NoteBookTwo-Density-ByYear-ByGroup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Three year summary Lake Geneva\n",
    "\n",
    "#### Notebook 2\n",
    "\n",
    "__Purpose:__ Present an analysis method for survey results from beach litter inventories on Lake Geneva.\n",
    "\n",
    "__Background:__ This is in the context of the global movement to reduce plastic debris in the maritime environment. Riverine inputs are major contributors of plastic debris (and all types of refuse) to the oceans. This is an analysis of the data collected on the shores of Lake Geneva over a three year period. The maritime protocol was modified in very specific ways to adjust for the local geography and population density.\n",
    "\n",
    "#### Research question: Is this a representative sample ?\n",
    "\n",
    "##### from notebook one we can assume the following:\n",
    "\n",
    "1. The data was collected at different locations\n",
    "2. The data was collected by different groups of people\n",
    "3. For each year there is one group that collected 50% or more of the samples\n",
    "\n",
    "\n",
    "If sampling all the trash is not possibile, what if we sample as much as possible and see what that looks like? The following questions could be answered:\n",
    "\n",
    "1. What does the distribution of survey results look like?\n",
    "2. Do different groups of people produce different survey results?\n",
    "3. How different are the survey results from one location to another?\n",
    "4. What are the most abundant objects?\n",
    "5. How different are the survey results year over year?\n",
    "\n",
    "#### From Notebook 1 IMPORTANT!\n",
    "\n",
    "_In notebook one the conclusion was that the data has different geographic centers and those centers reflect different land use patterns._\n",
    "\n",
    "_The data is now local so it is allways best to run notebook one first_\n",
    "\n",
    "_This notebook and all subsequent use the directory establsihed in notebook one (see above)_\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "import json\n",
    "import csv\n",
    "import datetime\n",
    "import collections\n",
    "# import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib import colors as mcolors\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "from Utilities.utility_functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory already in place\n"
     ]
    }
   ],
   "source": [
    "# The data is off of the API at https://mwshovel.pythonanywhere.com/dirt/api_home.html \n",
    "# You will have the opportunity to save the data locally\n",
    "# get some file structures in place\n",
    "\n",
    "# Required variables:\n",
    "folders = [\"Data\", \"Charts\", \"Utilities\"]\n",
    "here = os.getcwd()\n",
    "\n",
    "# check for existing and if not make folders\n",
    "check_for_folders(folders, here)\n",
    "\n",
    "# make a dictionary to call path by directory name\n",
    "my_folders = make_folders(folders, here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "#### Read in the json data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Read the JSON data in from local\n",
    "# there is no need to hit the server a million times\n",
    "\n",
    "# required variables\n",
    "the_jsons = [\n",
    "    '/home/mw-shovel/three_year_final/Data/beach_info.json',\n",
    "    '/home/mw-shovel/three_year_final/Data/all_data.json'\n",
    "]\n",
    "\n",
    "idx = pd.IndexSlice\n",
    "# all the survey results by code, location, date, project:\n",
    "my_survey_results = jsonFileGet(the_jsons[1])\n",
    "def changeProjectName(aList, otherList, projectName):\n",
    "    aNewList = []   \n",
    "    for eachDict in aList:       \n",
    "        if eachDict[\"location_id\"] in otherList:\n",
    "            eachDict['project_id'] = projectName\n",
    "            aNewList.append(eachDict)\n",
    "        else:\n",
    "            aNewList.append(eachDict)\n",
    "    return aNewList\n",
    "            \n",
    "location_list = [\"Plage-de-St-Sulpice\", \"Parc-des-Pierrettes\",  \"Tiger-duck-beach\", \"Saint-Sulpice\"]\n",
    "project_name = \"EPFL\"\n",
    "my_survey_resultsx = changeProjectName(my_survey_results, location_list, project_name)\n",
    "\n",
    "# all the location information:\n",
    "beach_info = jsonFileGet(the_jsons[0])\n",
    "\n",
    "# make a data frame:\n",
    "the_survey_results = pd.DataFrame(my_survey_resultsx)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make pcs/m, make grouping levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# making a pcs/m column:\n",
    "\n",
    "the_survey_results[\"pcs_m\"] = the_survey_results[\"quantity\"]/the_survey_results[\"length\"]\n",
    "\n",
    "# making a month, day and year column from a datetime object\n",
    "# makes sorting easier\n",
    "the_survey_results[\"py_date\"] = pd.to_datetime(the_survey_results[\"date\"])\n",
    "the_survey_results[\"py_month\"] = the_survey_results[\"py_date\"].dt.month\n",
    "the_survey_results[\"py_year\"] = the_survey_results[\"py_date\"].dt.year\n",
    "the_survey_results[\"py_day\"] = the_survey_results[\"py_date\"].dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#### one df for abundance and one df for density:\n",
    "abundance = the_survey_results[[\"project_id\", \"location_id\", \"date\",\"py_date\", \"py_month\", \"py_year\", \"code_id\", \"quantity\"]].copy()\n",
    "density = the_survey_results[[\"project_id\", \"location_id\", \"date\",\"py_date\", \"py_day\",\"py_month\", \"py_year\", \"code_id\", \"pcs_m\"]].copy()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results: Total pieces of trash per meter\n",
    "\n",
    "#### Cumulative results\n",
    "\n",
    "Reported as the total number of objects found, the cumulative and year over year results are given in the table below:\n",
    "\n",
    "| Statistic     | Year one | Year two | Year three | All   |   \n",
    "|--------------:|:--------:|:--------:|:----------:|:-----:|\n",
    "| __Samples__   | <span style=\"color:red\">83</span>       |41        |24          | 148   |\n",
    "| __mean__      | 8.77     |<span style=\"color:red\">9.99</span>      |9.01        | 9.16  |\n",
    "| __median__    | 4.83     |<span style=\"color:red\">6.84</span>     |4.24        | 5.52  |\n",
    "| __Std dev__   | 9.92     |8.52      |<span style=\"color:red\">16.14</span>      |10.75  |\n",
    "| __25\\%ile__   | 3.113    |<span style=\"color:red\">4.41</span>     |2.36        | 3.18  |\n",
    "| __75\\%ile__   | 10.405   |<span style=\"color:red\">12.35</span>    |7.79        | 11.24 | \n",
    "| __Minimum__   | 0.68   |0.57      |<span style=\"color:red\">0.11 </span>        | 0.11  | \n",
    "| __Maximum__   | 50.075   |39.54     |<span style=\"color:red\">77.05 </span>      | 77.05| \n",
    "| _MCBP samples_   |<span style=\"color:red\">80</span>  |<span style=\"color:red\">22</span>    |5   | <span style=\"color:red\">107</span>| \n",
    "| _SLR samples_   |0  |18    |<span style=\"color:red\">15</span>      | 33|\n",
    "| _EPFL samples_  |2 |2     |2| 6| \n",
    "| _PC samples_  | 0 |0   |2       | 2| \n",
    "\n",
    "Year two has the highest average, median and the greatest innerquartile range. The lowest and the highest daily values were reported in year three. In each year there is one group that collected at least 50% of all the samples.\n",
    "\n",
    "For each year the mean is greater than the median, suggesting a right skewed distribution. The mean and median are clossest at year two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "daily_total_pcs_m = density[[\"project_id\",\"location_id\", \"py_date\", \"pcs_m\"]].groupby([\"project_id\",\"location_id\", \"py_date\"]).sum()\n",
    "\n",
    "end_date = datetime.datetime.strptime(\"2018-11-14\", \"%Y-%m-%d\")\n",
    "start_date = datetime.datetime.strptime(\"2015-11-15\", \"%Y-%m-%d\")\n",
    "daily_total_pcs_m = daily_total_pcs_m.loc[idx[:,:,start_date:end_date],:]\n",
    "\n",
    "daily_total_stats = daily_total_pcs_m.describe().to_dict()['pcs_m']\n",
    "project_names = daily_total_pcs_m.index.get_level_values(\"project_id\").unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_all_dict = daily_total_pcs_m.to_dict()['pcs_m']\n",
    "data_all_list = list(data_all_dict.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# required variables\n",
    "x = [1 for x in data_all_list]\n",
    "y = [x[1] for x in data_all_list]\n",
    "# y axis \n",
    "y_limit = max(y)\n",
    "y_max = y_limit + 2\n",
    "\n",
    "# title and sup title\n",
    "title_color = \"black\"\n",
    "min_date = \"Nov-2015\"\n",
    "max_date = \"Nov-2018\"\n",
    "n_samples = len(daily_total_pcs_m[\"pcs_m\"].values)\n",
    "font_styles = {\n",
    "    'fontsize': 12,\n",
    "    'fontfamily':'sans-serif',\n",
    "    'fontweight' : 'normal',\n",
    "    'verticalalignment': 'baseline',\n",
    "    'horizontalalignment': 'center'\n",
    "}\n",
    "title = \"Pieces of trash per meter\"\n",
    "sub_title = 'All samples {} - {}\\nLake Geneva, n={}'.format( min_date, max_date, n_samples)\n",
    "IQR = \"IQR of all data\"\n",
    "pad = 15\n",
    "\n",
    "# legend\n",
    "b_box = (1,0.9)\n",
    "frameAlpha=1\n",
    "frameEdgeColor=\"black\"\n",
    "loc=\"best\"\n",
    "# labels\n",
    "\n",
    "y_label_size = 12\n",
    "y_label_color = \"black\"\n",
    "y_label_padding = 15\n",
    "\n",
    "# annotate\n",
    "data_coords = (-0.2, 5.66)\n",
    "text_coords = (-0.25, 35)\n",
    "connection_style=\"arc3, rad=0.3\"\n",
    "# plots\n",
    "opac=0.5\n",
    "marker_size = 10\n",
    "edge_color = \"#ffffff\"\n",
    "flierprops = dict(marker='o', markersize=6, markerfacecolor='black')\n",
    "# storing output\n",
    "file_prefix = my_folders[\"Charts\"]\n",
    "file_suffix = [\n",
    "    '.svg',\n",
    "    '.png',    \n",
    "]\n",
    "file_names=\"boxPlotAllData\"\n",
    "\n",
    "# category colors\n",
    "PROJECT_COLORS = [\n",
    "    \"indigo\",\n",
    "    \"darkcyan\",\n",
    "    \"olive\",\n",
    "    \"darkred\",\n",
    "    \n",
    "]\n",
    "project_colors = dict(zip(project_names, PROJECT_COLORS))\n",
    "color = \"wheat\"\n",
    "# box plot stuff\n",
    "sat = 1\n",
    "l_width = 1\n",
    "\n",
    "\n",
    "def makeBoxPlots(y,aDf, project_names):   \n",
    "    fig, ax = plt.subplots(figsize=(4, 7))\n",
    "    ax = sns.boxplot(y, orient='v', saturation=sat, linewidth=l_width, color= color, flierprops=flierprops)\n",
    "    for project in project_names:       \n",
    "        this_y = aDf.loc[project]['pcs_m'].values\n",
    "        the_count = len(this_y)\n",
    "        this_x = [1 for x in this_y]\n",
    "       \n",
    "        ax = sns.stripplot(x=this_x, y=this_y, jitter=True, label='{}: {}'.format(project, the_count), size=marker_size, color=project_colors[project], linewidth=1,edgecolor=edge_color)\n",
    "    ax.margins(0.05)  \n",
    "    ax.set_xticks([])\n",
    "    ax.annotate(\n",
    "        IQR,\n",
    "        xy=data_coords,\n",
    "        xycoords='data',\n",
    "        xytext= text_coords ,\n",
    "        textcoords='data',\n",
    "        horizontalalignment=\"center\",\n",
    "        fontsize=12,\n",
    "        arrowprops=dict(\n",
    "            arrowstyle=\"->\",\n",
    "            connectionstyle=connection_style)\n",
    "        )\n",
    "    plt.setp(ax.artists, edgecolor = 'k')\n",
    "    plt.setp(ax.lines, color='k')\n",
    "    plt.subplots_adjust(top=0.85)\n",
    "    plt.ylabel('Pieces of trash per meter', labelpad=y_label_padding, color=y_label_color, size=y_label_size)\n",
    "    plt.yticks(np.arange(0, y_max, step=10))\n",
    "    plt.xticks([])\n",
    "    plt.ylim(-1, y_max)\n",
    "    plt.suptitle(title, fontfamily=\"sans-serif\", fontweight=\"roman\", fontsize=14, x=0.13, ha=\"left\", color=title_color)\n",
    "    plt.title(sub_title, fontdict=font_styles, pad=pad, x=0, ha=\"left\", color=title_color)\n",
    "    plt.legend(loc=loc, title=\"Samples per group\", bbox_to_anchor=b_box, framealpha=frameAlpha, edgecolor=frameEdgeColor)\n",
    "    for x in file_suffix:\n",
    "        save_me = '{}/{}{}'.format(file_prefix, file_names, x)\n",
    "        plt.savefig(save_me, bbox_inches=\"tight\")  \n",
    "    \n",
    "    plt.show()\n",
    "    plt.close()  \n",
    "    \n",
    "def makeBoxPlot(y,aDf, project_names):   \n",
    "    makeBoxPlots(y,aDf, project_names)\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All data Nov 2015 - Nov 2018: mean, median, count..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pcs_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>148.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.160844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.752655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.183535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.526347</td>\n",
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       "      <th>75%</th>\n",
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      "text/plain": [
       "            pcs_m\n",
       "count  148.000000\n",
       "mean     9.160844\n",
       "std     10.752655\n",
       "min      0.117647\n",
       "25%      3.183535\n",
       "50%      5.526347\n",
       "75%     11.240594\n",
       "max     77.055556"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_total_pcs_m.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": true,
    "scrolled": false
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   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 288x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeBoxPlot(y,daily_total_pcs_m, project_names)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Year one: Nov 2015 - Nov 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# index of d_t_p_m [\"project_id\",\"py_year\",\"location_id\", \"py_date\"]\n",
    "start_end = convertStringToDate([(\"2015-11-15\", \"2016-11-14\")])\n",
    "year_one = daily_total_pcs_m.loc[idx[:,:,start_end[0][0]:start_end[0][1]], :]\n",
    "project_names = year_one.index.get_level_values(\"project_id\").unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "year_one_dict = year_one.to_dict()['pcs_m']\n",
    "year_one_list = list(year_one_dict.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# required variables\n",
    "x = [1 for x in year_one_list]\n",
    "y = [x[1] for x in year_one_list]\n",
    "# y axis \n",
    "y_limit = max(y)\n",
    "y_max = y_limit + 2\n",
    "# title and sup title\n",
    "title_color = \"black\"\n",
    "min_date = \"Nov-2015\"\n",
    "max_date = \"Nov-2016\"\n",
    "n_samples = len(year_one)\n",
    "font_styles = {\n",
    "    'fontsize': 12,\n",
    "    'fontfamily':'sans-serif',\n",
    "    'fontweight' : 'normal',\n",
    "    'verticalalignment': 'baseline',\n",
    "    'horizontalalignment': 'center'\n",
    "}\n",
    "title = \"Year one trash per meter\"\n",
    "sub_title = '{} - {}\\nLake Geneva, n={}'.format( min_date, max_date, n_samples)\n",
    "IQR = \"IQR year one\"\n",
    "# legend\n",
    "b_box = (.5,0.93)\n",
    "loc=\"upper left\"\n",
    "# labels\n",
    "\n",
    "y_label_size = 12\n",
    "y_label_color = \"black\"\n",
    "y_label_padding = 15\n",
    "# annotate\n",
    "data_coords = (-0.2, 4.832864)\n",
    "text_coords = (-0.25, 30)\n",
    "connection_style=\"arc3, rad=0.3\"\n",
    "# plots\n",
    "opac=0.5\n",
    "marker_size = 10\n",
    "edge_color = \"#ffffff\"\n",
    "flierprops = dict(marker='o', markersize=6, markerfacecolor='black')\n",
    "file_names=\"boxPlotYearOne\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "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>pcs_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.708838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.882661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.681159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.149679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.796875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.155738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.075000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pcs_m\n",
       "count  83.000000\n",
       "mean    8.708838\n",
       "std     9.882661\n",
       "min     0.681159\n",
       "25%     3.149679\n",
       "50%     4.796875\n",
       "75%    10.155738\n",
       "max    50.075000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_one.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hide_input": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeBoxPlot(y,year_one, project_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Year two: Nov 2016 - Nov 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# index of d_t_p_m [\"project_id\",\"py_year\",\"location_id\", \"py_date\"]\n",
    "start_end = convertStringToDate([(\"2016-11-15\", \"2017-11-14\")])\n",
    "year_two = daily_total_pcs_m.loc[idx[:,:,start_end[0][0]:start_end[0][1]], :]\n",
    "project_names = year_two.index.get_level_values(\"project_id\").unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "year_two_dict = year_two.to_dict()['pcs_m']\n",
    "year_two_list = list(year_two_dict.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# required variables\n",
    "x = [1 for x in year_two_list]\n",
    "y = [x[1] for x in year_two_list]\n",
    "# y axis \n",
    "y_limit = max(y)\n",
    "y_max = y_limit + 2\n",
    "\n",
    "# title and sup title\n",
    "title_color = \"black\"\n",
    "min_date = \"Nov-2016\"\n",
    "max_date = \"Nov-2017\"\n",
    "n_samples = len(year_two)\n",
    "font_styles = {\n",
    "    'fontsize': 12,\n",
    "    'fontfamily':'sans-serif',\n",
    "    'fontweight' : 'normal',\n",
    "    'verticalalignment': 'baseline',\n",
    "    'horizontalalignment': 'center'\n",
    "}\n",
    "title = \"Year two trash per meter\"\n",
    "sub_title = '{} - {}\\nLake Geneva, n={}'.format( min_date, max_date, n_samples)\n",
    "IQR = \"IQR year two\"\n",
    "# legend\n",
    "b_box = (.5,0.93)\n",
    "loc=\"upper left\"\n",
    "# labels\n",
    "y_label_size = 12\n",
    "y_label_color = \"black\"\n",
    "y_label_padding = 15\n",
    "# annotate\n",
    "data_coords = (-0.2, 6.84)\n",
    "text_coords = (-0.25, 30)\n",
    "connection_style=\"arc3, rad=0.3\"\n",
    "# plots\n",
    "opac=0.5\n",
    "marker_size = 10\n",
    "edge_color = \"#ffffff\"\n",
    "flierprops = dict(marker='o', markersize=6, markerfacecolor='black')\n",
    "file_names=\"boxPlotYearTwo\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "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>pcs_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.158769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.569256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.576471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.387097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.540541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pcs_m\n",
       "count  41.000000\n",
       "mean   10.158769\n",
       "std     8.569256\n",
       "min     0.576471\n",
       "25%     5.666667\n",
       "50%     6.857143\n",
       "75%    12.387097\n",
       "max    39.540541"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_two.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hide_input": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeBoxPlot(y,year_two, project_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Year three: Nov 2017 - Nov 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# index of d_t_p_m [\"project_id\",\"py_year\",\"location_id\", \"py_date\"]\n",
    "start_end = convertStringToDate([(\"2017-11-15\", \"2018-11-14\")])\n",
    "year_three = daily_total_pcs_m.loc[idx[:,:,start_end[0][0]:start_end[0][1]], :]\n",
    "project_names = year_three.index.get_level_values(\"project_id\").unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "year_three_dict = year_three.to_dict()['pcs_m']\n",
    "year_three_list = list(year_three_dict.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hide_input": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# required variables\n",
    "x = [1 for x in year_three_list]\n",
    "y = [x[1] for x in year_three_list]\n",
    "# y axis \n",
    "y_limit = max(y)\n",
    "y_max = y_limit + 2\n",
    "# title and sup title\n",
    "title_color = \"black\"\n",
    "min_date = \"Nov-2017\"\n",
    "max_date = \"Nov-2018\"\n",
    "n_samples = len(year_three)\n",
    "font_styles = {\n",
    "    'fontsize': 12,\n",
    "    'fontfamily':'sans-serif',\n",
    "    'fontweight' : 'normal',\n",
    "    'verticalalignment': 'baseline',\n",
    "    'horizontalalignment': 'center'\n",
    "}\n",
    "title = \"Year three trash per meter\"\n",
    "sub_title = '{} - {}\\nLake Geneva, n={}'.format( min_date, max_date, n_samples)\n",
    "IQR = \"IQR year three\"\n",
    "# legend\n",
    "b_box = (.25,0.93)\n",
    "loc=\"upper left\"\n",
    "# labels\n",
    "y_label_size = 12\n",
    "y_label_color = \"black\"\n",
    "y_label_padding = 15\n",
    "# annotate\n",
    "data_coords = (-0.2, 4.24)\n",
    "text_coords = (-0.25, 45)\n",
    "connection_style=\"arc3, rad=0.3\"\n",
    "# plots\n",
    "opac=0.5\n",
    "marker_size = 10\n",
    "edge_color = \"#ffffff\"\n",
    "flierprops = dict(marker='o', markersize=6, markerfacecolor='black')\n",
    "file_names=\"boxPlotYearThree\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "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>pcs_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.019244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.140460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.366757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.244992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pcs_m\n",
       "count  24.000000\n",
       "mean    9.019244\n",
       "std    16.140460\n",
       "min     0.117647\n",
       "25%     2.366757\n",
       "50%     4.244992\n",
       "75%     7.791667\n",
       "max    77.055556"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_three.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeBoxPlot(y,year_three, project_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}