SBER_DELIVERY_CMF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8088e151",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color_pal = sns.color_palette()\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a0e767c",
   "metadata": {},
   "source": [
    "# Прогноз по дням "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf2da7e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('orders.csv', decimal=';')\n",
    "df2 = pd.read_csv('partners_delays.csv',decimal=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "04162b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['date_orders'] = pd.to_datetime(df1['date'].str[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53758c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>delivery_area_id</th>\n",
       "      <th>date</th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>date_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 10:00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 11:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 13:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 14:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 15:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884851</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 16:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884852</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 17:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884853</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 18:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884854</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 19:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884855</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 20:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-11-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884856 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        delivery_area_id                 date  orders_cnt date_orders\n",
       "0                      0  2021-04-01 10:00:00           9  2021-04-01\n",
       "1                      0  2021-04-01 11:00:00           1  2021-04-01\n",
       "2                      0  2021-04-01 13:00:00           1  2021-04-01\n",
       "3                      0  2021-04-01 14:00:00           1  2021-04-01\n",
       "4                      0  2021-04-01 15:00:00           2  2021-04-01\n",
       "...                  ...                  ...         ...         ...\n",
       "884851               592  2021-11-30 16:00:00           6  2021-11-30\n",
       "884852               592  2021-11-30 17:00:00           3  2021-11-30\n",
       "884853               592  2021-11-30 18:00:00           2  2021-11-30\n",
       "884854               592  2021-11-30 19:00:00           4  2021-11-30\n",
       "884855               592  2021-11-30 20:00:00           4  2021-11-30\n",
       "\n",
       "[884856 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f979cf59",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_sum = df1.groupby(['date_orders'])['orders_cnt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d8258c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sum = pd.DataFrame(data= df_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "863e195f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features(data):\n",
    "    \"\"\"\n",
    "    Create time series features based on time series index.\n",
    "    \"\"\"\n",
    "    data = data.copy()\n",
    "    \n",
    "    data['dayofweek'] = data.index.dayofweek\n",
    "    data['quarter'] = data.index.quarter\n",
    "    data['month'] = data.index.month\n",
    "    data['year'] = data.index.year\n",
    "    data['dayofyear'] = data.index.dayofyear\n",
    "    data['dayofmonth'] = data.index.day\n",
    "    data['weekofyear'] = data.index.isocalendar().week.astype(int)\n",
    "    \n",
    "    data['Percent of orders'] = data.orders_cnt/data.orders_cnt.sum()\n",
    "    data['MA_7_orders'] = data.orders_cnt.rolling(window=7).mean()\n",
    "    data['MA_30_orders'] = data.orders_cnt.rolling(window=30).mean()\n",
    "    return data\n",
    "\n",
    "df_sum = create_features(df_sum)\n",
    "df_sum = df_sum.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "792b9f85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>Percent of orders</th>\n",
       "      <th>MA_7_orders</th>\n",
       "      <th>MA_30_orders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_orders</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>1280</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-02</th>\n",
       "      <td>1192</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-03</th>\n",
       "      <td>1269</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>93</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-04</th>\n",
       "      <td>1261</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>94</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>1928</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>95</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-26</th>\n",
       "      <td>34853</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>330</td>\n",
       "      <td>26</td>\n",
       "      <td>47</td>\n",
       "      <td>0.010393</td>\n",
       "      <td>35473.428571</td>\n",
       "      <td>31937.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-27</th>\n",
       "      <td>40550</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>331</td>\n",
       "      <td>27</td>\n",
       "      <td>47</td>\n",
       "      <td>0.012091</td>\n",
       "      <td>35463.571429</td>\n",
       "      <td>32297.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-28</th>\n",
       "      <td>40440</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>332</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>0.012059</td>\n",
       "      <td>35601.428571</td>\n",
       "      <td>32697.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-29</th>\n",
       "      <td>33392</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>333</td>\n",
       "      <td>29</td>\n",
       "      <td>48</td>\n",
       "      <td>0.009957</td>\n",
       "      <td>35925.285714</td>\n",
       "      <td>32773.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>36311</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>0.010827</td>\n",
       "      <td>36415.142857</td>\n",
       "      <td>32968.633333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             orders_cnt  dayofweek  quarter  month  year  dayofyear  \\\n",
       "date_orders                                                           \n",
       "2021-04-01         1280          3        2      4  2021         91   \n",
       "2021-04-02         1192          4        2      4  2021         92   \n",
       "2021-04-03         1269          5        2      4  2021         93   \n",
       "2021-04-04         1261          6        2      4  2021         94   \n",
       "2021-04-05         1928          0        2      4  2021         95   \n",
       "...                 ...        ...      ...    ...   ...        ...   \n",
       "2021-11-26        34853          4        4     11  2021        330   \n",
       "2021-11-27        40550          5        4     11  2021        331   \n",
       "2021-11-28        40440          6        4     11  2021        332   \n",
       "2021-11-29        33392          0        4     11  2021        333   \n",
       "2021-11-30        36311          1        4     11  2021        334   \n",
       "\n",
       "             dayofmonth  weekofyear  Percent of orders   MA_7_orders  \\\n",
       "date_orders                                                            \n",
       "2021-04-01            1          13           0.000382      0.000000   \n",
       "2021-04-02            2          13           0.000355      0.000000   \n",
       "2021-04-03            3          13           0.000378      0.000000   \n",
       "2021-04-04            4          13           0.000376      0.000000   \n",
       "2021-04-05            5          14           0.000575      0.000000   \n",
       "...                 ...         ...                ...           ...   \n",
       "2021-11-26           26          47           0.010393  35473.428571   \n",
       "2021-11-27           27          47           0.012091  35463.571429   \n",
       "2021-11-28           28          47           0.012059  35601.428571   \n",
       "2021-11-29           29          48           0.009957  35925.285714   \n",
       "2021-11-30           30          48           0.010827  36415.142857   \n",
       "\n",
       "             MA_30_orders  \n",
       "date_orders                \n",
       "2021-04-01       0.000000  \n",
       "2021-04-02       0.000000  \n",
       "2021-04-03       0.000000  \n",
       "2021-04-04       0.000000  \n",
       "2021-04-05       0.000000  \n",
       "...                   ...  \n",
       "2021-11-26   31937.900000  \n",
       "2021-11-27   32297.166667  \n",
       "2021-11-28   32697.066667  \n",
       "2021-11-29   32773.933333  \n",
       "2021-11-30   32968.633333  \n",
       "\n",
       "[244 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "25d0b120",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from datetime import datetime,date\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import GridSearchCV, cross_validate, KFold\n",
    "from sklearn.linear_model import Ridge, RidgeCV, Lasso\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from plotly.offline import init_notebook_mode\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "import lightgbm as ltb\n",
    "from lightgbm import LGBMRegressor\n",
    "from lightgbm import plot_importance,plot_metric\n",
    "import torch\n",
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "61a98c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tts(data):\n",
    "    data_train = data[:-14]\n",
    "    data_test = data[-14:]\n",
    "    train = data_train.iloc[:-7]\n",
    "    target = data_train.iloc[7:]['orders_cnt']\n",
    "    test = data_test.iloc[:-7]\n",
    "    test_target = data_test.iloc[7:]['orders_cnt']\n",
    "    print('Train:{}\\tTarget:{}\\tTest:{}\\tTest_target:{}'.format(\n",
    "        train.shape,target.shape,test.shape,test_target.shape))\n",
    "    return train,target,test,test_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dc95056c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:(223, 11)\tTarget:(223,)\tTest:(7, 11)\tTest_target:(7,)\n"
     ]
    }
   ],
   "source": [
    "df_sum_train,df_sum_target,df_sum_test,df_sum_test_target = tts(df_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "93527655",
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=5,shuffle=True,random_state=0)\n",
    "def fit_model(train,target):\n",
    "    lgb_models = []\n",
    "    fold = 1\n",
    "    for (train_idx,valid_idx) in kf.split(train,target):\n",
    "        print(f'Fold:{fold}')\n",
    "        x_train, x_valid = train.values[train_idx], train.values[valid_idx]\n",
    "        y_train, y_valid = target.values[train_idx], target.values[valid_idx]\n",
    "        model = LGBMRegressor(n_estimators=5000,n_jobs=-1,subsample=0.7,subsample_for_bin=512,\n",
    "                             objective='regression',device='cpu',reg_lambda=0.01,learning_rate=0.05)\n",
    "        model.fit(x_train,y_train,eval_set=[(x_valid,y_valid)],eval_metric=['mape'],verbose=100,\n",
    "                  early_stopping_rounds=300\n",
    "                  )\n",
    "        lgb_models.append(model)\n",
    "        fold += 1\n",
    "    return lgb_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "55a6ea86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------DF_SUM--------------\n",
      "Fold:1\n",
      "[100]\tvalid_0's mape: 0.0892742\tvalid_0's l2: 2.55719e+06\n",
      "[200]\tvalid_0's mape: 0.0855559\tvalid_0's l2: 2.32376e+06\n",
      "[300]\tvalid_0's mape: 0.0832793\tvalid_0's l2: 2.13628e+06\n",
      "[400]\tvalid_0's mape: 0.0817755\tvalid_0's l2: 2.01354e+06\n",
      "[500]\tvalid_0's mape: 0.0787502\tvalid_0's l2: 1.91369e+06\n",
      "[600]\tvalid_0's mape: 0.0770437\tvalid_0's l2: 1.85872e+06\n",
      "[700]\tvalid_0's mape: 0.0749509\tvalid_0's l2: 1.81361e+06\n",
      "[800]\tvalid_0's mape: 0.0722558\tvalid_0's l2: 1.76767e+06\n",
      "[900]\tvalid_0's mape: 0.0707172\tvalid_0's l2: 1.72832e+06\n",
      "[1000]\tvalid_0's mape: 0.0692989\tvalid_0's l2: 1.71427e+06\n",
      "[1100]\tvalid_0's mape: 0.068326\tvalid_0's l2: 1.69225e+06\n",
      "[1200]\tvalid_0's mape: 0.0679163\tvalid_0's l2: 1.68244e+06\n",
      "[1300]\tvalid_0's mape: 0.0686004\tvalid_0's l2: 1.66921e+06\n",
      "[1400]\tvalid_0's mape: 0.0688923\tvalid_0's l2: 1.66386e+06\n",
      "Fold:2\n",
      "[100]\tvalid_0's mape: 0.0973445\tvalid_0's l2: 882430\n",
      "[200]\tvalid_0's mape: 0.09388\tvalid_0's l2: 780187\n",
      "[300]\tvalid_0's mape: 0.0932158\tvalid_0's l2: 767425\n",
      "[400]\tvalid_0's mape: 0.0938319\tvalid_0's l2: 759447\n",
      "[500]\tvalid_0's mape: 0.089928\tvalid_0's l2: 722062\n",
      "[600]\tvalid_0's mape: 0.0903037\tvalid_0's l2: 717087\n",
      "[700]\tvalid_0's mape: 0.0910451\tvalid_0's l2: 733950\n",
      "[800]\tvalid_0's mape: 0.0915161\tvalid_0's l2: 736636\n",
      "Fold:3\n",
      "[100]\tvalid_0's mape: 0.110742\tvalid_0's l2: 1.16408e+06\n",
      "[200]\tvalid_0's mape: 0.098756\tvalid_0's l2: 925329\n",
      "[300]\tvalid_0's mape: 0.101011\tvalid_0's l2: 896253\n",
      "[400]\tvalid_0's mape: 0.10092\tvalid_0's l2: 880360\n",
      "[500]\tvalid_0's mape: 0.100188\tvalid_0's l2: 855044\n",
      "Fold:4\n",
      "[100]\tvalid_0's mape: 0.12805\tvalid_0's l2: 1.13144e+06\n",
      "[200]\tvalid_0's mape: 0.113828\tvalid_0's l2: 920022\n",
      "[300]\tvalid_0's mape: 0.106427\tvalid_0's l2: 870144\n",
      "[400]\tvalid_0's mape: 0.103161\tvalid_0's l2: 848867\n",
      "[500]\tvalid_0's mape: 0.102587\tvalid_0's l2: 881677\n",
      "[600]\tvalid_0's mape: 0.101934\tvalid_0's l2: 908534\n",
      "[700]\tvalid_0's mape: 0.102112\tvalid_0's l2: 925302\n",
      "Fold:5\n",
      "[100]\tvalid_0's mape: 0.0662413\tvalid_0's l2: 1.3531e+06\n",
      "[200]\tvalid_0's mape: 0.0724791\tvalid_0's l2: 1.43708e+06\n",
      "[300]\tvalid_0's mape: 0.0742495\tvalid_0's l2: 1.3717e+06\n"
     ]
    }
   ],
   "source": [
    "print('-----------DF_SUM--------------')\n",
    "df_sum_models = fit_model(df_sum_train,df_sum_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "78f23a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_importance(model,train):\n",
    "    plt.figure(figsize=(15,10))\n",
    "    feature = pd.Series(model.feature_importances_,index=train.columns)\n",
    "    sort_feature = feature.sort_values(ascending=False)\n",
    "    return sns.barplot(x=sort_feature,y=sort_feature.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5385f05b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance(df_sum_models[0],df_sum_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f6d4c09a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ensemble(lgb_models,test):\n",
    "    result = []\n",
    "    for model in lgb_models:\n",
    "        pred = model.predict(test)\n",
    "        result.append(pred)\n",
    "    pred_deaths = np.mean(result,axis=0)\n",
    "    return pred_deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5a40a1b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orders_cnt             int64\n",
       "dayofweek              int64\n",
       "quarter                int64\n",
       "month                  int64\n",
       "year                   int64\n",
       "dayofyear              int64\n",
       "dayofmonth             int64\n",
       "weekofyear             int64\n",
       "Percent of orders    float64\n",
       "MA_7_orders          float64\n",
       "MA_30_orders         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "124214ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sum_models = df_sum_models[3]\n",
    "df_sum_models_pred_orders = df_sum_models.predict(df_sum_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b0344b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_orders</th>\n",
       "      <th>pred_new_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-11-24</td>\n",
       "      <td>34571</td>\n",
       "      <td>26862.154889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-25</td>\n",
       "      <td>34789</td>\n",
       "      <td>28822.493191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-11-26</td>\n",
       "      <td>34853</td>\n",
       "      <td>28822.493191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-11-27</td>\n",
       "      <td>40550</td>\n",
       "      <td>29538.743146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-11-28</td>\n",
       "      <td>40440</td>\n",
       "      <td>30204.512608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-11-29</td>\n",
       "      <td>33392</td>\n",
       "      <td>26668.635455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>36311</td>\n",
       "      <td>27314.070558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  new_orders  pred_new_orders\n",
       "0 2021-11-24       34571     26862.154889\n",
       "1 2021-11-25       34789     28822.493191\n",
       "2 2021-11-26       34853     28822.493191\n",
       "3 2021-11-27       40550     29538.743146\n",
       "4 2021-11-28       40440     30204.512608\n",
       "5 2021-11-29       33392     26668.635455\n",
       "6 2021-11-30       36311     27314.070558"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum_result = pd.DataFrame({'date':df_sum_test_target.index,'new_orders':df_sum_test_target.values,\n",
    "                           'pred_new_orders':df_sum_models_pred_orders})\n",
    "df_sum_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f76e601",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "689b5c64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f89885b3",
   "metadata": {},
   "source": [
    "# Часовое моделирование "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7718c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('orders.csv', decimal=';')\n",
    "df2 = pd.read_csv('partners_delays.csv',decimal=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e7be198",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['date_orders'] = pd.to_datetime(df1['date'].str[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cc8f1999",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['date'] = pd.to_datetime(df1['date'],format='%Y-%m-%d %H:%M:%S')\n",
    "df2['dttm'] = pd.to_datetime(df2['dttm'],format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "05213b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.set_index(df1.date, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0e0b1991",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.drop(['date','delivery_area_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2e9a0a38",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_h = df1.groupby(['date'])['orders_cnt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3453ad69",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_h = pd.DataFrame(data= df1_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8cd017d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-01 07:00:00</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 08:00:00</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 09:00:00</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 10:00:00</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 11:00:00</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 18:00:00</th>\n",
       "      <td>3578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 19:00:00</th>\n",
       "      <td>3755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 20:00:00</th>\n",
       "      <td>3460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 21:00:00</th>\n",
       "      <td>1132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 22:00:00</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3669 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     orders_cnt\n",
       "date                           \n",
       "2021-04-01 07:00:00           1\n",
       "2021-04-01 08:00:00           2\n",
       "2021-04-01 09:00:00           7\n",
       "2021-04-01 10:00:00         200\n",
       "2021-04-01 11:00:00         110\n",
       "...                         ...\n",
       "2021-11-30 18:00:00        3578\n",
       "2021-11-30 19:00:00        3755\n",
       "2021-11-30 20:00:00        3460\n",
       "2021-11-30 21:00:00        1132\n",
       "2021-11-30 22:00:00          30\n",
       "\n",
       "[3669 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "023a0790",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def create_features(df1):\n",
    "    \"\"\"\n",
    "    Create time series features based on time series index.\n",
    "    \"\"\"\n",
    "    df1 = df1.copy()\n",
    "    df1['hour'] = df1.index.hour\n",
    "    df1['dayofweek'] = df1.index.dayofweek\n",
    "    df1['quarter'] = df1.index.quarter\n",
    "    df1['month'] = df1.index.month\n",
    "    df1['year'] = df1.index.year\n",
    "    df1['dayofyear'] = df1.index.dayofyear\n",
    "    df1['dayofmonth'] = df1.index.day\n",
    "    df1['weekofyear'] = df1.index.isocalendar().week\n",
    "    return df1\n",
    "\n",
    "df1_h = create_features(df1_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5edbf90f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_h.set_index(df1_h.hour,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1fb9ba05",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_h = df1_h.drop(['hour'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6fae795f",
   "metadata": {},
   "outputs": [],
   "source": [
    "    df1_h['Until 12'] = df1_h.index < 12\n",
    "    df1_h['Before 12'] = df1_h.index >= 12\n",
    "    df1_h['Every 6'] = (df1_h.index % 6 == 0)\n",
    "    df1_h['Every 3'] = (df1_h.index % 3 == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3e9f3010",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_h = df1_h.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b04b5dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>Until 12</th>\n",
       "      <th>Before 12</th>\n",
       "      <th>Every 6</th>\n",
       "      <th>Every 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3578</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3755</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3460</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1132</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3669 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      orders_cnt  dayofweek  quarter  month  year  dayofyear  dayofmonth  \\\n",
       "hour                                                                       \n",
       "7              1          3        2      4  2021         91           1   \n",
       "8              2          3        2      4  2021         91           1   \n",
       "9              7          3        2      4  2021         91           1   \n",
       "10           200          3        2      4  2021         91           1   \n",
       "11           110          3        2      4  2021         91           1   \n",
       "...          ...        ...      ...    ...   ...        ...         ...   \n",
       "18          3578          1        4     11  2021        334          30   \n",
       "19          3755          1        4     11  2021        334          30   \n",
       "20          3460          1        4     11  2021        334          30   \n",
       "21          1132          1        4     11  2021        334          30   \n",
       "22            30          1        4     11  2021        334          30   \n",
       "\n",
       "      weekofyear  Until 12  Before 12  Every 6  Every 3  \n",
       "hour                                                     \n",
       "7             13         1          0        0        0  \n",
       "8             13         1          0        0        0  \n",
       "9             13         1          0        0        1  \n",
       "10            13         1          0        0        0  \n",
       "11            13         1          0        0        0  \n",
       "...          ...       ...        ...      ...      ...  \n",
       "18            48         0          1        1        1  \n",
       "19            48         0          1        0        0  \n",
       "20            48         0          1        0        0  \n",
       "21            48         0          1        0        1  \n",
       "22            48         0          1        0        0  \n",
       "\n",
       "[3669 rows x 12 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b22dfc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tts(data):\n",
    "    data_train = data.loc[(data['dayofyear']<=320)]\n",
    "    data_test = data.loc[(data['dayofyear']>=320)]\n",
    "    train = data_train.loc[(data_train['dayofyear']<=313)]\n",
    "    target = data_train.loc[(data_train['dayofyear']>=98)]['orders_cnt']\n",
    "    test = data_test.loc[data_test['dayofyear']<=327]\n",
    "    test_target = data_test.loc[data_test['dayofyear']>=327]['orders_cnt']\n",
    "    print('Train:{}\\tTarget:{}\\tTest:{}\\tTest_target:{}'.format(\n",
    "        train.shape,target.shape,test.shape,test_target.shape))\n",
    "    return train,target,test,test_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ffa9d54e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:(3353, 12)\tTarget:(3349,)\tTest:(121, 12)\tTest_target:(120,)\n"
     ]
    }
   ],
   "source": [
    "h_train,h_target,h_test,h_test_target = tts(df1_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5be15ec",
   "metadata": {},
   "source": [
    "### основная пробелма с развером тест и трайн выборки \n",
    "* я тут высчитывал окно в 2 недели по столбцу dayofyear с окном 7 дней и 14 \n",
    "* но тут крч есть проблемка с временем заказов, так как в один из дней заказы могли быть с 7 утра и до 22 вечера, а в другой с 8 до 22 \n",
    "* если кто то придумает другой способ было бы збс как посчитать по часам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ed402002",
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=5,shuffle=True,random_state=0)\n",
    "def fit_model(train,target):\n",
    "    lgb_models = []\n",
    "    fold = 1\n",
    "    for (train_idx,valid_idx) in kf.split(train,target):\n",
    "        print(f'Fold:{fold}')\n",
    "        x_train, x_valid = train.values[train_idx], train.values[valid_idx]\n",
    "        y_train, y_valid = target.values[train_idx], target.values[valid_idx]\n",
    "        model = LGBMRegressor(n_estimators=5000,n_jobs=-1,subsample=0.7,subsample_for_bin=512,\n",
    "                             objective='regression',device='cpu',reg_lambda=0.01,learning_rate=0.05)\n",
    "        model.fit(x_train,y_train,eval_set=[(x_valid,y_valid)],eval_metric=['mape'],verbose=100,\n",
    "                  early_stopping_rounds=300\n",
    "                  )\n",
    "        lgb_models.append(model)\n",
    "        fold += 1\n",
    "    return lgb_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "c5b96240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------ITA--------------\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [3353, 3349]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [149]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-----------ITA--------------\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m h_models \u001b[38;5;241m=\u001b[39m \u001b[43mfit_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43mh_target\u001b[49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[0;32mIn [148]\u001b[0m, in \u001b[0;36mfit_model\u001b[0;34m(train, target)\u001b[0m\n\u001b[1;32m      3\u001b[0m lgb_models \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m      4\u001b[0m fold \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m (train_idx,valid_idx) \u001b[38;5;129;01min\u001b[39;00m kf\u001b[38;5;241m.\u001b[39msplit(train,target):\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFold:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfold\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      7\u001b[0m     x_train, x_valid \u001b[38;5;241m=\u001b[39m train\u001b[38;5;241m.\u001b[39mvalues[train_idx], train\u001b[38;5;241m.\u001b[39mvalues[valid_idx]\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_split.py:330\u001b[0m, in \u001b[0;36m_BaseKFold.split\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m    306\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msplit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, groups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    307\u001b[0m     \u001b[38;5;124;03m\"\"\"Generate indices to split data into training and test set.\u001b[39;00m\n\u001b[1;32m    308\u001b[0m \n\u001b[1;32m    309\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    328\u001b[0m \u001b[38;5;124;03m        The testing set indices for that split.\u001b[39;00m\n\u001b[1;32m    329\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 330\u001b[0m     X, y, groups \u001b[38;5;241m=\u001b[39m \u001b[43mindexable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    331\u001b[0m     n_samples \u001b[38;5;241m=\u001b[39m _num_samples(X)\n\u001b[1;32m    332\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_splits \u001b[38;5;241m>\u001b[39m n_samples:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:433\u001b[0m, in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    414\u001b[0m \u001b[38;5;124;03m\"\"\"Make arrays indexable for cross-validation.\u001b[39;00m\n\u001b[1;32m    415\u001b[0m \n\u001b[1;32m    416\u001b[0m \u001b[38;5;124;03mChecks consistent length, passes through None, and ensures that everything\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[38;5;124;03m    sparse matrix, or dataframe) or `None`.\u001b[39;00m\n\u001b[1;32m    430\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    432\u001b[0m result \u001b[38;5;241m=\u001b[39m [_make_indexable(X) \u001b[38;5;28;01mfor\u001b[39;00m X \u001b[38;5;129;01min\u001b[39;00m iterables]\n\u001b[0;32m--> 433\u001b[0m \u001b[43mcheck_consistent_length\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:387\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    385\u001b[0m uniques \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(lengths)\n\u001b[1;32m    386\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 387\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    388\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    389\u001b[0m         \u001b[38;5;241m%\u001b[39m [\u001b[38;5;28mint\u001b[39m(l) \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m lengths]\n\u001b[1;32m    390\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [3353, 3349]"
     ]
    }
   ],
   "source": [
    "print('-----------H_MODELS--------------')\n",
    "h_models = fit_model(h_train,h_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c79253",
   "metadata": {},
   "source": [
    "# Обычный анализ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49d236c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('orders.csv', decimal=';')\n",
    "df2 = pd.read_csv('partners_delays.csv',decimal=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04fc6d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['date_orders'] = pd.to_datetime(df1['date'].str[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0f15820",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['date'] = pd.to_datetime(df1['date'],format='%Y-%m-%d %H:%M:%S')\n",
    "df2['dttm'] = pd.to_datetime(df2['dttm'],format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff3bde8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.set_index(df1.date, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2306bf26",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features(df1):\n",
    "    \"\"\"\n",
    "    Create time series features based on time series index.\n",
    "    \"\"\"\n",
    "    df1 = df1.copy()\n",
    "    df1['hour'] = df1.index.hour\n",
    "    df1['dayofweek'] = df1.index.dayofweek\n",
    "    df1['quarter'] = df1.index.quarter\n",
    "    df1['month'] = df1.index.month\n",
    "    df1['year'] = df1.index.year\n",
    "    df1['dayofyear'] = df1.index.dayofyear\n",
    "    df1['dayofmonth'] = df1.index.day\n",
    "    df1['weekofyear'] = df1.index.isocalendar().week\n",
    "    df1['DayOfWeek'] = df1.index.day_name()\n",
    "    df1['Percent of orders'] = df1.orders_cnt/df1.orders_cnt.sum()\n",
    "    df1['MA_7_orders'] = df1.orders_cnt.rolling(window=7).mean()\n",
    "    df1['MA_30_orders'] = df1.orders_cnt.rolling(window=30).mean()\n",
    "    return df1\n",
    "\n",
    "df1 = create_features(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "124f5765",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.set_index(df1.date_orders, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0c167ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.drop(['date_orders'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0cda98b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6c43d546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>date_orders</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Percent of orders</th>\n",
       "      <th>MA_7_orders</th>\n",
       "      <th>MA_30_orders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <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>2021-04-01 10:00:00</th>\n",
       "      <td>9</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2.683653e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 11:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2.981836e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 13:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2.981836e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 14:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2.981836e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 15:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>5.963673e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 16:00:00</th>\n",
       "      <td>6</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1.789102e-06</td>\n",
       "      <td>5.714286</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 17:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>8.945509e-07</td>\n",
       "      <td>4.714286</td>\n",
       "      <td>5.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 18:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>5.963673e-07</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>5.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 19:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1.192735e-06</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 20:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1.192735e-06</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>5.066667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884856 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     orders_cnt date_orders  hour  dayofweek  quarter  month  \\\n",
       "date                                                                           \n",
       "2021-04-01 10:00:00           9  2021-04-01    10          3        2      4   \n",
       "2021-04-01 11:00:00           1  2021-04-01    11          3        2      4   \n",
       "2021-04-01 13:00:00           1  2021-04-01    13          3        2      4   \n",
       "2021-04-01 14:00:00           1  2021-04-01    14          3        2      4   \n",
       "2021-04-01 15:00:00           2  2021-04-01    15          3        2      4   \n",
       "...                         ...         ...   ...        ...      ...    ...   \n",
       "2021-11-30 16:00:00           6  2021-11-30    16          1        4     11   \n",
       "2021-11-30 17:00:00           3  2021-11-30    17          1        4     11   \n",
       "2021-11-30 18:00:00           2  2021-11-30    18          1        4     11   \n",
       "2021-11-30 19:00:00           4  2021-11-30    19          1        4     11   \n",
       "2021-11-30 20:00:00           4  2021-11-30    20          1        4     11   \n",
       "\n",
       "                     year  dayofyear  dayofmonth  weekofyear DayOfWeek  \\\n",
       "date                                                                     \n",
       "2021-04-01 10:00:00  2021         91           1          13  Thursday   \n",
       "2021-04-01 11:00:00  2021         91           1          13  Thursday   \n",
       "2021-04-01 13:00:00  2021         91           1          13  Thursday   \n",
       "2021-04-01 14:00:00  2021         91           1          13  Thursday   \n",
       "2021-04-01 15:00:00  2021         91           1          13  Thursday   \n",
       "...                   ...        ...         ...         ...       ...   \n",
       "2021-11-30 16:00:00  2021        334          30          48   Tuesday   \n",
       "2021-11-30 17:00:00  2021        334          30          48   Tuesday   \n",
       "2021-11-30 18:00:00  2021        334          30          48   Tuesday   \n",
       "2021-11-30 19:00:00  2021        334          30          48   Tuesday   \n",
       "2021-11-30 20:00:00  2021        334          30          48   Tuesday   \n",
       "\n",
       "                     Percent of orders  MA_7_orders  MA_30_orders  \n",
       "date                                                               \n",
       "2021-04-01 10:00:00       2.683653e-06     0.000000      0.000000  \n",
       "2021-04-01 11:00:00       2.981836e-07     0.000000      0.000000  \n",
       "2021-04-01 13:00:00       2.981836e-07     0.000000      0.000000  \n",
       "2021-04-01 14:00:00       2.981836e-07     0.000000      0.000000  \n",
       "2021-04-01 15:00:00       5.963673e-07     0.000000      0.000000  \n",
       "...                                ...          ...           ...  \n",
       "2021-11-30 16:00:00       1.789102e-06     5.714286      5.200000  \n",
       "2021-11-30 17:00:00       8.945509e-07     4.714286      5.166667  \n",
       "2021-11-30 18:00:00       5.963673e-07     3.857143      5.066667  \n",
       "2021-11-30 19:00:00       1.192735e-06     3.285714      5.000000  \n",
       "2021-11-30 20:00:00       1.192735e-06     3.428571      5.066667  \n",
       "\n",
       "[884856 rows x 14 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b0b558ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
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       "      <th>weekofyear</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Percent of orders</th>\n",
       "      <th>MA_7_orders</th>\n",
       "      <th>MA_30_orders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_orders</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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>5.963673e-07</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>5.963673e-07</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
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       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2.981836e-07</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-02</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>Friday</td>\n",
       "      <td>8.945509e-07</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-02</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2.981836e-07</td>\n",
       "      <td>2.000000</td>\n",
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       "      <th>2021-11-30</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1.789102e-06</td>\n",
       "      <td>5.714286</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
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       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>8.945509e-07</td>\n",
       "      <td>4.714286</td>\n",
       "      <td>5.166667</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
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       "      <td>18</td>\n",
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       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>5.963673e-07</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>5.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
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       "      <td>4</td>\n",
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       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1.192735e-06</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1.192735e-06</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>5.066667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884849 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             orders_cnt  hour  dayofweek  quarter  month  year  dayofyear  \\\n",
       "date_orders                                                                 \n",
       "2021-04-01            2    19          3        2      4  2021         91   \n",
       "2021-04-01            2    20          3        2      4  2021         91   \n",
       "2021-04-01            1    21          3        2      4  2021         91   \n",
       "2021-04-02            3    10          4        2      4  2021         92   \n",
       "2021-04-02            1    11          4        2      4  2021         92   \n",
       "...                 ...   ...        ...      ...    ...   ...        ...   \n",
       "2021-11-30            6    16          1        4     11  2021        334   \n",
       "2021-11-30            3    17          1        4     11  2021        334   \n",
       "2021-11-30            2    18          1        4     11  2021        334   \n",
       "2021-11-30            4    19          1        4     11  2021        334   \n",
       "2021-11-30            4    20          1        4     11  2021        334   \n",
       "\n",
       "             dayofmonth  weekofyear DayOfWeek  Percent of orders  MA_7_orders  \\\n",
       "date_orders                                                                     \n",
       "2021-04-01            1          13  Thursday       5.963673e-07     1.714286   \n",
       "2021-04-01            1          13  Thursday       5.963673e-07     1.857143   \n",
       "2021-04-01            1          13  Thursday       2.981836e-07     1.857143   \n",
       "2021-04-02            2          13    Friday       8.945509e-07     2.142857   \n",
       "2021-04-02            2          13    Friday       2.981836e-07     2.000000   \n",
       "...                 ...         ...       ...                ...          ...   \n",
       "2021-11-30           30          48   Tuesday       1.789102e-06     5.714286   \n",
       "2021-11-30           30          48   Tuesday       8.945509e-07     4.714286   \n",
       "2021-11-30           30          48   Tuesday       5.963673e-07     3.857143   \n",
       "2021-11-30           30          48   Tuesday       1.192735e-06     3.285714   \n",
       "2021-11-30           30          48   Tuesday       1.192735e-06     3.428571   \n",
       "\n",
       "             MA_30_orders  \n",
       "date_orders                \n",
       "2021-04-01       0.000000  \n",
       "2021-04-01       0.000000  \n",
       "2021-04-01       0.000000  \n",
       "2021-04-02       0.000000  \n",
       "2021-04-02       0.000000  \n",
       "...                   ...  \n",
       "2021-11-30       5.200000  \n",
       "2021-11-30       5.166667  \n",
       "2021-11-30       5.066667  \n",
       "2021-11-30       5.000000  \n",
       "2021-11-30       5.066667  \n",
       "\n",
       "[884849 rows x 13 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[7:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e77c99ec",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.boxplot(data=df1, x='hour', y='orders_cnt')\n",
    "ax.set_title('MW by Hour')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d75a9d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Percent of orders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-01 10:00:00</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2.683653e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 11:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2.981836e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 13:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2.981836e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 14:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2.981836e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01 15:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>5.963673e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 16:00:00</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1.789102e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 17:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>8.945509e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 18:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>5.963673e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 19:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1.192735e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 20:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>334</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1.192735e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884856 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     orders_cnt  hour  dayofweek  quarter  month  year  \\\n",
       "date                                                                     \n",
       "2021-04-01 10:00:00           9    10          3        2      4  2021   \n",
       "2021-04-01 11:00:00           1    11          3        2      4  2021   \n",
       "2021-04-01 13:00:00           1    13          3        2      4  2021   \n",
       "2021-04-01 14:00:00           1    14          3        2      4  2021   \n",
       "2021-04-01 15:00:00           2    15          3        2      4  2021   \n",
       "...                         ...   ...        ...      ...    ...   ...   \n",
       "2021-11-30 16:00:00           6    16          1        4     11  2021   \n",
       "2021-11-30 17:00:00           3    17          1        4     11  2021   \n",
       "2021-11-30 18:00:00           2    18          1        4     11  2021   \n",
       "2021-11-30 19:00:00           4    19          1        4     11  2021   \n",
       "2021-11-30 20:00:00           4    20          1        4     11  2021   \n",
       "\n",
       "                     dayofyear  dayofmonth  weekofyear DayOfWeek  \\\n",
       "date                                                               \n",
       "2021-04-01 10:00:00         91           1          13  Thursday   \n",
       "2021-04-01 11:00:00         91           1          13  Thursday   \n",
       "2021-04-01 13:00:00         91           1          13  Thursday   \n",
       "2021-04-01 14:00:00         91           1          13  Thursday   \n",
       "2021-04-01 15:00:00         91           1          13  Thursday   \n",
       "...                        ...         ...         ...       ...   \n",
       "2021-11-30 16:00:00        334          30          48   Tuesday   \n",
       "2021-11-30 17:00:00        334          30          48   Tuesday   \n",
       "2021-11-30 18:00:00        334          30          48   Tuesday   \n",
       "2021-11-30 19:00:00        334          30          48   Tuesday   \n",
       "2021-11-30 20:00:00        334          30          48   Tuesday   \n",
       "\n",
       "                     Percent of orders  \n",
       "date                                    \n",
       "2021-04-01 10:00:00       2.683653e-06  \n",
       "2021-04-01 11:00:00       2.981836e-07  \n",
       "2021-04-01 13:00:00       2.981836e-07  \n",
       "2021-04-01 14:00:00       2.981836e-07  \n",
       "2021-04-01 15:00:00       5.963673e-07  \n",
       "...                                ...  \n",
       "2021-11-30 16:00:00       1.789102e-06  \n",
       "2021-11-30 17:00:00       8.945509e-07  \n",
       "2021-11-30 18:00:00       5.963673e-07  \n",
       "2021-11-30 19:00:00       1.192735e-06  \n",
       "2021-11-30 20:00:00       1.192735e-06  \n",
       "\n",
       "[884856 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "91537cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.boxplot(data=df1, x='month', y='orders_cnt', palette='Blues')\n",
    "ax.set_title('MW by Month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "85b2fcdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.boxplot(data=df1, x='DayOfWeek', y='orders_cnt', palette='Blues')\n",
    "ax.set_title('MW by Month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "58f572ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/02/g50s5v214l3859l53xfk9xzr0000gn/T/ipykernel_4529/2700130030.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df1.groupby(['hour']).sum()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>weekofyear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</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>7</th>\n",
       "      <td>108</td>\n",
       "      <td>282</td>\n",
       "      <td>192</td>\n",
       "      <td>459</td>\n",
       "      <td>194016</td>\n",
       "      <td>12433</td>\n",
       "      <td>1524</td>\n",
       "      <td>1777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3498</td>\n",
       "      <td>7518</td>\n",
       "      <td>7549</td>\n",
       "      <td>20110</td>\n",
       "      <td>5020164</td>\n",
       "      <td>573576</td>\n",
       "      <td>39603</td>\n",
       "      <td>81930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30756</td>\n",
       "      <td>43579</td>\n",
       "      <td>48317</td>\n",
       "      <td>129270</td>\n",
       "      <td>29072085</td>\n",
       "      <td>3719237</td>\n",
       "      <td>234742</td>\n",
       "      <td>531259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>362565</td>\n",
       "      <td>229658</td>\n",
       "      <td>249829</td>\n",
       "      <td>664568</td>\n",
       "      <td>153226157</td>\n",
       "      <td>19082009</td>\n",
       "      <td>1230341</td>\n",
       "      <td>2725686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>272829</td>\n",
       "      <td>223432</td>\n",
       "      <td>243365</td>\n",
       "      <td>648082</td>\n",
       "      <td>148005914</td>\n",
       "      <td>18625116</td>\n",
       "      <td>1193010</td>\n",
       "      <td>2660198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>273107</td>\n",
       "      <td>222271</td>\n",
       "      <td>242067</td>\n",
       "      <td>644651</td>\n",
       "      <td>147292501</td>\n",
       "      <td>18522177</td>\n",
       "      <td>1183486</td>\n",
       "      <td>2645507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>269195</td>\n",
       "      <td>221631</td>\n",
       "      <td>241367</td>\n",
       "      <td>642864</td>\n",
       "      <td>146773104</td>\n",
       "      <td>18473189</td>\n",
       "      <td>1180698</td>\n",
       "      <td>2638490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>264873</td>\n",
       "      <td>220234</td>\n",
       "      <td>240735</td>\n",
       "      <td>641329</td>\n",
       "      <td>146419429</td>\n",
       "      <td>18428910</td>\n",
       "      <td>1177622</td>\n",
       "      <td>2632289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>266061</td>\n",
       "      <td>220304</td>\n",
       "      <td>241808</td>\n",
       "      <td>644275</td>\n",
       "      <td>146900427</td>\n",
       "      <td>18516991</td>\n",
       "      <td>1183357</td>\n",
       "      <td>2644964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>275376</td>\n",
       "      <td>223646</td>\n",
       "      <td>245313</td>\n",
       "      <td>653530</td>\n",
       "      <td>149052792</td>\n",
       "      <td>18783593</td>\n",
       "      <td>1201662</td>\n",
       "      <td>2683029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>289392</td>\n",
       "      <td>225801</td>\n",
       "      <td>248434</td>\n",
       "      <td>661763</td>\n",
       "      <td>150910091</td>\n",
       "      <td>19018191</td>\n",
       "      <td>1214505</td>\n",
       "      <td>2716629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>309720</td>\n",
       "      <td>229287</td>\n",
       "      <td>253405</td>\n",
       "      <td>674854</td>\n",
       "      <td>154115397</td>\n",
       "      <td>19393722</td>\n",
       "      <td>1241475</td>\n",
       "      <td>2770458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>322706</td>\n",
       "      <td>231079</td>\n",
       "      <td>256794</td>\n",
       "      <td>683952</td>\n",
       "      <td>156514324</td>\n",
       "      <td>19652465</td>\n",
       "      <td>1260674</td>\n",
       "      <td>2807674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>296823</td>\n",
       "      <td>226939</td>\n",
       "      <td>252482</td>\n",
       "      <td>672290</td>\n",
       "      <td>154157838</td>\n",
       "      <td>19313205</td>\n",
       "      <td>1240120</td>\n",
       "      <td>2759300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>114664</td>\n",
       "      <td>144240</td>\n",
       "      <td>161602</td>\n",
       "      <td>430294</td>\n",
       "      <td>98491414</td>\n",
       "      <td>12363538</td>\n",
       "      <td>792988</td>\n",
       "      <td>1766500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1964</td>\n",
       "      <td>3080</td>\n",
       "      <td>3551</td>\n",
       "      <td>9408</td>\n",
       "      <td>2146302</td>\n",
       "      <td>271193</td>\n",
       "      <td>18047</td>\n",
       "      <td>38757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>321</td>\n",
       "      <td>17</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      orders_cnt  dayofweek  quarter   month       year  dayofyear  \\\n",
       "hour                                                                 \n",
       "7            108        282      192     459     194016      12433   \n",
       "8           3498       7518     7549   20110    5020164     573576   \n",
       "9          30756      43579    48317  129270   29072085    3719237   \n",
       "10        362565     229658   249829  664568  153226157   19082009   \n",
       "11        272829     223432   243365  648082  148005914   18625116   \n",
       "12        273107     222271   242067  644651  147292501   18522177   \n",
       "13        269195     221631   241367  642864  146773104   18473189   \n",
       "14        264873     220234   240735  641329  146419429   18428910   \n",
       "15        266061     220304   241808  644275  146900427   18516991   \n",
       "16        275376     223646   245313  653530  149052792   18783593   \n",
       "17        289392     225801   248434  661763  150910091   19018191   \n",
       "18        309720     229287   253405  674854  154115397   19393722   \n",
       "19        322706     231079   256794  683952  156514324   19652465   \n",
       "20        296823     226939   252482  672290  154157838   19313205   \n",
       "21        114664     144240   161602  430294   98491414   12363538   \n",
       "22          1964       3080     3551    9408    2146302     271193   \n",
       "23             1          2        4      11       2021        321   \n",
       "\n",
       "      dayofmonth  weekofyear  \n",
       "hour                          \n",
       "7           1524        1777  \n",
       "8          39603       81930  \n",
       "9         234742      531259  \n",
       "10       1230341     2725686  \n",
       "11       1193010     2660198  \n",
       "12       1183486     2645507  \n",
       "13       1180698     2638490  \n",
       "14       1177622     2632289  \n",
       "15       1183357     2644964  \n",
       "16       1201662     2683029  \n",
       "17       1214505     2716629  \n",
       "18       1241475     2770458  \n",
       "19       1260674     2807674  \n",
       "20       1240120     2759300  \n",
       "21        792988     1766500  \n",
       "22         18047       38757  \n",
       "23            17          46  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(['hour']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b7d37ae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/02/g50s5v214l3859l53xfk9xzr0000gn/T/ipykernel_4529/145907901.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df1.groupby(['month']).sum()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>weekofyear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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>4</th>\n",
       "      <td>65819</td>\n",
       "      <td>306845</td>\n",
       "      <td>61163</td>\n",
       "      <td>40094</td>\n",
       "      <td>40514987</td>\n",
       "      <td>2127622</td>\n",
       "      <td>323392</td>\n",
       "      <td>303800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>75968</td>\n",
       "      <td>368494</td>\n",
       "      <td>74088</td>\n",
       "      <td>48338</td>\n",
       "      <td>48845549</td>\n",
       "      <td>3315514</td>\n",
       "      <td>415234</td>\n",
       "      <td>473419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>145416</td>\n",
       "      <td>728836</td>\n",
       "      <td>138020</td>\n",
       "      <td>95734</td>\n",
       "      <td>96739207</td>\n",
       "      <td>8115679</td>\n",
       "      <td>887762</td>\n",
       "      <td>1160180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>280568</td>\n",
       "      <td>1693551</td>\n",
       "      <td>345787</td>\n",
       "      <td>333300</td>\n",
       "      <td>224533100</td>\n",
       "      <td>21991927</td>\n",
       "      <td>1882827</td>\n",
       "      <td>3139920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>420332</td>\n",
       "      <td>2209916</td>\n",
       "      <td>429949</td>\n",
       "      <td>434868</td>\n",
       "      <td>292956076</td>\n",
       "      <td>33118885</td>\n",
       "      <td>2388213</td>\n",
       "      <td>4731972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>596768</td>\n",
       "      <td>2477606</td>\n",
       "      <td>486528</td>\n",
       "      <td>487164</td>\n",
       "      <td>328186148</td>\n",
       "      <td>42009675</td>\n",
       "      <td>2549391</td>\n",
       "      <td>6001473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>779708</td>\n",
       "      <td>2793573</td>\n",
       "      <td>590572</td>\n",
       "      <td>731936</td>\n",
       "      <td>369810664</td>\n",
       "      <td>52919844</td>\n",
       "      <td>2965212</td>\n",
       "      <td>7554032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>989059</td>\n",
       "      <td>2924069</td>\n",
       "      <td>546876</td>\n",
       "      <td>765380</td>\n",
       "      <td>386708245</td>\n",
       "      <td>61150720</td>\n",
       "      <td>2981840</td>\n",
       "      <td>8739697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       orders_cnt     hour  dayofweek  quarter       year  dayofyear  \\\n",
       "month                                                                  \n",
       "4           65819   306845      61163    40094   40514987    2127622   \n",
       "5           75968   368494      74088    48338   48845549    3315514   \n",
       "6          145416   728836     138020    95734   96739207    8115679   \n",
       "7          280568  1693551     345787   333300  224533100   21991927   \n",
       "8          420332  2209916     429949   434868  292956076   33118885   \n",
       "9          596768  2477606     486528   487164  328186148   42009675   \n",
       "10         779708  2793573     590572   731936  369810664   52919844   \n",
       "11         989059  2924069     546876   765380  386708245   61150720   \n",
       "\n",
       "       dayofmonth  weekofyear  \n",
       "month                          \n",
       "4          323392      303800  \n",
       "5          415234      473419  \n",
       "6          887762     1160180  \n",
       "7         1882827     3139920  \n",
       "8         2388213     4731972  \n",
       "9         2549391     6001473  \n",
       "10        2965212     7554032  \n",
       "11        2981840     8739697  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(['month']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "adba3214",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [69]\u001b[0m, in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo. of students enrolled\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStudents enrolled in different courses\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 10\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/pyplot.py:409\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    365\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    406\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    408\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_backend_mod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:43\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     40\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[1;32m     41\u001b[0m         display(\n\u001b[1;32m     42\u001b[0m             figure_manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[0;32m---> 43\u001b[0m             metadata\u001b[38;5;241m=\u001b[39m\u001b[43m_fetch_figure_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     44\u001b[0m         )\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     46\u001b[0m     show\u001b[38;5;241m.\u001b[39m_to_draw \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:231\u001b[0m, in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[38;5;66;03m# determine if a background is needed for legibility\u001b[39;00m\n\u001b[1;32m    229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_transparent(fig\u001b[38;5;241m.\u001b[39mget_facecolor()):\n\u001b[1;32m    230\u001b[0m     \u001b[38;5;66;03m# the background is transparent\u001b[39;00m\n\u001b[0;32m--> 231\u001b[0m     ticksLight \u001b[38;5;241m=\u001b[39m _is_light([label\u001b[38;5;241m.\u001b[39mget_color()\n\u001b[1;32m    232\u001b[0m                             \u001b[38;5;28;01mfor\u001b[39;00m axes \u001b[38;5;129;01min\u001b[39;00m fig\u001b[38;5;241m.\u001b[39maxes\n\u001b[1;32m    233\u001b[0m                             \u001b[38;5;28;01mfor\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m (axes\u001b[38;5;241m.\u001b[39mxaxis, axes\u001b[38;5;241m.\u001b[39myaxis)\n\u001b[1;32m    234\u001b[0m                             \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mget_ticklabels()])\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ticksLight\u001b[38;5;241m.\u001b[39msize \u001b[38;5;129;01mand\u001b[39;00m (ticksLight \u001b[38;5;241m==\u001b[39m ticksLight[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m    236\u001b[0m         \u001b[38;5;66;03m# there are one or more tick labels, all with the same lightness\u001b[39;00m\n\u001b[1;32m    237\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneeds_background\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdark\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ticksLight[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlight\u001b[39m\u001b[38;5;124m'\u001b[39m}\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:234\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[38;5;66;03m# determine if a background is needed for legibility\u001b[39;00m\n\u001b[1;32m    229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_transparent(fig\u001b[38;5;241m.\u001b[39mget_facecolor()):\n\u001b[1;32m    230\u001b[0m     \u001b[38;5;66;03m# the background is transparent\u001b[39;00m\n\u001b[1;32m    231\u001b[0m     ticksLight \u001b[38;5;241m=\u001b[39m _is_light([label\u001b[38;5;241m.\u001b[39mget_color()\n\u001b[1;32m    232\u001b[0m                             \u001b[38;5;28;01mfor\u001b[39;00m axes \u001b[38;5;129;01min\u001b[39;00m fig\u001b[38;5;241m.\u001b[39maxes\n\u001b[1;32m    233\u001b[0m                             \u001b[38;5;28;01mfor\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m (axes\u001b[38;5;241m.\u001b[39mxaxis, axes\u001b[38;5;241m.\u001b[39myaxis)\n\u001b[0;32m--> 234\u001b[0m                             \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_ticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m])\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ticksLight\u001b[38;5;241m.\u001b[39msize \u001b[38;5;129;01mand\u001b[39;00m (ticksLight \u001b[38;5;241m==\u001b[39m ticksLight[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m    236\u001b[0m         \u001b[38;5;66;03m# there are one or more tick labels, all with the same lightness\u001b[39;00m\n\u001b[1;32m    237\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneeds_background\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdark\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ticksLight[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlight\u001b[39m\u001b[38;5;124m'\u001b[39m}\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py:1388\u001b[0m, in \u001b[0;36mAxis.get_ticklabels\u001b[0;34m(self, minor, which)\u001b[0m\n\u001b[1;32m   1386\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m minor:\n\u001b[1;32m   1387\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_minorticklabels()\n\u001b[0;32m-> 1388\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_majorticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py:1345\u001b[0m, in \u001b[0;36mAxis.get_majorticklabels\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1343\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_majorticklabels\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1344\u001b[0m     \u001b[38;5;124;03m\"\"\"Return this Axis' major tick labels, as a list of `~.text.Text`.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1345\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_ticks\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1346\u001b[0m     ticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_major_ticks()\n\u001b[1;32m   1347\u001b[0m     labels1 \u001b[38;5;241m=\u001b[39m [tick\u001b[38;5;241m.\u001b[39mlabel1 \u001b[38;5;28;01mfor\u001b[39;00m tick \u001b[38;5;129;01min\u001b[39;00m ticks \u001b[38;5;28;01mif\u001b[39;00m tick\u001b[38;5;241m.\u001b[39mlabel1\u001b[38;5;241m.\u001b[39mget_visible()]\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py:1190\u001b[0m, in \u001b[0;36mAxis._update_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1185\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_update_ticks\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1186\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1187\u001b[0m \u001b[38;5;124;03m    Update ticks (position and labels) using the current data interval of\u001b[39;00m\n\u001b[1;32m   1188\u001b[0m \u001b[38;5;124;03m    the axes.  Return the list of ticks that will be drawn.\u001b[39;00m\n\u001b[1;32m   1189\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1190\u001b[0m     major_locs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_majorticklocs\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1191\u001b[0m     major_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajor\u001b[38;5;241m.\u001b[39mformatter\u001b[38;5;241m.\u001b[39mformat_ticks(major_locs)\n\u001b[1;32m   1192\u001b[0m     major_ticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_major_ticks(\u001b[38;5;28mlen\u001b[39m(major_locs))\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py:1416\u001b[0m, in \u001b[0;36mAxis.get_majorticklocs\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1414\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_majorticklocs\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1415\u001b[0m     \u001b[38;5;124;03m\"\"\"Return this Axis' major tick locations in data coordinates.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1416\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmajor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/ticker.py:2142\u001b[0m, in \u001b[0;36mMaxNLocator.__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2141\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m-> 2142\u001b[0m     vmin, vmax \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_view_interval\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2143\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtick_values(vmin, vmax)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py:2162\u001b[0m, in \u001b[0;36m_make_getset_interval.<locals>.getter\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2160\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgetter\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   2161\u001b[0m     \u001b[38;5;66;03m# docstring inherited.\u001b[39;00m\n\u001b[0;32m-> 2162\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlim_name\u001b[49m\u001b[43m)\u001b[49m, attr_name)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py:815\u001b[0m, in \u001b[0;36m_AxesBase.viewLim\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    813\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    814\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mviewLim\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 815\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_unstale_viewLim\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    816\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_viewLim\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py:810\u001b[0m, in \u001b[0;36m_AxesBase._unstale_viewLim\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    808\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shared_axes[name]\u001b[38;5;241m.\u001b[39mget_siblings(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    809\u001b[0m         ax\u001b[38;5;241m.\u001b[39m_stale_viewlims[name] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 810\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautoscale_view\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mscale\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\n\u001b[1;32m    811\u001b[0m \u001b[43m                       \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mneed_scale\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py:2894\u001b[0m, in \u001b[0;36m_AxesBase.autoscale_view\u001b[0;34m(self, tight, scalex, scaley)\u001b[0m\n\u001b[1;32m   2887\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_sticky_edges:\n\u001b[1;32m   2888\u001b[0m     \u001b[38;5;66;03m# Only iterate over Axes and artists if needed.  The check for\u001b[39;00m\n\u001b[1;32m   2889\u001b[0m     \u001b[38;5;66;03m# ``hasattr(ax, \"_children\")`` is necessary because this can be\u001b[39;00m\n\u001b[1;32m   2890\u001b[0m     \u001b[38;5;66;03m# called very early in the Axes init process (e.g., for twin Axes)\u001b[39;00m\n\u001b[1;32m   2891\u001b[0m     \u001b[38;5;66;03m# when these attributes don't even exist yet, in which case\u001b[39;00m\n\u001b[1;32m   2892\u001b[0m     \u001b[38;5;66;03m# `get_children` would raise an AttributeError.\u001b[39;00m\n\u001b[1;32m   2893\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xmargin \u001b[38;5;129;01mand\u001b[39;00m scalex \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_autoscalex_on():\n\u001b[0;32m-> 2894\u001b[0m         x_stickies \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(np\u001b[38;5;241m.\u001b[39mconcatenate([\n\u001b[1;32m   2895\u001b[0m             artist\u001b[38;5;241m.\u001b[39msticky_edges\u001b[38;5;241m.\u001b[39mx\n\u001b[1;32m   2896\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shared_axes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget_siblings(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   2897\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_children\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2898\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mget_children()]))\n\u001b[1;32m   2899\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ymargin \u001b[38;5;129;01mand\u001b[39;00m scaley \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_autoscaley_on():\n\u001b[1;32m   2900\u001b[0m         y_stickies \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(np\u001b[38;5;241m.\u001b[39mconcatenate([\n\u001b[1;32m   2901\u001b[0m             artist\u001b[38;5;241m.\u001b[39msticky_edges\u001b[38;5;241m.\u001b[39my\n\u001b[1;32m   2902\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shared_axes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget_siblings(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   2903\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_children\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2904\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mget_children()]))\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py:2895\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   2887\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_sticky_edges:\n\u001b[1;32m   2888\u001b[0m     \u001b[38;5;66;03m# Only iterate over Axes and artists if needed.  The check for\u001b[39;00m\n\u001b[1;32m   2889\u001b[0m     \u001b[38;5;66;03m# ``hasattr(ax, \"_children\")`` is necessary because this can be\u001b[39;00m\n\u001b[1;32m   2890\u001b[0m     \u001b[38;5;66;03m# called very early in the Axes init process (e.g., for twin Axes)\u001b[39;00m\n\u001b[1;32m   2891\u001b[0m     \u001b[38;5;66;03m# when these attributes don't even exist yet, in which case\u001b[39;00m\n\u001b[1;32m   2892\u001b[0m     \u001b[38;5;66;03m# `get_children` would raise an AttributeError.\u001b[39;00m\n\u001b[1;32m   2893\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xmargin \u001b[38;5;129;01mand\u001b[39;00m scalex \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_autoscalex_on():\n\u001b[1;32m   2894\u001b[0m         x_stickies \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(np\u001b[38;5;241m.\u001b[39mconcatenate([\n\u001b[0;32m-> 2895\u001b[0m             \u001b[43martist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msticky_edges\u001b[49m\u001b[38;5;241m.\u001b[39mx\n\u001b[1;32m   2896\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shared_axes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget_siblings(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   2897\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_children\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2898\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mget_children()]))\n\u001b[1;32m   2899\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ymargin \u001b[38;5;129;01mand\u001b[39;00m scaley \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_autoscaley_on():\n\u001b[1;32m   2900\u001b[0m         y_stickies \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(np\u001b[38;5;241m.\u001b[39mconcatenate([\n\u001b[1;32m   2901\u001b[0m             artist\u001b[38;5;241m.\u001b[39msticky_edges\u001b[38;5;241m.\u001b[39my\n\u001b[1;32m   2902\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shared_axes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget_siblings(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   2903\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_children\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2904\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mget_children()]))\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:1120\u001b[0m, in \u001b[0;36mArtist.sticky_edges\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1095\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m   1096\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msticky_edges\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1097\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1098\u001b[0m \u001b[38;5;124;03m    ``x`` and ``y`` sticky edge lists for autoscaling.\u001b[39;00m\n\u001b[1;32m   1099\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1118\u001b[0m \n\u001b[1;32m   1119\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1120\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sticky_edges\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(df1['hour'], df1['orders_cnt'], color ='maroon',\n",
    "        width = 0.4)\n",
    " \n",
    "plt.xlabel(\"Courses offered\")\n",
    "plt.ylabel(\"No. of students enrolled\")\n",
    "plt.title(\"Students enrolled in different courses\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6aa10726",
   "metadata": {},
   "outputs": [],
   "source": [
    "dow = df1.loc[(df1['DayOfWeek']=='Sunday')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0b067a5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/02/g50s5v214l3859l53xfk9xzr0000gn/T/ipykernel_4529/1330809844.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  dow.groupby(['hour']).sum()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>weekofyear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
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       "      <td>18189</td>\n",
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       "      <th>8</th>\n",
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       "      <td>2468</td>\n",
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       "      <td>4928</td>\n",
       "      <td>9909</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4443</td>\n",
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       "      <td>6921</td>\n",
       "      <td>18491</td>\n",
       "      <td>4155176</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>55122</td>\n",
       "      <td>66480</td>\n",
       "      <td>36645</td>\n",
       "      <td>97302</td>\n",
       "      <td>22392680</td>\n",
       "      <td>2797190</td>\n",
       "      <td>182861</td>\n",
       "      <td>394850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>46091</td>\n",
       "      <td>65982</td>\n",
       "      <td>36682</td>\n",
       "      <td>97479</td>\n",
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       "      <td>2804634</td>\n",
       "      <td>182146</td>\n",
       "      <td>395949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>46810</td>\n",
       "      <td>66000</td>\n",
       "      <td>36630</td>\n",
       "      <td>97308</td>\n",
       "      <td>22231000</td>\n",
       "      <td>2799267</td>\n",
       "      <td>182114</td>\n",
       "      <td>395181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>46085</td>\n",
       "      <td>66138</td>\n",
       "      <td>36783</td>\n",
       "      <td>97700</td>\n",
       "      <td>22277483</td>\n",
       "      <td>2810851</td>\n",
       "      <td>182463</td>\n",
       "      <td>396826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>44935</td>\n",
       "      <td>65280</td>\n",
       "      <td>36254</td>\n",
       "      <td>96358</td>\n",
       "      <td>21988480</td>\n",
       "      <td>2772300</td>\n",
       "      <td>180273</td>\n",
       "      <td>391380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>44998</td>\n",
       "      <td>65376</td>\n",
       "      <td>36377</td>\n",
       "      <td>96696</td>\n",
       "      <td>22020816</td>\n",
       "      <td>2782442</td>\n",
       "      <td>180599</td>\n",
       "      <td>392822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>45611</td>\n",
       "      <td>65922</td>\n",
       "      <td>36679</td>\n",
       "      <td>97431</td>\n",
       "      <td>22204727</td>\n",
       "      <td>2803176</td>\n",
       "      <td>181843</td>\n",
       "      <td>395745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>47092</td>\n",
       "      <td>66426</td>\n",
       "      <td>36914</td>\n",
       "      <td>98067</td>\n",
       "      <td>22374491</td>\n",
       "      <td>2821677</td>\n",
       "      <td>183638</td>\n",
       "      <td>398352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>47616</td>\n",
       "      <td>66702</td>\n",
       "      <td>37059</td>\n",
       "      <td>98443</td>\n",
       "      <td>22467457</td>\n",
       "      <td>2833295</td>\n",
       "      <td>185263</td>\n",
       "      <td>399992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>45611</td>\n",
       "      <td>66456</td>\n",
       "      <td>36865</td>\n",
       "      <td>97921</td>\n",
       "      <td>22384596</td>\n",
       "      <td>2817240</td>\n",
       "      <td>183834</td>\n",
       "      <td>397716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>39721</td>\n",
       "      <td>64902</td>\n",
       "      <td>35949</td>\n",
       "      <td>95505</td>\n",
       "      <td>21861157</td>\n",
       "      <td>2746988</td>\n",
       "      <td>179018</td>\n",
       "      <td>387791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15198</td>\n",
       "      <td>40422</td>\n",
       "      <td>22464</td>\n",
       "      <td>59622</td>\n",
       "      <td>13615477</td>\n",
       "      <td>1715688</td>\n",
       "      <td>112040</td>\n",
       "      <td>242211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>282</td>\n",
       "      <td>876</td>\n",
       "      <td>491</td>\n",
       "      <td>1302</td>\n",
       "      <td>295066</td>\n",
       "      <td>37755</td>\n",
       "      <td>2689</td>\n",
       "      <td>5331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      orders_cnt  dayofweek  quarter  month      year  dayofyear  dayofmonth  \\\n",
       "hour                                                                           \n",
       "7              9         54       18     43     18189       1140         119   \n",
       "8            444       1860      926   2468    626510      70293        4928   \n",
       "9           4443      12336     6921  18491   4155176     533051       34579   \n",
       "10         55122      66480    36645  97302  22392680    2797190      182861   \n",
       "11         46091      65982    36682  97479  22224937    2804634      182146   \n",
       "12         46810      66000    36630  97308  22231000    2799267      182114   \n",
       "13         46085      66138    36783  97700  22277483    2810851      182463   \n",
       "14         44935      65280    36254  96358  21988480    2772300      180273   \n",
       "15         44998      65376    36377  96696  22020816    2782442      180599   \n",
       "16         45611      65922    36679  97431  22204727    2803176      181843   \n",
       "17         47092      66426    36914  98067  22374491    2821677      183638   \n",
       "18         47616      66702    37059  98443  22467457    2833295      185263   \n",
       "19         45611      66456    36865  97921  22384596    2817240      183834   \n",
       "20         39721      64902    35949  95505  21861157    2746988      179018   \n",
       "21         15198      40422    22464  59622  13615477    1715688      112040   \n",
       "22           282        876      491   1302    295066      37755        2689   \n",
       "\n",
       "      weekofyear  \n",
       "hour              \n",
       "7            159  \n",
       "8           9909  \n",
       "9          75269  \n",
       "10        394850  \n",
       "11        395949  \n",
       "12        395181  \n",
       "13        396826  \n",
       "14        391380  \n",
       "15        392822  \n",
       "16        395745  \n",
       "17        398352  \n",
       "18        399992  \n",
       "19        397716  \n",
       "20        387791  \n",
       "21        242211  \n",
       "22          5331  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow.groupby(['hour']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "301bfb0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dow_sat = df1.loc[(df1['DayOfWeek']=='Saturday')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f2bb9184",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/02/g50s5v214l3859l53xfk9xzr0000gn/T/ipykernel_4529/2907702380.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  dow_sat.groupby(['hour']).sum().iloc[:,:1]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>59263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>45921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>45179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>44634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>42677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>43127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>43903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>44801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>42884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>38191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      orders_cnt\n",
       "hour            \n",
       "7             17\n",
       "8            556\n",
       "9           4909\n",
       "10         59263\n",
       "11         45921\n",
       "12         45179\n",
       "13         44634\n",
       "14         42677\n",
       "15         42003\n",
       "16         43127\n",
       "17         43903\n",
       "18         44801\n",
       "19         42884\n",
       "20         38191\n",
       "21         14906\n",
       "22           292"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_sat.groupby(['hour']).sum().iloc[:,:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7113091a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dow_fr = df1.loc[(df1['DayOfWeek']=='Friday')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "676d01d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/02/g50s5v214l3859l53xfk9xzr0000gn/T/ipykernel_4529/1149989609.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  dow_fr.groupby(['hour']).sum().iloc[:,:1]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>51486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>37683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>37383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>36680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>36413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>36350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>40663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>44493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>45620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>42563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      orders_cnt\n",
       "hour            \n",
       "7             21\n",
       "8            542\n",
       "9           4381\n",
       "10         51486\n",
       "11         37683\n",
       "12         37383\n",
       "13         36680\n",
       "14         36413\n",
       "15         36350\n",
       "16         38014\n",
       "17         40663\n",
       "18         44493\n",
       "19         45620\n",
       "20         42563\n",
       "21         16827\n",
       "22           268"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_fr.groupby(['hour']).sum().iloc[:,:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9680abb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_cnt</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>weekofyear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayOfWeek</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>469387</td>\n",
       "      <td>1917906</td>\n",
       "      <td>502420</td>\n",
       "      <td>419196</td>\n",
       "      <td>1108919</td>\n",
       "      <td>253847705</td>\n",
       "      <td>31798232</td>\n",
       "      <td>1982521</td>\n",
       "      <td>4524661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>452568</td>\n",
       "      <td>1921999</td>\n",
       "      <td>0</td>\n",
       "      <td>417989</td>\n",
       "      <td>1116540</td>\n",
       "      <td>253789096</td>\n",
       "      <td>32073746</td>\n",
       "      <td>2020413</td>\n",
       "      <td>4635782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>513263</td>\n",
       "      <td>1946626</td>\n",
       "      <td>641495</td>\n",
       "      <td>428108</td>\n",
       "      <td>1133704</td>\n",
       "      <td>259292279</td>\n",
       "      <td>32619656</td>\n",
       "      <td>2134122</td>\n",
       "      <td>4623294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>530068</td>\n",
       "      <td>1979451</td>\n",
       "      <td>781212</td>\n",
       "      <td>433657</td>\n",
       "      <td>1152136</td>\n",
       "      <td>263138242</td>\n",
       "      <td>33146987</td>\n",
       "      <td>2158407</td>\n",
       "      <td>4679483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>467863</td>\n",
       "      <td>1906066</td>\n",
       "      <td>374259</td>\n",
       "      <td>411362</td>\n",
       "      <td>1097060</td>\n",
       "      <td>252125813</td>\n",
       "      <td>31490074</td>\n",
       "      <td>2006086</td>\n",
       "      <td>4498582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>469606</td>\n",
       "      <td>1946144</td>\n",
       "      <td>127245</td>\n",
       "      <td>423230</td>\n",
       "      <td>1131473</td>\n",
       "      <td>257162145</td>\n",
       "      <td>32598729</td>\n",
       "      <td>2142722</td>\n",
       "      <td>4693317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>450883</td>\n",
       "      <td>1884698</td>\n",
       "      <td>246352</td>\n",
       "      <td>403272</td>\n",
       "      <td>1081878</td>\n",
       "      <td>248938696</td>\n",
       "      <td>31022442</td>\n",
       "      <td>1949600</td>\n",
       "      <td>4449374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           orders_cnt     hour  dayofweek  quarter    month       year  \\\n",
       "DayOfWeek                                                                \n",
       "Friday         469387  1917906     502420   419196  1108919  253847705   \n",
       "Monday         452568  1921999          0   417989  1116540  253789096   \n",
       "Saturday       513263  1946626     641495   428108  1133704  259292279   \n",
       "Sunday         530068  1979451     781212   433657  1152136  263138242   \n",
       "Thursday       467863  1906066     374259   411362  1097060  252125813   \n",
       "Tuesday        469606  1946144     127245   423230  1131473  257162145   \n",
       "Wednesday      450883  1884698     246352   403272  1081878  248938696   \n",
       "\n",
       "           dayofyear  dayofmonth  weekofyear  \n",
       "DayOfWeek                                     \n",
       "Friday      31798232     1982521     4524661  \n",
       "Monday      32073746     2020413     4635782  \n",
       "Saturday    32619656     2134122     4623294  \n",
       "Sunday      33146987     2158407     4679483  \n",
       "Thursday    31490074     2006086     4498582  \n",
       "Tuesday     32598729     2142722     4693317  \n",
       "Wednesday   31022442     1949600     4449374  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(['DayOfWeek']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a9d6fd8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(df1['month'], df1['orders_cnt'], color ='maroon',\n",
    "        width = 0.4)\n",
    " \n",
    "plt.xlabel(\"Courses offered\")\n",
    "plt.ylabel(\"No. of students enrolled\")\n",
    "plt.title(\"Students enrolled in different courses\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b05e5de0",
   "metadata": {
    "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>delivery_area_id</th>\n",
       "      <th>dttm</th>\n",
       "      <th>partners_cnt</th>\n",
       "      <th>delay_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 09:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 10:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.1111111111111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 11:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 12:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-01 13:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247466</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 17:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247467</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 18:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247468</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 19:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247469</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 20:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247470</th>\n",
       "      <td>592</td>\n",
       "      <td>2021-11-30 21:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1247471 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         delivery_area_id                 dttm partners_cnt  \\\n",
       "0                       0  2021-04-01 09:00:00          3.0   \n",
       "1                       0  2021-04-01 10:00:00          4.0   \n",
       "2                       0  2021-04-01 11:00:00          4.0   \n",
       "3                       0  2021-04-01 12:00:00          4.0   \n",
       "4                       0  2021-04-01 13:00:00          1.0   \n",
       "...                   ...                  ...          ...   \n",
       "1247466               592  2021-11-30 17:00:00          2.0   \n",
       "1247467               592  2021-11-30 18:00:00          2.0   \n",
       "1247468               592  2021-11-30 19:00:00          2.0   \n",
       "1247469               592  2021-11-30 20:00:00          2.0   \n",
       "1247470               592  2021-11-30 21:00:00          1.0   \n",
       "\n",
       "                 delay_rate  \n",
       "0                       0.0  \n",
       "1        0.1111111111111111  \n",
       "2                       0.0  \n",
       "3                       0.0  \n",
       "4                       0.0  \n",
       "...                     ...  \n",
       "1247466                 0.0  \n",
       "1247467                 0.0  \n",
       "1247468                 0.0  \n",
       "1247469                 0.0  \n",
       "1247470                 0.0  \n",
       "\n",
       "[1247471 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1923b5fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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