bike_sharing_analysis_part_2_plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20aadaee-3c47-45e6-8bfd-4d4ffecba1fd",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80452283-627a-44f2-b86b-bd477a3eb2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "17a2323c-c1bf-4cee-9ce8-b396d04f80c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = pd.read_csv('data_sets/dc_bikes_2.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cddfa85c-1ca7-4b3a-9072-6a7783e15762",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
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       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month_number</th>\n",
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       "      <th>week_day</th>\n",
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       "      <th>hour_of_day</th>\n",
       "      <th>part_of_day</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>January</td>\n",
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       "      <td>Saturday</td>\n",
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       "      <th>2011-01-01 01:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>night</td>\n",
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       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  year  \\\n",
       "datetime                                                                    \n",
       "2011-01-01 00:00:00        81        0.0     3.0        13.0     16  2011   \n",
       "2011-01-01 01:00:00        80        0.0     8.0        32.0     40  2011   \n",
       "2011-01-01 02:00:00        80        0.0     5.0        27.0     32  2011   \n",
       "2011-01-01 03:00:00        75        0.0     3.0        10.0     13  2011   \n",
       "2011-01-01 04:00:00        75        0.0     0.0         1.0      1  2011   \n",
       "\n",
       "                     month_number    month  day_of_month  week_day  hour_day  \\\n",
       "datetime                                                                       \n",
       "2011-01-01 00:00:00             1  January             1  Saturday         0   \n",
       "2011-01-01 01:00:00             1  January             1  Saturday         1   \n",
       "2011-01-01 02:00:00             1  January             1  Saturday         2   \n",
       "2011-01-01 03:00:00             1  January             1  Saturday         3   \n",
       "2011-01-01 04:00:00             1  January             1  Saturday         4   \n",
       "\n",
       "                    season_name  hour_of_day part_of_day  \n",
       "datetime                                                  \n",
       "2011-01-01 00:00:00      winter            0       night  \n",
       "2011-01-01 01:00:00      winter            1       night  \n",
       "2011-01-01 02:00:00      winter            2       night  \n",
       "2011-01-01 03:00:00      winter            3       night  \n",
       "2011-01-01 04:00:00      winter            4       night  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ab3360b-fe01-4bc1-86e0-6c49622fa6ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2011-01-01 00:00:00', '2011-01-01 01:00:00', '2011-01-01 02:00:00',\n",
       "       '2011-01-01 03:00:00', '2011-01-01 04:00:00', '2011-01-01 05:00:00',\n",
       "       '2011-01-01 06:00:00', '2011-01-01 07:00:00', '2011-01-01 08:00:00',\n",
       "       '2011-01-01 09:00:00',\n",
       "       ...\n",
       "       '2012-12-31 14:00:00', '2012-12-31 15:00:00', '2012-12-31 16:00:00',\n",
       "       '2012-12-31 17:00:00', '2012-12-31 18:00:00', '2012-12-31 19:00:00',\n",
       "       '2012-12-31 20:00:00', '2012-12-31 21:00:00', '2012-12-31 22:00:00',\n",
       "       '2012-12-31 23:00:00'],\n",
       "      dtype='object', name='datetime', length=17379)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d66707e-20ef-421d-98c5-9a55534062de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.index = pd.to_datetime(df_2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d285f944-3b0a-435c-850c-4af99f350442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 01:00:00',\n",
       "               '2011-01-01 02:00:00', '2011-01-01 03:00:00',\n",
       "               '2011-01-01 04:00:00', '2011-01-01 05:00:00',\n",
       "               '2011-01-01 06:00:00', '2011-01-01 07:00:00',\n",
       "               '2011-01-01 08:00:00', '2011-01-01 09:00:00',\n",
       "               ...\n",
       "               '2012-12-31 14:00:00', '2012-12-31 15:00:00',\n",
       "               '2012-12-31 16:00:00', '2012-12-31 17:00:00',\n",
       "               '2012-12-31 18:00:00', '2012-12-31 19:00:00',\n",
       "               '2012-12-31 20:00:00', '2012-12-31 21:00:00',\n",
       "               '2012-12-31 22:00:00', '2012-12-31 23:00:00'],\n",
       "              dtype='datetime64[ns]', name='datetime', length=17379, freq=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c1367a8-96b9-4eb6-bcff-478242679f3f",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2. Plot small sections of the count data over time (1 day, 1 week, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01f730e2-74b4-43d4-a57f-3f330c8336e2",
   "metadata": {},
   "source": [
    "#### 2.1.  Day Plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "631aa77f-84aa-40aa-9228-66618adc7f7f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2011-01-01 00:00:00</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>night</td>\n",
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       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>winter</td>\n",
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      ],
      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  year  \\\n",
       "datetime                                                                    \n",
       "2011-01-01 00:00:00        81        0.0     3.0        13.0     16  2011   \n",
       "2011-01-01 01:00:00        80        0.0     8.0        32.0     40  2011   \n",
       "2011-01-01 02:00:00        80        0.0     5.0        27.0     32  2011   \n",
       "2011-01-01 03:00:00        75        0.0     3.0        10.0     13  2011   \n",
       "2011-01-01 04:00:00        75        0.0     0.0         1.0      1  2011   \n",
       "\n",
       "                     month_number    month  day_of_month  week_day  hour_day  \\\n",
       "datetime                                                                       \n",
       "2011-01-01 00:00:00             1  January             1  Saturday         0   \n",
       "2011-01-01 01:00:00             1  January             1  Saturday         1   \n",
       "2011-01-01 02:00:00             1  January             1  Saturday         2   \n",
       "2011-01-01 03:00:00             1  January             1  Saturday         3   \n",
       "2011-01-01 04:00:00             1  January             1  Saturday         4   \n",
       "\n",
       "                    season_name  hour_of_day part_of_day  \n",
       "datetime                                                  \n",
       "2011-01-01 00:00:00      winter            0       night  \n",
       "2011-01-01 01:00:00      winter            1       night  \n",
       "2011-01-01 02:00:00      winter            2       night  \n",
       "2011-01-01 03:00:00      winter            3       night  \n",
       "2011-01-01 04:00:00      winter            4       night  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.loc['2011-01-01'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b700ca9b-7e42-4556-8dde-fcf8e8a4492c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "985"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1 day count: January 1st, 2011:\n",
    "\n",
    "day_count = df_2.loc['2011-01-01']['count'].sum()\n",
    "day_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e07fdaf-6991-42b6-b92c-732bae287ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.loc['2011-01-01']['count'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83a93bfd-ba25-49e1-887f-b464450993a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "2011-01-01 00:00:00    16\n",
       "2011-01-01 01:00:00    40\n",
       "2011-01-01 02:00:00    32\n",
       "2011-01-01 03:00:00    13\n",
       "2011-01-01 04:00:00     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Option 1: Creat a new data frame only with the column 'count' and the index:\n",
    "\n",
    "# 1. Create the data series:\n",
    "\n",
    "ds_day1= df_2.loc['2011-01-01']['count']\n",
    "ds_day1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ab38ec0-0502-4be2-8a63-b4a320471480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Create the plot from the data series:\n",
    "\n",
    "ds_day1.plot(x= 'datetime', y= 'count',label= 'count')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17417fd8-ef87-48d6-97d7-1e9b8cc46fac",
   "metadata": {
    "tags": []
   },
   "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>count</th>\n",
       "      <th>hour_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  hour_day\n",
       "datetime                            \n",
       "2011-01-01 00:00:00     16         0\n",
       "2011-01-01 01:00:00     40         1\n",
       "2011-01-01 02:00:00     32         2\n",
       "2011-01-01 03:00:00     13         3\n",
       "2011-01-01 04:00:00      1         4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Option 2: Creat a new data frame only with the columns I need:\n",
    "\n",
    "# 1. Create the data frame with the needed columns:\n",
    "\n",
    "df_day= df_2.loc['2011-01-01'][['count','hour_day']]\n",
    "df_day.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45801878-b78e-472f-a47f-f4a07d6e9a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Plot the information from the columns. \n",
    "\n",
    "#plt.figure(figsize= (6,10), dpi = 80)\n",
    "sns.lineplot(x= 'hour_day', y= 'count', data= df_day,label= 'count')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e34f9d68-8969-4328-a3e4-0892328fb75b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Option 3: Plot directly from the original data frame:\n",
    "\n",
    "#plt.figure(figsize= (8,10), dpi = 80)\n",
    "\n",
    "# 1. Extract the information from the day needed: \n",
    "\n",
    "day_one= df_2.loc['2011-01-01']\n",
    "\n",
    "# 2. Use this variable in the plotting code line:\n",
    "\n",
    "sns.lineplot(x= day_one['hour_day'], y = df_2['count'], data= df_2, label= 'count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8667e8d3-06e0-4e59-b3ca-424b1cec7299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Alternative to Option 3: Do not create a new variable, use the .loc directly\n",
    "# on the ploting line:\n",
    "\n",
    "sns.lineplot(x= df_2.loc['2011-01-01']['hour_day'], y = df_2['count'], data= df_2, label= 'count' )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6782558d-73bf-4203-af7f-83fa29a4fc7f",
   "metadata": {},
   "source": [
    "#### 2.2.  Week Plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dfdc91ef-0be0-47e1-bcd3-e874fcccdb4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "week_1 = df_2.loc['2011-01-01':'2011-01-07']\n",
    "\n",
    "sns.lineplot(x= df_2['day_of_month'], y = week_1['count'], data= df_2, label= 'count', ci = None )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fc2f3ffb-d919-4e7f-afd8-f06f4cf05ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "week_2 = df_2.loc['2011-01-08':'2011-01-15']\n",
    "\n",
    "sns.lineplot(x= week_2['day_of_month'], y = week_2['count'], data= df_2, label= 'count',ci = None )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbcdb3fd-ba97-4e46-95ac-04c71ff588f1",
   "metadata": {},
   "source": [
    "#### 2.3.  Month Plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4193b502-8fc2-47aa-ae46-f5736cae09a4",
   "metadata": {
    "tags": []
   },
   "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month_number</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>week_day</th>\n",
       "      <th>hour_day</th>\n",
       "      <th>season_name</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>part_of_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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",
       "      <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>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>winter</td>\n",
       "      <td>0</td>\n",
       "      <td>night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>40</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>winter</td>\n",
       "      <td>1</td>\n",
       "      <td>night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2</td>\n",
       "      <td>winter</td>\n",
       "      <td>2</td>\n",
       "      <td>night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>3</td>\n",
       "      <td>winter</td>\n",
       "      <td>3</td>\n",
       "      <td>night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>winter</td>\n",
       "      <td>4</td>\n",
       "      <td>night</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-31 19:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.30</td>\n",
       "      <td>15.910</td>\n",
       "      <td>61</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>19</td>\n",
       "      <td>winter</td>\n",
       "      <td>19</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-31 20:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.335</td>\n",
       "      <td>59</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>20</td>\n",
       "      <td>winter</td>\n",
       "      <td>20</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-31 21:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.56</td>\n",
       "      <td>9.850</td>\n",
       "      <td>59</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>21</td>\n",
       "      <td>winter</td>\n",
       "      <td>21</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-31 22:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6.56</td>\n",
       "      <td>9.090</td>\n",
       "      <td>59</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>22</td>\n",
       "      <td>winter</td>\n",
       "      <td>22</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-31 23:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6.56</td>\n",
       "      <td>9.850</td>\n",
       "      <td>64</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>23</td>\n",
       "      <td>winter</td>\n",
       "      <td>23</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>688 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "...                     ...      ...         ...      ...    ...     ...   \n",
       "2011-01-31 19:00:00       1        0           1        1  12.30  15.910   \n",
       "2011-01-31 20:00:00       1        0           1        3   6.56   8.335   \n",
       "2011-01-31 21:00:00       1        0           1        3   6.56   9.850   \n",
       "2011-01-31 22:00:00       1        0           1        2   6.56   9.090   \n",
       "2011-01-31 23:00:00       1        0           1        2   6.56   9.850   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  year  \\\n",
       "datetime                                                                    \n",
       "2011-01-01 00:00:00        81     0.0000     3.0        13.0     16  2011   \n",
       "2011-01-01 01:00:00        80     0.0000     8.0        32.0     40  2011   \n",
       "2011-01-01 02:00:00        80     0.0000     5.0        27.0     32  2011   \n",
       "2011-01-01 03:00:00        75     0.0000     3.0        10.0     13  2011   \n",
       "2011-01-01 04:00:00        75     0.0000     0.0         1.0      1  2011   \n",
       "...                       ...        ...     ...         ...    ...   ...   \n",
       "2011-01-31 19:00:00        61     7.0015     NaN         NaN    137  2011   \n",
       "2011-01-31 20:00:00        59    11.0014     NaN         NaN     32  2011   \n",
       "2011-01-31 21:00:00        59     6.0032     NaN         NaN     23  2011   \n",
       "2011-01-31 22:00:00        59     7.0015     NaN         NaN     22  2011   \n",
       "2011-01-31 23:00:00        64     6.0032     NaN         NaN     12  2011   \n",
       "\n",
       "                     month_number    month  day_of_month  week_day  hour_day  \\\n",
       "datetime                                                                       \n",
       "2011-01-01 00:00:00             1  January             1  Saturday         0   \n",
       "2011-01-01 01:00:00             1  January             1  Saturday         1   \n",
       "2011-01-01 02:00:00             1  January             1  Saturday         2   \n",
       "2011-01-01 03:00:00             1  January             1  Saturday         3   \n",
       "2011-01-01 04:00:00             1  January             1  Saturday         4   \n",
       "...                           ...      ...           ...       ...       ...   \n",
       "2011-01-31 19:00:00             1  January            31    Monday        19   \n",
       "2011-01-31 20:00:00             1  January            31    Monday        20   \n",
       "2011-01-31 21:00:00             1  January            31    Monday        21   \n",
       "2011-01-31 22:00:00             1  January            31    Monday        22   \n",
       "2011-01-31 23:00:00             1  January            31    Monday        23   \n",
       "\n",
       "                    season_name  hour_of_day part_of_day  \n",
       "datetime                                                  \n",
       "2011-01-01 00:00:00      winter            0       night  \n",
       "2011-01-01 01:00:00      winter            1       night  \n",
       "2011-01-01 02:00:00      winter            2       night  \n",
       "2011-01-01 03:00:00      winter            3       night  \n",
       "2011-01-01 04:00:00      winter            4       night  \n",
       "...                         ...          ...         ...  \n",
       "2011-01-31 19:00:00      winter           19     evening  \n",
       "2011-01-31 20:00:00      winter           20     evening  \n",
       "2011-01-31 21:00:00      winter           21     evening  \n",
       "2011-01-31 22:00:00      winter           22     evening  \n",
       "2011-01-31 23:00:00      winter           23     evening  \n",
       "\n",
       "[688 rows x 20 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "january = df_2[(df_2['month'] == 'January') & (df_2['year'] == 2011)]\n",
    "january"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "51e91985-9393-4442-87ef-2fda3c15fd1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x= january['day_of_month'], y = df_2['count'], data= df_2, label= 'count',ci = None )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9b2a2c-0699-434c-9ac7-cb74367e1ca3",
   "metadata": {},
   "source": [
    "#### 2.4.  Quarter Plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8fbc2721-4001-4222-a8e7-1f9404af03b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q1_2011 = df_2.loc['2011-01-01':'2011-03-31']\n",
    "sns.lineplot(x= Q1_2011['month'], y = df_2['count'], data= df_2, label= 'count',ci = None )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bbe63b00-8aff-4bda-aec3-184b0762c65c",
   "metadata": {
    "tags": []
   },
   "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month_number</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>week_day</th>\n",
       "      <th>hour_day</th>\n",
       "      <th>season_name</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>part_of_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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",
       "      <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>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>winter</td>\n",
       "      <td>0</td>\n",
       "      <td>night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>40</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>winter</td>\n",
       "      <td>1</td>\n",
       "      <td>night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2</td>\n",
       "      <td>winter</td>\n",
       "      <td>2</td>\n",
       "      <td>night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>3</td>\n",
       "      <td>winter</td>\n",
       "      <td>3</td>\n",
       "      <td>night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>winter</td>\n",
       "      <td>4</td>\n",
       "      <td>night</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31 19:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>12.30</td>\n",
       "      <td>15.150</td>\n",
       "      <td>87</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "      <td>31</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>19</td>\n",
       "      <td>spring</td>\n",
       "      <td>19</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31 20:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11.48</td>\n",
       "      <td>12.880</td>\n",
       "      <td>93</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "      <td>31</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>20</td>\n",
       "      <td>spring</td>\n",
       "      <td>20</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31 21:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11.48</td>\n",
       "      <td>13.635</td>\n",
       "      <td>93</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "      <td>31</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>21</td>\n",
       "      <td>spring</td>\n",
       "      <td>21</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31 22:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11.48</td>\n",
       "      <td>13.635</td>\n",
       "      <td>93</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "      <td>31</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>22</td>\n",
       "      <td>spring</td>\n",
       "      <td>22</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31 23:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>100</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "      <td>31</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>23</td>\n",
       "      <td>spring</td>\n",
       "      <td>23</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2067 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "...                     ...      ...         ...      ...    ...     ...   \n",
       "2011-03-31 19:00:00       2        0           1        2  12.30  15.150   \n",
       "2011-03-31 20:00:00       2        0           1        3  11.48  12.880   \n",
       "2011-03-31 21:00:00       2        0           1        2  11.48  13.635   \n",
       "2011-03-31 22:00:00       2        0           1        3  11.48  13.635   \n",
       "2011-03-31 23:00:00       2        0           1        3  10.66  12.880   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  year  \\\n",
       "datetime                                                                    \n",
       "2011-01-01 00:00:00        81     0.0000     3.0        13.0     16  2011   \n",
       "2011-01-01 01:00:00        80     0.0000     8.0        32.0     40  2011   \n",
       "2011-01-01 02:00:00        80     0.0000     5.0        27.0     32  2011   \n",
       "2011-01-01 03:00:00        75     0.0000     3.0        10.0     13  2011   \n",
       "2011-01-01 04:00:00        75     0.0000     0.0         1.0      1  2011   \n",
       "...                       ...        ...     ...         ...    ...   ...   \n",
       "2011-03-31 19:00:00        87     8.9981     NaN         NaN    136  2011   \n",
       "2011-03-31 20:00:00        93    19.0012     NaN         NaN     46  2011   \n",
       "2011-03-31 21:00:00        93    11.0014     NaN         NaN     70  2011   \n",
       "2011-03-31 22:00:00        93    16.9979     NaN         NaN     29  2011   \n",
       "2011-03-31 23:00:00       100    11.0014     NaN         NaN     18  2011   \n",
       "\n",
       "                     month_number    month  day_of_month  week_day  hour_day  \\\n",
       "datetime                                                                       \n",
       "2011-01-01 00:00:00             1  January             1  Saturday         0   \n",
       "2011-01-01 01:00:00             1  January             1  Saturday         1   \n",
       "2011-01-01 02:00:00             1  January             1  Saturday         2   \n",
       "2011-01-01 03:00:00             1  January             1  Saturday         3   \n",
       "2011-01-01 04:00:00             1  January             1  Saturday         4   \n",
       "...                           ...      ...           ...       ...       ...   \n",
       "2011-03-31 19:00:00             3    March            31  Thursday        19   \n",
       "2011-03-31 20:00:00             3    March            31  Thursday        20   \n",
       "2011-03-31 21:00:00             3    March            31  Thursday        21   \n",
       "2011-03-31 22:00:00             3    March            31  Thursday        22   \n",
       "2011-03-31 23:00:00             3    March            31  Thursday        23   \n",
       "\n",
       "                    season_name  hour_of_day part_of_day  \n",
       "datetime                                                  \n",
       "2011-01-01 00:00:00      winter            0       night  \n",
       "2011-01-01 01:00:00      winter            1       night  \n",
       "2011-01-01 02:00:00      winter            2       night  \n",
       "2011-01-01 03:00:00      winter            3       night  \n",
       "2011-01-01 04:00:00      winter            4       night  \n",
       "...                         ...          ...         ...  \n",
       "2011-03-31 19:00:00      spring           19     evening  \n",
       "2011-03-31 20:00:00      spring           20     evening  \n",
       "2011-03-31 21:00:00      spring           21     evening  \n",
       "2011-03-31 22:00:00      spring           22     evening  \n",
       "2011-03-31 23:00:00      spring           23     evening  \n",
       "\n",
       "[2067 rows x 20 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1_2011_2 = df_2[((df_2['month'] == 'January') | (df_2['month'] == 'February')\n",
    "                   | (df_2['month'] == 'March')) & (df_2['year'] == 2011)]\n",
    "Q1_2011_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "adfc649d-c947-42ff-9a7a-564a387618a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uLF2/jVGT59CpbTOmjc6lXQslbYocjBq/xIXVWyJJmy2bJjF9zBDS2ihpU+RQ1Pgl5m3cFknarKiqZvqYXLq3V9KmyOGo8UtMK91Vzi0TZ7N1RzlTRuXSt5OSNkWORFk9ErN2lVcyasocVm7eyeRRg8nsnhztkkRigkb8EpP2JW3OX1PKYzdlcdYJStoUqS2N+CXmVFU73312Pv9cvpkHrh3IRQOUtClyNDTil5ji7vzPywt5feF6/ufSU7heSZsiR02NX2LKr95YxozZa7j9/BO49T96R7sckZikxi8x43fvr+B376/gq0My+N5/KmlT5Fip8UtMmDG7kPv/+hGXn9aVn185QEmbIsdBjV8avL8sXM+PX1rIuSem8fB1p9FYSZsixyXUxm9md5jZYjNbZGYzzKy5mbU3s7fMbHnwOyXMGiS2/XN5Md/+YwHZGSn8btggmiZprCJyvEJ7F5lZN2AckOPuA4DGwI3AXcDb7t4XeDu4L/IF+YUljJ0WSdqcOFJJmyJ1JezhUxLQwsySgJbAOuBKYGqwfipwVcg1SAxatmE7oybPoWPbZkwdPVhJmyJ1KLTG7+5rgYeAQmA9UObubwKd3H198Jj1QMeDbW9mY80sz8zyiouLwypTGqDCLbsYPnEWzZs04ukxQ+jYpnm0SxKJK2FO9aQQGd33AroCrcxsWG23d/fx7p7j7jlpaWlhlSkNzKYgabO8qprpY4YoaVMkBGFO9VwArHT3YnevAF4EzgQ2mlkXgOD3phBrkBhStquCWybNZvOOvUweOZgTlbQpEoowG38hcLqZtbTIRddDgaXAq8CI4DEjgFdCrEFiRCRpczafFu9k/PAcsjJ0sZdIWEILaXP3WWb2PJAPVAIFwHigNfCcmY0h8sfhurBqkNhQXlnN15/OZ96aUn771WzO7qukTZEwhZrO6e53A3cfsHgvkdG/CFXVzh3PzeMfHxfzwDUDuWhAl2iXJBL39GkYiZpI0uYiXl+wnh9dcjLXD1bSpkh9UOOXqHnwb8uYMbuQb57Xh7Hn9Il2OSIJQ41fomL8P1bw2/dWcPOQDP77yydFuxyRhKLGL/Xu2TmF/PIvH3HZwC78QkmbIvWuVo3fzN6uzTKRI3lj0Xp++GIkafOR6zOVtCkSBYe9qsfMmhPJ2EkNPom7713alsincUVq7V/LNzNuxjyyMlJ4cli2kjZFouRIl3PeBnyHSJOfy2eNfxvwf+GVJfGmoLCEsdPz6J3WikkjBtOyaahXEovIYRz23efuvwF+Y2bfcvfH66kmiTMfb9zOyMlzSG3djGmjc2nXUkmbItFUq2GXuz9uZmcCPWtu4+7TQqpL4sSarZGkzWZJQdJmWyVtikRbrRq/mU0H+gDzgKpgsQNq/HJIm7ZHkjb3VFTz3G1nkNFBSZsiDUFtJ1pzgH7u7mEWI/GjbHcFt0ycTfH2vTx96xBO6qykTZGGoraXVSwCOodZiMSPXeWVjJ4yhxXFO/j98EFkK2lTpEGp7Yg/FVhiZrOJhKwB4O5XhFKVxKzyymq+8XQ+BYUlPHFzNv/RV1+iI9LQ1Lbx3xNmERIfqqqd7z43j/c/Lub+r5zKJacqaVOkIartVT3vh12IxDZ356evLOK1Bev54cUnc2NuRrRLEpFDqO1VPduJXMUD0BRoAux097ZhFSax5eE3P+aZWYV8/dw+3HaukjZFGrLajvg/d0mGmV0F5IZRkMSep/7xKU+8+wk35XbnBxcpaVOkoTumsBR3fxn4Ut2WIrHoubw13PeXpVx6ahfuvepUJW2KxIDaTvV8pcbdRkSu69c1/QnujUUbuOuFBfxH31QeueE0JW2KxIjaXtVzeY3blcAq4Mo6r0ZixsxPNjNuRgGZ3ZP5/fBBNEtqHO2SRKSWajvHPyrsQiR2zFtTytem5dErtRWTRippUyTW1PaLWNLN7CUz22RmG83sBTNLD7s4aXiWb9zOyMmzSW3djOljcklu2TTaJYnIUartyd3JwKtEcvm7AX8Olh2SmZ1kZvNq/Gwzs++Y2T1mtrbG8kuO7yVIfYkkbc6mSWMlbYrEsto2/jR3n+zulcHPFOCwn8V392XununumcAgYBfwUrD61/vWuftfjrV4qT/F2/cyfOIsdpVXMn1MrpI2RWJYbRv/ZjMbZmaNg59hwJajeJ6hwAp3X330JUq0le2u4JZJs9m4bS+TR+Vycmd9bk8kltW28Y8Grgc2AOuBa4GjOeF7IzCjxv3bzWyBmU0KvstXGqjd5VXcOnUOn2zazu+HD2JQD/3vEol1tW38vwBGuHuau3ck8ofgntpsaGZNgSuAPwWLniTypS6ZRP6IPHyI7caaWZ6Z5RUXF9eyTKlLFVXVfPOZueStLuHRG7I450QlbYrEg9o2/oHuXrLvjrtvBbJque3FQL67bwy23ejuVe5eDTzFIaIf3H28u+e4e05amhpOfauudr733HzeXVbML68+lUsHKmlTJF7UtvE3qjklY2btqf2Hv26ixjSPmdXsIFcT+ZIXaUDcnbtfXcyr89fxg4tO5iYlbYrEldo274eBD8zseSJRDdcD9x1pIzNrCVwI3FZj8QNmlhnsZ9UB66QBeOStj5n+4WpuO7c33zhPSZsi8aa2n9ydZmZ5RILZDPiKuy+pxXa7gA4HLBt+LIVK/Zjwz095/J1PuHFwd+666ORolyMiIaj1Z+2DRn/EZi+x6/m5Rdz7+lIuObUz912tpE2ReHVMscwSf/62eAM/CJI2f31DppI2ReKYGr/wwSeb+dYfCji1Wzt+N0xJmyLxTo0/wc2vkbQ5ZdRgWjVT0qZIvFPjT2CfbIokbbZv3ZRpStoUSRhq/AmqqGQXwybMJilI2uykpE2RhKHGn4A279jL8Imz2VVeybTRufTo0CraJYlIPdKEboLZtqeCWybOZn3Zbp65dQindFHSpkii0Yg/gewur+LWKXks37Sd3w0bxKAe7aNdkohEgUb8CaKiqpr/+kM+c1Zv5bEbszjvpI7RLklEokQj/gRQXe18/0/zeeejTdx71QAuP61rtEsSkShS449z7s7P/ryYV+at47+/fBJfHdIj2iWJSJSp8ce5X/99OVP/vZqx5/Tmm0raFBHU+OPapH+t5LG3l3N9Tjo/vPhkha6JCKDGH7demFvEz19bwkX9O/NLJW2KSA1q/HHorSUbufOFBZx1Qgd+c1MmSY31v1lEPqOOEGf+vWIL//WHfAZ0a8f44TlK2hSRL1DjjyMLi8r42rQ8erRvyZSRStoUkYNT448Tn2zawYjJs0lu2YTpY4aQ0kpJmyJycGr8cWBt6W6GT5xFIzOeHjOEzu2UtCkih6bGH+M279jL8Amz2LE3krTZM1VJmyJyeGr8MWzbngpGTJrNurLdTB45mH5dlbQpIkemxh+j9lRUcevUPJZt2M6TwwaR01NJmyJSO6E1fjM7yczm1fjZZmbfMbP2ZvaWmS0PfqeEVUO8qqiq5vY/5DNn1VYeuSGT85W0KSJHIbTG7+7L3D3T3TOBQcAu4CXgLuBtd+8LvB3cl1qqrnbufH4Bf1+6iZ9fOYArlLQpIkepvqZ6hgIr3H01cCUwNVg+FbiqnmqIee7Oz19bwksFa/nvL5/E8NOVtCkiR6++Gv+NwIzgdid3Xw8Q/D7oPIWZjTWzPDPLKy4urqcyG7bfvL2cKR+s4tazeylpU0SOWeiN38yaAlcAfzqa7dx9vLvnuHtOWlpaOMXFkMkzV/Lo35dz3aB0fnzpKQpdE5FjVh8j/ouBfHffGNzfaGZdAILfm+qhhpj2UkERP/vzEv6zXyf+9ytK2hSR41Mfjf8mPpvmAXgVGBHcHgG8Ug81xKy/L9nI9/+0gDP7dOCxm7KUtCkixy3ULmJmLYELgRdrLL4fuNDMlgfr7g+zhlj24adB0mbXtoy/JYfmTZS0KSLHL9T4RnffBXQ4YNkWIlf5yGEsWlvGrVPz6N6+JVNG5dJaSZsiUkc0b9AArSjewS2TZtOuRROmj8lV0qaI1Ck1/gZmXeluhk+YRSODp28dQpd2LaJdkojEGTX+BmTLjr0MmziL7XsqmTo6l15K2hSREGjiuIHYvqeCkZPnsK50N9PHDKF/13bRLklE4pRG/A3AvqTNpeu38eRXBzFYSZsiEiKN+KOssqqa2/9QwOxVW3n0hkzOP1lJmyISLo34o6i62rnzhQX8felGfn5Ff67M7BbtkkQkAajxR4m784vXl/Bi/lq+d+GJDD+jZ7RLEpEEocYfJY+/8wmTZ65izNm9uP1LJ0S7HBFJIGr8UTD1g1U88tbHXJOdzo8vUdKmiNQvNf569nLBWu5+dTEX9uvEr645lUaN1PRFpH6p8dejt5du5Ht/ms8ZvTvwuJI2RSRK1HnqyaxPt/DNZ/Lp37UtT41Q0qaIRI8afz3Yl7SZntJCSZsiEnVq/CH7tHgHIybNpm2LJkwfM4T2StoUkShT4w/R+rLdDJ84G4DpY3LpmqykTRGJPjX+kGzdWc6wCbPYtruCqaNz6Z3WOtoliYgAyuoJxY69lYycPJuikt1MG53LgG5K2hSRhkONv47tqajia1PzWLxuG+OHD2JI7w5H3khEpB5pqqcOVVZV860ZBfz70y08fN1pDD2lU7RLEhH5AjX+OlJd7dz14kLeWrKRn13Rn6uylLQpIg2TGn8dcHfufX0pz88t4o4LTmTEmT2jXZKIyCGp8deBJ975hEkzVzLqrJ6MG6qkTRFp2EJt/GaWbGbPm9lHZrbUzM4ws3vMbK2ZzQt+LgmzhrBN//cqHn7rY76S1Y2fXNpPSZsi0uCFfVXPb4A33P1aM2sKtAS+DPza3R8K+blD98q8tfz01cVccEonfnXtQCVtikhMCK3xm1lb4BxgJIC7lwPl8TIifvejTXzvufkM6dWeJ27OoomSNkUkRoTZrXoDxcBkMyswswlm1ipYd7uZLTCzSWaWcrCNzWysmeWZWV5xcXGIZR692Su38vWn53JKl7Y8dYuSNkUktoTZ+JOAbOBJd88CdgJ3AU8CfYBMYD3w8ME2dvfx7p7j7jlpaWkhlnl0Fq8rY8yUOXRLacGUUYNp07xJtEsSETkqYTb+IqDI3WcF958Hst19o7tXuXs18BSQG2INdWrl5p2MmDSbNs2TeHrMEDq0bhbtkkREjlpojd/dNwBrzOykYNFQYImZdanxsKuBRWHVUJfWl+1m2IRZVDtMv3WIkjZFJGaFfVXPt4Bngit6PgVGAY+ZWSbgwCrgtpBrOG5bd5YzfOJsynZX8Mexp9NHSZsiEsNCbfzuPg/IOWDx8DCfs67t2FvJqMmzWbN1F1OVtCkicUDpnIexp6KKsdPyWLRuG78fNojTlbQpInFAF58fQmVVNeNmFPDBii08dN1ALuinpE0RiQ9q/Afh7vzwxYW8uWQjd1/ej6uz0qNdkohInVHjP4C7c9/rS/nT3CK+c0FfRp3VK9oliYjUKTX+A/z2vRVM+NdKRp7Zk28P7RvtckRE6pwafw1Pf7iaB/+2jKuzuvHTy5S0KSLxSY0/8Or8dfzklUVccEpHHlDSpojEMTV+4N1lm/jus/MY3LM9T9ycraRNEYlrCd/h8lZt5RtPz+XkLm2YMEJJmyIS/xK68S9Zt41RU+bQtV0LpozKpa2SNkUkASRs41+1eSe3TJpN62ZJTL91CKlK2hSRBJGQjX9D2R6GTZxFtTvTxwyhm5I2RSSBJFzjL9lZzvCJsyjdVcHUUbmc0FFJmyKSWBIqpG3n3kpGTpnD6q27mDoql1PTlbQpIoknYUb8eyurGDs9j0Vry/i/m7M5o4+SNkUkMSVE46+squbbM+Yx85MtPHDNQC5U0qaIJLC4b/zuzo9eWsgbizfw08v6cc0gJW2KSGKL68bv7vzvXz/iubwixg3ty+izlbQpIhLXjf/J91cw/h+fMuKMHtxxgZI2RUQgzht/j/atuG5QOndf3l9JmyIigbi+nPPSgV24dGCXaJchItKgxPWIX0REvkiNX0QkwYTa+M0s2cyeN7OPzGypmZ1hZu3N7C0zWx78TgmzBhER+bywR/y/Ad5w95OB04ClwF3A2+7eF3g7uC8iIvUktMZvZm2Bc4CJAO5e7u6lwJXA1OBhU4GrwqpBRES+KMwRf2+gGJhsZgVmNsHMWgGd3H09QPC748E2NrOxZpZnZnnFxcUhlikikljCbPxJQDbwpLtnATs5imkddx/v7jnunpOWlhZWjSIiCSfMxl8EFLn7rOD+80T+EGw0sy4Awe9NIdYgIiIHMHcPb+dm/wRudfdlZnYP0CpYtcXd7zezu4D27n7nEfZTDKw+xjJSgc3HuK3Ikej4krAdzzHWw92/MGUSduPPBCYATYFPgVFE/pXxHJABFALXufvWEGvIc/ecsPYviU3Hl4QtjGMs1MgGd58HHKzgoWE+r4iIHJo+uSsikmASofGPj3YBEtd0fEnY6vwYC3WOX0REGp5EGPGLiEgNavwiIgmmQTd+M9sR7RokfphZlZnNq/HT8zCPfc/MdJmm1AkzczObXuN+kpkVm9lrdbT/o+qVcf0NXMfCzBq7e1W065BQ7Hb3zLramZkluXvlce5Dx1ti2AkMMLMW7r4buBBYezQ7qIvjbZ8GPeIHMLPWZva2meWb2UIzuzJY3jPI+H/KzBab2Ztm1iJYt3+0ZmapZraqxjb/DPaVb2ZnBsvPM7N3zewPwEIz+4WZfbtGDfeZ2bj6fu0SPjMbZGbvm9lcM/vbvjiRwDAz+8DMFplZbvD4e8xsvJm9CUwzs5Fm9kSN/b1mZucFt58MggYXm9nPajxmlZn91Mz+BdxlZvk11vU1s7khv2yJjr8Clwa3bwJm7FthZrnBsVYQ/D4pWD7SzP5kZn8G3gz64eSgFy4ws2tq7OM+M5tvZh+aWafDVuLuDfYH2EHkXyVtg/upwCeAAT2BSiAzWPccMCy4/R6QU2ObVcHtlkDz4HZfIC+4fR6Rv8i9gvs9gfzgdiNgBdAh2v899HPcx1MVMC/4eQloAnwApAXrbwAm1TiGngpunwMsCm7fA8wFWgT3RwJP1HiO14Dzgtvtg9+Ng/0NDO6vAu6ssc27NY7jXwLfivZ/K/3U+bG3AxhIJLOseXAMnge8FqxvCyQFty8AXqhxfBXVOJZ+BTxaY78pwW8HLg9uPwD8z+HqiYWpHgN+aWbnANVAN2DfX7OVHvl0METejD2PsK8mwBNBlEQVcGKNdbPdfSWAu68ysy1mlhU8V4G7b6mD1yLR9bmpHjMbAAwA3jIziDTo9TUePwPA3f9hZm3NLDlY/qpH/rl+JNeb2Vgig5cuQD9gQbDu2RqPmwCMMrPvEvnjk3uUr0tigLsvCM4r3QT85YDV7YCpZtaXSBNvUmPdW/5ZrM0FwI019lkS3CwnMuiASC+88HC1xELj/yqQBgxy94pg2qZ5sG5vjcdVAS2C25V8No3VvMZj7gA2Evk2sEbAnhrrdh7wvBOI/LXtDEw6rlcgDZUBi939jEOsP/BDLvvu1zxWah5rEBxvZtYL+D4w2N1LzGwKnz8Wa+7jBeBu4B1grgYZce1V4CEio/0ONZb/AnjX3a8O/ji8V2NdzWPF+OJxCVDhwXCfSC88bG9v8HP8RP4Sbgqa/vlAj1psswoYFNy+9oB9rXf3amA4kRHeobwEXAQMBv52tEVLTFgGpJnZGQBm1sTM+tdYf0Ow/GygzN3LDrKPVUCmmTUys+58NlpvS+QNWxbMt158qCLcfQ+RY+xJYPLxvSRp4CYBP3f3hQcsb8dnJ3tHHmb7N4Hb992xY/zO8gbb+M0siciI/hkgx8zyiIz+P6rF5g8B3zCzD4jM8e/zW2CEmX1IZJrnwFH+fu5eTmTu9TnXVRdxKfh/fC3wKzObT2Te9cwaDykJjqHfAWMOsZuZwEpgIZHjLj/Y93ygAFhM5M0+8wjlPENkJPfmsbwWiQ3uXuTuvznIqgeA/zWzmRx+QHovkBJccDAfOP9Y6miwkQ1mdhqRk2tRme80s0ZE3sTXufvyaNQgicPMvg+0c/efRLsWiX8Nco7fzL4OjAO+E6Xn70fkRMlLavoSNjN7CegDfCnatUhiaLAjfhERCUeDneMXEZFwqPGLiCQYNX4RkQSjxi8SAjNLNrNv1rh/Xl0lMYocLzV+kXAkA9880oNEokGNXxJekNr6kZlNCD4Y84yZXWBmM81seZCc2N7MXg4SET80s4HBtveY2aQgEfbTGimu9wN9LJL7/2CwrLWZPR881zMWBASJ1LcGeR2/SBScAFwHjAXmADcDZwNXAD8C1hAJ67vKzL4ETAMyg21PJvIJyjbAMjN7ErgLGLAvFC6Ias4C+gPriHyS9yzgX6G/MpEDaMQvErHS3RcGOU6LgbeD0KuFRFJfzwamA7j7O0AHM2sXbPu6u+91983AJj5Ljz3Q7OAj+9VE4iF6hvViRA5HjV8kombSa3WN+9VE/mV8sGmZfZ9+PDAl9lD/kq7t40RCpcYvUjv/IBISuG/aZrO7bzvM47cTmfoRaXA04hCpnXuAyWa2ANgFjDjcg919S3ByeBGRr9x7PfwSRWpHWT0iIglGUz0iIglGjV9EJMGo8YuIJBg1fhGRBKPGLyKSYNT4RUQSjBq/iEiC+X9VcG3W0+2VGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x= Q1_2011_2['month'], y = Q1_2011_2['count'], data= df_2, label= 'count',ci = None )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db0af715-61d2-4c80-a91a-bf0470c04972",
   "metadata": {},
   "source": [
    "#### 2.5.  Semester Plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "06f98a04-996b-43c2-b53e-9dfa68c0cba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "S1_2011 = df_2.loc['2011-01-01':'2011-06-30']\n",
    "sns.lineplot(x= S1_2011['month'], y = S1_2011['count'], data= df_2, label= 'count',ci = None )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9539ea-768a-4933-bfa0-3df568b710f9",
   "metadata": {},
   "source": [
    "#### 2.6.  Year Plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3b05c48c-dc54-459c-b13d-df93bfd7eb27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (15,5), dpi = 80)\n",
    "\n",
    "Y1_2011 = df_2.loc['2011-01-01':'2011-12-31']\n",
    "sns.lineplot(x= df_2['month'], y = df_2['count'], data= df_2, label= 'count',ci = None )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6796ffa0-1400-4352-b837-d8b7df4cd4d9",
   "metadata": {},
   "source": [
    "#### 2.7.  Two Years Plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "86f58c79-ed39-4597-8053-9c682c295224",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_2['rolling_mean'] = df_2['count'].rolling(window=1000).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "17d1cbf1-5994-4cb5-a51a-f0594eb1141e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (15,5), dpi = 80)\n",
    "\n",
    "sns.lineplot(x= df_2.index, y = df_2['rolling_mean'], data= df_2, label= 'count',ci = None )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "169ab561-3d54-4a74-9129-7b1fa8effb30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288.347"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['rolling_mean'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a18b7966-1e46-473a-8277-4dc1867ec4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "2012-10-18 22:00:00    October\n",
       "Name: month, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['month'][df_2['rolling_mean'] == df_2['rolling_mean'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "28350d4a-68cf-47dd-bd59-d6d47a658adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.14"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['rolling_mean'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a9f4c5f5-f270-46ee-8cee-84a94be2b899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "2011-02-14 07:00:00    February\n",
       "Name: month, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['month'][df_2['rolling_mean'] == df_2['rolling_mean'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbd6959-4915-4af6-b2eb-c007454e01cf",
   "metadata": {},
   "source": [
    "### 3. Create a part_of_day column for the bike data. It should have the following labels: morning, afternoon, evening, night"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d9fea077-b593-4f36-9a75-a2511cd5e685",
   "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>...</th>\n",
       "      <th>year</th>\n",
       "      <th>month_number</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>week_day</th>\n",
       "      <th>hour_day</th>\n",
       "      <th>season_name</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>part_of_day</th>\n",
       "      <th>rolling_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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",
       "      <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>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>winter</td>\n",
       "      <td>0</td>\n",
       "      <td>night</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>winter</td>\n",
       "      <td>1</td>\n",
       "      <td>night</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2</td>\n",
       "      <td>winter</td>\n",
       "      <td>2</td>\n",
       "      <td>night</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>3</td>\n",
       "      <td>winter</td>\n",
       "      <td>3</td>\n",
       "      <td>night</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>winter</td>\n",
       "      <td>4</td>\n",
       "      <td>night</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  ...  year  \\\n",
       "datetime                                                      ...         \n",
       "2011-01-01 00:00:00        81        0.0     3.0        13.0  ...  2011   \n",
       "2011-01-01 01:00:00        80        0.0     8.0        32.0  ...  2011   \n",
       "2011-01-01 02:00:00        80        0.0     5.0        27.0  ...  2011   \n",
       "2011-01-01 03:00:00        75        0.0     3.0        10.0  ...  2011   \n",
       "2011-01-01 04:00:00        75        0.0     0.0         1.0  ...  2011   \n",
       "\n",
       "                     month_number    month day_of_month  week_day hour_day  \\\n",
       "datetime                                                                     \n",
       "2011-01-01 00:00:00             1  January            1  Saturday        0   \n",
       "2011-01-01 01:00:00             1  January            1  Saturday        1   \n",
       "2011-01-01 02:00:00             1  January            1  Saturday        2   \n",
       "2011-01-01 03:00:00             1  January            1  Saturday        3   \n",
       "2011-01-01 04:00:00             1  January            1  Saturday        4   \n",
       "\n",
       "                     season_name hour_of_day  part_of_day rolling_mean  \n",
       "datetime                                                                \n",
       "2011-01-01 00:00:00       winter           0        night          NaN  \n",
       "2011-01-01 01:00:00       winter           1        night          NaN  \n",
       "2011-01-01 02:00:00       winter           2        night          NaN  \n",
       "2011-01-01 03:00:00       winter           3        night          NaN  \n",
       "2011-01-01 04:00:00       winter           4        night          NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['hour_of_day'] =df_2.index.hour\n",
    "df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e4fbf69d-3351-4201-8c85-0d28d5a03480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 23)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['hour_of_day'].min(), df_2['hour_of_day'].max() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "750959ff-aaae-4b0c-b0f1-6254d4fbb666",
   "metadata": {},
   "source": [
    "#### Option 1: dividing the day in 3 segments as done in Latinameria: Morning, Afternoon, Evening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ee5c6fd8-a53a-49a9-9790-197a63a356de",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_labels = ['evening_2','morning', 'afternoon', 'evening']\n",
    "bin_boundaries = [-0.1, 4.9, 12, 18, 23.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2d45bb31-4597-4f03-9bba-416d9e5e7e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2['part_of_day']= pd.cut(df_2['hour_of_day'], bins=bin_boundaries, labels=time_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4a30f679-a134-4f3e-aee8-c27a9368a52b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>20</td>\n",
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       "      <td>20</td>\n",
       "      <td>evening</td>\n",
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       "      <td>1</td>\n",
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       "      <td>31</td>\n",
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       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2012-12-31 19:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 20:00:00       1        0           1        2  10.66  12.880   \n",
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       "2012-12-31 23:00:00       1        0           1        1  10.66  13.635   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  ...  year  \\\n",
       "datetime                                                      ...         \n",
       "2012-12-31 19:00:00        60    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 20:00:00        60    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 21:00:00        60    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 22:00:00        56     8.9981     NaN         NaN  ...  2012   \n",
       "2012-12-31 23:00:00        65     8.9981     NaN         NaN  ...  2012   \n",
       "\n",
       "                     month_number     month day_of_month  week_day hour_day  \\\n",
       "datetime                                                                      \n",
       "2012-12-31 19:00:00            12  December           31    Monday       19   \n",
       "2012-12-31 20:00:00            12  December           31    Monday       20   \n",
       "2012-12-31 21:00:00            12  December           31    Monday       21   \n",
       "2012-12-31 22:00:00            12  December           31    Monday       22   \n",
       "2012-12-31 23:00:00            12  December           31    Monday       23   \n",
       "\n",
       "                     season_name hour_of_day  part_of_day rolling_mean  \n",
       "datetime                                                                \n",
       "2012-12-31 19:00:00       winter          19      evening      171.682  \n",
       "2012-12-31 20:00:00       winter          20      evening      171.764  \n",
       "2012-12-31 21:00:00       winter          21      evening      171.819  \n",
       "2012-12-31 22:00:00       winter          22      evening      171.852  \n",
       "2012-12-31 23:00:00       winter          23      evening      171.869  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.tail()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 36,
   "id": "431112ac-f477-40df-8e41-57979bb8d621",
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>winter</td>\n",
       "      <td>0</td>\n",
       "      <td>evening</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>80</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>winter</td>\n",
       "      <td>1</td>\n",
       "      <td>evening</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>9.02</td>\n",
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       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2</td>\n",
       "      <td>winter</td>\n",
       "      <td>2</td>\n",
       "      <td>evening</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
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       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>3</td>\n",
       "      <td>winter</td>\n",
       "      <td>3</td>\n",
       "      <td>evening</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>winter</td>\n",
       "      <td>4</td>\n",
       "      <td>evening</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  ...  year  \\\n",
       "datetime                                                      ...         \n",
       "2011-01-01 00:00:00        81        0.0     3.0        13.0  ...  2011   \n",
       "2011-01-01 01:00:00        80        0.0     8.0        32.0  ...  2011   \n",
       "2011-01-01 02:00:00        80        0.0     5.0        27.0  ...  2011   \n",
       "2011-01-01 03:00:00        75        0.0     3.0        10.0  ...  2011   \n",
       "2011-01-01 04:00:00        75        0.0     0.0         1.0  ...  2011   \n",
       "\n",
       "                     month_number    month day_of_month  week_day hour_day  \\\n",
       "datetime                                                                     \n",
       "2011-01-01 00:00:00             1  January            1  Saturday        0   \n",
       "2011-01-01 01:00:00             1  January            1  Saturday        1   \n",
       "2011-01-01 02:00:00             1  January            1  Saturday        2   \n",
       "2011-01-01 03:00:00             1  January            1  Saturday        3   \n",
       "2011-01-01 04:00:00             1  January            1  Saturday        4   \n",
       "\n",
       "                     season_name hour_of_day  part_of_day rolling_mean  \n",
       "datetime                                                                \n",
       "2011-01-01 00:00:00       winter           0      evening          NaN  \n",
       "2011-01-01 01:00:00       winter           1      evening          NaN  \n",
       "2011-01-01 02:00:00       winter           2      evening          NaN  \n",
       "2011-01-01 03:00:00       winter           3      evening          NaN  \n",
       "2011-01-01 04:00:00       winter           4      evening          NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = df_2[df_2['part_of_day']== 'evening_2'].replace({'evening_2': 'evening'})\n",
    "df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fa241eeb-a62e-4de9-84f0-826adc3d9f7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['evening']\n",
       "Categories (3, object): ['morning' < 'afternoon' < 'evening']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['part_of_day'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6131b292-fc5d-4774-a39e-f97f6192fca1",
   "metadata": {},
   "source": [
    "#### Option 2: dividing the day in 4 segments: Morning, Afternoon, Evening, night."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c19827f2-2944-46a9-a6b3-45b4cfe3485b",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_labels = ['night','morning', 'afternoon', 'evening']\n",
    "bin_boundaries = [-0.1, 4.9, 12, 18, 23.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2367aa01-567d-418c-ae5b-9cfc04ccf0f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2['part_of_day']= pd.cut(df_2['hour_of_day'], bins=bin_boundaries, labels=time_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "576f68d6-74bf-4edb-8220-514f84e1ba5a",
   "metadata": {},
   "outputs": [
    {
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       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  ...  year  \\\n",
       "datetime                                                      ...         \n",
       "2011-01-01 00:00:00        81        0.0     3.0        13.0  ...  2011   \n",
       "2011-01-01 01:00:00        80        0.0     8.0        32.0  ...  2011   \n",
       "2011-01-01 02:00:00        80        0.0     5.0        27.0  ...  2011   \n",
       "2011-01-01 03:00:00        75        0.0     3.0        10.0  ...  2011   \n",
       "2011-01-01 04:00:00        75        0.0     0.0         1.0  ...  2011   \n",
       "\n",
       "                     month_number    month day_of_month  week_day hour_day  \\\n",
       "datetime                                                                     \n",
       "2011-01-01 00:00:00             1  January            1  Saturday        0   \n",
       "2011-01-01 01:00:00             1  January            1  Saturday        1   \n",
       "2011-01-01 02:00:00             1  January            1  Saturday        2   \n",
       "2011-01-01 03:00:00             1  January            1  Saturday        3   \n",
       "2011-01-01 04:00:00             1  January            1  Saturday        4   \n",
       "\n",
       "                     season_name hour_of_day  part_of_day rolling_mean  \n",
       "datetime                                                                \n",
       "2011-01-01 00:00:00       winter           0        night          NaN  \n",
       "2011-01-01 01:00:00       winter           1        night          NaN  \n",
       "2011-01-01 02:00:00       winter           2        night          NaN  \n",
       "2011-01-01 03:00:00       winter           3        night          NaN  \n",
       "2011-01-01 04:00:00       winter           4        night          NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
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       "      <th>2012-12-31 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.38</td>\n",
       "      <td>9.090</td>\n",
       "      <td>55</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "      <td>winter</td>\n",
       "      <td>0</td>\n",
       "      <td>night</td>\n",
       "      <td>175.458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.38</td>\n",
       "      <td>9.090</td>\n",
       "      <td>55</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>winter</td>\n",
       "      <td>1</td>\n",
       "      <td>night</td>\n",
       "      <td>175.332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.335</td>\n",
       "      <td>59</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2</td>\n",
       "      <td>winter</td>\n",
       "      <td>2</td>\n",
       "      <td>night</td>\n",
       "      <td>174.949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.56</td>\n",
       "      <td>9.090</td>\n",
       "      <td>59</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>winter</td>\n",
       "      <td>3</td>\n",
       "      <td>night</td>\n",
       "      <td>174.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.74</td>\n",
       "      <td>8.335</td>\n",
       "      <td>69</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>4</td>\n",
       "      <td>winter</td>\n",
       "      <td>4</td>\n",
       "      <td>night</td>\n",
       "      <td>174.012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather  temp  atemp  \\\n",
       "datetime                                                                 \n",
       "2012-12-31 00:00:00       1        0           1        1  7.38  9.090   \n",
       "2012-12-31 01:00:00       1        0           1        1  7.38  9.090   \n",
       "2012-12-31 02:00:00       1        0           1        1  6.56  8.335   \n",
       "2012-12-31 03:00:00       1        0           1        1  6.56  9.090   \n",
       "2012-12-31 04:00:00       1        0           1        1  5.74  8.335   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  ...  year  \\\n",
       "datetime                                                      ...         \n",
       "2012-12-31 00:00:00        55    12.9980     NaN         NaN  ...  2012   \n",
       "2012-12-31 01:00:00        55    12.9980     NaN         NaN  ...  2012   \n",
       "2012-12-31 02:00:00        59    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 03:00:00        59     7.0015     NaN         NaN  ...  2012   \n",
       "2012-12-31 04:00:00        69     7.0015     NaN         NaN  ...  2012   \n",
       "\n",
       "                     month_number     month day_of_month  week_day hour_day  \\\n",
       "datetime                                                                      \n",
       "2012-12-31 00:00:00            12  December           31    Monday        0   \n",
       "2012-12-31 01:00:00            12  December           31    Monday        1   \n",
       "2012-12-31 02:00:00            12  December           31    Monday        2   \n",
       "2012-12-31 03:00:00            12  December           31    Monday        3   \n",
       "2012-12-31 04:00:00            12  December           31    Monday        4   \n",
       "\n",
       "                     season_name hour_of_day  part_of_day rolling_mean  \n",
       "datetime                                                                \n",
       "2012-12-31 00:00:00       winter           0        night      175.458  \n",
       "2012-12-31 01:00:00       winter           1        night      175.332  \n",
       "2012-12-31 02:00:00       winter           2        night      174.949  \n",
       "2012-12-31 03:00:00       winter           3        night      174.288  \n",
       "2012-12-31 04:00:00       winter           4        night      174.012  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6125c7-daa6-4c26-a040-cc0f09d082dd",
   "metadata": {},
   "source": [
    "### 4. Group by a time feature and observe grouped means"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1961aa4-315c-4227-9f10-a9277c985a28",
   "metadata": {},
   "source": [
    "#### Group by a time feature and observe grouped means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "41a9579f-d864-4726-b645-b304dea5167a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011    18.427534\n",
       "2012    18.982524\n",
       "Name: temp, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.groupby('year')['temp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0b44c09c-4b28-44b3-84c8-7870c88cda09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  month    \n",
       "2011  April        17.291544\n",
       "      August       26.726533\n",
       "      December     12.138158\n",
       "      February     10.056860\n",
       "      January       7.283150\n",
       "      July         28.636516\n",
       "      June         25.950267\n",
       "      March        11.944476\n",
       "      May          21.039613\n",
       "      November     14.875570\n",
       "      October      17.614026\n",
       "      September    23.696327\n",
       "2012  April        17.275405\n",
       "      August       27.001806\n",
       "      December     12.433987\n",
       "      February     11.846383\n",
       "      January      10.320132\n",
       "      July         28.430194\n",
       "      June         25.042800\n",
       "      March        17.007013\n",
       "      May          23.049935\n",
       "      November     12.061757\n",
       "      October      19.011644\n",
       "      September    23.348133\n",
       "Name: temp, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.groupby(['year','month'])['temp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7daacbb1-4737-44cc-b00e-17a6206fea33",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011     8.636452\n",
       "2012    11.054608\n",
       "Name: temp, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2[(df_2['month'] == 'January') | (df_2['month'] == 'February')].groupby('year')['temp'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7fae691c-a249-4b1a-9c78-194efe1d3da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011    27.315410\n",
       "2012    26.764262\n",
       "Name: temp, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2[(df_2['month'] == 'June') | (df_2['month'] == 'July')].groupby('year')['temp'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "63d871c8-7294-4a8e-8e74-633a84b1075e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011    73.30879\n",
       "2012    70.04870\n",
       "Name: humidity, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.groupby('year')['humidity'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ca567d15-3699-4b82-b98f-e74e1541e895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011    10.444967\n",
       "2012    10.510068\n",
       "Name: windspeed, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.groupby('year')['windspeed'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6445d0df-2ae8-4939-9b4f-0ee2b501178f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011    40552\n",
       "2012    53656\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.groupby('year')['count'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd9a452-3e36-457f-bddf-1c416252f92d",
   "metadata": {},
   "source": [
    "#### Make subset of data which only includes January 2011 observation. Add rolling average of the count to the new subset dataframe and plot the rolling average and count versus time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ee4b2e80-c6cc-4034-9ff2-e70e14b09f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "january_2011= df_2[((df_2['month'] == 'January') & (df_2['year'] == 2011))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6c317dcd-c4cf-47c5-b582-6fbe76c14984",
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling_mean_jan_2011 = january_2011['count'].rolling(window=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c9b15e6c-052e-4d25-8faa-988f7a3c06da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize= (15,5), dpi = 80)\n",
    "\n",
    "sns.lineplot(x= january_2011['day_of_month'], y = rolling_mean_jan_2011, data= df_2, label= 'count',ci = None )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a7e1efa9-ab96-4f36-af52-153383101713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp',\n",
       "       'humidity', 'windspeed', 'casual', 'registered', 'count', 'year',\n",
       "       'month_number', 'month', 'day_of_month', 'week_day', 'hour_day',\n",
       "       'season_name', 'hour_of_day', 'part_of_day', 'rolling_mean'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "91d41dc1-4a1f-49d2-81c2-e7c981050512",
   "metadata": {
    "tags": []
   },
   "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>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>32.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 01:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3559 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     registered  count\n",
       "datetime                              \n",
       "2011-01-01 00:00:00        13.0     16\n",
       "2011-01-01 01:00:00        32.0     40\n",
       "2011-01-01 02:00:00        27.0     32\n",
       "2011-01-01 03:00:00        10.0     13\n",
       "2011-01-01 04:00:00         1.0      1\n",
       "...                         ...    ...\n",
       "2012-12-31 00:00:00         NaN      9\n",
       "2012-12-31 01:00:00         NaN      5\n",
       "2012-12-31 02:00:00         NaN      3\n",
       "2012-12-31 03:00:00         NaN      2\n",
       "2012-12-31 04:00:00         NaN      2\n",
       "\n",
       "[3559 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop columns that are not needed\n",
    "\n",
    "df_dt2 = df_2.drop(['season', 'holiday','workingday','weather','temp','atemp','humidity','windspeed','casual','year','month_number', 'month', 'day_of_month', 'week_day', 'hour_day','season_name', 'hour_of_day', 'part_of_day', 'rolling_mean'], axis=1)\n",
    "df_dt2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d7ee1833-f6c7-4810-b35d-5cdacafa3298",
   "metadata": {
    "tags": []
   },
   "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>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>week_day</th>\n",
       "      <th>month_number</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>2011-01-01 00:00:00</th>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>40</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 00:00:00</th>\n",
       "      <td>9</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 01:00:00</th>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 02:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 03:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 04:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3559 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  year     month  day_of_month  week_day  \\\n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00     16  2011   January             1  Saturday   \n",
       "2011-01-01 01:00:00     40  2011   January             1  Saturday   \n",
       "2011-01-01 02:00:00     32  2011   January             1  Saturday   \n",
       "2011-01-01 03:00:00     13  2011   January             1  Saturday   \n",
       "2011-01-01 04:00:00      1  2011   January             1  Saturday   \n",
       "...                    ...   ...       ...           ...       ...   \n",
       "2012-12-31 00:00:00      9  2012  December            31    Monday   \n",
       "2012-12-31 01:00:00      5  2012  December            31    Monday   \n",
       "2012-12-31 02:00:00      3  2012  December            31    Monday   \n",
       "2012-12-31 03:00:00      2  2012  December            31    Monday   \n",
       "2012-12-31 04:00:00      2  2012  December            31    Monday   \n",
       "\n",
       "                     month_number  \n",
       "datetime                           \n",
       "2011-01-01 00:00:00             1  \n",
       "2011-01-01 01:00:00             1  \n",
       "2011-01-01 02:00:00             1  \n",
       "2011-01-01 03:00:00             1  \n",
       "2011-01-01 04:00:00             1  \n",
       "...                           ...  \n",
       "2012-12-31 00:00:00            12  \n",
       "2012-12-31 01:00:00            12  \n",
       "2012-12-31 02:00:00            12  \n",
       "2012-12-31 03:00:00            12  \n",
       "2012-12-31 04:00:00            12  \n",
       "\n",
       "[3559 rows x 6 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting columns option to reduce the table\n",
    "\n",
    "df_dt2_2= df_2[['count','year','month','day_of_month','week_day','month_number']]\n",
    "df_dt2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0fccabd8-001d-4257-b749-6ee9af51f9e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>January</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "                     count  year    month  day_of_month  week_day  \\\n",
       "datetime                                                            \n",
       "2011-01-01 00:00:00     16  2011  January             1  Saturday   \n",
       "2011-01-01 01:00:00     40  2011  January             1  Saturday   \n",
       "2011-01-01 02:00:00     32  2011  January             1  Saturday   \n",
       "2011-01-01 03:00:00     13  2011  January             1  Saturday   \n",
       "2011-01-01 04:00:00      1  2011  January             1  Saturday   \n",
       "\n",
       "                     month_number  \n",
       "datetime                           \n",
       "2011-01-01 00:00:00             1  \n",
       "2011-01-01 01:00:00             1  \n",
       "2011-01-01 02:00:00             1  \n",
       "2011-01-01 03:00:00             1  \n",
       "2011-01-01 04:00:00             1  "
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   ],
   "source": [
    "df_dt2_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3da226aa-a652-4c13-806a-5586f20a2484",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yq/6pm1lcds6ln5qlsjc5y8fly00000gn/T/ipykernel_4536/303696026.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_dt2_2['year']= df_dt2_2['year'].astype(str)\n"
     ]
    }
   ],
   "source": [
    "# Transform 'year' column type from 'int' to 'str' to later concatinate.\n",
    "\n",
    "df_dt2_2['year']= df_dt2_2['year'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4bde8134-ed93-431a-9428-baca5eae20fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 3559 entries, 2011-01-01 00:00:00 to 2012-12-31 04:00:00\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   count         3559 non-null   int64 \n",
      " 1   year          3559 non-null   object\n",
      " 2   month         3559 non-null   object\n",
      " 3   day_of_month  3559 non-null   int64 \n",
      " 4   week_day      3559 non-null   object\n",
      " 5   month_number  3559 non-null   int64 \n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 194.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_dt2_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "36eb1069-e5fd-4254-ae85-76d08a2dea51",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yq/6pm1lcds6ln5qlsjc5y8fly00000gn/T/ipykernel_4536/566047724.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_dt2_2['month_number']= df_dt2_2['month_number'].astype(str)\n"
     ]
    }
   ],
   "source": [
    "# Transform 'month_number' column type from 'int' to 'str' to later concatinate.\n",
    "\n",
    "df_dt2_2['month_number']= df_dt2_2['month_number'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1e29d0dc-f0c4-4e53-b207-b601313a2cab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 3559 entries, 2011-01-01 00:00:00 to 2012-12-31 04:00:00\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   count         3559 non-null   int64 \n",
      " 1   year          3559 non-null   object\n",
      " 2   month         3559 non-null   object\n",
      " 3   day_of_month  3559 non-null   int64 \n",
      " 4   week_day      3559 non-null   object\n",
      " 5   month_number  3559 non-null   object\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 194.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_dt2_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "07dd1a9f-597d-4a20-9346-5aa4b0f14501",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yq/6pm1lcds6ln5qlsjc5y8fly00000gn/T/ipykernel_4536/823846508.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_dt2_2['date'] = pd.to_datetime(df_dt2_2['year'] + '/' + df_dt2_2['month_number'])\n"
     ]
    },
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <th>2012-12-31 01:00:00</th>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
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       "      <td>12</td>\n",
       "      <td>2012-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 02:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
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       "      <td>12</td>\n",
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       "      <th>2012-12-31 03:00:00</th>\n",
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       "      <td>2012-12-01</td>\n",
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       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "      <td>2012-12-01</td>\n",
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       "<p>3559 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "                     count  year     month  day_of_month  week_day  \\\n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00     16  2011   January             1  Saturday   \n",
       "2011-01-01 01:00:00     40  2011   January             1  Saturday   \n",
       "2011-01-01 02:00:00     32  2011   January             1  Saturday   \n",
       "2011-01-01 03:00:00     13  2011   January             1  Saturday   \n",
       "2011-01-01 04:00:00      1  2011   January             1  Saturday   \n",
       "...                    ...   ...       ...           ...       ...   \n",
       "2012-12-31 00:00:00      9  2012  December            31    Monday   \n",
       "2012-12-31 01:00:00      5  2012  December            31    Monday   \n",
       "2012-12-31 02:00:00      3  2012  December            31    Monday   \n",
       "2012-12-31 03:00:00      2  2012  December            31    Monday   \n",
       "2012-12-31 04:00:00      2  2012  December            31    Monday   \n",
       "\n",
       "                    month_number       date  \n",
       "datetime                                     \n",
       "2011-01-01 00:00:00            1 2011-01-01  \n",
       "2011-01-01 01:00:00            1 2011-01-01  \n",
       "2011-01-01 02:00:00            1 2011-01-01  \n",
       "2011-01-01 03:00:00            1 2011-01-01  \n",
       "2011-01-01 04:00:00            1 2011-01-01  \n",
       "...                          ...        ...  \n",
       "2012-12-31 00:00:00           12 2012-12-01  \n",
       "2012-12-31 01:00:00           12 2012-12-01  \n",
       "2012-12-31 02:00:00           12 2012-12-01  \n",
       "2012-12-31 03:00:00           12 2012-12-01  \n",
       "2012-12-31 04:00:00           12 2012-12-01  \n",
       "\n",
       "[3559 rows x 7 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatinate 'year' and 'month_number' to create a new column called 'date' as \n",
    "# string type.\n",
    "\n",
    "df_dt2_2['date'] = pd.to_datetime(df_dt2_2['year'] + '/' + df_dt2_2['month_number'])\n",
    "df_dt2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "47888fd3-8302-42b8-8117-e71f6ee3d2f3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yq/6pm1lcds6ln5qlsjc5y8fly00000gn/T/ipykernel_4536/2812342474.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_dt2_2['date'] = pd.to_datetime(df_dt2_2['date'])\n"
     ]
    },
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       "                     count  year     month  day_of_month  week_day  \\\n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00     16  2011   January             1  Saturday   \n",
       "2011-01-01 01:00:00     40  2011   January             1  Saturday   \n",
       "2011-01-01 02:00:00     32  2011   January             1  Saturday   \n",
       "2011-01-01 03:00:00     13  2011   January             1  Saturday   \n",
       "2011-01-01 04:00:00      1  2011   January             1  Saturday   \n",
       "...                    ...   ...       ...           ...       ...   \n",
       "2012-12-31 00:00:00      9  2012  December            31    Monday   \n",
       "2012-12-31 01:00:00      5  2012  December            31    Monday   \n",
       "2012-12-31 02:00:00      3  2012  December            31    Monday   \n",
       "2012-12-31 03:00:00      2  2012  December            31    Monday   \n",
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       "\n",
       "                    month_number       date  \n",
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       "2011-01-01 00:00:00            1 2011-01-01  \n",
       "2011-01-01 01:00:00            1 2011-01-01  \n",
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       "2011-01-01 03:00:00            1 2011-01-01  \n",
       "2011-01-01 04:00:00            1 2011-01-01  \n",
       "...                          ...        ...  \n",
       "2012-12-31 00:00:00           12 2012-12-01  \n",
       "2012-12-31 01:00:00           12 2012-12-01  \n",
       "2012-12-31 02:00:00           12 2012-12-01  \n",
       "2012-12-31 03:00:00           12 2012-12-01  \n",
       "2012-12-31 04:00:00           12 2012-12-01  \n",
       "\n",
       "[3559 rows x 7 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform 'date' column into a date.time type.\n",
    "\n",
    "df_dt2_2['date'] = pd.to_datetime(df_dt2_2['date'])\n",
    "df_dt2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a92d18db-dc78-4dcc-89c5-9d371cc0e62a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 3559 entries, 2011-01-01 00:00:00 to 2012-12-31 04:00:00\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   count         3559 non-null   int64         \n",
      " 1   year          3559 non-null   object        \n",
      " 2   month         3559 non-null   object        \n",
      " 3   day_of_month  3559 non-null   int64         \n",
      " 4   week_day      3559 non-null   object        \n",
      " 5   month_number  3559 non-null   object        \n",
      " 6   date          3559 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(2), object(4)\n",
      "memory usage: 222.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_dt2_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d378640d-3828-445d-bfde-d7993ef45d1c",
   "metadata": {
    "tags": []
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       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>3</td>\n",
       "      <td>winter</td>\n",
       "      <td>3</td>\n",
       "      <td>night</td>\n",
       "      <td>174.288</td>\n",
       "      <td>2012-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.74</td>\n",
       "      <td>8.335</td>\n",
       "      <td>69</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>4</td>\n",
       "      <td>winter</td>\n",
       "      <td>4</td>\n",
       "      <td>night</td>\n",
       "      <td>174.012</td>\n",
       "      <td>2012-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3559 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "...                     ...      ...         ...      ...   ...     ...   \n",
       "2012-12-31 00:00:00       1        0           1        1  7.38   9.090   \n",
       "2012-12-31 01:00:00       1        0           1        1  7.38   9.090   \n",
       "2012-12-31 02:00:00       1        0           1        1  6.56   8.335   \n",
       "2012-12-31 03:00:00       1        0           1        1  6.56   9.090   \n",
       "2012-12-31 04:00:00       1        0           1        1  5.74   8.335   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  ...  \\\n",
       "datetime                                                      ...   \n",
       "2011-01-01 00:00:00        81     0.0000     3.0        13.0  ...   \n",
       "2011-01-01 01:00:00        80     0.0000     8.0        32.0  ...   \n",
       "2011-01-01 02:00:00        80     0.0000     5.0        27.0  ...   \n",
       "2011-01-01 03:00:00        75     0.0000     3.0        10.0  ...   \n",
       "2011-01-01 04:00:00        75     0.0000     0.0         1.0  ...   \n",
       "...                       ...        ...     ...         ...  ...   \n",
       "2012-12-31 00:00:00        55    12.9980     NaN         NaN  ...   \n",
       "2012-12-31 01:00:00        55    12.9980     NaN         NaN  ...   \n",
       "2012-12-31 02:00:00        59    11.0014     NaN         NaN  ...   \n",
       "2012-12-31 03:00:00        59     7.0015     NaN         NaN  ...   \n",
       "2012-12-31 04:00:00        69     7.0015     NaN         NaN  ...   \n",
       "\n",
       "                     month_number     month  day_of_month  week_day  hour_day  \\\n",
       "datetime                                                                        \n",
       "2011-01-01 00:00:00             1   January             1  Saturday         0   \n",
       "2011-01-01 01:00:00             1   January             1  Saturday         1   \n",
       "2011-01-01 02:00:00             1   January             1  Saturday         2   \n",
       "2011-01-01 03:00:00             1   January             1  Saturday         3   \n",
       "2011-01-01 04:00:00             1   January             1  Saturday         4   \n",
       "...                           ...       ...           ...       ...       ...   \n",
       "2012-12-31 00:00:00            12  December            31    Monday         0   \n",
       "2012-12-31 01:00:00            12  December            31    Monday         1   \n",
       "2012-12-31 02:00:00            12  December            31    Monday         2   \n",
       "2012-12-31 03:00:00            12  December            31    Monday         3   \n",
       "2012-12-31 04:00:00            12  December            31    Monday         4   \n",
       "\n",
       "                    season_name  hour_of_day part_of_day  rolling_mean  \\\n",
       "datetime                                                                 \n",
       "2011-01-01 00:00:00      winter            0       night           NaN   \n",
       "2011-01-01 01:00:00      winter            1       night           NaN   \n",
       "2011-01-01 02:00:00      winter            2       night           NaN   \n",
       "2011-01-01 03:00:00      winter            3       night           NaN   \n",
       "2011-01-01 04:00:00      winter            4       night           NaN   \n",
       "...                         ...          ...         ...           ...   \n",
       "2012-12-31 00:00:00      winter            0       night       175.458   \n",
       "2012-12-31 01:00:00      winter            1       night       175.332   \n",
       "2012-12-31 02:00:00      winter            2       night       174.949   \n",
       "2012-12-31 03:00:00      winter            3       night       174.288   \n",
       "2012-12-31 04:00:00      winter            4       night       174.012   \n",
       "\n",
       "                        date  \n",
       "datetime                      \n",
       "2011-01-01 00:00:00  2011-01  \n",
       "2011-01-01 01:00:00  2011-01  \n",
       "2011-01-01 02:00:00  2011-01  \n",
       "2011-01-01 03:00:00  2011-01  \n",
       "2011-01-01 04:00:00  2011-01  \n",
       "...                      ...  \n",
       "2012-12-31 00:00:00  2012-12  \n",
       "2012-12-31 01:00:00  2012-12  \n",
       "2012-12-31 02:00:00  2012-12  \n",
       "2012-12-31 03:00:00  2012-12  \n",
       "2012-12-31 04:00:00  2012-12  \n",
       "\n",
       "[3559 rows x 22 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column only with year and month\n",
    "\n",
    "df_2['date'] = df_2.index.to_period('M')\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "df6caf2b-5aa9-4589-a032-da99d0d56028",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_day</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01</th>\n",
       "      <td>7.294118</td>\n",
       "      <td>4.636364</td>\n",
       "      <td>16.16</td>\n",
       "      <td>14.041667</td>\n",
       "      <td>7.714286</td>\n",
       "      <td>4.272727</td>\n",
       "      <td>4.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02</th>\n",
       "      <td>10.312500</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>19.80</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>7.470588</td>\n",
       "      <td>4.846154</td>\n",
       "      <td>5.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03</th>\n",
       "      <td>8.166667</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.10</td>\n",
       "      <td>24.421053</td>\n",
       "      <td>5.260870</td>\n",
       "      <td>5.958333</td>\n",
       "      <td>5.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04</th>\n",
       "      <td>15.200000</td>\n",
       "      <td>9.789474</td>\n",
       "      <td>26.20</td>\n",
       "      <td>31.050000</td>\n",
       "      <td>15.850000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>8.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05</th>\n",
       "      <td>23.700000</td>\n",
       "      <td>33.120000</td>\n",
       "      <td>54.75</td>\n",
       "      <td>50.200000</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>11.840000</td>\n",
       "      <td>11.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06</th>\n",
       "      <td>21.950000</td>\n",
       "      <td>15.150000</td>\n",
       "      <td>66.80</td>\n",
       "      <td>71.700000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>17.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07</th>\n",
       "      <td>27.800000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>64.44</td>\n",
       "      <td>77.040000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>17.550000</td>\n",
       "      <td>18.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08</th>\n",
       "      <td>21.400000</td>\n",
       "      <td>14.520000</td>\n",
       "      <td>61.80</td>\n",
       "      <td>57.733333</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>16.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.210526</td>\n",
       "      <td>56.75</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>16.125000</td>\n",
       "      <td>11.157895</td>\n",
       "      <td>12.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10</th>\n",
       "      <td>23.900000</td>\n",
       "      <td>14.440000</td>\n",
       "      <td>50.68</td>\n",
       "      <td>45.480000</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>14.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11</th>\n",
       "      <td>18.150000</td>\n",
       "      <td>11.631579</td>\n",
       "      <td>37.25</td>\n",
       "      <td>53.400000</td>\n",
       "      <td>19.550000</td>\n",
       "      <td>10.520000</td>\n",
       "      <td>10.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>15.600000</td>\n",
       "      <td>9.368421</td>\n",
       "      <td>38.60</td>\n",
       "      <td>40.736842</td>\n",
       "      <td>11.520000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>10.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01</th>\n",
       "      <td>13.050000</td>\n",
       "      <td>9.041667</td>\n",
       "      <td>27.60</td>\n",
       "      <td>38.280000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>6.478261</td>\n",
       "      <td>6.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02</th>\n",
       "      <td>16.200000</td>\n",
       "      <td>10.777778</td>\n",
       "      <td>35.05</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.842105</td>\n",
       "      <td>9.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03</th>\n",
       "      <td>17.480000</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>45.24</td>\n",
       "      <td>49.947368</td>\n",
       "      <td>17.640000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>11.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04</th>\n",
       "      <td>23.100000</td>\n",
       "      <td>12.083333</td>\n",
       "      <td>50.55</td>\n",
       "      <td>48.360000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>13.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05</th>\n",
       "      <td>32.100000</td>\n",
       "      <td>21.650000</td>\n",
       "      <td>69.85</td>\n",
       "      <td>73.200000</td>\n",
       "      <td>22.240000</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>15.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06</th>\n",
       "      <td>32.560000</td>\n",
       "      <td>18.050000</td>\n",
       "      <td>62.72</td>\n",
       "      <td>87.600000</td>\n",
       "      <td>23.550000</td>\n",
       "      <td>16.400000</td>\n",
       "      <td>21.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07</th>\n",
       "      <td>30.650000</td>\n",
       "      <td>17.240000</td>\n",
       "      <td>68.15</td>\n",
       "      <td>74.120000</td>\n",
       "      <td>24.400000</td>\n",
       "      <td>18.440000</td>\n",
       "      <td>35.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08</th>\n",
       "      <td>28.640000</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>73.90</td>\n",
       "      <td>76.150000</td>\n",
       "      <td>23.320000</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>20.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09</th>\n",
       "      <td>28.950000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>77.36</td>\n",
       "      <td>79.640000</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>12.950000</td>\n",
       "      <td>16.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10</th>\n",
       "      <td>26.800000</td>\n",
       "      <td>20.238095</td>\n",
       "      <td>75.00</td>\n",
       "      <td>68.550000</td>\n",
       "      <td>20.850000</td>\n",
       "      <td>13.650000</td>\n",
       "      <td>17.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11</th>\n",
       "      <td>20.120000</td>\n",
       "      <td>14.550000</td>\n",
       "      <td>55.15</td>\n",
       "      <td>67.550000</td>\n",
       "      <td>22.260870</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>34.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>8.208333</td>\n",
       "      <td>41.12</td>\n",
       "      <td>47.320000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>12.526316</td>\n",
       "      <td>12.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count                                                       \\\n",
       "week_day     Friday     Monday Saturday     Sunday   Thursday    Tuesday   \n",
       "date                                                                       \n",
       "2011-01    7.294118   4.636364    16.16  14.041667   7.714286   4.272727   \n",
       "2011-02   10.312500  10.500000    19.80  23.100000   7.470588   4.846154   \n",
       "2011-03    8.166667   6.000000    22.10  24.421053   5.260870   5.958333   \n",
       "2011-04   15.200000   9.789474    26.20  31.050000  15.850000  11.400000   \n",
       "2011-05   23.700000  33.120000    54.75  50.200000  16.100000  11.840000   \n",
       "2011-06   21.950000  15.150000    66.80  71.700000  19.840000  13.800000   \n",
       "2011-07   27.800000  32.000000    64.44  77.040000  21.300000  17.550000   \n",
       "2011-08   21.400000  14.520000    61.80  57.733333  18.400000  13.800000   \n",
       "2011-09   20.000000  20.210526    56.75  61.800000  16.125000  11.157895   \n",
       "2011-10   23.900000  14.440000    50.68  45.480000  16.850000  11.900000   \n",
       "2011-11   18.150000  11.631579    37.25  53.400000  19.550000  10.520000   \n",
       "2011-12   15.600000   9.368421    38.60  40.736842  11.520000  10.300000   \n",
       "2012-01   13.050000   9.041667    27.60  38.280000   5.700000   6.478261   \n",
       "2012-02   16.200000  10.777778    35.05  31.000000  11.000000   8.842105   \n",
       "2012-03   17.480000  11.050000    45.24  49.947368  17.640000   8.400000   \n",
       "2012-04   23.100000  12.083333    50.55  48.360000  12.600000  10.300000   \n",
       "2012-05   32.100000  21.650000    69.85  73.200000  22.240000  15.400000   \n",
       "2012-06   32.560000  18.050000    62.72  87.600000  23.550000  16.400000   \n",
       "2012-07   30.650000  17.240000    68.15  74.120000  24.400000  18.440000   \n",
       "2012-08   28.640000  15.200000    73.90  76.150000  23.320000  16.850000   \n",
       "2012-09   28.950000  25.750000    77.36  79.640000  21.900000  12.950000   \n",
       "2012-10   26.800000  20.238095    75.00  68.550000  20.850000  13.650000   \n",
       "2012-11   20.120000  14.550000    55.15  67.550000  22.260870  13.500000   \n",
       "2012-12   16.500000   8.208333    41.12  47.320000  13.200000  12.526316   \n",
       "\n",
       "                     \n",
       "week_day  Wednesday  \n",
       "date                 \n",
       "2011-01    4.785714  \n",
       "2011-02    5.352941  \n",
       "2011-03    5.434783  \n",
       "2011-04    8.600000  \n",
       "2011-05   11.750000  \n",
       "2011-06   17.280000  \n",
       "2011-07   18.950000  \n",
       "2011-08   16.280000  \n",
       "2011-09   12.750000  \n",
       "2011-10   14.684211  \n",
       "2011-11   10.480000  \n",
       "2011-12   10.052632  \n",
       "2012-01    6.450000  \n",
       "2012-02    9.208333  \n",
       "2012-03   11.300000  \n",
       "2012-04   13.473684  \n",
       "2012-05   15.440000  \n",
       "2012-06   21.250000  \n",
       "2012-07   35.550000  \n",
       "2012-08   20.680000  \n",
       "2012-09   16.150000  \n",
       "2012-10   17.760000  \n",
       "2012-11   34.650000  \n",
       "2012-12   12.100000  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.pivot_table(values=['count'],\n",
    "                      columns='week_day',\n",
    "                      index='date',\n",
    "                      aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2777eca6-def7-49e7-bd3c-ad4dd8971da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table = df_2.pivot_table(values=['count', 'registered','casual'] ,\n",
    "                      columns='week_day',\n",
    "                      aggfunc='mean');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0bc777ec-adcc-4a22-a67c-9a404799e462",
   "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>week_day</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>3.929712</td>\n",
       "      <td>4.490506</td>\n",
       "      <td>8.878788</td>\n",
       "      <td>11.128049</td>\n",
       "      <td>2.550314</td>\n",
       "      <td>2.434084</td>\n",
       "      <td>2.310127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21.135029</td>\n",
       "      <td>15.367793</td>\n",
       "      <td>49.624762</td>\n",
       "      <td>53.881783</td>\n",
       "      <td>16.687747</td>\n",
       "      <td>11.987830</td>\n",
       "      <td>14.788119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>16.888179</td>\n",
       "      <td>10.958861</td>\n",
       "      <td>40.981818</td>\n",
       "      <td>44.472561</td>\n",
       "      <td>11.940252</td>\n",
       "      <td>8.675241</td>\n",
       "      <td>11.898734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "week_day       Friday     Monday   Saturday     Sunday   Thursday    Tuesday  \\\n",
       "casual       3.929712   4.490506   8.878788  11.128049   2.550314   2.434084   \n",
       "count       21.135029  15.367793  49.624762  53.881783  16.687747  11.987830   \n",
       "registered  16.888179  10.958861  40.981818  44.472561  11.940252   8.675241   \n",
       "\n",
       "week_day    Wednesday  \n",
       "casual       2.310127  \n",
       "count       14.788119  \n",
       "registered  11.898734  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0d737c9b-7a82-499d-9185-7332508471af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2.pivot_table(values=['count', 'registered','casual'] ,\n",
    "                      columns='week_day',\n",
    "                      aggfunc='mean').plot(kind='bar');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}