1. Taxi-rides_cleaning_analysis_visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "938e4a1e",
   "metadata": {},
   "source": [
    "# Flyber - Midterm - Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "192c9b4e",
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd3de99",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "### Reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4733d2e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the taxi_rides data into the pandas Dataframe\n",
    "df = pd.read_csv('../taxi-rides.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a16280a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 12\n",
      "Unnamed: 13\n",
      "Unnamed: 14\n",
      "distance\n",
      "dropoff_datetime\n",
      "dropoff_latitude\n",
      "dropoff_longitude\n",
      "duration\n",
      "id\n",
      "passenger_count\n",
      "pickup_datetime\n",
      "pickup_latitude\n",
      "pickup_longitude\n",
      "store_and_fwd_flag\n",
      "vendor_id\n"
     ]
    }
   ],
   "source": [
    "#listing the columns pertaining to the rides table:\n",
    "for col in sorted(df.columns):\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540298bc",
   "metadata": {},
   "source": [
    "### Removing the empty columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f11a2e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=[col for col in df.columns if 'Unnamed' in col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a353ade3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1048468 entries, 0 to 1048467\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count    Dtype  \n",
      "---  ------              --------------    -----  \n",
      " 0   id                  1048468 non-null  object \n",
      " 1   vendor_id           1048468 non-null  int64  \n",
      " 2   pickup_datetime     1048468 non-null  object \n",
      " 3   dropoff_datetime    1048468 non-null  object \n",
      " 4   passenger_count     1048468 non-null  int64  \n",
      " 5   pickup_longitude    1048468 non-null  float64\n",
      " 6   pickup_latitude     1048468 non-null  float64\n",
      " 7   dropoff_longitude   1048468 non-null  float64\n",
      " 8   dropoff_latitude    1048468 non-null  float64\n",
      " 9   store_and_fwd_flag  1048468 non-null  object \n",
      " 10  duration            1048468 non-null  int64  \n",
      " 11  distance            1048468 non-null  float64\n",
      "dtypes: float64(5), int64(3), object(4)\n",
      "memory usage: 96.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # 1.05 Million observations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7e087c",
   "metadata": {},
   "source": [
    "### Converting columns to the right datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0fcb8ee6",
   "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",
    "df['dropoff_datetime']= pd.to_datetime(df['dropoff_datetime'])\n",
    "\n",
    "#converting vendor_id to type category\n",
    "df['vendor_id'] = df['vendor_id'].astype('category') \n",
    "\n",
    "#converting object to boolean for the 'store_and_fwd_flag'\n",
    "df['store_and_fwd_flag'] = df['store_and_fwd_flag'].map({'N': False, 'Y': True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b25155b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                            object\n",
       "vendor_id                   category\n",
       "pickup_datetime       datetime64[ns]\n",
       "dropoff_datetime      datetime64[ns]\n",
       "passenger_count                int64\n",
       "pickup_longitude             float64\n",
       "pickup_latitude              float64\n",
       "dropoff_longitude            float64\n",
       "dropoff_latitude             float64\n",
       "store_and_fwd_flag              bool\n",
       "duration                       int64\n",
       "distance                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verifying the datatimes for each of the columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b86cc5",
   "metadata": {},
   "source": [
    "### Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "296f78ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    0\n",
       "vendor_id             0\n",
       "pickup_datetime       0\n",
       "dropoff_datetime      0\n",
       "passenger_count       0\n",
       "pickup_longitude      0\n",
       "pickup_latitude       0\n",
       "dropoff_longitude     0\n",
       "dropoff_latitude      0\n",
       "store_and_fwd_flag    0\n",
       "duration              0\n",
       "distance              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8ed41b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
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       "      <td>id3487442</td>\n",
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       "      <td>2016-02-29 18:39:00</td>\n",
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       "      <td>2016-05-16 23:15:00</td>\n",
       "      <td>2016-05-16 23:31:00</td>\n",
       "      <td>6</td>\n",
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       "      <td>-73.808487</td>\n",
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       "      <td>947</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3499387</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-25 19:45:00</td>\n",
       "      <td>2016-01-25 19:54:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.786720</td>\n",
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       "      <td>-73.786720</td>\n",
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       "      <td>2016-01-27 22:29:00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1</td>\n",
       "      <td>2016-01-22 16:13:00</td>\n",
       "      <td>2016-01-22 16:13:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.776314</td>\n",
       "      <td>40.645454</td>\n",
       "      <td>-73.776314</td>\n",
       "      <td>40.645454</td>\n",
       "      <td>False</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id3487442         2 2016-02-29 18:39:00 2016-02-29 18:42:00   \n",
       "1  id1145809         2 2016-05-16 23:15:00 2016-05-16 23:31:00   \n",
       "2  id3499387         1 2016-01-25 19:45:00 2016-01-25 19:54:00   \n",
       "3  id0753277         1 2016-01-27 22:29:00 2016-01-27 22:29:00   \n",
       "4  id2134482         1 2016-01-22 16:13:00 2016-01-22 16:13:00   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.981819        40.768963         -73.981819   \n",
       "1                6        -73.808487        40.687336         -73.808487   \n",
       "2                1        -73.786720        40.647041         -73.786720   \n",
       "3                1        -74.209854        40.816853         -74.209854   \n",
       "4                1        -73.776314        40.645454         -73.776314   \n",
       "\n",
       "   dropoff_latitude  store_and_fwd_flag  duration  distance  \n",
       "0         40.768963               False       227       0.0  \n",
       "1         40.687336               False       947       0.0  \n",
       "2         40.647041               False       580       0.0  \n",
       "3         40.816853               False        27       0.0  \n",
       "4         40.645454               False        19       0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6784f8a5",
   "metadata": {},
   "source": [
    "### Saving the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "744c3061",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Saving the dataframe \n",
    "\n",
    "df.to_csv('./data/1_preprocessed_set.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fff9ace",
   "metadata": {},
   "source": [
    "# Exploring the Data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4da186d2",
   "metadata": {},
   "source": [
    "##  High-level understanding of the granularity and scope of the dataset.\n",
    "\n",
    "### Q1. How many records are in the dataset?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "054c7046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048468, 12)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79816d25",
   "metadata": {},
   "source": [
    "### Q2. What does each record represent?\n",
    "\n",
    "1. `id` = Trip ID allocated to each complete taxi ride\n",
    "    \n",
    "2. `vendor_id` = This field corresponds to two categories of taxi services in NYC. Yello taxi and Private taxi.\n",
    "    \n",
    "3. `pickup_datetime` = This is the timestamp of the pickup time for the associated trip id.\n",
    "    \n",
    "4. `dropoff_datetime` = This is the timestamp of the dropoff time for the associated trip id.\n",
    "\n",
    "5. `passenger_count` = This corresponds to the number of passengers taking the taxiride for correponding trip id.\n",
    "\n",
    "6. `pickup_latitude` = This is the GPS coordinates in DMS(Degrees, Minutes and Seconds unit system) corresponding to the pickup_latitude.\n",
    "\n",
    "7. `pickup_longitude` = This is the GPS coordinates in DMS(Degrees, Minutes and Seconds unit system) corresponding to the pickup_longitude.\n",
    "\n",
    "8. `dropoff_latitude` = This is the GPS coordinates in DMS(Degrees, Minutes and Seconds unit system) corresponding to the dropoff_latitude.\n",
    "\n",
    "9. `dropoff_longitude` = This is the GPS coordinates in DMS(Degrees, Minutes and Seconds unit system) corresponding to the dropoff_longitude.\n",
    "\n",
    "10. `store_and_fwd_flag` = \n",
    "\n",
    "11. `duration` = This column presents the duration of each trip in seconds.\n",
    "\n",
    "12. `distance` = This column presents the distance in km travelled on each of the trips. The unit of this dimension is confirmed by verifying with computed 'great circle' and manhattan distance' to confirm the units of measure.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a466d1d",
   "metadata": {},
   "source": [
    "### 3. What is the primary key?\n",
    "\n",
    "The `id` column is the primary key for this dataframe. Let's verify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72cd3668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1048468 entries, 0 to 1048467\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count    Dtype         \n",
      "---  ------              --------------    -----         \n",
      " 0   id                  1048468 non-null  object        \n",
      " 1   vendor_id           1048468 non-null  category      \n",
      " 2   pickup_datetime     1048468 non-null  datetime64[ns]\n",
      " 3   dropoff_datetime    1048468 non-null  datetime64[ns]\n",
      " 4   passenger_count     1048468 non-null  int64         \n",
      " 5   pickup_longitude    1048468 non-null  float64       \n",
      " 6   pickup_latitude     1048468 non-null  float64       \n",
      " 7   dropoff_longitude   1048468 non-null  float64       \n",
      " 8   dropoff_latitude    1048468 non-null  float64       \n",
      " 9   store_and_fwd_flag  1048468 non-null  bool          \n",
      " 10  duration            1048468 non-null  int64         \n",
      " 11  distance            1048468 non-null  float64       \n",
      "dtypes: bool(1), category(1), datetime64[ns](2), float64(5), int64(2), object(1)\n",
      "memory usage: 82.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # the count of observations correspond to 1048468."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "51b1e8ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    1048468\n",
       "vendor_id                   2\n",
       "pickup_datetime        237124\n",
       "dropoff_datetime       237437\n",
       "passenger_count            10\n",
       "pickup_longitude        21368\n",
       "pickup_latitude         42575\n",
       "dropoff_longitude       30913\n",
       "dropoff_latitude        58283\n",
       "store_and_fwd_flag          2\n",
       "duration                 6883\n",
       "distance              1042359\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique() # the unique count of id's is 1048468 and is the primary key for our dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac5d596",
   "metadata": {},
   "source": [
    "### 4. What date range is your dataset bound to?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb47ff29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-01-01 00:00:00 , 2016-07-01 23:02:00 , 182 days 23:02:00\n"
     ]
    }
   ],
   "source": [
    "start = df.pickup_datetime.min()\n",
    "end = df.dropoff_datetime.max()\n",
    "print(start,\",\", end,\",\", end-start)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf128a3",
   "metadata": {},
   "source": [
    "The range of observations start at 1st January 2016 to midnight of 1st July 2016\n",
    "The total time window constitutes 182 days and 23 hours and 2 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541e1f57",
   "metadata": {},
   "source": [
    "### 5. What are the geographical bounds of this dataset? Is it limited to Manhattan, or is Brooklyn, Queens, Staten Island, the Bronx, and New Jersey included? Where are most of the data points centralized at? Are there outliers?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e576464",
   "metadata": {},
   "source": [
    "This details of this question are in the Map Analysis notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379826f2",
   "metadata": {},
   "source": [
    "--------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a492c6",
   "metadata": {},
   "source": [
    "# Data Analysis & Data Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d412c1e5",
   "metadata": {},
   "source": [
    "\n",
    "For getting hang of the data distributions - let's first create additional datafields. \n",
    "\n",
    "Additonal columns we need to create are `price` `distance` `manhattan_dist` `dur_to_dist` `dur_to_dist_m` `dist_to_dur` and for further analysis down the road we require parsing of `datetimeIndex`.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ac4956",
   "metadata": {},
   "source": [
    "## Deriving Additional columns - `Distance`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077dcd2b",
   "metadata": {},
   "source": [
    "### Computing Manhattan distance. \n",
    "\n",
    "After much research and even working on computing distance from the geodesic-circle functions available on geopandas library. I figured to make use of the Manhattan distance to perform the computation. I used this open-source-code available at this location to compute the 'manhattan' distance as the computation procedure aptly applies to manhattan location area owing to the perpendicular street ways. \n",
    "\n",
    "Ref: https://medium.com/@simplyjk/why-manhattan-distance-formula-doesnt-apply-to-manhattan-7db0ebb1c5f6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "949c9896",
   "metadata": {},
   "outputs": [],
   "source": [
    "def haversine(lat1, lon1, lat2, lon2):     \n",
    "    # R = 3958.76 # Earth radius in miles    \n",
    "    R = 6371 # Earth radisu in KM\n",
    "    dLat, dLon, lat1, lat2 = np.radians(lat2 - lat1), np.radians(lon2 - lon1), np.radians(lat1), np.radians(lat2)     \n",
    "    \n",
    "    a =  np.sin(dLat/2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dLon/2) ** 2     \n",
    "\n",
    "    c = 2 * np.arcsin(np.sqrt(a))          \n",
    "    \n",
    "    return R * c\n",
    "  \n",
    "def manhattan_dist(lat1, lon1, lat2, lon2):\n",
    "    \n",
    "    # Pickup coordinates\n",
    "    p = np.stack([lat1, lon1], axis = 1)\n",
    "    \n",
    "    # Dropoff coordinates\n",
    "    d = np.stack([lat2, lon2], axis = 1)\n",
    "    \n",
    "    theta1 = np.radians(-28.904)\n",
    "    theta2 = np.radians(28.904)\n",
    "    \n",
    "    ## Rotation matrix\n",
    "    R1 = np.array([[np.cos(theta1), np.sin(theta1)], \n",
    "                   [-np.sin(theta1), np.cos(theta1)]]\n",
    "                 )\n",
    "    R2 = np.array([[np.cos(theta2), np.sin(theta2)], \n",
    "                   [-np.sin(theta2), np.cos(theta2)]]\n",
    "                 )\n",
    "    \n",
    "    # Rotate Pickup and Dropoff coordinates by -29 degress in World2\n",
    "    pT = R1 @ p.T  \n",
    "    dT = R1 @ d.T  \n",
    "    \n",
    "    # Coordinates of Hinge point in the rotated world \n",
    "    vT = np.stack((pT[0,:], dT[1,:]))\n",
    "    # Coordinates of Hinge point in the real world \n",
    "    v = R2 @ vT\n",
    "\n",
    "    \n",
    "    return (haversine(p.T[0], p.T[1], v[0], v[1]) + \n",
    "            haversine(v[0], v[1], d.T[0], d.T[1])\n",
    "           )\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f166a066",
   "metadata": {},
   "outputs": [],
   "source": [
    "#passing the parameters to the manhattan_dist function defined in the above cell\n",
    "\n",
    "df[\"manhattan_dist\"] = manhattan_dist(df[\"pickup_latitude\"], df[\"pickup_longitude\"], df[\"dropoff_latitude\"], df[\"dropoff_longitude\"] )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ca2fda",
   "metadata": {},
   "source": [
    "### Replacing 'zero' `distance` values with mean distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "234fa18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3573, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## calculating number of observations where distance = 0\n",
    "\n",
    "(df[df.distance== 0][['id','distance','duration','manhattan_dist']]).shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf7f815b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
      "0  id3487442         2 2016-02-29 18:39:00 2016-02-29 18:42:00   \n",
      "1  id1145809         2 2016-05-16 23:15:00 2016-05-16 23:31:00   \n",
      "2  id3499387         1 2016-01-25 19:45:00 2016-01-25 19:54:00   \n",
      "3  id0753277         1 2016-01-27 22:29:00 2016-01-27 22:29:00   \n",
      "4  id2134482         1 2016-01-22 16:13:00 2016-01-22 16:13:00   \n",
      "\n",
      "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
      "0                1        -73.981819        40.768963         -73.981819   \n",
      "1                6        -73.808487        40.687336         -73.808487   \n",
      "2                1        -73.786720        40.647041         -73.786720   \n",
      "3                1        -74.209854        40.816853         -74.209854   \n",
      "4                1        -73.776314        40.645454         -73.776314   \n",
      "\n",
      "   dropoff_latitude  store_and_fwd_flag  duration  distance  manhattan_dist  \n",
      "0         40.768963               False       227       0.0    0.000000e+00  \n",
      "1         40.687336               False       947       0.0    0.000000e+00  \n",
      "2         40.647041               False       580       0.0    0.000000e+00  \n",
      "3         40.816853               False        27       0.0    2.866614e-12  \n",
      "4         40.645454               False        19       0.0    2.397931e-12  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(862, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating number of observations where manhattan_dist = 0\n",
    "#subset view\n",
    "\n",
    "print(df.head(5))\n",
    "\n",
    "(df[df.manhattan_dist == 0][['id','distance','duration','manhattan_dist']]).shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d591703",
   "metadata": {},
   "source": [
    "**Note:**\n",
    "\n",
    "The computed distance based on all reasoning above, has been able to reduce the 'errors' in analysis and reporting and a **75% reduction** in zeros is great for imputing for zero's occurring in the `manhattan_distance` column alone\n",
    "\n",
    "Moving the computation of all additional columns to after adding a random number from 0.1KM to 0.322 KM for 862 observations of where manhattan_dist values are '0'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cf47e33a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19992031701023075"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating the mean and assigning that value for the 0.0 value cells in `manhattan_dist`\n",
    "\n",
    "range = np.random.uniform(0.1, 0.3, 862)\n",
    "range.mean() # 0.201529.\n",
    "# the mean keeps varying for now however - I am sticking with the above computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "95a26592",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding the corrected dataset for each of the 862 observations.\n",
    "# reference https://stackoverflow.com/questions/37506488/python-pandas-dataframe-replace-0-with-median-value\n",
    "df = df.replace({'manhattan_dist': {0: 0.201529}})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a6719851",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "205038      1.372800\n",
      "524841      2.475106\n",
      "1035974    28.886648\n",
      "Name: manhattan_dist, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 4)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verifying if all zero cells are imputed with the mean calculated above.\n",
    "print(df['manhattan_dist'].sample(3))\n",
    "(df[df.manhattan_dist == 0][['id','distance','duration','manhattan_dist']]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0210406d",
   "metadata": {},
   "source": [
    "### Creating additional analytic columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "972dc51d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting duration to hours \n",
    "\n",
    "df['duration_hours'] = df[\"duration\"]/3600\n",
    "df['duration_min'] = df[\"duration\"]/60\n",
    "\n",
    "# computing additional duration to distance column \n",
    "\n",
    "df['dur_to_dist'] = df['duration_hours']/df['distance']\n",
    "df['dur_to_dist_m'] = df['duration_hours']/df['manhattan_dist']\n",
    "\n",
    "# computing additional distance to duration column \n",
    "df['speed_kmph'] = df['distance'] /df['duration_hours']\n",
    "df['speed_kmph_m'] = df['manhattan_dist'] /df['duration_hours']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8144de09",
   "metadata": {},
   "source": [
    "## Deriving Additional columns - `Price`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce3687c",
   "metadata": {},
   "source": [
    "As the taxt ride price is a 'derived column' I am using the inputs available on the NYC webpage to arrive at the price for each of the 1m+ rides present in the dataset. Firstly, planning out the conditional criterion that help derive the price function are presented and grouped below.\n",
    "\n",
    "\n",
    "### Fare calculation formula is derived from here: ( https://www.estimate.taxi/rates/united-states/new-york)\n",
    "\n",
    "`Price` is computed as below:\n",
    "\n",
    "C1. Base price is **$2.50** for first **0.322km**\n",
    "\n",
    "     Additional price for every **0.322km** is **$0.50**\n",
    "\n",
    "\n",
    "C2. Waiting time is **$30.00/hour**\n",
    "\n",
    "\n",
    "C3. Additional Peak rate for (**16:00 - 19:59** on Monday, Tuesday, Wednesday, Thursday, Friday only) of **$1.00**\n",
    "\n",
    "\n",
    "       Additional night fee of **$0.50**\n",
    "\n",
    "\n",
    "C4. Including another criterion to passenger count. If passenger count is > 2, price billed is 1.2 times the price computed based on all criterion put together\n",
    "\n",
    "\n",
    "C5. Further, taxi rates go the highest peak To/From JFK and any location in Manhattan. **$52.00** plus tolls + many surcharges...\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e596cf1",
   "metadata": {},
   "source": [
    "###  Defining the `price` function per above criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84866bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# writing a function dependent on the distance variable - based on C1 condition.\n",
    "\n",
    "def criterion_1(row):\n",
    "    if row['distance'] < 0.322:\n",
    "        return 2.5\n",
    "    else:\n",
    "        return 0.5 * (row['distance']-0.322)/0.322\n",
    "    \n",
    "df['criterion_1'] = df.apply(criterion_1, axis=1)   \n",
    "    \n",
    "# writing a function dependent on the duration variable - based on C2 condition.\n",
    "\n",
    "def criterion_2(row):\n",
    "    if row['duration'] > 3600 and row ['duration'] < 7200:\n",
    "        return 30\n",
    "    else:\n",
    "        return 30 * (row['duration']/3600)\n",
    "    \n",
    "df['criterion_2'] = df.apply(criterion_2, axis=1)  \n",
    "\n",
    "# Before applying the C3 bove to the dataframe - we require the columns of hour and day_name for deriving the \n",
    "#price based of C3.\n",
    "\n",
    "# obtaining columns of hour and day_name utilizing the datetime pandas indexing\n",
    "\n",
    "df[\"day_name\"] = df['pickup_datetime'].dt.day_name()\n",
    "df[\"hour\"] = df['pickup_datetime'].dt.hour\n",
    "\n",
    "def criterion_3(row):\n",
    "    if row['hour'] > 16 and row ['hour'] < 20 and row['day_name'] == ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']:\n",
    "        return 1\n",
    "    elif row['hour'] > 20 and row['hour'] < 6: \n",
    "        return 0.5\n",
    "    else: \n",
    "        return 0\n",
    "    \n",
    "df['criterion_3'] = df.apply(criterion_3, axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6343fc60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# deriving the price as a sum of the 3 conditional functions\n",
    "\n",
    "df['price']= df['criterion_1']+df['criterion_2']+df['criterion_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9c26f5c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# writing a function for the duration based conditional based on C4 above.\n",
    "\n",
    "def criterion_4(row):\n",
    "    if row['passenger_count'] <= 2 :\n",
    "        return (row['price'] * 1)\n",
    "    else:\n",
    "        return (row['price'] * 1.2 )\n",
    "    \n",
    "df['criterion_4'] = df.apply(criterion_4, axis=1)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "79a4c908",
   "metadata": {},
   "outputs": [],
   "source": [
    "# deriving the price as a sum of all **4 conditional functions**\n",
    "\n",
    "df['price']= df['criterion_1']+df['criterion_2']+df['criterion_3']+df['criterion_4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7f748c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.048468e+06\n",
       "mean     2.613925e+01\n",
       "std      1.003303e+02\n",
       "min      9.031562e-02\n",
       "25%      1.013871e+01\n",
       "50%      1.725429e+01\n",
       "75%      2.961762e+01\n",
       "max      5.883217e+04\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'].describe().T #Mean price $ 26.14 and Standard Deviation $ 100.33. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11409d15",
   "metadata": {},
   "source": [
    "The Dataset above has all of the outliers included, which are falling outside of the bounds of NYC. A better estimate would be to exclude outliers and then computing the descriptive stats of `price` variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b61e7d0",
   "metadata": {},
   "source": [
    "## Datetime column parsing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "987ffe04",
   "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": 29,
   "id": "0a53ac8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# additional columns being parsed.\n",
    "df[\"day_name\"] = df['pickup_datetime'].dt.day_name().astype(day_cat)\n",
    "df[\"month_name\"] = df['pickup_datetime'].dt.month_name().astype(month_cat)\n",
    "df[\"day\"] = df['pickup_datetime'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "70482f61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.range.RangeIndex"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cdd2a6ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1048468 entries, 0 to 1048467\n",
      "Data columns (total 28 columns):\n",
      " #   Column              Non-Null Count    Dtype         \n",
      "---  ------              --------------    -----         \n",
      " 0   id                  1048468 non-null  object        \n",
      " 1   vendor_id           1048468 non-null  category      \n",
      " 2   pickup_datetime     1048468 non-null  datetime64[ns]\n",
      " 3   dropoff_datetime    1048468 non-null  datetime64[ns]\n",
      " 4   passenger_count     1048468 non-null  int64         \n",
      " 5   pickup_longitude    1048468 non-null  float64       \n",
      " 6   pickup_latitude     1048468 non-null  float64       \n",
      " 7   dropoff_longitude   1048468 non-null  float64       \n",
      " 8   dropoff_latitude    1048468 non-null  float64       \n",
      " 9   store_and_fwd_flag  1048468 non-null  bool          \n",
      " 10  duration            1048468 non-null  int64         \n",
      " 11  distance            1048468 non-null  float64       \n",
      " 12  manhattan_dist      1048468 non-null  float64       \n",
      " 13  duration_hours      1048468 non-null  float64       \n",
      " 14  duration_min        1048468 non-null  float64       \n",
      " 15  dur_to_dist         1048468 non-null  float64       \n",
      " 16  dur_to_dist_m       1048468 non-null  float64       \n",
      " 17  speed_kmph          1048468 non-null  float64       \n",
      " 18  speed_kmph_m        1048468 non-null  float64       \n",
      " 19  criterion_1         1048468 non-null  float64       \n",
      " 20  criterion_2         1048468 non-null  float64       \n",
      " 21  day_name            1048468 non-null  category      \n",
      " 22  hour                1048468 non-null  int64         \n",
      " 23  criterion_3         1048468 non-null  int64         \n",
      " 24  price               1048468 non-null  float64       \n",
      " 25  criterion_4         1048468 non-null  float64       \n",
      " 26  month_name          1048468 non-null  category      \n",
      " 27  day                 1048468 non-null  int64         \n",
      "dtypes: bool(1), category(3), datetime64[ns](2), float64(16), int64(5), object(1)\n",
      "memory usage: 196.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # 1.05 Million observations "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb3ca4e",
   "metadata": {},
   "source": [
    "### Saving the dataset with additional columns and date time features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1db88095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1048468, 24)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1048468 entries, 0 to 1048467\n",
      "Data columns (total 24 columns):\n",
      " #   Column              Non-Null Count    Dtype         \n",
      "---  ------              --------------    -----         \n",
      " 0   id                  1048468 non-null  object        \n",
      " 1   vendor_id           1048468 non-null  category      \n",
      " 2   pickup_datetime     1048468 non-null  datetime64[ns]\n",
      " 3   dropoff_datetime    1048468 non-null  datetime64[ns]\n",
      " 4   passenger_count     1048468 non-null  int64         \n",
      " 5   pickup_longitude    1048468 non-null  float64       \n",
      " 6   pickup_latitude     1048468 non-null  float64       \n",
      " 7   dropoff_longitude   1048468 non-null  float64       \n",
      " 8   dropoff_latitude    1048468 non-null  float64       \n",
      " 9   store_and_fwd_flag  1048468 non-null  bool          \n",
      " 10  duration            1048468 non-null  int64         \n",
      " 11  distance            1048468 non-null  float64       \n",
      " 12  manhattan_dist      1048468 non-null  float64       \n",
      " 13  duration_hours      1048468 non-null  float64       \n",
      " 14  duration_min        1048468 non-null  float64       \n",
      " 15  dur_to_dist         1048468 non-null  float64       \n",
      " 16  dur_to_dist_m       1048468 non-null  float64       \n",
      " 17  speed_kmph          1048468 non-null  float64       \n",
      " 18  speed_kmph_m        1048468 non-null  float64       \n",
      " 19  day_name            1048468 non-null  category      \n",
      " 20  hour                1048468 non-null  int64         \n",
      " 21  price               1048468 non-null  float64       \n",
      " 22  month_name          1048468 non-null  category      \n",
      " 23  day                 1048468 non-null  int64         \n",
      "dtypes: bool(1), category(3), datetime64[ns](2), float64(13), int64(4), object(1)\n",
      "memory usage: 164.0+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(columns=[col for col in df.columns if 'criterion' in col])\n",
    "print(df.shape)\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3d756fb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Saving the dataframe \n",
    "\n",
    "df.to_csv('./data/2_additional_col_set.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a2baa3b",
   "metadata": {},
   "source": [
    "--------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b405c00",
   "metadata": {},
   "source": [
    "# Descriptive Statistics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f784c40c",
   "metadata": {},
   "source": [
    "### Exploring `passenger_count` `distance`  and `duration` columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d98f4f7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='passenger_count', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,figsize =(10,8))\n",
    "\n",
    "sns.countplot(ax=ax, data=df, x ='passenger_count', color='C0' )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dbee944",
   "metadata": {},
   "source": [
    "Low Volume picks ups that constitute **(1-2 passengers) comprise 85%** of the entire dataset ~ 895K riders.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7fceedbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1048468, 24)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>dur_to_dist</th>\n",
       "      <th>dur_to_dist_m</th>\n",
       "      <th>speed_kmph</th>\n",
       "      <th>speed_kmph_m</th>\n",
       "      <th>day_name</th>\n",
       "      <th>hour</th>\n",
       "      <th>price</th>\n",
       "      <th>month_name</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id3487442</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-29 18:39:00</td>\n",
       "      <td>2016-02-29 18:42:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.981819</td>\n",
       "      <td>40.768963</td>\n",
       "      <td>-73.981819</td>\n",
       "      <td>40.768963</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>3.783333</td>\n",
       "      <td>inf</td>\n",
       "      <td>3.128858e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.196055e+00</td>\n",
       "      <td>Monday</td>\n",
       "      <td>18</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>February</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id1145809</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-16 23:15:00</td>\n",
       "      <td>2016-05-16 23:31:00</td>\n",
       "      <td>6</td>\n",
       "      <td>-73.808487</td>\n",
       "      <td>40.687336</td>\n",
       "      <td>-73.808487</td>\n",
       "      <td>40.687336</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>15.783333</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.305299e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.661081e-01</td>\n",
       "      <td>Monday</td>\n",
       "      <td>23</td>\n",
       "      <td>22.861667</td>\n",
       "      <td>May</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3499387</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-25 19:45:00</td>\n",
       "      <td>2016-01-25 19:54:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.786720</td>\n",
       "      <td>40.647041</td>\n",
       "      <td>-73.786720</td>\n",
       "      <td>40.647041</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>inf</td>\n",
       "      <td>7.994438e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.250870e+00</td>\n",
       "      <td>Monday</td>\n",
       "      <td>19</td>\n",
       "      <td>14.666667</td>\n",
       "      <td>January</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id0753277</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-27 22:29:00</td>\n",
       "      <td>2016-01-27 22:29:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.209854</td>\n",
       "      <td>40.816853</td>\n",
       "      <td>-74.209854</td>\n",
       "      <td>40.816853</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>inf</td>\n",
       "      <td>2.616327e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.822152e-10</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>22</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>January</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id2134482</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-22 16:13:00</td>\n",
       "      <td>2016-01-22 16:13:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.776314</td>\n",
       "      <td>40.645454</td>\n",
       "      <td>-73.776314</td>\n",
       "      <td>40.645454</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.316667</td>\n",
       "      <td>inf</td>\n",
       "      <td>2.200972e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.543447e-10</td>\n",
       "      <td>Friday</td>\n",
       "      <td>16</td>\n",
       "      <td>5.316667</td>\n",
       "      <td>January</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id3487442         2 2016-02-29 18:39:00 2016-02-29 18:42:00   \n",
       "1  id1145809         2 2016-05-16 23:15:00 2016-05-16 23:31:00   \n",
       "2  id3499387         1 2016-01-25 19:45:00 2016-01-25 19:54:00   \n",
       "3  id0753277         1 2016-01-27 22:29:00 2016-01-27 22:29:00   \n",
       "4  id2134482         1 2016-01-22 16:13:00 2016-01-22 16:13:00   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.981819        40.768963         -73.981819   \n",
       "1                6        -73.808487        40.687336         -73.808487   \n",
       "2                1        -73.786720        40.647041         -73.786720   \n",
       "3                1        -74.209854        40.816853         -74.209854   \n",
       "4                1        -73.776314        40.645454         -73.776314   \n",
       "\n",
       "   dropoff_latitude  store_and_fwd_flag  ...  duration_min  dur_to_dist  \\\n",
       "0         40.768963               False  ...      3.783333          inf   \n",
       "1         40.687336               False  ...     15.783333          inf   \n",
       "2         40.647041               False  ...      9.666667          inf   \n",
       "3         40.816853               False  ...      0.450000          inf   \n",
       "4         40.645454               False  ...      0.316667          inf   \n",
       "\n",
       "   dur_to_dist_m  speed_kmph  speed_kmph_m   day_name  hour      price  \\\n",
       "0   3.128858e-01         0.0  3.196055e+00     Monday    18   8.783333   \n",
       "1   1.305299e+00         0.0  7.661081e-01     Monday    23  22.861667   \n",
       "2   7.994438e-01         0.0  1.250870e+00     Monday    19  14.666667   \n",
       "3   2.616327e+09         0.0  3.822152e-10  Wednesday    22   5.450000   \n",
       "4   2.200972e+09         0.0  4.543447e-10     Friday    16   5.316667   \n",
       "\n",
       "   month_name day  \n",
       "0    February  29  \n",
       "1         May  16  \n",
       "2     January  25  \n",
       "3     January  27  \n",
       "4     January  22  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9f54f6",
   "metadata": {},
   "source": [
    "##  Let’s understand the scope and distribution various dimensions within the dataset. \n",
    "\n",
    "Calculate the average, median, and the first & second standard deviation of the mean for the following measures:\n",
    "1. duration\n",
    "2. distance ",
    "\n",
    "3. passenger counts ",
    "\n",
    "4. duration-to-distance ratio ",
    "\n",
    "5. price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "24b7e077",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>1048468.00000</td>\n",
       "      <td>962.19563</td>\n",
       "      <td>5853.29685</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>397.00000</td>\n",
       "      <td>662.00000</td>\n",
       "      <td>1075.00000</td>\n",
       "      <td>3526282.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_hours</th>\n",
       "      <td>1048468.00000</td>\n",
       "      <td>0.26728</td>\n",
       "      <td>1.62592</td>\n",
       "      <td>0.00028</td>\n",
       "      <td>0.11028</td>\n",
       "      <td>0.18389</td>\n",
       "      <td>0.29861</td>\n",
       "      <td>979.52278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>1048468.00000</td>\n",
       "      <td>1.66438</td>\n",
       "      <td>1.31424</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1048468.00000</td>\n",
       "      <td>26.13925</td>\n",
       "      <td>100.33031</td>\n",
       "      <td>0.09032</td>\n",
       "      <td>10.13871</td>\n",
       "      <td>17.25429</td>\n",
       "      <td>29.61762</td>\n",
       "      <td>58832.16996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>1048468.00000</td>\n",
       "      <td>3.44228</td>\n",
       "      <td>4.38206</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.23223</td>\n",
       "      <td>2.09464</td>\n",
       "      <td>3.87690</td>\n",
       "      <td>1240.90868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manhattan_dist</th>\n",
       "      <td>1048468.00000</td>\n",
       "      <td>4.47743</td>\n",
       "      <td>6.10515</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.55843</td>\n",
       "      <td>2.62660</td>\n",
       "      <td>4.77034</td>\n",
       "      <td>1485.25957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speed_kmph_m</th>\n",
       "      <td>1048468.00000</td>\n",
       "      <td>18.43069</td>\n",
       "      <td>18.40331</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>11.37285</td>\n",
       "      <td>16.08565</td>\n",
       "      <td>22.71542</td>\n",
       "      <td>8191.34609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count      mean        std     min       25%  \\\n",
       "duration        1048468.00000 962.19563 5853.29685 1.00000 397.00000   \n",
       "duration_hours  1048468.00000   0.26728    1.62592 0.00028   0.11028   \n",
       "passenger_count 1048468.00000   1.66438    1.31424 0.00000   1.00000   \n",
       "price           1048468.00000  26.13925  100.33031 0.09032  10.13871   \n",
       "distance        1048468.00000   3.44228    4.38206 0.00000   1.23223   \n",
       "manhattan_dist  1048468.00000   4.47743    6.10515 0.00000   1.55843   \n",
       "speed_kmph_m    1048468.00000  18.43069   18.40331 0.00000  11.37285   \n",
       "\n",
       "                      50%        75%           max  \n",
       "duration        662.00000 1075.00000 3526282.00000  \n",
       "duration_hours    0.18389    0.29861     979.52278  \n",
       "passenger_count   1.00000    2.00000       9.00000  \n",
       "price            17.25429   29.61762   58832.16996  \n",
       "distance          2.09464    3.87690    1240.90868  \n",
       "manhattan_dist    2.62660    4.77034    1485.25957  \n",
       "speed_kmph_m     16.08565   22.71542    8191.34609  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df[[\"duration\",\"duration_hours\", \"passenger_count\",'price','distance','manhattan_dist','speed_kmph_m']].describe().T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "faab7eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            background-color:  #f8fcc9;\n",
       "            color:  #000000;\n",
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       "            color:  #000000;\n",
       "        }#T_cfcb2_row5_col6{\n",
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       "            color:  #000000;\n",
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       "            background-color:  #f4fbc0;\n",
       "            color:  #000000;\n",
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       "            background-color:  #259ac1;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_cfcb2_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >duration</th>        <th class=\"col_heading level0 col1\" >duration_hours</th>        <th class=\"col_heading level0 col2\" >passenger_count</th>        <th class=\"col_heading level0 col3\" >price</th>        <th class=\"col_heading level0 col4\" >distance</th>        <th class=\"col_heading level0 col5\" >manhattan_dist</th>        <th class=\"col_heading level0 col6\" >speed_kmph_m</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_cfcb2_level0_row0\" class=\"row_heading level0 row0\" >duration</th>\n",
       "                        <td id=\"T_cfcb2_row0_col0\" class=\"data row0 col0\" >1.0</td>\n",
       "                        <td id=\"T_cfcb2_row0_col1\" class=\"data row0 col1\" >1.0</td>\n",
       "                        <td id=\"T_cfcb2_row0_col2\" class=\"data row0 col2\" >0.0072</td>\n",
       "                        <td id=\"T_cfcb2_row0_col3\" class=\"data row0 col3\" >0.99</td>\n",
       "                        <td id=\"T_cfcb2_row0_col4\" class=\"data row0 col4\" >0.084</td>\n",
       "                        <td id=\"T_cfcb2_row0_col5\" class=\"data row0 col5\" >0.084</td>\n",
       "                        <td id=\"T_cfcb2_row0_col6\" class=\"data row0 col6\" >-0.015</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cfcb2_level0_row1\" class=\"row_heading level0 row1\" >duration_hours</th>\n",
       "                        <td id=\"T_cfcb2_row1_col0\" class=\"data row1 col0\" >1.0</td>\n",
       "                        <td id=\"T_cfcb2_row1_col1\" class=\"data row1 col1\" >1.0</td>\n",
       "                        <td id=\"T_cfcb2_row1_col2\" class=\"data row1 col2\" >0.0072</td>\n",
       "                        <td id=\"T_cfcb2_row1_col3\" class=\"data row1 col3\" >0.99</td>\n",
       "                        <td id=\"T_cfcb2_row1_col4\" class=\"data row1 col4\" >0.084</td>\n",
       "                        <td id=\"T_cfcb2_row1_col5\" class=\"data row1 col5\" >0.084</td>\n",
       "                        <td id=\"T_cfcb2_row1_col6\" class=\"data row1 col6\" >-0.015</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cfcb2_level0_row2\" class=\"row_heading level0 row2\" >passenger_count</th>\n",
       "                        <td id=\"T_cfcb2_row2_col0\" class=\"data row2 col0\" >0.0072</td>\n",
       "                        <td id=\"T_cfcb2_row2_col1\" class=\"data row2 col1\" >0.0072</td>\n",
       "                        <td id=\"T_cfcb2_row2_col2\" class=\"data row2 col2\" >1.0</td>\n",
       "                        <td id=\"T_cfcb2_row2_col3\" class=\"data row2 col3\" >0.017</td>\n",
       "                        <td id=\"T_cfcb2_row2_col4\" class=\"data row2 col4\" >0.0097</td>\n",
       "                        <td id=\"T_cfcb2_row2_col5\" class=\"data row2 col5\" >0.01</td>\n",
       "                        <td id=\"T_cfcb2_row2_col6\" class=\"data row2 col6\" >-0.0012</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cfcb2_level0_row3\" class=\"row_heading level0 row3\" >price</th>\n",
       "                        <td id=\"T_cfcb2_row3_col0\" class=\"data row3 col0\" >0.99</td>\n",
       "                        <td id=\"T_cfcb2_row3_col1\" class=\"data row3 col1\" >0.99</td>\n",
       "                        <td id=\"T_cfcb2_row3_col2\" class=\"data row3 col2\" >0.017</td>\n",
       "                        <td id=\"T_cfcb2_row3_col3\" class=\"data row3 col3\" >1.0</td>\n",
       "                        <td id=\"T_cfcb2_row3_col4\" class=\"data row3 col4\" >0.22</td>\n",
       "                        <td id=\"T_cfcb2_row3_col5\" class=\"data row3 col5\" >0.22</td>\n",
       "                        <td id=\"T_cfcb2_row3_col6\" class=\"data row3 col6\" >0.068</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cfcb2_level0_row4\" class=\"row_heading level0 row4\" >distance</th>\n",
       "                        <td id=\"T_cfcb2_row4_col0\" class=\"data row4 col0\" >0.084</td>\n",
       "                        <td id=\"T_cfcb2_row4_col1\" class=\"data row4 col1\" >0.084</td>\n",
       "                        <td id=\"T_cfcb2_row4_col2\" class=\"data row4 col2\" >0.0097</td>\n",
       "                        <td id=\"T_cfcb2_row4_col3\" class=\"data row4 col3\" >0.22</td>\n",
       "                        <td id=\"T_cfcb2_row4_col4\" class=\"data row4 col4\" >1.0</td>\n",
       "                        <td id=\"T_cfcb2_row4_col5\" class=\"data row4 col5\" >0.99</td>\n",
       "                        <td id=\"T_cfcb2_row4_col6\" class=\"data row4 col6\" >0.6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cfcb2_level0_row5\" class=\"row_heading level0 row5\" >manhattan_dist</th>\n",
       "                        <td id=\"T_cfcb2_row5_col0\" class=\"data row5 col0\" >0.084</td>\n",
       "                        <td id=\"T_cfcb2_row5_col1\" class=\"data row5 col1\" >0.084</td>\n",
       "                        <td id=\"T_cfcb2_row5_col2\" class=\"data row5 col2\" >0.01</td>\n",
       "                        <td id=\"T_cfcb2_row5_col3\" class=\"data row5 col3\" >0.22</td>\n",
       "                        <td id=\"T_cfcb2_row5_col4\" class=\"data row5 col4\" >0.99</td>\n",
       "                        <td id=\"T_cfcb2_row5_col5\" class=\"data row5 col5\" >1.0</td>\n",
       "                        <td id=\"T_cfcb2_row5_col6\" class=\"data row5 col6\" >0.59</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cfcb2_level0_row6\" class=\"row_heading level0 row6\" >speed_kmph_m</th>\n",
       "                        <td id=\"T_cfcb2_row6_col0\" class=\"data row6 col0\" >-0.015</td>\n",
       "                        <td id=\"T_cfcb2_row6_col1\" class=\"data row6 col1\" >-0.015</td>\n",
       "                        <td id=\"T_cfcb2_row6_col2\" class=\"data row6 col2\" >-0.0012</td>\n",
       "                        <td id=\"T_cfcb2_row6_col3\" class=\"data row6 col3\" >0.068</td>\n",
       "                        <td id=\"T_cfcb2_row6_col4\" class=\"data row6 col4\" >0.6</td>\n",
       "                        <td id=\"T_cfcb2_row6_col5\" class=\"data row6 col5\" >0.59</td>\n",
       "                        <td id=\"T_cfcb2_row6_col6\" class=\"data row6 col6\" >1.0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdd75a65850>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"duration\",\"duration_hours\",\"passenger_count\",'price','distance','manhattan_dist','speed_kmph_m']].corr().style.format(\"{:.2}\").background_gradient(cmap=plt.get_cmap('YlGnBu'),axis=1)\n",
    "\n",
    "#this correlation matrix shows a high +ve of price to duration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32fe8bd0",
   "metadata": {},
   "source": [
    "***Note:**\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b086705",
   "metadata": {},
   "source": [
    "### Distance and Duration viz - entire taxi dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b2fb4b9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/envs/newenv/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/usr/local/anaconda3/envs/newenv/lib/python3.7/site-packages/seaborn/distributions.py:2056: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/usr/local/anaconda3/envs/newenv/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/usr/local/anaconda3/envs/newenv/lib/python3.7/site-packages/seaborn/distributions.py:2056: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distribution of Trip `Duration`'}, xlabel='duration_hours', ylabel='Counts'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "# Plot 1\n",
    "ax1 = plt.subplot(121)\n",
    "\n",
    "plt.xlabel('Distance (KM)', fontsize=14)\n",
    "plt.ylabel('Counts', fontsize=14, weight='bold')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Distribution of Trip `Distance`', color='navy', fontsize=16,  weight='bold')\n",
    "\n",
    "sns.distplot(df.manhattan_dist, ax=ax1, kde=True, rug=True, )\n",
    "\n",
    "# Plot 2\n",
    "ax2 = plt.subplot(122)\n",
    "\n",
    "plt.xlabel('Duration (seconds)', fontsize=14)\n",
    "plt.ylabel('Counts')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Distribution of Trip `Duration`', color='navy', fontsize=16,  weight='bold')\n",
    "\n",
    "sns.distplot(df.duration_hours, kde=True, rug=True, ax=ax2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c13285",
   "metadata": {},
   "source": [
    "### Trend line of taxi rides data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "07347327",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Trip Duration and Distance plot per pickup-dropoff pair'}, xlabel='manhattan_dist', ylabel='duration'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import style\n",
    "plt.style.use('ggplot')\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "sns.despine()\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "fig,ax = plt.subplots(1, figsize=(20,10))\n",
    "\n",
    "ax.set_xlabel('hour')\n",
    "ax.set_title('Trip Duration and Distance plot per pickup-dropoff pair')\n",
    "\n",
    "sns.regplot(x=\"manhattan_dist\", y=\"duration\", data=df, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f7204e",
   "metadata": {},
   "source": [
    "### Distance and Duration viz - inliers \n",
    "Output loaded from the geographical analysis of pickup points - book 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "084f4142",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp_df = pd.read_csv('./data/joined_inliers_p.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c800d597",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1032162 entries, 0 to 1032161\n",
      "Data columns (total 30 columns):\n",
      " #   Column              Non-Null Count    Dtype  \n",
      "---  ------              --------------    -----  \n",
      " 0   Unnamed: 0          1032162 non-null  int64  \n",
      " 1   id                  1032162 non-null  object \n",
      " 2   vendor_id           1032162 non-null  int64  \n",
      " 3   pickup_datetime     1032162 non-null  object \n",
      " 4   dropoff_datetime    1032162 non-null  object \n",
      " 5   passenger_count     1032162 non-null  int64  \n",
      " 6   pickup_longitude    1032162 non-null  float64\n",
      " 7   pickup_latitude     1032162 non-null  float64\n",
      " 8   dropoff_longitude   1032162 non-null  float64\n",
      " 9   dropoff_latitude    1032162 non-null  float64\n",
      " 10  store_and_fwd_flag  1032162 non-null  bool   \n",
      " 11  duration            1032162 non-null  int64  \n",
      " 12  distance            1032162 non-null  float64\n",
      " 13  manhattan_dist      1032162 non-null  float64\n",
      " 14  duration_hours      1032162 non-null  float64\n",
      " 15  duration_min        1032162 non-null  float64\n",
      " 16  dur_to_dist         1032162 non-null  float64\n",
      " 17  dur_to_dist_m       1032162 non-null  float64\n",
      " 18  speed_kmph          1032162 non-null  float64\n",
      " 19  speed_kmph_m        1032162 non-null  float64\n",
      " 20  day_name            1032162 non-null  object \n",
      " 21  hour                1032162 non-null  int64  \n",
      " 22  price               1032162 non-null  float64\n",
      " 23  month_name          1032162 non-null  object \n",
      " 24  day                 1032162 non-null  int64  \n",
      " 25  geometry            1032162 non-null  object \n",
      " 26  index_right         1032162 non-null  float64\n",
      " 27  zcta                1032162 non-null  int64  \n",
      " 28  bcode               1032162 non-null  int64  \n",
      " 29  note                341 non-null      object \n",
      "dtypes: bool(1), float64(14), int64(8), object(7)\n",
      "memory usage: 229.4+ MB\n",
      "None\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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>day_name</th>\n",
       "      <th>hour</th>\n",
       "      <th>price</th>\n",
       "      <th>month_name</th>\n",
       "      <th>day</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>zcta</th>\n",
       "      <th>bcode</th>\n",
       "      <th>note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>id3487442</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-29 18:39:00</td>\n",
       "      <td>2016-02-29 18:42:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.981819</td>\n",
       "      <td>40.768963</td>\n",
       "      <td>-73.981819</td>\n",
       "      <td>40.768963</td>\n",
       "      <td>...</td>\n",
       "      <td>Monday</td>\n",
       "      <td>18</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>February</td>\n",
       "      <td>29</td>\n",
       "      <td>POINT (-73.98181915000001 40.76896286)</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10023</td>\n",
       "      <td>36061</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>id1145809</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-16 23:15:00</td>\n",
       "      <td>2016-05-16 23:31:00</td>\n",
       "      <td>6</td>\n",
       "      <td>-73.808487</td>\n",
       "      <td>40.687336</td>\n",
       "      <td>-73.808487</td>\n",
       "      <td>40.687336</td>\n",
       "      <td>...</td>\n",
       "      <td>Monday</td>\n",
       "      <td>23</td>\n",
       "      <td>22.861667</td>\n",
       "      <td>May</td>\n",
       "      <td>16</td>\n",
       "      <td>POINT (-73.80848693999999 40.68733597)</td>\n",
       "      <td>191.0</td>\n",
       "      <td>11419</td>\n",
       "      <td>36081</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>id3499387</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-25 19:45:00</td>\n",
       "      <td>2016-01-25 19:54:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.786720</td>\n",
       "      <td>40.647041</td>\n",
       "      <td>-73.786720</td>\n",
       "      <td>40.647041</td>\n",
       "      <td>...</td>\n",
       "      <td>Monday</td>\n",
       "      <td>19</td>\n",
       "      <td>14.666667</td>\n",
       "      <td>January</td>\n",
       "      <td>25</td>\n",
       "      <td>POINT (-73.78672028 40.64704132)</td>\n",
       "      <td>202.0</td>\n",
       "      <td>11430</td>\n",
       "      <td>36081</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>id2134482</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-22 16:13:00</td>\n",
       "      <td>2016-01-22 16:13:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.776314</td>\n",
       "      <td>40.645454</td>\n",
       "      <td>-73.776314</td>\n",
       "      <td>40.645454</td>\n",
       "      <td>...</td>\n",
       "      <td>Friday</td>\n",
       "      <td>16</td>\n",
       "      <td>5.316667</td>\n",
       "      <td>January</td>\n",
       "      <td>22</td>\n",
       "      <td>POINT (-73.77631378 40.64545441)</td>\n",
       "      <td>202.0</td>\n",
       "      <td>11430</td>\n",
       "      <td>36081</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>id2892749</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-18 15:24:00</td>\n",
       "      <td>2016-01-18 15:28:00</td>\n",
       "      <td>5</td>\n",
       "      <td>-73.954666</td>\n",
       "      <td>40.821003</td>\n",
       "      <td>-73.954666</td>\n",
       "      <td>40.821003</td>\n",
       "      <td>...</td>\n",
       "      <td>Monday</td>\n",
       "      <td>15</td>\n",
       "      <td>10.156667</td>\n",
       "      <td>January</td>\n",
       "      <td>18</td>\n",
       "      <td>POINT (-73.95466614 40.82100296)</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10031</td>\n",
       "      <td>36061</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
       "0           0  id3487442          2  2016-02-29 18:39:00  2016-02-29 18:42:00   \n",
       "1           1  id1145809          2  2016-05-16 23:15:00  2016-05-16 23:31:00   \n",
       "2           2  id3499387          1  2016-01-25 19:45:00  2016-01-25 19:54:00   \n",
       "3           4  id2134482          1  2016-01-22 16:13:00  2016-01-22 16:13:00   \n",
       "4           5  id2892749          1  2016-01-18 15:24:00  2016-01-18 15:28:00   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.981819        40.768963         -73.981819   \n",
       "1                6        -73.808487        40.687336         -73.808487   \n",
       "2                1        -73.786720        40.647041         -73.786720   \n",
       "3                1        -73.776314        40.645454         -73.776314   \n",
       "4                5        -73.954666        40.821003         -73.954666   \n",
       "\n",
       "   dropoff_latitude  ...  day_name  hour      price  month_name  day  \\\n",
       "0         40.768963  ...    Monday    18   8.783333    February   29   \n",
       "1         40.687336  ...    Monday    23  22.861667         May   16   \n",
       "2         40.647041  ...    Monday    19  14.666667     January   25   \n",
       "3         40.645454  ...    Friday    16   5.316667     January   22   \n",
       "4         40.821003  ...    Monday    15  10.156667     January   18   \n",
       "\n",
       "                                 geometry  index_right   zcta  bcode  note  \n",
       "0  POINT (-73.98181915000001 40.76896286)         23.0  10023  36061   NaN  \n",
       "1  POINT (-73.80848693999999 40.68733597)        191.0  11419  36081   NaN  \n",
       "2        POINT (-73.78672028 40.64704132)        202.0  11430  36081   NaN  \n",
       "3        POINT (-73.77631378 40.64545441)        202.0  11430  36081   NaN  \n",
       "4        POINT (-73.95466614 40.82100296)         34.0  10031  36061   NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(inp_df.info())\n",
    "inp_df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b0f0401d",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>1032162.00000</td>\n",
       "      <td>962.94024</td>\n",
       "      <td>5882.81570</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>397.00000</td>\n",
       "      <td>663.00000</td>\n",
       "      <td>1076.00000</td>\n",
       "      <td>3526282.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_hours</th>\n",
       "      <td>1032162.00000</td>\n",
       "      <td>0.26748</td>\n",
       "      <td>1.63412</td>\n",
       "      <td>0.00028</td>\n",
       "      <td>0.11028</td>\n",
       "      <td>0.18417</td>\n",
       "      <td>0.29889</td>\n",
       "      <td>979.52278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>1032162.00000</td>\n",
       "      <td>1.66333</td>\n",
       "      <td>1.31349</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>8.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1032162.00000</td>\n",
       "      <td>26.17917</td>\n",
       "      <td>100.70145</td>\n",
       "      <td>0.09032</td>\n",
       "      <td>10.14404</td>\n",
       "      <td>17.28064</td>\n",
       "      <td>29.68508</td>\n",
       "      <td>58832.16996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>1032162.00000</td>\n",
       "      <td>3.45235</td>\n",
       "      <td>4.11956</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.23304</td>\n",
       "      <td>2.09923</td>\n",
       "      <td>3.89337</td>\n",
       "      <td>578.84282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manhattan_dist</th>\n",
       "      <td>1032162.00000</td>\n",
       "      <td>4.49181</td>\n",
       "      <td>5.80874</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.55894</td>\n",
       "      <td>2.63217</td>\n",
       "      <td>4.79259</td>\n",
       "      <td>872.88696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speed_kmph_m</th>\n",
       "      <td>1032162.00000</td>\n",
       "      <td>18.44746</td>\n",
       "      <td>15.32728</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>11.39293</td>\n",
       "      <td>16.11273</td>\n",
       "      <td>22.76501</td>\n",
       "      <td>8191.34609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count      mean        std     min       25%  \\\n",
       "duration        1032162.00000 962.94024 5882.81570 1.00000 397.00000   \n",
       "duration_hours  1032162.00000   0.26748    1.63412 0.00028   0.11028   \n",
       "passenger_count 1032162.00000   1.66333    1.31349 0.00000   1.00000   \n",
       "price           1032162.00000  26.17917  100.70145 0.09032  10.14404   \n",
       "distance        1032162.00000   3.45235    4.11956 0.00000   1.23304   \n",
       "manhattan_dist  1032162.00000   4.49181    5.80874 0.00000   1.55894   \n",
       "speed_kmph_m    1032162.00000  18.44746   15.32728 0.00000  11.39293   \n",
       "\n",
       "                      50%        75%           max  \n",
       "duration        663.00000 1076.00000 3526282.00000  \n",
       "duration_hours    0.18417    0.29889     979.52278  \n",
       "passenger_count   1.00000    2.00000       8.00000  \n",
       "price            17.28064   29.68508   58832.16996  \n",
       "distance          2.09923    3.89337     578.84282  \n",
       "manhattan_dist    2.63217    4.79259     872.88696  \n",
       "speed_kmph_m     16.11273   22.76501    8191.34609  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp_df[['duration','duration_hours','passenger_count','price','distance','manhattan_dist','speed_kmph_m']].describe().T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "21f7f5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            background-color:  #35aac3;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_e5726_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >duration</th>        <th class=\"col_heading level0 col1\" >duration_hours</th>        <th class=\"col_heading level0 col2\" >passenger_count</th>        <th class=\"col_heading level0 col3\" >price</th>        <th class=\"col_heading level0 col4\" >distance</th>        <th class=\"col_heading level0 col5\" >manhattan_dist</th>        <th class=\"col_heading level0 col6\" >speed_kmph_m</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_e5726_level0_row0\" class=\"row_heading level0 row0\" >duration</th>\n",
       "                        <td id=\"T_e5726_row0_col0\" class=\"data row0 col0\" >1.0</td>\n",
       "                        <td id=\"T_e5726_row0_col1\" class=\"data row0 col1\" >1.0</td>\n",
       "                        <td id=\"T_e5726_row0_col2\" class=\"data row0 col2\" >0.0072</td>\n",
       "                        <td id=\"T_e5726_row0_col3\" class=\"data row0 col3\" >0.99</td>\n",
       "                        <td id=\"T_e5726_row0_col4\" class=\"data row0 col4\" >0.089</td>\n",
       "                        <td id=\"T_e5726_row0_col5\" class=\"data row0 col5\" >0.089</td>\n",
       "                        <td id=\"T_e5726_row0_col6\" class=\"data row0 col6\" >-0.018</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5726_level0_row1\" class=\"row_heading level0 row1\" >duration_hours</th>\n",
       "                        <td id=\"T_e5726_row1_col0\" class=\"data row1 col0\" >1.0</td>\n",
       "                        <td id=\"T_e5726_row1_col1\" class=\"data row1 col1\" >1.0</td>\n",
       "                        <td id=\"T_e5726_row1_col2\" class=\"data row1 col2\" >0.0072</td>\n",
       "                        <td id=\"T_e5726_row1_col3\" class=\"data row1 col3\" >0.99</td>\n",
       "                        <td id=\"T_e5726_row1_col4\" class=\"data row1 col4\" >0.089</td>\n",
       "                        <td id=\"T_e5726_row1_col5\" class=\"data row1 col5\" >0.089</td>\n",
       "                        <td id=\"T_e5726_row1_col6\" class=\"data row1 col6\" >-0.018</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5726_level0_row2\" class=\"row_heading level0 row2\" >passenger_count</th>\n",
       "                        <td id=\"T_e5726_row2_col0\" class=\"data row2 col0\" >0.0072</td>\n",
       "                        <td id=\"T_e5726_row2_col1\" class=\"data row2 col1\" >0.0072</td>\n",
       "                        <td id=\"T_e5726_row2_col2\" class=\"data row2 col2\" >1.0</td>\n",
       "                        <td id=\"T_e5726_row2_col3\" class=\"data row2 col3\" >0.017</td>\n",
       "                        <td id=\"T_e5726_row2_col4\" class=\"data row2 col4\" >0.0098</td>\n",
       "                        <td id=\"T_e5726_row2_col5\" class=\"data row2 col5\" >0.01</td>\n",
       "                        <td id=\"T_e5726_row2_col6\" class=\"data row2 col6\" >-0.0026</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5726_level0_row3\" class=\"row_heading level0 row3\" >price</th>\n",
       "                        <td id=\"T_e5726_row3_col0\" class=\"data row3 col0\" >0.99</td>\n",
       "                        <td id=\"T_e5726_row3_col1\" class=\"data row3 col1\" >0.99</td>\n",
       "                        <td id=\"T_e5726_row3_col2\" class=\"data row3 col2\" >0.017</td>\n",
       "                        <td id=\"T_e5726_row3_col3\" class=\"data row3 col3\" >1.0</td>\n",
       "                        <td id=\"T_e5726_row3_col4\" class=\"data row3 col4\" >0.21</td>\n",
       "                        <td id=\"T_e5726_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
       "                        <td id=\"T_e5726_row3_col6\" class=\"data row3 col6\" >0.051</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5726_level0_row4\" class=\"row_heading level0 row4\" >distance</th>\n",
       "                        <td id=\"T_e5726_row4_col0\" class=\"data row4 col0\" >0.089</td>\n",
       "                        <td id=\"T_e5726_row4_col1\" class=\"data row4 col1\" >0.089</td>\n",
       "                        <td id=\"T_e5726_row4_col2\" class=\"data row4 col2\" >0.0098</td>\n",
       "                        <td id=\"T_e5726_row4_col3\" class=\"data row4 col3\" >0.21</td>\n",
       "                        <td id=\"T_e5726_row4_col4\" class=\"data row4 col4\" >1.0</td>\n",
       "                        <td id=\"T_e5726_row4_col5\" class=\"data row4 col5\" >0.99</td>\n",
       "                        <td id=\"T_e5726_row4_col6\" class=\"data row4 col6\" >0.54</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5726_level0_row5\" class=\"row_heading level0 row5\" >manhattan_dist</th>\n",
       "                        <td id=\"T_e5726_row5_col0\" class=\"data row5 col0\" >0.089</td>\n",
       "                        <td id=\"T_e5726_row5_col1\" class=\"data row5 col1\" >0.089</td>\n",
       "                        <td id=\"T_e5726_row5_col2\" class=\"data row5 col2\" >0.01</td>\n",
       "                        <td id=\"T_e5726_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
       "                        <td id=\"T_e5726_row5_col4\" class=\"data row5 col4\" >0.99</td>\n",
       "                        <td id=\"T_e5726_row5_col5\" class=\"data row5 col5\" >1.0</td>\n",
       "                        <td id=\"T_e5726_row5_col6\" class=\"data row5 col6\" >0.53</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5726_level0_row6\" class=\"row_heading level0 row6\" >speed_kmph_m</th>\n",
       "                        <td id=\"T_e5726_row6_col0\" class=\"data row6 col0\" >-0.018</td>\n",
       "                        <td id=\"T_e5726_row6_col1\" class=\"data row6 col1\" >-0.018</td>\n",
       "                        <td id=\"T_e5726_row6_col2\" class=\"data row6 col2\" >-0.0026</td>\n",
       "                        <td id=\"T_e5726_row6_col3\" class=\"data row6 col3\" >0.051</td>\n",
       "                        <td id=\"T_e5726_row6_col4\" class=\"data row6 col4\" >0.54</td>\n",
       "                        <td id=\"T_e5726_row6_col5\" class=\"data row6 col5\" >0.53</td>\n",
       "                        <td id=\"T_e5726_row6_col6\" class=\"data row6 col6\" >1.0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdd6ce009d0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp_df[['duration','duration_hours','passenger_count','price','distance','manhattan_dist','speed_kmph_m']].corr().style.format(\"{:.2}\").background_gradient(cmap=plt.get_cmap('YlGnBu'),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7a523796",
   "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",
    "# Plot 1\n",
    "ax1 = plt.subplot(121)\n",
    "\n",
    "plt.xlabel('Distance (KM)', fontsize=14)\n",
    "plt.ylabel('Counts', fontsize=14, weight='bold')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Distribution of Trip `Distance`', color='navy', fontsize=16,  weight='bold')\n",
    "\n",
    "plt.xlim([-1,30])\n",
    "ax1= sns.histplot(data= inp_df, x='manhattan_dist', kde=True)\n",
    "\n",
    "# ax1.set_xticks(range(-1,30))\n",
    "\n",
    "# Plot 2\n",
    "ax2 = plt.subplot(122)\n",
    "\n",
    "plt.xlabel('Duration (seconds)', fontsize=14)\n",
    "plt.ylabel('Counts',fontsize=14, weight='bold')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.title('Distribution of Trip `Duration`', color='navy', fontsize=16,  weight='bold')\n",
    "\n",
    "plt.xlim([0,6200])\n",
    "ax2= sns.histplot(data=inp_df, x='duration', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d88119e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Trip Duration and Distance plot per pickup-dropoff pair'}, xlabel='manhattan_dist', ylabel='duration'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qfOc738nDDz+cn/3sZ5kyZUqbj+vSpUtGjRqVJNljjz0q85vXaw6dDj/88Kyzzjrp3LlzJZR5PyxtTKPm5+Liiy/OySefnNtvvz3f/va3c8EFFyxzjKvevXvnF7/4Ra6//vq8/PLL+c1vflMJpObNm7fE+oceemg6deqUj3/849loo42SpNJdqLnFy7Bhw7LBBhskSU444YQ88MAD+fWvf91qnVtuuSW77LJLhg0blokTJybJct1Zqvl57dKlS44++ugkyVNPPdXm+a2rq6uEhyeeeGJ69eqVmpqanHbaaamurs4bb7yRf/7zn4X7XNE6mo/xqaeeyl577ZWhQ4fmhhtuaPMYq6urM3z48JRKpcLXfP/+/TNy5Mgki1qnNYeTi3enbNa8r2233bZyfRx00EH561//mj/96U9LDPbcPN7WnnvuWWnd82506tQpxx57bEqlUjbbbLPK62RZ703Ngcg222yT7bffPkkycuTIfPSjH21z/cXfb5rfAwYNGpT99tsvSTJgwIB84QtfSPLOe8jy1ti8/le+8pX0798/pVIpxx13XHr37p36+vpW3SHfq2HDhmWzzTar7KO5VdyECRMqLSj/+Mc/Zu7cubn22mtz++23J2n79bm4QYMG5WMf+1i6du36gYzNBECxVb4725w5c3LwwQfnF7/4Rfr377/U9f7zn//knHPOyZtvvpnevXvnoosucvcPgHawIn/9/9CHPrTE49566613td/msVWKPsg2NDS0Ob93796tphsbG3PeeeflxhtvzIIFC/Kxj32s0uWmrXFflqW59c3iP6f69++fCRMmLNE6Z911161839xaZln7nDt3bs4555zcfvvtaWhoyMYbb1zpVrX449Zee+1K4Nayq01zuNMcqLR8Pt5ti45mzV1jkqWfn2OPPTZvvvlmbrrpptx2222VVj077rhjfvKTn6Rnz55L3f7Pf/7z/PrXv87s2bPz0Y9+tPL8tfWctXxum4+/eb3mMVrWWWedyjrdu3dv1aKueZ033nhjiW03D1K8LG1d8+VyudW4N83eeOONSija8trp3r171l133cycOTMzZ84s3OeK1tF8jPPmzVvig/7ix7j22msv9x21WrZIa7nfpb3m2zofnTp1Sr9+/dpcv/m5uuWWW/K1r30tH/nIR5ZZz9LeC9Zee+3U1tYutc6TTjop//rXvyrL99prr8r5a+sYX3nllSX2sfj7TfP7V1vvEcmSLfiKamzrPaeqqiof/ehHM2PGjHfdYrMtLY+5U6dOWWeddTJjxozMnj07SXLjjTfm4osvzowZM/LhD38466+/fpLlex9d/HkCoOOt0i2RHnvssRxyyCF54YUXlrleuVzO8ccfn6OPPjp/+MMfMmDAgFx22WUfTJEAa5jOnTsv97rNrWWSVMYWavmBcUU0/wW+ucVCy1YKzQMsJ0v/wNpyvJFkUfe0q6++Ov37989f//rX3HHHHa0G0k6yzPFfWmr+kNXyeJNFAz63XN6s5fgwy7OPn/70p7n11luz9dZb5/7778+tt96arbbaqs11i7bd/AeWlgN5Fw3qXaS5pUl1dXU++clPtrlOdXV1vvnNb+bvf/97rrnmmnzjG99I3759c++991Z+ZrdV71//+tf8v//3/1JbW5vbbrstd999dw466KCl1lJdXV35fvHtNR97y6DkjTfeyE033ZQJEyakXC5XwpfRo0fnqaeeylNPPZV//vOfeeqpp3L55ZcXPhdtXfOlUqnNkGzdddet1NvycXPnzq0Ec4tfO8trWXU0H+Ohhx5aOcYJEyZk0qRJefzxx1ttZ/HXTdE+WwYHRa/5ts5HQ0NDrr/++jzyyCOtXtdJcuSRR2a33XZLXV1dq/HDVvS9YNasWa3Cs8XrfP311zNt2rTK15tvvtlmrW1NN1v8eWt+zpf3PaKoxrbec5qamiqB1ru9btrSch91dXWVgHWdddbJ008/nW9961t58803c/311+e+++7LCSecsNzbXpHrC4APxiodIt1www0555xzWv2F9Pe//33233//jBgxImeccUYWLlyYJ554It26dcvOO++cJDnuuOMqTfkBeH8tb7CSJGPGjMmcOXMyZ86cXHvttUnSahDo5VFXV5fRo0fnueeeS6lUqnT/aPmhvDlg+vOf/7zULhSL1908RlCXLl3Sq1evzJgxI3/5y1+SvNNqp2UgM2fOnKW2bGgec+bGG2/Mk08+mST5wx/+UOk6tuuuu67AES+pudbu3btnnXXWyfPPP59//OMfrWpdXs1djK6++uq88cYbmT9/fsaMGfOua3vqqacqH+iHDRvWZmBQLpdz0EEHZauttsrll1+erbfeOsccc0wlCGsOTFoGQM3Pd/Ng5bW1tenbt2/mzJmTP/7xj0lW/Ni32WabJMmdd96Z5557LklyxRVX5Iwzzsgpp5ySUqlUuT6vuuqqzJ07N3PmzMn++++fbbfddom7uLXlyiuvzMyZM1NXV5crrrgiSfLJT36yzQGYa2trK4OQjx49Oq+//noaGhpywQUXpLGxMb169cqnPvWpJO9ci8u6Dpe3juZj/NOf/pSXX345TU1NOeOMMzJ48OAlBkJekdf7jBkzKgMljx8/PuPHj0+y9Nd887GPHz++ctv5sWPH5uyzz84xxxyzxPk99dRTc9JJJ6VUKuWWW26p3FVwRd8LGhsbc8kll6SpqSn/+c9/cuedd7aq8+qrr66Ea0899VTOP//8yuvm0UcfrXRBvP7669tshZQs+bw1v0c89thjueWWW5IkTz75ZGWMo8UD7KIam7f361//uhLeXXbZZZkxY0Zqa2uz0047tVnXu3HnnXdWuvNdccUVqa+vT21tbQYOHJhnn3025XI5VVVV6devX+rq6io3W1ie1+eKXF8AfDBW6e5s3//+91tNP/PMM7nhhhty3XXXpXPnzvnxj3+cyy+/PB/72Mfy4Q9/OGeccUYmTZqUT3ziE/nWt77VQVUD0OyJJ57I9ttvn3K5XPngceyxxxY+7oorrsiNN96YpqamzJ49OwsXLkyy6I5TzePcbLTRRpW7px111FHZYIMN8uKLL7a6o9qybLXVVvntb3+biRMnZrvttsvChQsrH86bu66su+666datW+bNm5dDDjkkO+20U5t3UDv00ENzyy235Pnnn8+IESMqd0RKki996UsZOHDg8j1hy6j1b3/7W8aNG5dtttkmc+fOXaJ72vI69thjc/fdd+ff//53dthhh1RXV6d79+6prq5e7vGmmv9oU1dXVwmAevfu3erW7S2VSqXsvffe+de//pVLL700V1xxRZqamjJv3rxUVVVl3333TdK6a87OO++cL3zhC/nc5z6XZNHt1Xfcccc0NjZWWpus6LGPHDkyV199dZ5//vkMHz48PXr0qLRWab4N+zHHHJM777wzDz30ULbbbrtUV1dn/vz56devX+WD+7JMnTo1O++8c2pra7NgwYKUSqVltsw49dRTc8ghh+Sxxx7LjjvumNra2syfPz9VVVU5++yzKy3/1ltvvSSLwq0bbrghf/3rX5fZBXBZdey333751a9+lZdffjl77LFHunfvnrfeeiu1tbXZc889l+/JbEO3bt3ygx/8IBdffHHl+h80aFDlelncTjvtlG222SYPPfRQvvSlL6Vnz56VLlJHHXXUEq1UmscH2nPPPfOnP/0pP/nJT/LTn/50hd8LOnXqlKuvvjrXX3995s+fn3K5nPXWW68yVlFbhg4dmkGDBmXChAk58sgjK+8Ly/t+s/XWW2efffbJH//4x5x66qn5zne+U3mOBg4cuMQfP4tqPOGEE3LXXXflpZdeym677ZauXbtWQrNvfvOb6du3b2FNy6umpiYHHnhgq/e1Qw89NOuuu2622GKLyjX2uc99LjU1NZXBxlf09QnAymGVbom0uAcffDAvvvhiDjzwwIwYMSLjxo3Lf/7znzQ0NOShhx7KIYcckptvvjnrr79+zj///I4uF2CNd8kll1QClE022SSXXXZZNt9888LHzZ07N9OmTcuMGTNSU1OTwYMH58ILL8xJJ51UWae6ujo/+9nPsvnmm6dUKqWmpiY//elPs+mmmy5XbSNGjKgMRFsqlbLlllvmO9/5TpJF3bPq6+vTqVOnnHLKKendu3fK5fJSb4Xdo0ePXH/99Tn00EPzkY98JHV1dfn4xz+es846q3K3qffiqKOOykEHHZR11lknVVVV+cxnPpP/+Z//SZJKi6TltcUWW+Syyy7LJptskqqqqmy++ea54oorWrUCKtLczWfOnDn5yEc+koMOOig33XTTMj+4Hn744TnvvPOy+eabp6mpKbW1tRkyZEguu+yybLfddkkW3W78+OOPr4xr1KNHj3z605/Ot771rXz0ox9NqVTKxhtvnB/+8IepqqrKM888s0JjBnXt2jX/93//l/322y9rr712Ghoa8slPfjIXXnhhRowYkSTZbLPN8pvf/KYyKHKnTp2y22675aqrrmo13tLSfPe7381OO+2UpqambLDBBrnwwguX2RJtwIAB+d3vfpdhw4alR48eaWpqyuDBg/OrX/0qe+21V2W9I444IltssUVqamrSu3fvpd4VbHnq6NKlS66++urss88+6dmzZxoaGrLllltmzJgx7+lOfVtssUW+973vpUePHunatWv22muvjBkzZqnXVlVVVX7xi1/k0EMPTe/evbNw4cJsuOGGOfPMM3PcccctdT8nnnhiqqqq8pe//CX/+te/Vvi9oHfv3rn00kvTu3fvdOrUKTvuuGOuvPLKZd7qvrq6OmPGjMlee+2Vrl27Zu211863vvWtFWpl+KMf/Sinn356Nt5449TX16d379457LDD8pvf/GaJbsJFNfbr1y9jx47NyJEj8+EPfzj19fUZMGBAfvzjH1fuOPh+Oeyww3LYYYelVCpl3XXXzXHHHVd5//mv//qv/OhHP8rHPvaxVFdXp3///vnBD36QtddeO2+99VZlUHoAVh2l8oqODroS2nXXXXPVVVdl3Lhxefnllyu/kM+dOzeNjY154oknct555+UPf/hDkuTZZ5/NSSedtFy34gXg/TV58uRK14xx48Yt86YIsLpoDiyuuuqqSjetNaWOSy+9NKNHj84222yzxK3qVyZjx47N6aefnvXWW2+JOyauLFamGg899NA89NBDOeGEE3LiiSd2aC0AfHBWq5ZI2267be6888689tprKZfL+fa3v50rr7wygwcPzqxZsyrjUNx1113L9ZduAAAAABZZpcdEWtxmm22WE044IYcffniampoyYMCAHHPMMencuXN++tOf5qyzzqqMW3DBBRd0dLkAAAAAq4zVojsbAAAAAO1rterOBgAAAED7WCW7sy1YsCATJ05M7969V+huMQAAAAC0rbGxMTNmzMgWW2yRLl26LLF8lQyRJk6cmFGjRnV0GQAAAACrnWuuuSZDhgxZYv4qGSL17t07yaKD6tevXwdXAwAAALDqmzp1akaNGlXJXRa3SoZIzV3Y+vXrl/79+3dwNQAAAACrj6UNHWRgbQAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoVNPRBaxOxk+alrH3PJtps+alb69uGbnLRhkyoG9HlwUAAADwnmmJ9D4ZP2laxoydkNdnz89aXWvy+uz5GTN2QsZPmtbRpQEAAAC8Z0Kk98nYe55NTU0pXTrVpFRa9H9NTSlj73m2o0sDAAAAeM+ESO+TabPmpXNtdat5nWurM33WvA6qCAAAAOD9065jIl188cW54447UiqVcsABB+SII45otXz06NG56aab0rNnzyTJgQcemFGjRrVnSe2mb69ueX32/HTp9M5TurC+MX16devAqgAAAADeH+0WIj300EP5xz/+kT/84Q9paGjI3nvvnaFDh+YTn/hEZZ2JEyfmoosuyuDBg9urjA/MyF02ypixE7IgDelcW52F9Y1paChn5C4bdXRpAAAAAO9Zu4VI22yzTa666qrU1NRk2rRpaWxsTLdurVvlTJw4MWPGjMmUKVPy6U9/Ov/7v/+bzp07t1pn9uzZmT17dqt5U6dOba+y37UhA/omIwdl7D3PZvqseenj7mwAAADAaqRdu7PV1tbmkksuya9//evsueee6dv3nUBl7ty5GTBgQE455ZRssMEGOe200/Kzn/0sJ598cqttXHnllRk9enR7lvm+GTKgr9AIAAAAWC2VyuVyub13Mn/+/Bx33HHZe++9c9BBB7W5zr///e+cccYZ+f3vf99q/tJaIo0aNSrjxo1L//7926tsAAAAgDXG5MmTs9tuuy01b2m3lkjPPfdc6urqMmDAgHTt2jV77LFHnnrqqcryV155Jffff38OOOCAJEm5XE5NzZLl9OzZszLwNgAAAAAdo6q9Njx58uScddZZqaurS11dXcaNG5ett966srxLly750Y9+lJdffjnlcjnXXHNNdt999/YqBwAAAID3oN1aIg0dOjQTJkzIfvvtl+rq6uyxxx4ZPnx4jj766Jx00kkZOHBgzj333Bx//PGpr6/Ppz71qRxxxBHtVQ4AAAAA78EHMibS+62ojx4AAAAAK6Yob2m37mwAAAAArD6ESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIXaNUS6+OKLs/fee2f48OG54oorllg+adKkjBw5MsOGDcuZZ56ZhoaG9iwHAAAAgHep3UKkhx56KP/4xz/yhz/8ITfddFOuvvrq/Oc//2m1zimnnJKzzz47d9xxR8rlcm644Yb2KgcAAACA96DdQqRtttkmV111VWpqavLaa6+lsbEx3bp1qyyfMmVKFixYkK222ipJMnLkyNx+++1LbGf27NmZPHlyq6+pU6e2V9kAAAAAtKGmPTdeW1ubSy65JL/+9a+z5557pm/fvpVl06dPT+/evSvTvXv3zrRp05bYxpVXXpnRo0e3Z5kAAAAAFGj3gbVPOumkPPDAA3n11VdbdVdrampKqVSqTJfL5VbTzQ4//PCMGzeu1dc111zT3mUDAAAA0EK7tUR67rnnUldXlwEDBqRr167ZY4898tRTT1WW9+vXLzNmzKhMz5w5M3369FliOz179kzPnj3bq0wAAAAAlkO7tUSaPHlyzjrrrNTV1aWuri7jxo3L1ltvXVm+3nrrpXPnznnkkUeSJLfcckt23nnn9ioHAAAAgPeg3UKkoUOHZpdddsl+++2Xz3/+8xk8eHCGDx+eo48+Oo8//niS5MILL8x5552XPffcM/Pmzcthhx3WXuUAAAAA8B6UyuVyuaOLWFGTJ0/ObrvtlnHjxqV///4dXQ4AAADAKq8ob2n3gbUBAAAAWPUJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAArVtOfGR48enT/96U9JkqFDh+bUU09dYvlNN92Unj17JkkOPPDAjBo1qj1LAgAAAOBdaLcQ6f7778+9996bm2++OaVSKUcddVTuvPPO7L777pV1Jk6cmIsuuiiDBw9urzIAAAAAeB+0W4jUu3fvnHbaaenUqVOSZMMNN8wrr7zSap2JEydmzJgxmTJlSj796U/nf//3f9O5c+dW68yePTuzZ89uNW/q1KntVTYAAAAAbWi3EGnjjTeufP/CCy/kT3/6U377299W5s2dOzcDBgzIKaeckg022CCnnXZafvazn+Xkk09utZ0rr7wyo0ePbq8yAQAAAFgOpXK5XG7PHTzzzDM59thjc+KJJ2b//fdf6nr//ve/c8YZZ+T3v/99q/lLa4k0atSojBs3Lv3792+PsgEAAADWKJMnT85uu+221LylXQfWfuSRR3LSSSfljDPOyPDhw1ste+WVV3L//ffngAMOSJKUy+XU1CxZTs+ePSsDbwMAAADQMaraa8Ovvvpqvva1r+XCCy9cIkBKki5duuRHP/pRXn755ZTL5VxzzTWtBt0GAAAAYOXRbi2RLr/88ixcuDDnn39+Zd7BBx+cu+66KyeddFIGDhyYc889N8cff3zq6+vzqU99KkcccUR7lQMAAADAe9DuYyK1h6I+egAAAACsmKK8pd26swEAAACw+hAiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUqunoAt6Lx5+dmZ/d+mKmzZqXvr26ZeQuG2XIgL4dXRYAAADAameVbol07R1P5vXZ87NW15q8Pnt+xoydkPGTpnV0WQAAAACrnVU6RKquLqVLp5qUSov+r6kpZew9z3Z0WQAAAACrnVU6ROpU27r8zrXVmT5rXgdVAwAAALD6WqVDpLr6plbTC+sb06dXtw6qBgAAAGD1tUqHSI2N5Syoa0i5vOj/hoZyRu6yUUeXBQAAALDaWaVDpC8O2yzr9uyaOfMbsm7Prjl25CB3ZwMAAABoBzUdXcB7MXCjD2evXbbq6DIAAAAAVnurdEskAAAAAD4YQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEI1HV1Aexs/aVrG3vNsps2al769umXkLhtlyIC+HV0WAAAAwCpltW6JNH7StIwZOyGvz56ftbrW5PXZ8zNm7ISMnzSto0sDAAAAWKWs1iHS2HueTU1NKV061aRUWvR/TU0pY+95tqNLAwAAAFilrNYh0rRZ89K5trrVvM611Zk+a14HVQQAAACwalqtQ6S+vbplYX1jq3kL6xvTp1e3DqoIAAAAYNW0WodII3fZKA0N5Syoa0i5vOj/hoZyRu6yUUeXBgAAALBKWa1DpCED+ubYkYOybs+umTO/Iev27JpjRw5ydzYAAACAFVTTnhsfPXp0/vSnPyVJhg4dmlNPPbXV8kmTJuXMM8/M3LlzM2TIkHznO99JTc37W9KQAX2FRgAAAADvUbu1RLr//vtz77335uabb87vf//7PPHEE7nzzjtbrXPKKafk7LPPzh133JFyuZwbbrihvcoBAAAA4D1otxCpd+/eOe2009KpU6fU1tZmww03zCuvvFJZPmXKlCxYsCBbbbVVkmTkyJG5/fbbl9jO7NmzM3ny5FZfU6dOba+yAQAAAGhDu3Vn23jjjSvfv/DCC/nTn/6U3/72t5V506dPT+/evSvTvXv3zrRp05bYzpVXXpnRo0e3V5kAAAAALId2HRMpSZ555pkce+yxOfXUU/Oxj32sMr+pqSmlUqkyXS6XW003O/zww7P//vu3mjd16tSMGjWq3WoGAAAAoLV2DZEeeeSRnHTSSTnjjDMyfPjwVsv69euXGTNmVKZnzpyZPn36LLGNnj17pmfPnu1ZJgAAAAAF2m1MpFdffTVf+9rXcuGFFy4RICXJeuutl86dO+eRRx5Jktxyyy3Zeeed26scAAAAAN6DdmuJdPnll2fhwoU5//zzK/MOPvjg3HXXXTnppJMycODAXHjhhTnrrLMyZ86cbL755jnssMPaqxwAAAAA3oNSuVwud3QRK2ry5MnZbbfdMm7cuPTv37+jywEAAABY5RXlLe0+sPbqbvykaRl7z7OZNmte+vbqlpG7bJQhA/p2dFkAAAAA76t2GxNpTTB+0rSMGTshr8+en7W61uT12fMzZuyEjJ80raNLAwAAAHhfCZHeg7H3PJuamlK6dKpJqbTo/5qaUsbe82xHlwYAAADwvhIivQfTZs1L59rqVvM611Zn+qx5HVQRAAAAQPsQIr0HfXt1y8L6xlbzFtY3pk+vbh1UEQAAAED7ECK9ByN32SgNDeUsqGtIubzo/4aGckbuslFHlwYAAADwvhIivQdDBvTNsSMHZd2eXTNnfkPW7dk1x44c5O5sAAAAwGqnpqMLWNUNGdBXaAQAAACs9rREAgAAAKDQcrVEmj9/fm6//fa8+eabKZfLlflHHHFEuxUGAAAAwMpjuUKkU089NVOmTMkmm2ySUqnU3jUBAAAAsJJZrhDpqaeeym233ZaaGkMoAQAAAKyJlmtMpH79+rV3HQAAAACsxJaradEmm2ySww47LDvttFO6dOlSmW9MJAAAAIA1w3KFSHPnzs0GG2yQl156qb3rAQAAAGAltFwh0nnnnZckmTJlShoaGrLBBhu0a1EAAAAArFyWK0R68cUX89WvfjXTp09PU1NT1l133YwZMyYbbrhhe9cHAAAAwEpguQbWPvfcc3PUUUfl4YcfziOPPJLjjz8+3/nOd9q7NgAAAABWEssVIr322mvZf//9K9Of//zn8/rrr7dbUQAAAACsXJYrRGpsbMwbb7xRmZ41a1Z71QMAAADASmi5xkT60pe+lIMOOih77bVXSqVSbrvtthx++OHtXRsAAAAAK4nlCpEOOuigbLDBBvn73/+epqamnHPOOfnMZz7T3rUBAAAAsJJYZoj03HPPZcMNN8wTTzyRtdZaK3vvvXdl2RNPPJHNN9+83QsEAAAAoOMtM0S64IILMmbMmJx44olLLCuVShk3bly7FQYAAADAymOZIdKYMWOSJNdee2369evXatkzzzzTflUBAAAAsFJZ5t3Z3njjjbzxxhs55phj8uabb+aNN97Im2++mZkzZ7bZOgkAAACA1dMyWyL993//d+67774kybbbbvvOg2pqMmzYsPatDAAAAICVxjJDpMsvvzxJcvrpp+e88877QAoCAAAAYOWzzBCp2XnnnZc33ngj8+fPT7lcTmNjY1566aXssMMO7V0fAAAAACuB5QqRLrnkksog29XV1amvr89GG22UW2+9tV2LAwAAAGDlsMyBtZv9/ve/z913351hw4blz3/+c84777xstNFG7V0bAAAAACuJ5QqRevXqlT59+uQTn/hEnnzyyey33355+umn27s2AAAAAFYSyxUi1dTU5KWXXsonPvGJjB8/Pg0NDVm4cGF71wYAAADASmK5QqTjjjsu3/rWt7LLLrvkzjvvzC677JLtttuuvWsDAAAAYCWxXANrNzQ05Morr0yyaHykF198MZtuumm7FgYAAADAymO5WiL95Cc/qXzftWvXbLbZZimVSu1WFAAAAAArl+VqibTJJpvk5z//eYYMGZJu3bpV5m+++ebtVhgAAAAAK4/lCpEee+yxPPbYY7nxxhsr80qlUsaNG9duhQEAAACw8liuEOmuu+5q7zoAAAAAWIktV4h0xRVXtDn/iCOOeF+LAQAAAGDltFwh0tNPP135vq6uLg8//HC23377disKAAAAgJXLcoVI5513XqvpadOm5cwzz2yXggAAAABY+VS9mwf17ds3U6ZMeb9rAQAAAGAltcJjIpXL5Tz++OP50Ic+1G5FAQAAALByWeExkUqlUtZbb72cdtpp7VYUAAAAACuXZYZIhx56aEqlUsrlcuX/JJk8eXJOOeWUXHXVVR9IkQAAAAB0rGWGSF/60peSJHfeeWfmzJmTz3/+86murs4tt9ySnj17fiAFAgAAANDxlhkiDRs2LEly+eWX57rrrktV1aJxuHfZZZccdNBB7V8dAAAAACuF5bo72+uvv56FCxdWpufOnZs333yz3YoCAAAAYOWyXANr77PPPjnwwAOz++67p1wu5/bbb8+BBx7Y3rUBAAAAsJJYrhDp61//ejbffPP84x//SJKcdtppGTp0aLsWBgAAAMDKY7lCpCT53Oc+l8997nPtWQsAAAAAK6nlGhMJAAAAgDWbEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgULuGSHPmzMk+++yTyZMnL7Fs9OjR+exnP5sRI0ZkxIgRueaaa9qzFAAAAADeg5r22vBjjz2Ws846Ky+88EKbyydOnJiLLroogwcPbq8SAAAAAHiftFuIdMMNN+Scc87Jqaee2ubyiRMnZsyYMZkyZUo+/elP53//93/TuXPnJdabPXt2Zs+e3Wre1KlT26VmAAAAANrWbiHS97///aUumzt3bgYMGJBTTjklG2ywQU477bT87Gc/y8knn7zEuldeeWVGjx7dXmUCAAAAsBzaLURalu7du+eXv/xlZfrII4/MGWec0WaIdPjhh2f//fdvNW/q1KkZNWpUu9cJAAAAwCIdEiK98soruf/++3PAAQckScrlcmpq2i6lZ8+e6dmz5wdZHgAAAACLade7sy1Nly5d8qMf/Sgvv/xyyuVyrrnmmuy+++4dUQoAAAAAy+EDDZGOPvroPP744+nVq1fOPffcHH/88dlzzz1TLpdzxBFHfJClAAAAALAC2r0721133VX5vuU4SMOGDcuwYcPae/cAAAAAvA86pDsbAAAAAKsWIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEAhIRIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACFhEgAAAAAFBIiAQAAAFBIiAQAAABAISESAAAAAIWESAAAAAAUEiIBAAAAUEiIBAAAAEChdg2R5syZk3322SeTJ09eYtmkSZMycuTIDBs2LGeeeWYaGhrasxQAAAAA3oN2C5Eee+yxHHLIIXnhhRfaXH7KKafk7LPPzh133JFyuZwbbrihvUoBAAAA4D1qtxDphhtuyDnnnJM+ffossWzKlClZsGBBttpqqyTJyJEjc/vtt7e5ndmzZ2fy5MmtvqZOndpeZQMAAADQhpr22vD3v//9pS6bPn16evfuXZnu3bt3pk2b1ua6V155ZUaPHv2+1wcAAADA8mu3EGlZmpqaUiqVKtPlcrnVdEuHH3549t9//1bzpk6dmlGjRrVrjQAAAAC8o0NCpH79+mXGjBmV6ZkzZ7bZ7S1JevbsmZ49e35QpQEAAADQhna9O9vSrLfeeuncuXMeeeSRJMktt9ySnXfeuSNKAQAAAGA5fKAh0tFHH53HH388SXLhhRfmvPPOy5577pl58+blsMMO+yBLAQAAAGAFtHt3trvuuqvy/S9/+cvK95tttll+97vftffuAQAAAHgfdEh3NgAAAABWLUIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQu0aIt16663Ze++9s8cee+Saa65ZYvno0aPz2c9+NiNGjMiIESPaXAcAAACAjlfTXhueNm1afvKTn2Ts2LHp1KlTDj744Gy77bbZaKONKutMnDgxF110UQYPHtxeZQAAAADwPmi3lkj3339/tttuu6yzzjrp1q1bhg0blttvv73VOhMnTsyYMWOy77775txzz83ChQvbqxwAAAAA3oN2C5GmT5+e3r17V6b79OmTadOmVabnzp2bAQMG5JRTTsnNN9+c2bNn52c/+9kS25k9e3YmT57c6mvq1KntVTYAAAAAbWi37mxNTU0plUqV6XK53Gq6e/fu+eUvf1mZPvLII3PGGWfk5JNPbrWdK6+8MqNHj26vMgEAAABYDu0WIvXr1y/jx4+vTM+YMSN9+vSpTL/yyiu5//77c8ABByRZFDLV1CxZzuGHH57999+/1bypU6dm1KhR7VQ5AAAAAItrt+5sn/nMZ/LAAw9k1qxZmT9/fv785z9n5513rizv0qVLfvSjH+Xll19OuVzONddck913332J7fTs2TP9+/dv9dWvX7/2KhsAAACANrRbiNS3b9+cfPLJOeyww7Lffvtln332yaBBg3L00Ufn8ccfT69evXLuuefm+OOPz5577plyuZwjjjiivcoBAAAA4D0olcvlckcXsaImT56c3XbbLePGjUv//v07uhwAAACAVV5R3tJuLZEAAAAAWH0IkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAArVdHQB78VR378ztd16veftVJWSHl1rMnteQ2VebU1VNvtYr4zcZaMMGdC3cBvjJ03L2HuezbRZ89K3V7cM3PBDefy51yrTy7sdAAAAgJXRKh0ivV+aymkVICVJfUNTHn92Zh5/duYS69dUl9LQWF7q9qbPmtfqcc3T6/TolE6dalapUGnxcGxVqRsAAAB4f+nO9i4sK0Baljfm1KW+viGvz56fMWMnZPykae9zZe+v8ZOmZczYCXl99vys1bVmlakbAAAAeP9pifQBe3NOfT60dtcsSEPG3vPsSt2qZ+w9z6amppQunRZdJl061awSdQMAAMB7US6X01ROmprKaSqXU377/+Z55XK5sqypqXn95ulyyi0eW5lusXyJdcpLeUyr5c37XLyGtHhMOY0t9lduKqdxqdtbfH/lvDFr+jKfFyHSB6ypXM6UGXNS39CUabPmZ/ykaSttIDNt1rys1bX1JdK5tjrTZ83roIoAAABWP5WgomUA0FYgsNTli4KEVtNNi8KN5Q0glnv54utUgpHF5jW9U1ObYUobwUpboUbL/bVet8XxLSXgKZfLaSynEgAten4W1dR2APPOsZbfXQekVV79vFnLXC5E6gAL6xpTVVVKVamUMWMnJCMHrZRBUt9e3fL67PmVlkhJsrC+MX16devAqgAAgHer3MYH7eYP321+2F+RVhJNiwcCi4UYi33Qbx04LCPIWK7lSz+uJcKWt0OExnLLsCFLPa7mea3DmYLnrcW8pbVIaXksrLlKSUpVpVSVklJpUU5QqkqqUmo1v1QqpaqN+VWltx+/+PyqUkqlLNpeKYums5T5pXceN292dZ6/a+n1CpE6QFN50YnvtXaXVFdlpe0eNnKXjTJm7IQsSEM611ZnYX1jGhrKGbnLRh1dGgAAq4AlWjIUdAdp/cF6BT6QtxF0vNM6Y1G40TIwaLv1xTvhQqt9lZezO0hbNS+1dcUywpZlBSNFYUrRca3BrStY5J3wYFEgUQkt2gwVlj5/eR67tDBj8XlVS6yz2PKq0jvzm9d5OyxpGYA0f1+9WJ0tt19qsb/melquX6pKZVnzNlvVV1VVqaHVsqp3aq+ueucYW9Ze3eKYqqta11Q5Pym1cdJanr9SW7NTavWw1ttoXlZqa2Yb25k8eXJu/sXSryEhUgfpvW7XdO9Sk3K5vNJ2DxsyoG8yclDG3vNsps+alz7uzgYArKSaux4sq7vHsloELG/rira6VqxYMPLuWlcsd+uHFWk1snj4sdixLk9LkqXW/fY0a65Smj9IL9a6okUY0CqEaP5AneYP3m0FEMuY//Z0dSVgaB1CtP6+RchRtWQN1aWqynTLIKLVfluECaUW6ywKFN4JO94JG97ZV6vHVLV+bloGE4uOtarVepXgo7r19DsBStVi4UyL0KVqUVywokHEioYQ73zfRiDCSq/ovAmRFlNdVUpjix94G3xkrXx5+OYZMqBvfvvnJ3P9X55O47u8O1uplJTLi17E3bsseupX9u5hQwb0FRoBwGLKbX24X0aI0DoQeG+tKxZf3ro7xjtjPbRsPbF4mLJ464oVX/7OcbVu3bFk2FBerAtIuZwlupAsa/kS65QXO87K/I6+KuhIS+0O0kbw0NY6xQFFy20tJVRoI0xYauuMxVo3VC8RULwdCLQIYZpbMZRabbtF64aq1sfXstVDqarqnboqLSSW3gqk0jKjarGam+utqmoVdLQKPlqEH5XzU2p9rtpaUGprXSEErHSESItpGSCVkkyeNicXX/dovn7wp7Lx+uumS2115jU25L38ntKj26IWSLqHAVBkeVpXLL58+QKKxT7wt9WaoM1uESvQbaJFALLEvHfV3aP18uVpVVLU+mOZx9Jy3mIhEWu2xQODUpsfxFuHDIuHGMsKIN4JG1pMt9mSYvF9LNa9o9W2Ws5vEQws9sG/9TqL1dlmF48lW3osrTVEpcVHi8CherH9NLeuKFVaeizZ6qJlV5HS261BSqWS1hAAHwAh0jI0J/DzFy66rf1bc+syf+GiAKmUrHCQ1K1LbT49oE9em71Q9zBgpdTyzh7ve7eJxe+e0WZI0MaH+xbzVmwwzaLwo+3jWtpgm5WuMMvRumLx7iBtt75YtC+tK1iWUtJGyLDk+BNtj2/RuqXB4q0hWj62rcE1q1p94G/ZsqJlqLGsrh5tBBVtLV+sZUjLlg6lVvt9pzVE87EtWqd14NEcTCx+vFVvBxSlt1t9VJZVtz2+RXWLLiDVrZ5jgQQAay4h0jI0NpVTVVVOuamUl6bOzuy5dZWB6N7N7/Rz59fnb/+akk411encqfp9rXVFjZ80LWPveTbTZs1L3w4Os9qqJclKUx/tZ9ldQIrGjFh2a4tlDabZ6sN/m8tbdwd5X1pXLCVYaa/WFW11tVniuN8+zqZW8zr6qqAjtdVyYlnhwxJhwxIhRuuQoq0wpGqxbbXdaqONFhIt61ssQGhr0My2u2S0Hnyz1UCYrUKOd1o6LAohluy+0fy46hb7aDloZsuuH0sGKEvOa/l8AACsLNo1RLr11lvz85//PA0NDTn88MMzatSoVssnTZqUM888M3Pnzs2QIUPyne98JzU1K0+uVU7S2LTouzfn1L0v22xqShbUNWZBXWMef3ZmHn925vuy3fdi+qx5y1VHVVUpXWqr06dXt8xdUJdZsxe2OT5U6e2xn5Kkc21VNtmgVwZu+KHcN+GVvDztrZTLzWNDpfIX9kV/aV2ylnV6dMorM97Keb95KFVVpdTVN7Z6zOJ7H7hhr6RUtUT49PC/p1ZCqT7rdsuInT+RLTfu3eqD+U13P5Pb//FiFixsSJdONdl9m/WzwUfWzh3/eDEz35yfD/Xskt0+/V8Z8LFebbeuWKzFQVO5nKdfej33T3glb8xZmLV7dM42n+yXj32kZ6V1QfOH9/+88mYefWp6Zs+ty1rdOmXQRh9O/z49lq+7xXK2rmgZSiwteFmRri9tdodZViDUxnLWXKUsNthmy24abYYNS+ne0caH7+Z5bYUBpbe7R7QZKpRKmT2nLq/OmpuFdY3p0qk66/ddKx9eu2urUGPxgTBbj0vRsjXFkq0gFu8y0jpUeCdkqG55XC26jFS3qL11AJLW04t1MymVmoOM1oFHVal1oAMAK4uV6Y/e78XqchysGcZPmparb3lkmeuUyuX2+bvztGnTcsghh2Ts2LHp1KlTDj744Fx00UXZaKN3xv/ZZ5998r3vfS9bbbVVzjjjjGyxxRb54he/WLjtyZMnZ7fddsvHdz0ttd16tUf5vI9ahkodsc930/WQ1cs7YcTiLSYWDy7aDibabOXQ3LqgZYuKxbqNtNl9Y/GQpKA1wjthQ9USoUJzKNDWfpa4tWlzC4m3B9ZcZkuNxWtbruWtA4u2W4q0bk2yshk/aVrGjJ2QmppSOtdWV8atO3bkoGX+sueXQwAotiI/L9/tz+SVzepyHKwZmq/X+vmz8sCNZ2fcuHHp37//Euu1W7Of+++/P9ttt13WWWedJMmwYcNy++2354QTTkiSTJkyJQsWLMhWW22VJBk5cmQuueSS5QqRWLV0RPeYlvv8oHff/MG50tKmVHq71cWiWqpKpfToWlv5ID1nXl2ayuVUvf2hujkAq66uSt9eXQu6ZbRuybHMsGKxAKRq8RCgzduKtl63uaVD8/evzJiTBx5/NdXVSU1VVRqamtLYmOzyqf75+HprL6VLShs1Fx3HsoKOZQYw74Q5UGTsPc+mpqaULp0W/Wjs0qkmC7JoTLzl+SV3ra41eX32/IwZOyHxyyEAVKzoz8t38zN5ZbS6HAdrhubrtap22UPvtFuINH369PTu3bsy3adPn0yYMGGpy3v37p1p06YtsZ3Zs2dn9uzZreZNnTq1HSpetZUq/7SYbv6utNitNNP6jhVJaRmPT4vHlxabbmt7rfbcahDyUouZ5RZrlltNpfXc8qIHlUvlJdKg1uHUovWb1+n3oW5vr1POtNfnp2+vrqlaFOUkpUXz5y9szBlHbJPqqlK+efHfUrNY2LCwvjFJskG/nu8cX7mcOQsacun/fHapY3U0+8r378xaXWtazSuXy5kzvyG/OnP35Vrv4m9+NiuzM35+Xzp3qqr8YEySBXUNefKl13PY8E92YGWw4qbNmpe1urb+sdi5tjrTZ81b6mP8cggAxVb05+W7+Zm8MlpdjoM1Q/P1On/BstdrtxCpqalpiQ/FLaeLlje78sorM3r06Db30aVTTTp1XnQIrTKMxVuhlBaFEUsLK975tvT2fysWWCz27dshSfmdeW+vXG652fcwQHdbll7PkseylEevVjq9nZ4uqGtIbU11yuWktsVg5gvqGtLvw92zUf91kix6M19Q15CaFmOCNJUXXS3V1aUWj2tM3w91T7cutYU19O3VLa/Pnt8qYFlY35g+vbq9q/VWRn4wsjp5N69FrwEAKLaiPy9X5d+PW1pdjoM1Q/P1WqSqvQro169fZsyYUZmeMWNG+vTps9TlM2fObLW82eGHH55x48a1+rrmmmuSLAoC5i9c9DWv+WtBi+/fXjZ/QfP3jZX13/lqfOerriHz6xqyYGFjZfDr5q+F9S2/mlJX35S6hne+6lt+NTalobGcxuavprfvgPT2gNLNYdLqF920rbZmUUudd6uqKunaefnvZte1c3XK5XIW1DWkoaGcETt/Ig0Ni6Zbzm++C1ySjNj5E0k5aWhqSrnclIamppSSdOtau8zHLcvIXTYq3O+KrLcy6turW6XFVjM/GFlVvZvXotcAABRb0Z+Xq/Lvxy2tLsfBmqH5el38tbq4dguRPvOZz+SBBx7IrFmzMn/+/Pz5z3/OzjvvXFm+3nrrpXPnznnkkUUjf99yyy2tljfr2bNn+vfv3+qrX79+7VU2y1BVVUq3zjX52Ed6pve6XVq10GmpZYOyzrVV2exjH8rBe2yaDT6y1tvj3ry9veb/q0rp3rV2UZezFldkTXUpXxy2ab515HbZaP11071rbaswqq29D9ywVzZaf93Mmd+QdXt2zbEjB+WQPTbLsSMHZd2eXVvNb9l09pA9NstBu2+SLp1q0tC0qJXbIcM2zf+M2nqZj1uWIQP6Fu53RdZbGfnByOrk3bwWvQYAoNiK/rxclX8/bml1OQ7WDM3X69o9uixzvXa7O1uS3HrrrRkzZkzq6+tzwAEH5Oijj87RRx+dk046KQMHDsyTTz6Zs846K3PmzMnmm2+e8847L506dSrc7gd1d7aqqlLW79sjXx6+easXevOdBabPmpf5C+vz1tz6NlsVfewjPXP48E96k2C11vL10MedqVgDeQ0AQDE/L2HV0Jy3LO3ubO0aIrWXooMCAAAAYMUU5S3t1p0NAAAAgNWHEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCQiQAAAAACgmRAAAAACgkRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAAACAQkIkAAAAAAoJkQAAAAAoJEQCAAAAoJAQCQAAAIBCNR1dwLvR2NiYJJk6dWoHVwIAAACwemjOWZpzl8WtkiHSCy+8kCQZNWpUxxYCAAAAsJqZMWNGNthggyXmr5Ih0vrrr58kueqqq7Leeut1cDV80KZOnZpRo0blmmuuSb9+/Tq6HD5gzv+azflfczn3azbnf83m/K/ZnP81m/P/wWtsbMyMGTOyxRZbtLl8lQyROnXqlCRZb7310r9//w6uho7Sr18/538N5vyv2Zz/NZdzv2Zz/tdszv+azflfszn/H6y2WiA1M7A2AAAAAIWESAAAAAAUEiIBAAAAUGiVDJF69uyZE044IT179uzoUugAzv+azflfszn/ay7nfs3m/K/ZnP81m/O/ZnP+Vz6lcrlc7ugiAAAAAFi5rZItkQAAAAD4YAmRAAAAACi0SoZIt956a/bee+/sscceueaaazq6HNrJnDlzss8++2Ty5MlJkvvvvz/77rtv9thjj/zkJz+prDdp0qSMHDkyw4YNy5lnnpmGhoaOKpn3yejRozN8+PAMHz48F1xwQRLnf01y8cUXZ++9987w4cNzxRVXJHH+1zQ//OEPc9pppyVx7tc0hx56aIYPH54RI0ZkxIgReeyxx1wDa4i77rorI0eOzF577ZXvfe97Sbz+1xQ33nhj5TU/YsSIbL311jn33HOd/zXILbfcUvnd/4c//GESr/+VWnkVM3Xq1PJnP/vZ8uuvv16eO3dued999y0/88wzHV0W77N//etf5X322ae8+eabl19++eXy/Pnzy0OHDi2/9NJL5fr6+vKRRx5Zvueee8rlcrk8fPjw8j//+c9yuVwun3766eVrrrmmAyvnvbrvvvvKBx10UHnhwoXlurq68mGHHVa+9dZbnf81xIMPPlg++OCDy/X19eX58+eXP/vZz5YnTZrk/K9B7r///vK2225b/t///V/v/WuYpqam8o477liur6+vzHMNrBleeuml8o477lh+9dVXy3V1deVDDjmkfM899zj3a6Cnn366vPvuu5dfeeUV538NMW/evPKnP/3p8muvvVaur68vH3DAAeVx48Y5/yuxVa4l0v3335/tttsu66yzTrp165Zhw4bl9ttv7+iyeJ/dcMMNOeecc9KnT58kyYQJE7LBBhtk/fXXT01NTfbdd9/cfvvtmTJlShYsWJCtttoqSTJy5EjXwyqud+/eOe2009KpU6fU1tZmww03zAsvvOD8ryG22WabXHXVVampqclrr72WxsbGzJ492/lfQ7zxxhv5yU9+kuOOOy6J9/41zX/+858kyZFHHpn/7//7//J///d/roE1xJ133pm99947/fr1S21tbX7yk5+ka9euzv0a6Nvf/nZOPvnkvPzyy87/GqKxsTFNTU2ZP39+Ghoa0tDQkB49ejj/K7FVLkSaPn16evfuXZnu06dPpk2b1oEV0R6+//3vZ8iQIZXppZ33xef37t3b9bCK23jjjSs/GF544YX86U9/SqlUcv7XILW1tbnkkksyfPjwbL/99l7/a5Czzz47J598cuU2vs79mmX27NnZfvvt89Of/jS/+c1vct111+WVV15xDawBXnzxxTQ2Nua4447LiBEjcu2113r9r4Huv//+LFiwIHvttZfzvwbp0aNHvv71r2evvfbK0KFDs9566zn/K7lVLkRqampKqVSqTJfL5VbTrJ6Wdt5dD6uvZ555JkceeWROPfXUrL/++s7/Guakk07KAw88kFdffTUvvPCC878GuPHGG/ORj3wk22+/fWWe9/41y+DBg3PBBRdkrbXWSq9evXLAAQfkkksucQ2sARobG/PAAw/kBz/4Qa6//vpMmDAhL7/8snO/hrnuuutyxBFHJPH+vyZ58sknc9NNN+Xuu+/O3//+91RVVfndbyVX09EFrKh+/fpl/PjxlekZM2ZUujyx+urXr19mzJhRmW4+74vPnzlzputhNfDII4/kpJNOyhlnnJHhw4fnoYcecv7XEM8991zq6uoyYMCAdO3aNXvssUduv/32VFdXV9Zx/ldPt912W2bMmJERI0bkzTffzLx58zJlyhTnfg0yfvz41NfXV4LEcrmc9dZbz/v/GuDDH/5wtt9++/Tq1StJ8rnPfc57/xqmrq4uDz/8cM4///wkfvdfk9x7773Zfvvt86EPfSjJoi5ql19+udf/SmyVa4n0mc98Jg888EBmzZqV+fPn589//nN23nnnji6Ldrblllvm+eefrzR3/uMf/5idd9456623Xjp37pxHHnkkyaKR/V0Pq7ZXX301X/va13LhhRdm+PDhSZz/NcnkyZNz1llnpa6uLnV1dRk3blwOPvhg538NcMUVV+SPf/xjbrnllpx00knZdddd86tf/cq5X4O89dZbueCCC7Jw4cLMmTMnN998c775zW+6BtYAn/3sZ3Pvvfdm9uzZaWxszN///vfsueeezv0a5KmnnsrHPvaxdOvWLYnf/dYkm222We6///7Mmzcv5XI5d911l/O/klvlWiL17ds3J598cg477LDU19fngAMOyKBBgzq6LNpZ586dc/755+fEE0/MwoULM3To0Oy5555JkgsvvDBnnXVW5syZk8033zyHHXZYB1fLe3H55Zdn4cKFlb9EJcnBBx/s/K8hhg4dmgkTJmS//fZLdXV19thjjwwfPjy9evVy/tdA3vvXLJ/97Gfz2GOPZb/99ktTU1O++MUvZvDgwa6BNcCWW26Zo446Kl/84hdTX1+fHXbYIYccckg+8YlPOPdriJdffjn9+vWrTHv/X3PsuOOO+fe//52RI0emtrY2AwcOzIknnpgddtjB+V9JlcrlcrmjiwAAAABg5bbKdWcDAAAA4IMnRAIAAACgkBAJAAAAgEJCJAAAAAAKCZEAAAAAKCREAgAAAKCQEAkAYBk23XTTzJo1a4Uec8899+Tiiy9Okrz11ls57LDD2qO0JTz++OPZddddkyS//e1vc9llly1z/RtvvDHXXHPNB1EaALAaqOnoAgAAVjePP/543nzzzSTJm2++mccff/wDr+GQQw4pXOeRRx7Jxhtv/AFUAwCsDoRIAMAq78EHH8xFF12Uj3zkI3n++efTtWvXHHPMMbn66qvz/PPPZ4899shpp52WH/zgB3nssccyd+7clMvlfO9738vWW2+d0047LT169MhTTz2VqVOnZtNNN80Pf/jDdO/ePUly6aWX5rHHHssbb7yRr3zlKxk1alTmzZuXb3/723nxxRfzxhtvpHv37rnwwgvz1ltv5brrrktjY2PWWmutPProo1mwYEFGjBiRsWPH5uabb87111+f+vr6vPnmmzn66KPzxS9+MWPHjs2dd96ZqqqqvPjii+nSpUt++MMfZsMNN1zmsV977bW58sor06NHj2yyySaV+Zdeemlef/31nH322bn22mtz3XXXpba2Np07d865556b559/PnfddVfuu+++dOnSJaNGjWrXcwQArPp0ZwMAVguPP/54jjnmmNxyyy3p0aNHLrvssowZMyZjx47Ntddem3/+85+ZPn16rr/++tx2223Zf//988tf/rLy+IkTJ+byyy/PbbfdlilTpuT222+vLFt//fUzduzYjB49Oueff37q6+vzt7/9LT179sz111+fO+64I1tssUWuueaabLnlljn44IOz99575+STT855552XLl265JZbbsmCBQty44035rLLLsvvf//7/OQnP8mPfvSjyn4efvjhfOtb38of//jHbLnlloXd0SZNmpTRo0fn//7v/3LTTTeltrZ2iXUaGxvzgx/8IL/61a9y00035cADD8wjjzyS3XffPbvuumu+/OUvC5AAgOWiJRIAsFro379/PvnJTyZJ/uu//itrrbVWOnXqlF69eqV79+5Za6218o1vfCPXXXddXn755Tz44IOVlkZJstNOO6VTp05Jkk022aTSHS1J9tlnnyTJgAEDUldXlzlz5mTPPffM+uuvn6uvvjovvvhiHnrooQwePHiZNXbv3j2/+MUv8te//jUvvPBCnnzyycybN6+yfPPNN0+/fv2SJJ/85Cdz5513LnN7DzzwQHbYYYf07t07SXLQQQfl3nvvbbVOdXV19txzzxx88MHZZZddsuOOO2bo0KHL3C4AQFu0RAIAVgvNAVCzmprWfyt74IEHcuyxxyZJdttttyXGDOrSpUvl+1KplHK5vMS2SqVSkqRcLufaa6/NmWeemS5dumTffffNPvvs0+oxbZk6dWr222+/TJkyJVtvvXW+8Y1vLHcNS9Nynerq6jbXufDCC/OLX/wi//Vf/5XLLrss3/zmNwu3CwCwOCESALBGuPvuu/PZz342X/ziF7PFFlvkL3/5SxobG9/19u69997sv//++cIXvpCPf/zjueuuuyrbq66uTkNDQ5JFAVRjY2PK5XImTpyYXr165atf/Wp23HHH3H333UnyruvYYYcdct9992Xq1KlJkptvvnmJdWbNmpWhQ4dmnXXWyZe//OV84xvfqAz03bJOAIAiQiQAYI1wxhln5KGHHsq+++6b/fffP+uvv34mT56cpqamd7W9I488Mtdff3323XffjBo1KptvvnleeumlJMl2222Xe++9N9/97nfTu3fvDBo0KMOHD8/AgQPTt2/f7Lnnntlrr73y6quvplevXnnxxRffVQ2bbrppTjnllBx++OEZOXJkFi5cuMQ6vXr1yvHHH58vf/nLGTlyZH784x/ne9/7XpJk5513znXXXZcxY8a8q/0DAGuWUnl52kkDAAAAsEYzsDYAwErsBz/4QR588ME2l51++unZbrvtPuCKAIA1lZZIAAAAABQyJhIAAAAAhYRIAAAAABQSIgEAAABQSIgEAAAAQCEhEgAAAACF/n9M48qmt3CbNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1, figsize=(20,10))\n",
    "\n",
    "ax.set_xlabel('hour')\n",
    "ax.set_title('Trip Duration and Distance plot per pickup-dropoff pair',fontsize=16,  weight='bold' )\n",
    "\n",
    "sns.regplot(x=\"manhattan_dist\", y=\"duration\", data=inp_df, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57236e69",
   "metadata": {},
   "source": [
    "### Distance and Duration viz - outliers\n",
    "Output loaded from the geographical analysis of pickup points - book 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "98840ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "outp_df = pd.read_csv('./data/joined_outliers_p.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ecd25de0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16306 entries, 0 to 16305\n",
      "Data columns (total 30 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Unnamed: 0          16306 non-null  int64  \n",
      " 1   id                  16306 non-null  object \n",
      " 2   vendor_id           16306 non-null  int64  \n",
      " 3   pickup_datetime     16306 non-null  object \n",
      " 4   dropoff_datetime    16306 non-null  object \n",
      " 5   passenger_count     16306 non-null  int64  \n",
      " 6   pickup_longitude    16306 non-null  float64\n",
      " 7   pickup_latitude     16306 non-null  float64\n",
      " 8   dropoff_longitude   16306 non-null  float64\n",
      " 9   dropoff_latitude    16306 non-null  float64\n",
      " 10  store_and_fwd_flag  16306 non-null  bool   \n",
      " 11  duration            16306 non-null  int64  \n",
      " 12  distance            16306 non-null  float64\n",
      " 13  manhattan_dist      16306 non-null  float64\n",
      " 14  duration_hours      16306 non-null  float64\n",
      " 15  duration_min        16306 non-null  float64\n",
      " 16  dur_to_dist         16306 non-null  float64\n",
      " 17  dur_to_dist_m       16306 non-null  float64\n",
      " 18  speed_kmph          16306 non-null  float64\n",
      " 19  speed_kmph_m        16306 non-null  float64\n",
      " 20  day_name            16306 non-null  object \n",
      " 21  hour                16306 non-null  int64  \n",
      " 22  price               16306 non-null  float64\n",
      " 23  month_name          16306 non-null  object \n",
      " 24  day                 16306 non-null  int64  \n",
      " 25  geometry            16306 non-null  object \n",
      " 26  index_right         0 non-null      float64\n",
      " 27  zcta                0 non-null      float64\n",
      " 28  bcode               0 non-null      float64\n",
      " 29  note                0 non-null      float64\n",
      "dtypes: bool(1), float64(17), int64(6), object(6)\n",
      "memory usage: 3.6+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>day_name</th>\n",
       "      <th>hour</th>\n",
       "      <th>price</th>\n",
       "      <th>month_name</th>\n",
       "      <th>day</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>zcta</th>\n",
       "      <th>bcode</th>\n",
       "      <th>note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>id0753277</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-27 22:29:00</td>\n",
       "      <td>2016-01-27 22:29:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>40.81685</td>\n",
       "      <td>-74.20985</td>\n",
       "      <td>40.81685</td>\n",
       "      <td>...</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>22</td>\n",
       "      <td>5.45000</td>\n",
       "      <td>January</td>\n",
       "      <td>27</td>\n",
       "      <td>POINT (-74.20985413 40.81685257)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>12</td>\n",
       "      <td>id1606138</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-27 15:14:00</td>\n",
       "      <td>2016-04-27 15:15:00</td>\n",
       "      <td>2</td>\n",
       "      <td>-74.17778</td>\n",
       "      <td>40.69028</td>\n",
       "      <td>-74.17778</td>\n",
       "      <td>40.69028</td>\n",
       "      <td>...</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>15</td>\n",
       "      <td>5.11667</td>\n",
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       "      <td>27</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>21</td>\n",
       "      <td>id0688948</td>\n",
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       "      <td>2016-04-14 20:13:00</td>\n",
       "      <td>2016-04-14 20:13:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.16180</td>\n",
       "      <td>40.83649</td>\n",
       "      <td>-74.16180</td>\n",
       "      <td>40.83649</td>\n",
       "      <td>...</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>20</td>\n",
       "      <td>5.25000</td>\n",
       "      <td>April</td>\n",
       "      <td>14</td>\n",
       "      <td>POINT (-74.16180420000001 40.83649445)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>id3535158</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-11 18:57:00</td>\n",
       "      <td>2016-02-11 19:14:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.73524</td>\n",
       "      <td>40.82271</td>\n",
       "      <td>-73.73524</td>\n",
       "      <td>40.82271</td>\n",
       "      <td>...</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>18</td>\n",
       "      <td>21.66667</td>\n",
       "      <td>February</td>\n",
       "      <td>11</td>\n",
       "      <td>POINT (-73.73523711999999 40.82271194)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>id1436954</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-25 13:18:00</td>\n",
       "      <td>2016-04-25 13:26:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.96654</td>\n",
       "      <td>40.78409</td>\n",
       "      <td>-73.96654</td>\n",
       "      <td>40.78409</td>\n",
       "      <td>...</td>\n",
       "      <td>Monday</td>\n",
       "      <td>13</td>\n",
       "      <td>12.26667</td>\n",
       "      <td>April</td>\n",
       "      <td>25</td>\n",
       "      <td>POINT (-73.96653748 40.78409195)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
       "0           3  id0753277          1  2016-01-27 22:29:00  2016-01-27 22:29:00   \n",
       "1          12  id1606138          2  2016-04-27 15:14:00  2016-04-27 15:15:00   \n",
       "2          21  id0688948          1  2016-04-14 20:13:00  2016-04-14 20:13:00   \n",
       "3          36  id3535158          1  2016-02-11 18:57:00  2016-02-11 19:14:00   \n",
       "4          50  id1436954          2  2016-04-25 13:18:00  2016-04-25 13:26:00   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1         -74.20985         40.81685          -74.20985   \n",
       "1                2         -74.17778         40.69028          -74.17778   \n",
       "2                1         -74.16180         40.83649          -74.16180   \n",
       "3                1         -73.73524         40.82271          -73.73524   \n",
       "4                1         -73.96654         40.78409          -73.96654   \n",
       "\n",
       "   dropoff_latitude  ...   day_name  hour    price  month_name  day  \\\n",
       "0          40.81685  ...  Wednesday    22  5.45000     January   27   \n",
       "1          40.69028  ...  Wednesday    15  5.11667       April   27   \n",
       "2          40.83649  ...   Thursday    20  5.25000       April   14   \n",
       "3          40.82271  ...   Thursday    18 21.66667    February   11   \n",
       "4          40.78409  ...     Monday    13 12.26667       April   25   \n",
       "\n",
       "                                 geometry  index_right  zcta  bcode  note  \n",
       "0        POINT (-74.20985413 40.81685257)          NaN   NaN    NaN   NaN  \n",
       "1        POINT (-74.17778015 40.69028091)          NaN   NaN    NaN   NaN  \n",
       "2  POINT (-74.16180420000001 40.83649445)          NaN   NaN    NaN   NaN  \n",
       "3  POINT (-73.73523711999999 40.82271194)          NaN   NaN    NaN   NaN  \n",
       "4        POINT (-73.96653748 40.78409195)          NaN   NaN    NaN   NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(outp_df.info())\n",
    "outp_df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9b887047",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>16306.00000</td>\n",
       "      <td>915.06219</td>\n",
       "      <td>3511.45797</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>385.00000</td>\n",
       "      <td>624.00000</td>\n",
       "      <td>1009.00000</td>\n",
       "      <td>86351.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_hours</th>\n",
       "      <td>16306.00000</td>\n",
       "      <td>0.25418</td>\n",
       "      <td>0.97540</td>\n",
       "      <td>0.00056</td>\n",
       "      <td>0.10694</td>\n",
       "      <td>0.17333</td>\n",
       "      <td>0.28028</td>\n",
       "      <td>23.98639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>16306.00000</td>\n",
       "      <td>1.73034</td>\n",
       "      <td>1.35972</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>16306.00000</td>\n",
       "      <td>23.61241</td>\n",
       "      <td>73.05641</td>\n",
       "      <td>0.37030</td>\n",
       "      <td>9.82637</td>\n",
       "      <td>15.87771</td>\n",
       "      <td>25.56325</td>\n",
       "      <td>3865.95366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>16306.00000</td>\n",
       "      <td>2.80470</td>\n",
       "      <td>12.65177</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.18096</td>\n",
       "      <td>1.87421</td>\n",
       "      <td>3.01807</td>\n",
       "      <td>1240.90868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manhattan_dist</th>\n",
       "      <td>16306.00000</td>\n",
       "      <td>3.56735</td>\n",
       "      <td>16.12400</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.52753</td>\n",
       "      <td>2.37140</td>\n",
       "      <td>3.67169</td>\n",
       "      <td>1485.25957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speed_kmph_m</th>\n",
       "      <td>16306.00000</td>\n",
       "      <td>17.36888</td>\n",
       "      <td>83.10048</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.26550</td>\n",
       "      <td>14.54535</td>\n",
       "      <td>19.83092</td>\n",
       "      <td>7524.00878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count      mean        std     min       25%       50%  \\\n",
       "duration        16306.00000 915.06219 3511.45797 2.00000 385.00000 624.00000   \n",
       "duration_hours  16306.00000   0.25418    0.97540 0.00056   0.10694   0.17333   \n",
       "passenger_count 16306.00000   1.73034    1.35972 0.00000   1.00000   1.00000   \n",
       "price           16306.00000  23.61241   73.05641 0.37030   9.82637  15.87771   \n",
       "distance        16306.00000   2.80470   12.65177 0.00000   1.18096   1.87421   \n",
       "manhattan_dist  16306.00000   3.56735   16.12400 0.00000   1.52753   2.37140   \n",
       "speed_kmph_m    16306.00000  17.36888   83.10048 0.00000  10.26550  14.54535   \n",
       "\n",
       "                       75%         max  \n",
       "duration        1009.00000 86351.00000  \n",
       "duration_hours     0.28028    23.98639  \n",
       "passenger_count    2.00000     9.00000  \n",
       "price             25.56325  3865.95366  \n",
       "distance           3.01807  1240.90868  \n",
       "manhattan_dist     3.67169  1485.25957  \n",
       "speed_kmph_m      19.83092  7524.00878  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outp_df[['duration','duration_hours','passenger_count','price','distance','manhattan_dist','speed_kmph_m']].describe().T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0ba45017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "            background-color:  #081d58;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f4870_row0_col2,#T_f4870_row1_col2,#T_f4870_row2_col3,#T_f4870_row4_col0,#T_f4870_row4_col1,#T_f4870_row5_col0,#T_f4870_row5_col1{\n",
       "            background-color:  #fdfed4;\n",
       "            color:  #000000;\n",
       "        }#T_f4870_row0_col3,#T_f4870_row1_col3{\n",
       "            background-color:  #24439b;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f4870_row0_col4,#T_f4870_row0_col5,#T_f4870_row1_col4,#T_f4870_row1_col5{\n",
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       "        }#T_f4870_row2_col6{\n",
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       "            color:  #000000;\n",
       "        }#T_f4870_row3_col0,#T_f4870_row3_col1{\n",
       "            background-color:  #24449c;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f4870_row3_col4,#T_f4870_row3_col5{\n",
       "            background-color:  #2ca1c2;\n",
       "            color:  #000000;\n",
       "        }#T_f4870_row3_col6{\n",
       "            background-color:  #42b6c4;\n",
       "            color:  #000000;\n",
       "        }#T_f4870_row4_col3,#T_f4870_row5_col3{\n",
       "            background-color:  #2b9fc2;\n",
       "            color:  #000000;\n",
       "        }#T_f4870_row4_col5,#T_f4870_row5_col4{\n",
       "            background-color:  #091e5a;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f4870_row4_col6{\n",
       "            background-color:  #192b7c;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f4870_row5_col6,#T_f4870_row6_col5{\n",
       "            background-color:  #152772;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f4870_row6_col2{\n",
       "            background-color:  #fcfed3;\n",
       "            color:  #000000;\n",
       "        }#T_f4870_row6_col3{\n",
       "            background-color:  #3cb1c3;\n",
       "            color:  #000000;\n",
       "        }#T_f4870_row6_col4{\n",
       "            background-color:  #182a7a;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_f4870_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >duration</th>        <th class=\"col_heading level0 col1\" >duration_hours</th>        <th class=\"col_heading level0 col2\" >passenger_count</th>        <th class=\"col_heading level0 col3\" >price</th>        <th class=\"col_heading level0 col4\" >distance</th>        <th class=\"col_heading level0 col5\" >manhattan_dist</th>        <th class=\"col_heading level0 col6\" >speed_kmph_m</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f4870_level0_row0\" class=\"row_heading level0 row0\" >duration</th>\n",
       "                        <td id=\"T_f4870_row0_col0\" class=\"data row0 col0\" >1.0</td>\n",
       "                        <td id=\"T_f4870_row0_col1\" class=\"data row0 col1\" >1.0</td>\n",
       "                        <td id=\"T_f4870_row0_col2\" class=\"data row0 col2\" >0.0066</td>\n",
       "                        <td id=\"T_f4870_row0_col3\" class=\"data row0 col3\" >0.83</td>\n",
       "                        <td id=\"T_f4870_row0_col4\" class=\"data row0 col4\" >0.033</td>\n",
       "                        <td id=\"T_f4870_row0_col5\" class=\"data row0 col5\" >0.033</td>\n",
       "                        <td id=\"T_f4870_row0_col6\" class=\"data row0 col6\" >-0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4870_level0_row1\" class=\"row_heading level0 row1\" >duration_hours</th>\n",
       "                        <td id=\"T_f4870_row1_col0\" class=\"data row1 col0\" >1.0</td>\n",
       "                        <td id=\"T_f4870_row1_col1\" class=\"data row1 col1\" >1.0</td>\n",
       "                        <td id=\"T_f4870_row1_col2\" class=\"data row1 col2\" >0.0066</td>\n",
       "                        <td id=\"T_f4870_row1_col3\" class=\"data row1 col3\" >0.83</td>\n",
       "                        <td id=\"T_f4870_row1_col4\" class=\"data row1 col4\" >0.033</td>\n",
       "                        <td id=\"T_f4870_row1_col5\" class=\"data row1 col5\" >0.033</td>\n",
       "                        <td id=\"T_f4870_row1_col6\" class=\"data row1 col6\" >-0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4870_level0_row2\" class=\"row_heading level0 row2\" >passenger_count</th>\n",
       "                        <td id=\"T_f4870_row2_col0\" class=\"data row2 col0\" >0.0066</td>\n",
       "                        <td id=\"T_f4870_row2_col1\" class=\"data row2 col1\" >0.0066</td>\n",
       "                        <td id=\"T_f4870_row2_col2\" class=\"data row2 col2\" >1.0</td>\n",
       "                        <td id=\"T_f4870_row2_col3\" class=\"data row2 col3\" >0.024</td>\n",
       "                        <td id=\"T_f4870_row2_col4\" class=\"data row2 col4\" >0.014</td>\n",
       "                        <td id=\"T_f4870_row2_col5\" class=\"data row2 col5\" >0.015</td>\n",
       "                        <td id=\"T_f4870_row2_col6\" class=\"data row2 col6\" >0.013</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4870_level0_row3\" class=\"row_heading level0 row3\" >price</th>\n",
       "                        <td id=\"T_f4870_row3_col0\" class=\"data row3 col0\" >0.83</td>\n",
       "                        <td id=\"T_f4870_row3_col1\" class=\"data row3 col1\" >0.83</td>\n",
       "                        <td id=\"T_f4870_row3_col2\" class=\"data row3 col2\" >0.024</td>\n",
       "                        <td id=\"T_f4870_row3_col3\" class=\"data row3 col3\" >1.0</td>\n",
       "                        <td id=\"T_f4870_row3_col4\" class=\"data row3 col4\" >0.58</td>\n",
       "                        <td id=\"T_f4870_row3_col5\" class=\"data row3 col5\" >0.58</td>\n",
       "                        <td id=\"T_f4870_row3_col6\" class=\"data row3 col6\" >0.51</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4870_level0_row4\" class=\"row_heading level0 row4\" >distance</th>\n",
       "                        <td id=\"T_f4870_row4_col0\" class=\"data row4 col0\" >0.033</td>\n",
       "                        <td id=\"T_f4870_row4_col1\" class=\"data row4 col1\" >0.033</td>\n",
       "                        <td id=\"T_f4870_row4_col2\" class=\"data row4 col2\" >0.014</td>\n",
       "                        <td id=\"T_f4870_row4_col3\" class=\"data row4 col3\" >0.58</td>\n",
       "                        <td id=\"T_f4870_row4_col4\" class=\"data row4 col4\" >1.0</td>\n",
       "                        <td id=\"T_f4870_row4_col5\" class=\"data row4 col5\" >0.99</td>\n",
       "                        <td id=\"T_f4870_row4_col6\" class=\"data row4 col6\" >0.93</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4870_level0_row5\" class=\"row_heading level0 row5\" >manhattan_dist</th>\n",
       "                        <td id=\"T_f4870_row5_col0\" class=\"data row5 col0\" >0.033</td>\n",
       "                        <td id=\"T_f4870_row5_col1\" class=\"data row5 col1\" >0.033</td>\n",
       "                        <td id=\"T_f4870_row5_col2\" class=\"data row5 col2\" >0.015</td>\n",
       "                        <td id=\"T_f4870_row5_col3\" class=\"data row5 col3\" >0.58</td>\n",
       "                        <td id=\"T_f4870_row5_col4\" class=\"data row5 col4\" >0.99</td>\n",
       "                        <td id=\"T_f4870_row5_col5\" class=\"data row5 col5\" >1.0</td>\n",
       "                        <td id=\"T_f4870_row5_col6\" class=\"data row5 col6\" >0.94</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4870_level0_row6\" class=\"row_heading level0 row6\" >speed_kmph_m</th>\n",
       "                        <td id=\"T_f4870_row6_col0\" class=\"data row6 col0\" >-0.01</td>\n",
       "                        <td id=\"T_f4870_row6_col1\" class=\"data row6 col1\" >-0.01</td>\n",
       "                        <td id=\"T_f4870_row6_col2\" class=\"data row6 col2\" >0.013</td>\n",
       "                        <td id=\"T_f4870_row6_col3\" class=\"data row6 col3\" >0.51</td>\n",
       "                        <td id=\"T_f4870_row6_col4\" class=\"data row6 col4\" >0.93</td>\n",
       "                        <td id=\"T_f4870_row6_col5\" class=\"data row6 col5\" >0.94</td>\n",
       "                        <td id=\"T_f4870_row6_col6\" class=\"data row6 col6\" >1.0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdd75a65b50>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outp_df[['duration','duration_hours','passenger_count','price','distance','manhattan_dist','speed_kmph_m']].corr().style.format(\"{:.2}\").background_gradient(cmap=plt.get_cmap('YlGnBu'),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1c77a524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Trip Duration and Distance plot per pickup-dropoff pair'}, xlabel='manhattan_dist', ylabel='duration'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1, figsize=(20,10))\n",
    "\n",
    "ax.set_xlabel('hour')\n",
    "ax.set_title('Trip Duration and Distance plot per pickup-dropoff pair')\n",
    "\n",
    "sns.regplot(x=\"manhattan_dist\", y=\"duration\", data=outp_df, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b82590e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp_df_manhattan = inp_df.query('bcode == 36061')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "25961533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zcta\n",
       "10001    52313\n",
       "10002    19528\n",
       "10003    54568\n",
       "10004     5777\n",
       "10005     4781\n",
       "         ...  \n",
       "10271       49\n",
       "10278      239\n",
       "10279      341\n",
       "10280     5601\n",
       "10282     4155\n",
       "Name: id, Length: 69, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp_df_manhattan.groupby(['zcta'])['id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f130f3fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "de351868",
   "metadata": {},
   "source": [
    "-------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1de3691",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "newenv",
   "language": "python",
   "name": "newenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}