Pandas Class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframes, Series and Panel Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([1,2,3,4,5])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[1,2,3],[4,5,6]])\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "e    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])\n",
    "\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data = [[1,2,3],[4,5,6]]\n",
    "data={'A':[1,2,3],'B':[4,5,6]}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: A, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)), \n",
    "        'Item2' : pd.DataFrame(np.random.randn(4, 2))}\n",
    "p = pd.Panel(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)\n",
       "Items axis: Item1 to Item2\n",
       "Major_axis axis: 0 to 3\n",
       "Minor_axis axis: 0 to 2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing and Exporting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_json('df.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children    hobby  weight\n",
       "alice         1985       NaN   Biking      68\n",
       "bob           1984       3.0  Dancing      83\n",
       "charles       1992       0.0     None     112"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('df.csv')\n",
    "#df.to_excel('df.xls')\n",
    "#df.to_json('df.json')\n",
    "#df.to_clipboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sleeping</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birthyear  children     hobby  weight\n",
       "malice       1986       NaN  Sleeping      69\n",
       "marles       1993       1.0       NaN     113\n",
       "mob          1985       4.0   Cycling      84"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people_dict2 = { \"weight\": pd.Series([69, 84, 113],index=[\"malice\", \"mob\", \"marles\"]),\n",
    "                \"birthyear\": pd.Series([1985, 1986, 1993], index=[\"mob\", \"malice\", \"marles\"],\n",
    "                name=\"year\"),\"children\": pd.Series([1, 4], index=[\"marles\", \"mob\"]),\n",
    "\"hobby\": pd.Series([\"Sleeping\", \"Cycling\"], index=[\"malice\", \"mob\"]),}\n",
    "\n",
    "df2 = pd.DataFrame(people_dict2)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df.append(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sleeping</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children     hobby  weight\n",
       "alice         1985       NaN    Biking      68\n",
       "bob           1984       3.0   Dancing      83\n",
       "charles       1992       0.0      None     112\n",
       "malice        1986       NaN  Sleeping      69\n",
       "marles        1993       1.0       NaN     113\n",
       "mob           1985       4.0   Cycling      84"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       birthyear  children    hobby  weight\n",
       "alice       1985       NaN   Biking      68\n",
       "bob         1984       3.0  Dancing      83"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sleeping</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children     hobby  weight\n",
       "charles       1992       0.0      None     112\n",
       "malice        1986       NaN  Sleeping      69\n",
       "marles        1993       1.0       NaN     113\n",
       "mob           1985       4.0   Cycling      84"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1987.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>88.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.937004</td>\n",
       "      <td>1.825742</td>\n",
       "      <td>20.014162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1984.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1985.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>72.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1985.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>83.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1990.500000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1993.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children      weight\n",
       "count     6.000000  4.000000    6.000000\n",
       "mean   1987.500000  2.000000   88.166667\n",
       "std       3.937004  1.825742   20.014162\n",
       "min    1984.000000  0.000000   68.000000\n",
       "25%    1985.000000  0.750000   72.500000\n",
       "50%    1985.500000  2.000000   83.500000\n",
       "75%    1990.500000  3.250000  105.000000\n",
       "max    1993.000000  4.000000  113.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 6 entries, alice to mob\n",
      "Data columns (total 4 columns):\n",
      "birthyear    6 non-null int64\n",
      "children     4 non-null float64\n",
      "hobby        4 non-null object\n",
      "weight       6 non-null int64\n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 240.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection from Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice      1985\n",
       "bob        1984\n",
       "charles    1992\n",
       "malice     1986\n",
       "marles     1993\n",
       "mob        1985\n",
       "Name: birthyear, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['birthyear']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>Sleeping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>Cycling</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear     hobby\n",
       "alice         1985    Biking\n",
       "bob           1984   Dancing\n",
       "charles       1992      None\n",
       "malice        1986  Sleeping\n",
       "marles        1993       NaN\n",
       "mob           1985   Cycling"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['birthyear','hobby']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sleeping</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children     hobby  weight\n",
       "alice         1985       NaN    Biking      68\n",
       "bob           1984       3.0   Dancing      83\n",
       "charles       1992       0.0      None     112\n",
       "malice        1986       NaN  Sleeping      69\n",
       "marles        1993       1.0       NaN     113\n",
       "mob           1985       4.0   Cycling      84"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['alice','children']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eobard/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "birthyear        1986\n",
       "children          NaN\n",
       "hobby        Sleeping\n",
       "weight             69\n",
       "Name: malice, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Difference between .loc, .iloc and .ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49   NaN\n",
       "48   NaN\n",
       "47   NaN\n",
       "46   NaN\n",
       "45   NaN\n",
       "1    NaN\n",
       "2    NaN\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.nan, index=[49,48,47,46,45, 1, 2, 3, 4, 5])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49   NaN\n",
       "48   NaN\n",
       "47   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sleeping</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a    b         c    d\n",
       "alice    1985  NaN    Biking   68\n",
       "bob      1984  3.0   Dancing   83\n",
       "charles  1992  0.0      None  112\n",
       "malice   1986  NaN  Sleeping   69\n",
       "marles   1993  1.0       NaN  113\n",
       "mob      1985  4.0   Cycling   84"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=['a','b','c','d']\n",
    "#df.columns=['birthyear','children','hobby','weight']\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a      b      c      d\n",
       "alice    False   True  False  False\n",
       "bob      False  False  False  False\n",
       "charles  False  False   True  False\n",
       "malice   False   True  False  False\n",
       "marles   False  False   True  False\n",
       "mob      False  False  False  False"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a      b      c     d\n",
       "alice    True  False   True  True\n",
       "bob      True   True   True  True\n",
       "charles  True   True  False  True\n",
       "malice   True  False   True  True\n",
       "marles   True   True  False  True\n",
       "mob      True   True   True  True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notnull(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a    d\n",
       "alice    1985   68\n",
       "bob      1984   83\n",
       "charles  1992  112\n",
       "malice   1986   69\n",
       "marles   1993  113\n",
       "mob      1985   84"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
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       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sleeping</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a    b         c    d\n",
       "bob      1984  3.0   Dancing   83\n",
       "charles  1992  0.0      None  112\n",
       "malice   1986  NaN  Sleeping   69\n",
       "marles   1993  1.0       NaN  113\n",
       "mob      1985  4.0   Cycling   84"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('alice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a    b    d\n",
       "alice    1985  NaN   68\n",
       "bob      1984  3.0   83\n",
       "charles  1992  0.0  112\n",
       "malice   1986  NaN   69\n",
       "marles   1993  1.0  113\n",
       "mob      1985  4.0   84"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('c',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Sleeping</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a    b         c    d\n",
       "alice    1985  6.0    Biking   68\n",
       "bob      1984  3.0   Dancing   83\n",
       "charles  1992  0.0         6  112\n",
       "malice   1986  6.0  Sleeping   69\n",
       "marles   1993  1.0         6  113\n",
       "mob      1985  4.0   Cycling   84"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49    6.0\n",
       "48    6.0\n",
       "47    6.0\n",
       "46    6.0\n",
       "45    6.0\n",
       "1     6.0\n",
       "2     6.0\n",
       "3     6.0\n",
       "4     6.0\n",
       "5     6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>bob</th>\n",
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       "      <td>7</td>\n",
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       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malice</th>\n",
       "      <td>1986</td>\n",
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       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marles</th>\n",
       "      <td>1993</td>\n",
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       "      <td>11</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mob</th>\n",
       "      <td>1985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a     b         c    d\n",
       "alice    1985  11.0    Biking   68\n",
       "bob      1984   3.0   Dancing   83\n",
       "charles     7   0.0        11  112\n",
       "malice   1986  11.0  Sleeping   69\n",
       "marles   1993   1.0        11  113\n",
       "mob      1985   4.0   Cycling   84"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace([1992,np.nan],[7,11])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining/Combining\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          1       Alex  Anderson\n",
       "1          2        Amy  Ackerman\n",
       "2          3      Allen       Ali\n",
       "3          4      Alice      Aoni\n",
       "4          5     Ayoung   Atiches"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "df_a = pd.DataFrame(raw_data, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "df_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          4      Billy    Bonder\n",
       "1          5      Brian     Black\n",
       "2          6       Bran   Balwner\n",
       "3          7      Bryce     Brice\n",
       "4          8      Betty    Btisan"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8'],\n",
    "        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
    "        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "df_b = pd.DataFrame(raw_data, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "df_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>Alice</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          1       Alex  Anderson\n",
       "1          2        Amy  Ackerman\n",
       "2          3      Allen       Ali\n",
       "3          4      Alice      Aoni\n",
       "4          5     Ayoung   Atiches\n",
       "0          4      Billy    Bonder\n",
       "1          5      Brian     Black\n",
       "2          6       Bran   Balwner\n",
       "3          7      Bryce     Brice\n",
       "4          8      Betty    Btisan"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c = df_a.append(df_b)\n",
    "df_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_d = pd.concat([df_a,df_b])\n",
    "df_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name_x</th>\n",
       "      <th>last_name_x</th>\n",
       "      <th>first_name_y</th>\n",
       "      <th>last_name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name_x last_name_x first_name_y last_name_y\n",
       "0          4        Alice        Aoni        Billy      Bonder\n",
       "1          5       Ayoung     Atiches        Brian       Black"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inner = pd.merge(df_a,df_b, on ='subject_id', how='inner') \n",
    "df_inner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name_x</th>\n",
       "      <th>last_name_x</th>\n",
       "      <th>first_name_y</th>\n",
       "      <th>last_name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name_x last_name_x first_name_y last_name_y\n",
       "0          1         Alex    Anderson          NaN         NaN\n",
       "1          2          Amy    Ackerman          NaN         NaN\n",
       "2          3        Allen         Ali          NaN         NaN\n",
       "3          4        Alice        Aoni        Billy      Bonder\n",
       "4          5       Ayoung     Atiches        Brian       Black\n",
       "5          6          NaN         NaN         Bran     Balwner\n",
       "6          7          NaN         NaN        Bryce       Brice\n",
       "7          8          NaN         NaN        Betty      Btisan"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_outer = pd.merge(df_a, df_b, on ='subject_id', how='outer') \n",
    "df_outer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_left = pd.merge(df_a, df_b, on ='subject_id', how='left') \n",
    "df_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_right = pd.merge(df_a, df_b, on ='subject_id', how='right') \n",
    "df_right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Class': [1, 3, 5, 2, 3, 1, 4, 2, 1],\n",
       " 'Marks': [29, 34, 55, 42, 31, 24, 67, 99, 35],\n",
       " 'Name': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'I', 'J']}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marks = {'Name':['A', 'B', 'C', 'D','E','F','G','I', 'J'],'Marks':[29,34,55,42,31,24,67,99,35],'Class':[1,3,5,2,3,1,4,2,1]}\n",
    "marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mrks = pd.DataFrame(marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mrks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Marks</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>J</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class  Marks Name\n",
       "1      3     34    B\n",
       "2      5     55    C\n",
       "3      2     42    D\n",
       "4      3     31    E\n",
       "6      4     67    G\n",
       "7      2     99    I\n",
       "8      1     35    J"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrks[mrks['Marks'] > 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "      <th>Marks</th>\n",
       "      <th>Name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class  Marks Name\n",
       "7      2     99    I\n",
       "6      4     67    G\n",
       "2      5     55    C\n",
       "3      2     42    D\n",
       "8      1     35    J\n",
       "1      3     34    B\n",
       "4      3     31    E\n",
       "0      1     29    A\n",
       "5      1     24    F"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrks.sort_values('Marks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls = mrks.groupby(['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x1158a4d68>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1,    Class  Marks Name\n",
       "  0      1     29    A\n",
       "  5      1     24    F\n",
       "  8      1     35    J), (2,    Class  Marks Name\n",
       "  3      2     42    D\n",
       "  7      2     99    I), (3,    Class  Marks Name\n",
       "  1      3     34    B\n",
       "  4      3     31    E), (4,    Class  Marks Name\n",
       "  6      4     67    G), (5,    Class  Marks Name\n",
       "  2      5     55    C)]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Marks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>5.507571</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.50</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>40.305087</td>\n",
       "      <td>42.0</td>\n",
       "      <td>56.25</td>\n",
       "      <td>70.5</td>\n",
       "      <td>84.75</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.75</td>\n",
       "      <td>32.5</td>\n",
       "      <td>33.25</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Marks                                                      \n",
       "      count       mean        std   min    25%   50%    75%   max\n",
       "Class                                                            \n",
       "1       3.0  29.333333   5.507571  24.0  26.50  29.0  32.00  35.0\n",
       "2       2.0  70.500000  40.305087  42.0  56.25  70.5  84.75  99.0\n",
       "3       2.0  32.500000   2.121320  31.0  31.75  32.5  33.25  34.0\n",
       "4       1.0  67.000000        NaN  67.0  67.00  67.0  67.00  67.0\n",
       "5       1.0  55.000000        NaN  55.0  55.00  55.0  55.00  55.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mrks.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class     2.444444\n",
       "Marks    46.222222\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrks.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class     5\n",
       "Marks    99\n",
       "Name      J\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrks.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class     2.0\n",
       "Marks    35.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrks.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class     1.424001\n",
       "Marks    24.004051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrks.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>Marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marks</th>\n",
       "      <td>0.333186</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Class     Marks\n",
       "Class  1.000000  0.333186\n",
       "Marks  0.333186  1.000000"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrks.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Practice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import weather_by_cities file and perform all functions"
   ]
  }
 ],
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