3. Datetime_manipulation_visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac03ab65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7.7 (default, Mar 23 2020, 17:31:31) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "Numpy version: 1.19.2\n",
      "Pandas version: 1.2.4\n",
      "Matplotlib version: 3.3.4\n",
      "Seaborn version: 0.11.1\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.version)\n",
    "\n",
    "#array and vector processing \n",
    "import numpy as np\n",
    "print(f'Numpy version:', np.__version__)\n",
    "\n",
    "import pandas as pd\n",
    "print(f'Pandas version:', pd.__version__)\n",
    "\n",
    "# Visualisation\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "print(f'Matplotlib version:', mpl.__version__)\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "print(f'Seaborn version:', sns.__version__)\n",
    "\n",
    "#using the datetime object properties in pandas\n",
    "import datetime as dt\n",
    "from datetime import timedelta\n",
    "\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3feaef17",
   "metadata": {},
   "source": [
    "### Reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "138ead5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/2_new_features_outliers_removed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50bea486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'index', 'id', 'vendor_id', 'pickup_datetime',\n",
       "       'dropoff_datetime', 'passenger_count', 'pickup_longitude',\n",
       "       'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude',\n",
       "       'store_and_fwd_flag', 'duration', 'distance', 'manhattan_dist',\n",
       "       'duration_hours', 'duration_min', 'dur_to_dist_m_hpkm',\n",
       "       'dur_to_dist_m_spkm', 'speed_kmph_m', 'day_name', 'hour', 'price',\n",
       "       'month_name', 'day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "093c4063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1033660 entries, 0 to 1033659\n",
      "Data columns (total 25 columns):\n",
      " #   Column              Non-Null Count    Dtype  \n",
      "---  ------              --------------    -----  \n",
      " 0   Unnamed: 0          1033660 non-null  int64  \n",
      " 1   index               1033660 non-null  int64  \n",
      " 2   id                  1033660 non-null  object \n",
      " 3   vendor_id           1033660 non-null  int64  \n",
      " 4   pickup_datetime     1033660 non-null  object \n",
      " 5   dropoff_datetime    1033660 non-null  object \n",
      " 6   passenger_count     1033660 non-null  int64  \n",
      " 7   pickup_longitude    1033660 non-null  float64\n",
      " 8   pickup_latitude     1033660 non-null  float64\n",
      " 9   dropoff_longitude   1033660 non-null  float64\n",
      " 10  dropoff_latitude    1033660 non-null  float64\n",
      " 11  store_and_fwd_flag  1033660 non-null  bool   \n",
      " 12  duration            1033660 non-null  int64  \n",
      " 13  distance            1033660 non-null  float64\n",
      " 14  manhattan_dist      1033660 non-null  float64\n",
      " 15  duration_hours      1033660 non-null  float64\n",
      " 16  duration_min        1033660 non-null  float64\n",
      " 17  dur_to_dist_m_hpkm  1033660 non-null  float64\n",
      " 18  dur_to_dist_m_spkm  1033660 non-null  float64\n",
      " 19  speed_kmph_m        1033660 non-null  float64\n",
      " 20  day_name            1033660 non-null  object \n",
      " 21  hour                1033660 non-null  int64  \n",
      " 22  price               1033660 non-null  float64\n",
      " 23  month_name          1033660 non-null  object \n",
      " 24  day                 1033660 non-null  int64  \n",
      "dtypes: bool(1), float64(12), int64(7), object(5)\n",
      "memory usage: 190.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d68ad32",
   "metadata": {},
   "source": [
    "### subsetting the required data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af797b6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['id','pickup_datetime','price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "28c7c831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1033660, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2966204</td>\n",
       "      <td>2016-05-01 10:37:00</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id3871388</td>\n",
       "      <td>2016-06-13 09:46:00</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id2922653</td>\n",
       "      <td>2016-05-02 11:27:00</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id      pickup_datetime  price\n",
       "0  id2966204  2016-05-01 10:37:00    3.5\n",
       "1  id3871388  2016-06-13 09:46:00    3.5\n",
       "2  id2922653  2016-05-02 11:27:00    3.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "283beb4d",
   "metadata": {},
   "source": [
    "Let's use this subset of the dataframe for our visualisation purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529d73e8",
   "metadata": {},
   "source": [
    "### Saving the sorted index and Parsing the datetime "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb83f595",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the data type for `pickup and dropoff` columns to datetimeIndex\n",
    "\n",
    "df['pickup_datetime']= pd.to_datetime(df['pickup_datetime'])\n",
    "\n",
    "df['dates'] = df['pickup_datetime'].dt.date\n",
    "df['dates'] = pd.to_datetime(df['dates'])\n",
    "# df['time'] = pd.to_datetime(df['pickup_datetime']).dt.time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74f91260",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # setting the index and sorting the df.\n",
    "\n",
    "# df_sort = df.set_index(\"pickup_datetime\").sort_index()\n",
    "# df = df_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f0a56cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# categories as default are unordered, setting the order of category for month and day names\n",
    "from pandas.api.types import CategoricalDtype\n",
    "\n",
    "month_cat = CategoricalDtype(categories=[\"January\", \"February\",\"March\",\"April\",\"May\",\"June\"], ordered = True)\n",
    "day_cat = CategoricalDtype(categories=[\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\",\"Sunday\"], ordered = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b8ae2a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"month_name\"] = df['pickup_datetime'].dt.month_name().astype(month_cat)\n",
    "df[\"day\"] = df['pickup_datetime'].dt.day\n",
    "df[\"day_name\"] = df['pickup_datetime'].dt.day_name().astype(day_cat)\n",
    "df[\"hour\"] = df['pickup_datetime'].dt.hour\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4cea2290",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['id','pickup_datetime','dates','month_name','day_name','hour','day','price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a04d00fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1033660 entries, 0 to 1033659\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count    Dtype         \n",
      "---  ------           --------------    -----         \n",
      " 0   id               1033660 non-null  object        \n",
      " 1   pickup_datetime  1033660 non-null  datetime64[ns]\n",
      " 2   dates            1033660 non-null  datetime64[ns]\n",
      " 3   month_name       1033660 non-null  category      \n",
      " 4   day_name         1033660 non-null  category      \n",
      " 5   hour             1033660 non-null  int64         \n",
      " 6   day              1033660 non-null  int64         \n",
      " 7   price            1033660 non-null  float64       \n",
      "dtypes: category(2), datetime64[ns](2), float64(1), int64(2), object(1)\n",
      "memory usage: 49.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87172813",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./data/3_df_sort_datetime_set.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84b2382",
   "metadata": {},
   "source": [
    "\n",
    "## Plotting the Trend Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a618c3b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count ')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot of 6 month data between 1st January 2016 to midnight of 1st July 2016\n",
    "# The total time window constitutes 182 days and 23 hours and 2 minutes.\n",
    "\n",
    "fig,ax = plt.subplots(1, figsize=(20,10))\n",
    "fig.suptitle('Daily rides trend from 1st January 2016 to midnight of 1st July 2016', size=16, y=0.95, weight='bold')\n",
    "\n",
    "# sns.countplot(x='dates', data=df, ax=ax)\n",
    "\n",
    "df['dates'].value_counts().plot(grid=True)\n",
    "ax.set_xlabel('Date', fontsize=16)\n",
    "ax.set_ylabel('Count ', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22aa1908",
   "metadata": {},
   "source": [
    "There are clear spikes in this data set for the 6 month taxi ride data - could be attributed to holidays / special occassios / others. Need to check the correlation with such events. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "21db091c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Trips by hour')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1296 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#adding figures\n",
    "\n",
    "fig = plt.figure(figsize=(16,18))\n",
    "ax1=fig.add_subplot(321)\n",
    "ax2=fig.add_subplot(322)\n",
    "ax3=fig.add_subplot(312)\n",
    "ax4=fig.add_subplot(313)\n",
    "\n",
    "#setting the styles\n",
    "from matplotlib import style\n",
    "\n",
    "#styles\n",
    "# style.use('ggplot')\n",
    "sns.set_theme()\n",
    "# sns.set_style(\"ticks\")\n",
    "# sns.set_context(\"paper\", font_scale = 1.3,)\n",
    "\n",
    "fig.suptitle('Density of trips between 1st January 2016 to midnight of 1st July 2016 ',fontsize='18', weight='bold',y=0.95)\n",
    "\n",
    "#plot1\n",
    "\n",
    "sns.countplot(x='month_name', data=df, ax=ax1) #color='b'\n",
    "ax1.set_title('Trips per month', fontsize='16',weight='bold')\n",
    "\n",
    "#plot2\n",
    "\n",
    "sns.countplot(x='day_name', data=df, ax=ax2) #color='b'\n",
    "ax2.set_title('Trips per weekday',fontsize='16',weight='bold')\n",
    "\n",
    "#plot3\n",
    "\n",
    "sns.countplot(x='day', data=df, ax=ax3)\n",
    "ax3.set_title('Trips by day',fontsize='16',weight='bold')\n",
    "\n",
    "#plot4\n",
    "\n",
    "sns.countplot(x='hour', data=df, ax=ax4) #color='C0'\n",
    "ax4.set_title('Trips by hour',fontsize='16', weight='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75cdefcf",
   "metadata": {},
   "source": [
    "### Weekend versus Weekday comparsion of rides. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34983fcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Friday       158455\n",
       "Saturday     156493\n",
       "Thursday     155088\n",
       "Wednesday    149072\n",
       "Tuesday      143343\n",
       "Sunday       138422\n",
       "Monday       132787\n",
       "Name: day_name, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.day_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58fe1a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "weekend = ['Saturday', 'Sunday']\n",
    "weekday = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\n",
    "c1_weekend = df.day_name.isin(weekend)\n",
    "c1_weekday = df.day_name.isin(weekday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b2eabf81-e0df-4d62-8106-82a22273b04f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11a0fff8e01f4477b6bc283eede83820",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectMultiple(description='MONTH_NAME', index=(5,), layout=Layout(width='max-content'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'layout'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-00d28fc15acd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0mgrid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridspecLayout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'300px'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontrols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/envs/newenv/lib/python3.7/site-packages/ipywidgets/widgets/widget_templates.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0mobj_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'widget%03d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m         \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid_area\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indices_from_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'layout'"
     ]
    }
   ],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual, GridspecLayout\n",
    "\n",
    "# output = widgets.Output()\n",
    "\n",
    "# with output:\n",
    "# #     fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))\n",
    "#     fig = plt.figure(figsize=(16,18))\n",
    "#     ax1=fig.add_subplot(321)\n",
    "#     ax2=fig.add_subplot(322)\n",
    "#     ax3=fig.add_subplot(312)\n",
    "\n",
    "def filter_datetime(MONTH_NAME ='January', DAY_NAME ='Sunday'):\n",
    "\n",
    "    if MONTH_NAME =='January':\n",
    "        t_df = df[(df['month_name'] == MONTH_NAME) & (df['day_name'] == DAY_NAME)]\n",
    "        display(t_df)\n",
    "        \n",
    "        if len(t_df > 1):\n",
    "            t_df['day_name'].value_count().plot.bar(grid=True, ax=ax1)\n",
    "            t_df['dates'].value_counts().plot(grid=True, ax=ax2)\n",
    "            t_df['hour'].value_counts().plot.bar(grid=True, ax=ax3)\n",
    "\n",
    "        else:\n",
    "            pass\n",
    "        \n",
    "    else:\n",
    "        display(df[(df['month_name'] == MONTH_NAME) & (df['day_name'] == DAY_NAME)])\n",
    "            \n",
    "\n",
    "\n",
    "controls = widgets.VBox([month_name_selector, day_name_selector])            \n",
    "month_name_selector = widgets.SelectMultiple(\n",
    "    options=df['month_name'].unique(),\n",
    "    value= ['January'],\n",
    "    layout={'width': 'max-content'},\n",
    "    description='MONTH_NAME',\n",
    "    disabled=False\n",
    "    )   \n",
    "\n",
    "day_name_selector = widgets.SelectMultiple(\n",
    "    options=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],\n",
    "    value=['Sunday'],\n",
    "    layout={'width': 'max-content'},\n",
    "    description='DAY_NAME',\n",
    "    )   \n",
    "\n",
    "\n",
    "interact(filter_datetime, MONTH_NAME = month_name_selector, DAY_NAME = day_name_selector )\n",
    "\n",
    "grid = GridspecLayout(4, 3, height='300px')\n",
    "grid[:3, 1:] = controls\n",
    "grid[:, 0] = ax1.grid(True)\n",
    "grid[3, 1] = ax1.grid(True)\n",
    "grid[3, 2] = ax1.grid(True)\n",
    "grid\n",
    "\n",
    "# widgets.HBox([month_name_selector, day_name_selector])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c6f5774d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'c1_weekend' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-bf34a645d5c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc1_weekend\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc1_weekday\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'c1_weekend' is not defined"
     ]
    }
   ],
   "source": [
    "print(df.loc[c1_weekend].shape)\n",
    "print(df.loc[c1_weekday].shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c3cbcf3",
   "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>id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dates</th>\n",
       "      <th>month_name</th>\n",
       "      <th>day_name</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2966204</td>\n",
       "      <td>2016-05-01 10:37:00</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>May</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>id3142312</td>\n",
       "      <td>2016-06-18 13:18:00</td>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>June</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>id1365326</td>\n",
       "      <td>2016-04-02 04:48:00</td>\n",
       "      <td>2016-04-02</td>\n",
       "      <td>April</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>id3604087</td>\n",
       "      <td>2016-04-09 17:51:00</td>\n",
       "      <td>2016-04-09</td>\n",
       "      <td>April</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>id1389089</td>\n",
       "      <td>2016-05-29 01:16:00</td>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>May</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id     pickup_datetime      dates month_name  day_name  hour  day  \\\n",
       "0   id2966204 2016-05-01 10:37:00 2016-05-01        May    Sunday    10    1   \n",
       "5   id3142312 2016-06-18 13:18:00 2016-06-18       June  Saturday    13   18   \n",
       "6   id1365326 2016-04-02 04:48:00 2016-04-02      April  Saturday     4    2   \n",
       "8   id3604087 2016-04-09 17:51:00 2016-04-09      April  Saturday    17    9   \n",
       "14  id1389089 2016-05-29 01:16:00 2016-05-29        May    Sunday     1   29   \n",
       "\n",
       "    price  \n",
       "0    3.50  \n",
       "5    4.55  \n",
       "6    3.50  \n",
       "8    3.50  \n",
       "14   3.50  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weekend = df.loc[c1_weekend]\n",
    "df_weekday = df.loc[c1_weekday]\n",
    "df_weekend.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1cebdaa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "ax1=fig.add_subplot(121)\n",
    "ax2=fig.add_subplot(122)\n",
    "\n",
    "fig.suptitle('Distribution of count of pickups across `weekday`and `weekend` data points',fontsize='16',weight='bold')\n",
    "ax1.set_title('Weekend', fontsize='16')\n",
    "ax2.set_title('Weekday',fontsize='16')\n",
    "\n",
    "sns.countplot(x='hour', data=df_weekend, ax=ax1 ) #eg: palette=\"rocket\", palette=\"Blues\" color='b'\n",
    "sns.countplot(x='hour', data=df_weekday, ax=ax2)\n",
    "\n",
    "\n",
    "# style.use('ggplot')\n",
    "sns.set_theme()\n",
    "sns.set_style(\"ticks\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816c2250",
   "metadata": {},
   "source": [
    "This comparison of 'weekend' and 'weekday' shows a clear high density pickup happening during the **midnight hours of 0:00 to 3:00 A.M** a clear winner for MVP planning for this time and weekend window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "efb937f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,12))\n",
    "\n",
    "table=df.groupby([df['day'],df['hour']]).size().unstack('day')\n",
    "\n",
    "# table.style.background_gradient(cmap='Blues')\n",
    "\n",
    "sns.heatmap(table, cmap='YlGnBu', linewidths=.35)   #alternate option             \n",
    "fig.savefig('densitymap of hour-day counts.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35b2b4ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "170e5932",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "794ee97c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45cc56c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da62fbdc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61af058a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lab",
   "language": "python",
   "name": "lab"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}