Complete_EDA.ipynb 112 KB
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{
 "cells": [
  {
   "cell_type": "code",
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   "execution_count": 25,
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   "metadata": {},
   "outputs": [],
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   "source": [
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    "import pandas as pd\n",
    "from tools import data_loader\n",
    "from utility.settings import load_settings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import paths as pt\n",
    "\n",
    "# Load data\n",
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    "data_settings = load_settings(pt.CONFIGS_DIR, \"data.yaml\")\n",
    "target_settings = load_settings(pt.CONFIGS_DIR, \"complete.yaml\")\n",
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    "ats_resolution = data_settings['ats_resolution']\n",
    "converters = {str(i)+'Ats':str for i in range(1, ats_resolution+1)}\n",
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    "dl = data_loader.CompleteDataLoader(pt.PROCESSED_DATA_DIR,\n",
    "                                    \"complete.csv\",\n",
    "                                    target_settings,\n",
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    "                                    converters=converters).load_data()\n",
    "X, y = dl.get_data()\n",
    "df = pd.concat([X, y], axis=1)\n",
    "\n",
    "# Add age feature\n",
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    "df['Age'] = df['BirthYear'].apply(lambda x: 121-x)"
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   ]
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  },
  {
   "cell_type": "code",
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   "execution_count": 26,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
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      "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>Gender</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>LoanPeriod</th>\n",
       "      <th>NumberAts</th>\n",
       "      <th>1Ats</th>\n",
       "      <th>2Ats</th>\n",
       "      <th>3Ats</th>\n",
       "      <th>4Ats</th>\n",
       "      <th>5Ats</th>\n",
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       "      <th>6Ats</th>\n",
       "      <th>7Ats</th>\n",
       "      <th>8Ats</th>\n",
       "      <th>9Ats</th>\n",
       "      <th>10Ats</th>\n",
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       "      <th>Complete</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>57</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>122203</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>57</td>\n",
       "      <td>173</td>\n",
       "      <td>6</td>\n",
       "      <td>122203</td>\n",
       "      <td>091218</td>\n",
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       "      <td>120606</td>\n",
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       "      <td>093307</td>\n",
       "      <td>123112</td>\n",
       "      <td>043303</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>64</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>38</td>\n",
       "      <td>470</td>\n",
       "      <td>6</td>\n",
       "      <td>091218</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>83</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>38</td>\n",
       "      <td>553</td>\n",
       "      <td>6</td>\n",
       "      <td>091218</td>\n",
       "      <td>180315</td>\n",
       "      <td>123103</td>\n",
       "      <td>123112</td>\n",
       "      <td>181210</td>\n",
       "      <td>043306</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>83</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>38</td>\n",
       "      <td>579</td>\n",
       "      <td>6</td>\n",
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       "      <td>180315</td>\n",
       "      <td>123103</td>\n",
       "      <td>123112</td>\n",
       "      <td>181210</td>\n",
       "      <td>043306</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>83</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      ],
      "text/plain": [
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       "   Gender  BirthYear  LoanPeriod  NumberAts    1Ats    2Ats    3Ats    4Ats  \\\n",
       "0       0         57          94          1  122203       0       0       0   \n",
       "1       0         57         173          6  122203  091218  120606  093307   \n",
       "2       0         38         470          6  091218  180315  123103  123112   \n",
       "3       0         38         553          6  091218  180315  123103  123112   \n",
       "4       0         38         579          6  091218  180315  123103  123112   \n",
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       "\n",
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       "     5Ats    6Ats 7Ats 8Ats 9Ats 10Ats  Complete  Age  \n",
       "0       0       0    0    0    0     0         0   64  \n",
       "1  123112  043303    0    0    0     0         0   64  \n",
       "2  181210  043306    0    0    0     0         1   83  \n",
       "3  181210  043306    0    0    0     0         1   83  \n",
       "4  181210  043306    0    0    0     0         0   83  "
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      ]
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     },
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     "execution_count": 26,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
   ],
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   "source": [
    "df.head()"
   ]
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  },
  {
   "cell_type": "code",
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   "execution_count": 27,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
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      "text/plain": [
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       "1    2468\n",
       "0    1092\n",
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       "Name: Complete, dtype: int64"
      ]
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     },
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     "execution_count": 27,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
   ],
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   "source": [
    "df.Complete.value_counts()"
   ]
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  },
  {
   "cell_type": "code",
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   "execution_count": 28,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
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      "image/png": 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",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      ]
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     },
     "metadata": {
      "needs_background": "light"
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     },
     "output_type": "display_data"
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    }
   ],
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   "source": [
    "import seaborn as sns\n",
    "var = df['Complete']\n",
    "varValue = var.value_counts()\n",
    "plt.figure()\n",
    "plt.bar(varValue.index, varValue)\n",
    "plt.xticks(varValue.index, varValue.index.values)\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title('Complete')\n",
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    "plt.show()"
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   ]
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  },
  {
   "cell_type": "code",
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   "execution_count": 29,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
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      "image/png": 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",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      ]
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     },
     "metadata": {
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      "needs_background": "light"
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     },
     "output_type": "display_data"
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    }
   ],
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   "source": [
    "plot = sns.scatterplot(data=df, x=\"Age\", y=\"NumberAts\", hue=\"Complete\")\n",
    "plt.title(\"Scatter plot of NumberAts vs Age\")\n",
    "fig = plot.get_figure()\n",
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    "plt.show()"
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   ]
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  },
  {
   "cell_type": "code",
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   "execution_count": 30,
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   "metadata": {},
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   "outputs": [
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    {
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     "name": "stderr",
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     "output_type": "stream",
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     "text": [
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      "C:\\Users\\cml\\miniconda3\\envs\\py38-air\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
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      "  warnings.warn(msg, FutureWarning)\n",
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      "C:\\Users\\cml\\miniconda3\\envs\\py38-air\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
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      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
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    {
     "data": {
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      "image/png": 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",
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      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
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      ]
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     },
     "metadata": {
      "needs_background": "light"
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     },
     "output_type": "display_data"
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    }
   ],
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   "source": [
    "g = sns.FacetGrid(df, col=\"Complete\")\n",
    "g.map(sns.distplot, \"Age\", bins=25)\n",
    "g.fig.suptitle(\"Number of citizens who complete given age\")\n",
    "g.fig.subplots_adjust(top=.8)\n",
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    "plt.show()"
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   ]
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  },
  {
   "cell_type": "code",
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   "execution_count": 31,
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   "metadata": {},
   "outputs": [],
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   "source": [
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    "def get_ats_list(df):\n",
    "    all_ats = []\n",
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    "    ats_cols = [f\"{i}Ats\" for i in range(1, ats_resolution+1)]\n",
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    "    for ats_col in ats_cols:\n",
    "        for ats_string in df[ats_col]:\n",
    "            for ats in ats_string.split(\",\"):\n",
    "                if ats != \"0\":\n",
    "                    all_ats.append(ats)\n",
    "    return all_ats\n",
    "\n",
    "ats_no_complete = pd.Series(get_ats_list(df.loc[df['Complete'] == 0]))\n",
    "ats_complete = pd.Series(get_ats_list(df.loc[df['Complete'] == 1]))\n",
    "\n",
    "a = pd.DataFrame(ats_no_complete.value_counts()[:20], columns=['No complete quantity'])\n",
    "b = pd.DataFrame(ats_complete.value_counts()[:20], columns=['Complete quantity'])\n",
    "\n",
    "ats_df = pd.concat([a, b], axis=1).fillna(0)\n",
    "ats_df.index.names = ['Ats']\n",
    "ats_df = ats_df.reset_index()\n",
    "\n",
    "ats_df['No complete quantity'] = ats_df['No complete quantity'] / len(ats_no_complete)\n",
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    "ats_df['Complete quantity'] = ats_df['Complete quantity'] / len(ats_complete)"
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   ]
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  },
  {
   "cell_type": "code",
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   "execution_count": 32,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
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      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      ]
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     },
     "metadata": {
      "needs_background": "light"
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     },
     "output_type": "display_data"
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    }
   ],
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   "source": [
    "plt.bar(ats_df[\"Ats\"], ats_df[\"No complete quantity\"], label=\"No complete\")\n",
    "plt.bar(ats_df[\"Ats\"], ats_df[\"Complete quantity\"], bottom=ats_df[\"No complete quantity\"], label=\"Complete\")\n",
    "plt.legend()\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Scaled ats usage\")\n",
    "plt.title('Scaled plot of ats usage for complete')\n",
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    "plt.show()"
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   ]
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  }
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 ],
 "metadata": {
  "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",
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   "version": "3.8.8"
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  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
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}