJ8r69vsZv94zFLV949p23P3U8foSZaeXlhaha/HcpO03Hk8yugsLpyiQZNpOv5/x21cPWaIQYVS3mJnxx61jxYJarFigrCpjqBQ4skGoLe7cvhdXdC3EHdv3htvkxgta8cDzb+KdY1O47fJTMTIxjet//FLM9c62zYIgCOlSENMnVJnUN2/uwerO5vByZIZ0oqxv1fYNj0XXZ2V2uqhPCE7xgUzbqavfCoUIq5Qt8oFQW6zubA4PiIFAm4SUJsJt3+ezvV8KgiAkQ0EMinVZ3ETRy6EM6URZ38nWZ1V2uqhPCE7xgUzbqavfCoUIq5Qt8oFQW+jUJEJ93cS0H36Dqrwd/VIQBCEZCmJQrMvijnxFSGSGdKKs72Trsyo7XdQnBKf4QKbt1NWvU3QxE4NWKVvkA5FtEa+vc5e4YJwVY0e/FARBSIaCGBSrMqk3rWvHtu6D4eXIDOlEWd+q7RvXRtdnZXa6qE8ITvGBTNupq7/DAoUIq5Qt8oFQW2zddRDrV7ZGtUlIaSKsztHisb1fCoIgJEPeJtpNTc2iu9+HwZEJeGvcWNlaj8cjsri9VWVYNPe4WkRbYzV2H3w3nLl+/ol1+MGnz9Buf9+JtXjg02dgaGQSDTVlWNZYicV1FeHltsaqqEz45Q3V6PONhzPyWzzl2DM0Et6+rL4Krw6PKjPnVVnoC2srHKFEIFhDLpUIdOhUJla21kfFTofXA5eLYmKyI6jsMD4+jd2DI+HY6WisQXl5ibb+vz5xblTstTVWobS0CB8+pREL51ZE1RNPlUK1PlLZImRjPJUJs0obdlMQMdoT2S+dtqAa37hsBaZmZ8Pt7fWUocTlwsK5FaivLsPIxDQ87hJsvf5svD48Hu7r7HSOgiAIyZKXg+KpqVls7u4Pv5I0dCd3XWcTltZXqbevacfdTwfUIxbVleO681uxYUtgu3G5a5EHl3ctCi8b9w8tP7zjTex40xfY//2tMfaE1CpU9Rkz5yOz0J2iRCBYS66UCFTofHBlaz229AzE+PrqtgZs6x2KWX9xWz0e7xmOiaXV7Q14et+RmPr/+sS5+EXPoZjyH2qvxy97h2Pqv6StEb94ZTBGUWJ1mxfbegdi1s+pKFEqKajiymwc2i1ujfZE9lNnL5mLVR1e/HTHAXz09IW4dVuvUn1i/cpWPLTjAK47/0T84fXDePGtd3HDytaodpW+SRAEp5CX0ye6+33hL0fguDpEd79Pv33LcfWI1Z3N4S9d1fJV5yyNWjbuH1q+6pylx/dX2BMqr6ovXta7U5QIhPxF54O7B9Sx1zM4qlz/yuBRZSz1DI4q6+8dHFOW3zN4VFn/7sERpaLEbo3ShBklBbNxaLe4NdoT2U9d/d4luGVLL646Z2l4QByyOVJ94o7te4P9Yy8ue89CrO5sjmlX6ZsEQXAKeTko1mWiD41MxN0eyqg2Zlwbl8cnZ5JSnxifmlHubyyvq0+X9e4UJQIhf9GrQExqYs9e63V2mlFSMBuHdotboz2R/dQ7R6cDfViCvi5SoeLI2KS2r5O+SRAEJ5CXg2JdJnpDjTvudqN6hHF7iIqy4qT2Ly8tjlrWldfVp8t6d4oSgZC/6HywsaZME3v2Wq+z04ySgtk4tFvc6uwBgLmVJXCXuBL2daH/3SUu1FWVRdURWV76JkEQnEBeDoo7mjzYuDY6i3zj2nZ0Nnn029ccV4/YuusgNq45vt24fP9z+6OWjfuHlh94bv/x/RX2hMqr6ouX9e4UJQIhf9GqQHjVsdfeWK1cf0pjpTKW2hurlfW3NVYpyy9vrFTW39GoVpTo8KrVKswoKZiNQ7vFrdGeyH7qf579C25d04b7n9uPW1a3adUn1q9sxbbug9i4pg0/e+EAtu46GNOu0jcJguAU8ibRzphFvabdi6XzKsPLncFMdyDwiuV1nU1R29saqrGssQqDvgk0etxonVeJJfOOq1Msb6zEknnHM+pPbqzE4nnHM+BPaazE0vrj9Z3SUIWl9cez2Nsaa9C1qDasHNDiKceyhuPHW1Yfffw2rz7r3Y5KBEJhEc8HL2lrjFJiCalJrGn3YkldRZQqhdtdjEvaG7F4Xmx5Xf2r2xuiyrc3VqOivDQmpkMxr1KUKC0twroVzWidHx1zLhdFqdTEi6t4baBTmbBT3EbaMzQygYrSIhABP7zmTBwem8SSeeVYOm85jk7N4IFPn4FDo5NorHGjmBBWnxibnMY3LluBRk8ZFtZV4PoLTsLC2gqcvrDWFucoCIJghrwYFKeS1V1aWoSuxXOj1q1YUIsVC+JnZevUIQCga7E7oT2RygGh4+mW42EnJQKhMFH54MyMX6n2sKajCdv3Ditjory8BGcsqUuqfgCoKC9VllfFtN/P+O2rh5THLS52KWPOTFypbEzUH9kpbl0uwuK6Svx5cBSf+v4LUfa2zvfA72ds3nUQN2/eqVSf2LSuHe87cT6Ki11YOPf4OdnpHAVBEJIlL6ZPWJ3VHS8rO1S/qEMIQiy9OlWHfl9OYiIXsei0+I9nr+p6RqpPxOsHBUEQnEZeDIqtzuqOl5UdWb+oQwhCNAM+nSpFbmIiF7HotPiPZ6/uekaqT+j6QUEQBKeRF4Niq7O642VlRy6LOoQgROP1lGvUHnITE7mIRafFfzx7ddczUn1C1w8KgiA4jbwYFFud1R0vKztUv6hDCEIsbV612kNnkycnMZGLWHRa/MezV3U9I9Un4vWDgiAITiPjiXZEtArA7QCKAHyXmf/TsJ2C2y8GcAzA1cy808wxrM7qVtVnVIsQdQhBiKW42KVUdSguduUkJnIRi06L/3j2ulwUcz2LXYSl8yoT9oOCIAhOI6ODYiIqAnA3gA8A6APwAhFtYeZXIop9CEBr8HMmgG8H/5rC6qxuVX2iDiEIidGpOuQqJnJxXKfFfzx7VdezrXlO9owTBEHIEpn+iX8GgH3MvJ+ZpwD8BMBaQ5m1AB7gAH8AMIeIvBm2SxAEQRAEQRDCZHpQ3AzgrYjlvuA6s2VARNcS0Q4i2jE8PGy5oYKQKuKbgh0RvxTsivimYFeIQ2nEmaic6GMAPsjMfxtc/iSAM5j5hogyvwDwVWZ+Nrj8JIAvMfOLceodBvBmxgw3xzwAh3NtRBzEvvSItO8wM6+KVziBb9r9XK2mkM431+ca1zdt0Gfmun3Sxen2A7k5h2T7zKNwfvuaIR/8yQx2PF+lb2Y60a4PQOTMwhYA/SmUiYKZ6y2xzgKIaAczd+XaDh1iX3qYtS+eb9r9XK2mkM7X7uea6z7T7u2TCKfbD9j3HJi53q62ZQo5X/uS6ekTLwBoJaIlRFQK4OMAthjKbAFwFQU4C4CPmQcybJcgCIIgCIIghMnonWJmniGi6wH8CgFJtvuYuZeIPhvcfg+AxxGQY9uHgCTbpzJpkyAIgiAIgiAYybhOMTM/jsDAN3LdPRH/M4DrMm1HBrk31wYkQOxLDyvts/u5Wk0hnW8hnWsqOL19nG4/YO9zsLNtmUDO16ZkNNFOEARBEARBEJyAvIpIEARBEARBKHhkUCwIgiAIgiAUPDIoTgMieoOIdhPRy0S0I9f2GCGiOUT0MyL6MxHtIaKzc20TABDRsmCbhT4jRPS5XNsVCRF9noh6iaiHiB4kIneK9SwgoqeC7d9LRDdabaudICI3Ef2JiHYFz/fWXNuUDYioiIheIqJtubbFLuST7zv9+tr1uwAAiGgVEb1KRPuI6P/NtT2ZhIjuI6JDRNSTa1syjVPjX+YUpwERvQGgi5ntJkoNACCi+wH8jpm/G5TEq2Dmd3NsVhREVATgIIAzmdkWL2QhomYAzwI4hZnHiehhAI8z8/dTqMsLwMvMO4moGsCLANYx8yuWGm0TiIgAVDLzGBGVINCONwZf4Z63ENFNALoA1DDz6lzbYwfyyfedfn3t+l0Q7P9fA/ABBN5Z8AKAK53oI8lARO8DMAbgAWZuz7U9mcSp8S93ivMUIqoB8D4A3wMAZp6yQyeo4AIAr9tlQBxBMYByIioGUIEEL5TRwcwDzLwz+P8ogD1QvMY8X+AAY8HFkuAnr395E1ELgA8D+G6ubbET+eL7Tr++Nv8uOAPAPmbez8xTAH4CYG2ObcoYzPwMgLdzbUc2cGr8y6A4PRjAr4noRSK6NtfGGFgKYBjA/wQf+32XiCpzbZSCjwN4MNdGRMLMBwF8E8ABAAMIvFDm1+nWS0SLAZwG4I/p1mVngo+aXwZwCMBvmDmvzxfAfwH4EgB/ju2wLQ73/f+Cs6+vnb8LmgG8FbHcBwcMnARzOCn+ZVCcHucy8+kAPgTguuCjEbtQDOB0AN9m5tMQeLe8reZrBR/jrQHw01zbEgkR1SJwt2IJgCYAlUT0N2nWWQXgEQCfY+aR9K20L8w8y8ynIvDK9jOIKG8fExLRagCHmPnFXNtiV5zs+3lyfe38XUCKdXn9ZKnQcFr8y6A4DZi5P/j3EICfI/AoyC70AeiLuEv3MwQ6RjvxIQA7mXko14YYuBDAX5h5mJmnATwK4JxUKwvOrX0EwI+Y+VGLbLQ9wUe0TwNYlVtLMsq5ANYE8wt+AmAlEf0wtybZhzzw/Xy4vnb+LugDsCBiuQUpTlUT7IcT418GxSlCRJXByeMIPoq6CIBtMkqZeRDAW0S0LLjqAgB2m+B+JWw2dSLIAQBnEVFFMHHsAgTmQ5kmuP/3AOxh5tsstNGWEFE9Ec0J/l+OwA+MP+fUqAzCzP/EzC3MvBiBqUDbmTmtpwr5Qj74fj5cX5t/F7wAoJWIlgSfHH4cwJYc2yRYgFPjXwbFqdMA4Fki2gXgTwB+wcxP5NgmIzcA+BERdQM4FcB/5Nac4xBRBQIZx7b79Ri8o/IzADsB7EYgTlJ9TeW5AD6JwB2mkATdxdZYaku8AJ4K+twLCMwpdqSMlZA2heb7dsaW3wXMPAPgegC/QuDGw8PM3JtbqzIHET0I4HkAy4ioj4iuybVNGcSR8S+SbIIgCIIgCELBI3eKBUEQBEEQhIJHBsWCIAiCIAhCwSODYkEQBEEQBKHgkUGxIAiCIAiCUPDIoFgQBEEQBEEoeGRQLAiCIAiCIBQ8MijOM4jofCLS6sIS0dVEdFcGjns1ETVFLL9BRPOsPo7gfBL5aBL7dxHRHZptbxDRPCKaQ0T/YNUxBedh7JPilPs+EV0WZ/vTRNRlsW3in4JlPprE/huJ6ELF+rDfBf8/J2JbWsd0KjIoFqziagAJg1sQ0oWZdzDz+gTF5gD4hwRlhPzmati3T5oD8U8hSz7KzBuY+bcJip0P4JwEZfIeGRTngOAron9BRLuIqIeIriCivyKi/yWiF4noV0TkDZZ9moj+i4ieC5Y9I7j+jOC6l4J/l8U/qtKOeiJ6hIheCH7ODa7/VyK6L3js/US0PmKffyGiPxPRb4joQSL6YvDXZBcCb0x6Ofh6XwC4gYh2EtFuIjo57YYTskYufTToL3MowBEiuiq4/gdEdKHh7kYdEf06eIzvAKBgNf8J4ISgP34juK6KiH4W9N8fERHFHl2wK0S0OHjt7iei7uC1rFD5papPIqINwX6uh4juTeX6E9FFRPR8sF/7KRFVBde/QUS3Gvu7YB/7m+D67xDRmxR4gib+mYfkwkeD/eyjwf/XEtE4EZUSkZuI9gfXh+/6EtGqoI3PArg0ZDeAzwL4fNCW84LVvy/Yd++nQrlrzMzyyfIHwEcB/HfEsgfAcwDqg8tXALgv+P/TobIA3gegJ/h/DYDi4P8XAngk+P/5ALbFOfbVAO4K/v9jAO8N/r8QgXeUA8C/Bu0pAzAPwBEAJQgE8MsAygFUA9gL4IsRdnZFHOcNADcE//8HAN/NdbvLxzE+eg+ADwNoR+BV0aG69wKoitwfwB0ANgT//zAADvrs4pAdEcf0AWhB4GbA8yHfl48zPsFrygDODS7fB+AfE/hlZJ80N+L/HwC4JPj/9wFcFue4Twf7vnkAngFQGVz/5QjfU/Z3AO4C8E/B/1eJf+b3Jxc+CqAYwF+C/38z2GeeC+CvSHnB/gAABFFJREFUATwYuT8AN4C3ALQicAPh4Yi+9F8R/D6P2OenQX88BcC+XLdvNj7FEHLBbgDfJKKvAdgG4B0EBgC/Cf4wLAIwEFH+QQBg5meIqIaI5iAwKL2fiFoRCMKSFOy4EMApET9Ga4ioOvj/L5h5EsAkER0C0ADgvQAeY+ZxACCirQnqfzT490UEf5EKjiGXPvo7BAbXbwL4NoBriagZwNvMPGa4efI+BH2LmX9BRO/EqfdPzNwHAET0MgJfYM8maZNgD95i5t8H//8hgH9GfL+M5P1E9CUAFQDmAugFkKgPi+QsBAYHvw8eqxSBwWsIVX/3XgAfAQBmfkL8syDIqo8y8wwR7SOi5QDOAHAbAv1iEQJ9aSQnIzCA3gsARPRDANfGqX4zM/sBvEJEDfHsyBdkUJwDmPk1IvorABcD+CqA3wDoZeazdbsolv8NwFPM/JHgo4+nUzDFBeDs0CA3RDBwJyNWzSLgK2Yf54XqCO0vOIQc++gzAK5D4OnFVxAYVFyG2A5ed2wdKp8WnIXxWo8ivl8CAIjIDeD/R+Cu3FtE9K8I3DUzAwH4DTNfqdmu6u/M9Jnin/lBLnz0dwA+BGAawG8RuMtbBOCLSdgXj0ifLIjpPDKnOAdQINv0GDP/EIHHHWcCqCeis4PbS4ioLWKXK4Lr3wvAx8w+BB5nHwxuvzpFU34N4PoIu05NUP5ZAJcE5ypVIfC4OsQoAncGhTwglz7KzG8h8Ii5lZn3I+B3X4R6UPwMgP8TPPaHANQG14s/5icLQz4I4EoAf4DeLyN9IDS4OBzsu1KZH/kHAOcS0YnBY1UQ0UkJ9nkWwOXB8hdB/LMQyIWPPgPgcwCeZ+ZhAHUI3BXuNZT7M4AlRHRChH0hxCchg+Jc0QHgT8FHZF8BsAGBAPgaEe1CYN5uZBboO0T0HAJzLa8Jrvs6gK8S0e8R+EWYCusBdAUTAl5BYKK9FmZ+AcAWALsQeFS4A4F5cEDgl+k9FJ1oJziXXPvoHwG8Fvz/dwCaoX6UfCsCySA7AVwE4AAAMPMRBB5z99DxRCbB+ewB8P8QUTcCj5fvhN4vv49gn4TAHa//RmBa0GYE5l2aIjjYuBrAg8Hj/wGBgUc8bgVwUdA/P4TAY/NR8c+8Jhc++kcEpjg+E1zuBtDNwcnBIZh5AoHpEr8IJtq9GbF5K4CPGBLtCg4ytJlgM4joaQQmv+/ItS0AQERVwXmdFQgE4LXMvDPXdgm5w24+KuQnwSk425i5Pde2JAsRlQGYDc77PBvAt5n51BybJWQIJ/qoEI3MWRLMci8RnYLAo577ZUAsCIKgZSGAh4nIBWAKwN/l2B5BEOIgd4rzFCL6FIAbDat/z8zX5cIeQTAiPirYCSL6OYAlhtVfZuZf5cIeQTAiPpp5ZFAsCIIgCIIgFDySaCcIgiAIgiAUPDIoFgRBEARBEAoeGRQLgiAIgiAIBY8MigVBEARBEISC5/8CJ/8erGRr1Z0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I hope you will not uncomment the following line; but if you need to, it is fine, do not worry:\n",
    "iris = sns.load_dataset('iris')\n",
    "iris\n",
    "sns.pairplot(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 **[1pt]** Start with plotting the pair-plot, using different colors depending on species. Explain the plots on the diagonal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hint: a common error in the past has been if the types have been altered by the fixing in 3.4. To avoid that: use `loc` or `iloc` specifying *always* both row and column. To fix it with bruteforce: re-cast the Series to the correct `dtype` as you did in 3.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f88ed9c1700>"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.pairplot(err_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The diagnol plots are basically the same features. So to represent them, their distribution on a historgarm is shown as which buckets to values lie in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots on the diagonal show the distribution of the (one-dimensional) values for each class. Distributions that intersect correspond to data points that are not linearly separable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2 **[2pt]** Plot the correlation matrix as a heatmap, adding the values on the squares as seen in the lecture's example. Explain what high values of covariance (>0.9 or <-0.9) mean from a statistical perspective, and the implications for the learning process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hint: could it be a \"common data problem\"? Also add the parameter `fmt=` to round to 1 decimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = err_df.corr()\n",
    "sns.heatmap(corr, cmap=\"Blues\", annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If value is close to one, the more one increases, so does the other in the sema direction. If its negative, then the increase in one increases the other in oppositite direction. This can be helpful to refer causality and/or correlation if any. If two columns give the same information, we can drop one to keep our model simpler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "High (linear) correlation (in magnitude: close to $+1$ or $-1$) means that the two feature vary together in an indistinguishable manner. Here the high correlation between petal width and length means that the petal proportion is constant, and having both is redundant. We can learn the same if we drop one of the two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 **[2pt]** Plot a violin plot of the features. Use a custom palette. Briefly explain the advantage of the violin plot over the box plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Palettes can be found [[here]](https://seaborn.pydata.org/tutorial/color_palettes.html). You can use `plt.xticks(rotation=45)` (or similar) to rotate the $x$ axis labels if they overlap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------\n",
    " Box plot || Violin Plot\n",
    "--------- || -----------\n",
    "Shows summary including mean, median mode, sd ||  Shows all summary along with distribution\n",
    "Does not show multiple Peaks | Shows multiple peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt = sns.violinplot(data=err_df, linewidth=2.1, palette=sns.color_palette(\"pastel\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The violin plot offers more details about the data distribution, especially when the feature is effectively composed of multiple clusters (e.g. both petal length and width)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Visualizing the performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 **[2pt]** Use scikit-learn to train a Linear Regression to predict the petal length ($\\hat{y}$) from the petal width ($x$). Generate the Prediction over Observation plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Careful, the integration between Pandas and Scikit-learn is relatively [recent](https://medium.com/dunder-data/from-pandas-to-scikit-learn-a-new-exciting-workflow-e88e2271ef62). If you find it confusing, stick to numpy arrays, for example:\n",
    "```python\n",
    "x = df['petal_width'].to_numpy().reshape((-1, 1))\n",
    "y = df['petal_length'].to_numpy()\n",
    "```\n",
    "Remember scikit-learn needs 2D input, so you need reshaping. To plot the diagonal line a simple line plot with two points will generate a segment, e.g. `sns.lineplot([0,7],[0,7])`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bhargavsolanki/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='observed', ylabel='predicted'>"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_s = list(list(temp) for temp in zip(err_df.petal_width))\n",
    "y_s = list(err_df.petal_length)\n",
    "x_s\n",
    "from sklearn.linear_model import LinearRegression\n",
    "clf = LinearRegression()\n",
    "x_s\n",
    "clf.fit(X=x_s, y=y_s)\n",
    "predicted = clf.predict(x_s)\n",
    "my_data = {\"observed\" : y_s, \"predicted\": predicted}\n",
    "my_df = pd.DataFrame.from_dict(my_data, orient='columns')\n",
    "my_df.head()\n",
    "sns.stripplot(x=\"observed\", y = \"predicted\" , data=my_df)\n",
    "sns.lineplot([0,35],[0,7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 **[2pt]** Using the same model and predictions from the last point, generate the Errors over Predictions plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you want at least one horizontal line for $y=0$: use the segment trick from the previous question. Ideally it looks best with two more lines at $y=+1$ and $y=-1$ for the standard deviation. These can be made thinner or lighter in color to look even better :) I simply used the `alpha` option to make them slightly transparent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bhargavsolanki/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/bhargavsolanki/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/bhargavsolanki/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = (y_s-predicted)\n",
    "my_res_plot_data = {\"errors\" : residuals, \"predictions\":predicted}\n",
    "sns.stripplot(x=\"predictions\", y = \"errors\" , data=my_res_plot_data)\n",
    "sns.lineplot([0,25], [1,1])\n",
    "sns.lineplot([0,25], [0,0])\n",
    "sns.lineplot([0,25], [-1,-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3 **[2pt]** For both Prediction over Observation and Errors over Prediction, write a few sentences commenting on the performance of the model learned by the Linear Regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicted values over observations, indicate that the  line is almost a straight line and that the model did a decent job in predeicting the values. Also, the errors over predictions, even though not standardised, have errors of about 1 unit. Which also suggest that the model has a done a decent job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# At the end of the exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bonus question with no points! Answering this will have no influence on your scoring, not at the assignment and not towards the exam score -- really feel free to ignore it with no consequence. But solving it will reward you with skills that will make the next lectures easier, give you real applications, and will be good practice towards the exam.\n",
    "\n",
    "The solution for this questions will not be included in the regular lab solutions pdf, but you are welcome to open a discussion on the Moodle: we will support your addressing it, and you may meet other students that choose to solve this, and find a teammate for the next assignment that is willing to do things for fun and not only for score :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BONUS **[ZERO pt]** Follow [this tutorial](https://becominghuman.ai/linear-regression-in-python-with-pandas-scikit-learn-72574a2ec1a5) to augment your answers to point 5 with 5-fold cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BONUS **[ZERO pt]** Follow [this tutorial](https://elitedatascience.com/python-seaborn-tutorial) but using our data, and generate all the plots of types that have not been mentioned in the lecture/lab. Comment a few sentences about which I think would be most useful to include in our set of basic plots for the next year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "#### BONUS **[ZERO pt]** Copy your Perceptron implementation from the second lab. Modify it so that at every update it computes the Risk (total Loss over all points) and saves it to a list (we did something similar at the first lab, remember?). Run your implementation on the data, then plot  the Risk over Iterations, using a `sns.lineplot()`."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "If you discuss these points on Moodle I will participate :) plus it will give you an opportunity to find other people in the class that interested with going beyond the points: I strongly encourage these to team together (remember you can change teammate at each assignment)."
   ]
  },
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   "source": [
    "### Final considerations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This lecture+lab actually gives you most of what you need for the high-pay job of Data Analyst, so I guess there is one direct market application from this course after all :)\n",
    "- You will need to be strong in all the topics seen so far to carry on with the course.\n",
    "- Fixing and polishing your past assignments will make passing the exam so much easier. Now is the perfect chance to prepare the first 3."
   ]
  }
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