Nisar_Taxi_data_assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import csv\n",
    "import numpy as np\n",
    "from math import radians, cos, sin, asin, sqrt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['medallion', 'hack_license', 'vendor_id', 'rate_code', 'store_and_fwd_flag', 'pickup_datetime', 'dropoff_datetime', 'passenger_count', 'trip_time_in_secs', 'trip_distance', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude']\n",
      "['1B5C0970F2AE8CFFBA8AE4584BEAED29', 'D961332334524990D1BBD462E2EFB8A4', 'CMT', '1', 'N', '2013-02-08 23:35:14', '2013-02-08 23:42:58', '1', '463', '.80', '-73.992439', '40.724487', '-73.984421', '40.718903']\n",
      "['B42249AE16E2B8E556F1CB1F940D6FB4', 'D4BB308D1F3FCB3434D9DB282CDC93D7', 'CMT', '1', 'N', '2013-02-07 12:20:16', '2013-02-07 12:50:27', '4', '1810', '3.10', '-73.989494', '40.769588', '-73.990303', '40.737347']\n",
      "['890699222C47C09FBC898758CEC69762', '6318C3AEC02248928C3345B5805EB905', 'CMT', '1', 'N', '2013-02-08 08:56:54', '2013-02-08 08:59:43', '1', '168', '1.00', '-73.963036', '40.799141', '-73.972168', '40.786446']\n",
      "['74B7D835C2CD98606D5256DA8A38E045', 'D5E278C918256D1F97680A1F04D290E0', 'CMT', '1', 'N', '2013-02-08 09:37:02', '2013-02-08 09:50:50', '1', '828', '2.10', '-73.987953', '40.728764', '-74.007118', '40.705399']\n",
      "['4003B8478418FEC5D761E2F37602769B', '0B766F1054A5C16D86BC023858BD8143', 'CMT', '1', 'N', '2013-02-08 19:31:25', '2013-02-08 19:46:23', '1', '897', '3.30', '-73.987282', '40.743042', '-74.010284', '40.703964']\n",
      "['D72DF7B12201912BFDBB93081EF04C96', 'AFD828EEF790A2485BBB0B568A8BE22E', 'CMT', '1', 'N', '2013-02-08 23:10:01', '2013-02-08 23:46:15', '4', '2173', '7.60', '-73.993004', '40.720154', '-73.959747', '40.80854']\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "f = open('trip_data_2.csv', 'r')\n",
    "reader = csv.reader(f)\n",
    "c=0\n",
    "for i in reader:\n",
    "    print(i)\n",
    "    if c > 5:\n",
    "        break\n",
    "    c+=1\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time data 'pickup_datetime' does not match format '%Y-%m-%d %H:%M:%S'\n",
      "CPU times: user 1min 43s, sys: 1.46 s, total: 1min 44s\n",
      "Wall time: 1min 45s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "f = open('trip_data_2.csv', 'r')\n",
    "reader = csv.reader(f)\n",
    "\n",
    "min_date = None\n",
    "max_date = None\n",
    "c = 0\n",
    "pickup_l, dropoff_l = [], []\n",
    "cols = None\n",
    "\n",
    "for row in reader:\n",
    "    dts_pickup, dts_dropoff = row[5], row[6]\n",
    "    dto_pickup, dto_dropoff = None, None\n",
    "    try:\n",
    "        dto_pickup = datetime.strptime(dts_pickup, '%Y-%m-%d %H:%M:%S')\n",
    "        dto_dropoff = datetime.strptime(dts_dropoff, '%Y-%m-%d %H:%M:%S')\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "    if not dto_pickup is None:\n",
    "        pickup_l.append(dto_pickup)\n",
    "    \n",
    "    if not dto_dropoff is None:\n",
    "        dropoff_l.append(dto_dropoff)\n",
    "    \n",
    "    if c == 0:\n",
    "        cols = row\n",
    "\n",
    "    c+=1\n",
    "\n",
    "pickup_dt_range = max(pickup_l) - min(pickup_l)\n",
    "dropoff_dt_range = max(dropoff_l) - min(dropoff_l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. What datetime range does your data cover?  How many rows are there total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1. What datetime range does your data cover?  How many rows are there total?\n",
      "min pickup datetime: 2013-02-01 00:00:00 | max pickup datetime: 2013-02-28 23:59:59\n",
      "min dropoff datetime: 2013-02-01 00:00:55 | max dropoff datetime: 2013-03-01 01:15:48\n",
      "\n",
      "Pickup datetime range: 27 days, 23:59:59 \n",
      "Dropoff datetime range: 28 days, 1:14:53 \n",
      "Total number of rows: 13990176\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('\\n1. What datetime range does your data cover?  How many rows are there total?')\n",
    "print(f'min pickup datetime: {min(pickup_l)} | max pickup datetime: {max(pickup_l)}')\n",
    "print(f'min dropoff datetime: {min(dropoff_l)} | max dropoff datetime: {max(dropoff_l)}')\n",
    "print(f'\\nPickup datetime range: {pickup_dt_range} \\nDropoff datetime range: {dropoff_dt_range} \\nTotal number of rows: {c-1}')\n",
    "print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. What are the field names?  Give descriptions for each field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Field Names:\n",
      "medallion\n",
      "hack_license\n",
      "vendor_id\n",
      "rate_code\n",
      "store_and_fwd_flag\n",
      "pickup_datetime\n",
      "dropoff_datetime\n",
      "passenger_count\n",
      "trip_time_in_secs\n",
      "trip_distance\n",
      "pickup_longitude\n",
      "pickup_latitude\n",
      "dropoff_longitude\n",
      "dropoff_latitude\n"
     ]
    }
   ],
   "source": [
    "f=open(\"trip_data_2.csv\",'r')\n",
    "reader=csv.reader(f)\n",
    "for row in reader:\n",
    "    print(\"Field Names:\",*row,sep='\\n')\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What are the field names?  Give descriptions for each field.\n",
      "Field names --  ['medallion', 'hack_license', 'vendor_id', 'rate_code', 'store_and_fwd_flag', 'pickup_datetime', 'dropoff_datetime', 'passenger_count', 'trip_time_in_secs', 'trip_distance', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude']\n",
      "\n",
      "Columns            | Description \n",
      "-------------------|------------------------\n",
      "medallion          | A medallion is a permit that allows a driver to operate a taxi in United States\n",
      "hack_license       | A license needed to ride the yellow medalian taxicab\n",
      "vendor_id          | A code indicating the provider associated with the trip record\n",
      "rate_code          | Rating provide for the trip\n",
      "store_and_fwd_flag | This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the server - Y=store and forward; N=not a store and forward trip\n",
      "pickup_datetime    | Date and time when the trip was started\n",
      "dropoff_datetime   | Date and time when the trip was ended\n",
      "passenger_count    | Total number of passangers boarded in the taxi\n",
      "trip_time_in_secs  | Total time take to complete the trip in seconds\n",
      "trip_distance      | Total distance covered to complete the trip\n",
      "pickup_longitude   | The longitude of the pickup point\n",
      "pickup_latitude    | The latitude of the pickup point\n",
      "dropoff_longitude  | The longitude of the dropoff point\n",
      "dropoff_latitude   | The longitude of the dropoff point\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"What are the field names?  Give descriptions for each field.\")\n",
    "print('Field names -- ', cols)\n",
    "print('''\\nColumns            | Description \n",
    "-------------------|------------------------\n",
    "medallion          | A medallion is a permit that allows a driver to operate a taxi in United States\n",
    "hack_license       | A license needed to ride the yellow medalian taxicab\n",
    "vendor_id          | A code indicating the provider associated with the trip record\n",
    "rate_code          | Rating provide for the trip\n",
    "store_and_fwd_flag | This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the server - Y=store and forward; N=not a store and forward trip\n",
    "pickup_datetime    | Date and time when the trip was started\n",
    "dropoff_datetime   | Date and time when the trip was ended\n",
    "passenger_count    | Total number of passangers boarded in the taxi\n",
    "trip_time_in_secs  | Total time take to complete the trip in seconds\n",
    "trip_distance      | Total distance covered to complete the trip\n",
    "pickup_longitude   | The longitude of the pickup point\n",
    "pickup_latitude    | The latitude of the pickup point\n",
    "dropoff_longitude  | The longitude of the dropoff point\n",
    "dropoff_latitude   | The longitude of the dropoff point\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Give some sample data for each field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Give some sample data for each field.\n",
      "medallion: ['1B5C0970F2AE8CFFBA8AE4584BEAED29', 'B42249AE16E2B8E556F1CB1F940D6FB4', '890699222C47C09FBC898758CEC69762', '74B7D835C2CD98606D5256DA8A38E045', '4003B8478418FEC5D761E2F37602769B', 'D72DF7B12201912BFDBB93081EF04C96', 'FA5337E245DE9B2E124AFA735B41B4DF', 'BE63343BAD5CD6F99EC435812E332445', '58598FD2F8811C4F52A264D15D6FECAF', 'FE13CDF111CE4A3811F3E5D942BFD3B9'],\n",
      "\n",
      "hack_license: ['D961332334524990D1BBD462E2EFB8A4', 'D4BB308D1F3FCB3434D9DB282CDC93D7', '6318C3AEC02248928C3345B5805EB905', 'D5E278C918256D1F97680A1F04D290E0', '0B766F1054A5C16D86BC023858BD8143', 'AFD828EEF790A2485BBB0B568A8BE22E', '6E6B7D73303D5AE3808A9ABF4D3FF65B', '88CF6CAA78CE8B2000FBCF39DFAB3E69', 'F6F9DF7755F186C183EEBB5B03DBA23C', 'BAC511461590FAF7D7BB4C8F29C31AC5'],\n",
      "\n",
      "vendor_id: ['CMT', 'CMT', 'CMT', 'CMT', 'CMT', 'CMT', 'CMT', 'CMT', 'CMT', 'CMT'],\n",
      "\n",
      "rate_code: ['1', '1', '1', '1', '1', '1', '1', '1', '1', '1'],\n",
      "\n",
      "store_and_fwd_flag: ['N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N'],\n",
      "\n",
      "pickup_datetime: ['2013-02-08 23:35:14', '2013-02-07 12:20:16', '2013-02-08 08:56:54', '2013-02-08 09:37:02', '2013-02-08 19:31:25', '2013-02-08 23:10:01', '2013-02-07 22:02:30', '2013-02-06 22:25:26', '2013-02-09 02:57:33', '2013-02-09 09:43:39'],\n",
      "\n",
      "dropoff_datetime: ['2013-02-08 23:42:58', '2013-02-07 12:50:27', '2013-02-08 08:59:43', '2013-02-08 09:50:50', '2013-02-08 19:46:23', '2013-02-08 23:46:15', '2013-02-07 22:06:18', '2013-02-06 22:41:32', '2013-02-09 03:01:41', '2013-02-09 09:49:10'],\n",
      "\n",
      "passenger_count: ['1', '4', '1', '1', '1', '4', '1', '1', '3', '1'],\n",
      "\n",
      "trip_time_in_secs: ['463', '1810', '168', '828', '897', '2173', '227', '966', '247', '331'],\n",
      "\n",
      "trip_distance: ['.80', '3.10', '1.00', '2.10', '3.30', '7.60', '.70', '3.80', '.50', '2.60'],\n",
      "\n",
      "pickup_longitude: ['-73.992439', '-73.989494', '-73.963036', '-73.987953', '-73.987282', '-73.993004', '-73.979965', '-73.978508', '-73.982338', '-74.005486'],\n",
      "\n",
      "pickup_latitude: ['40.724487', '40.769588', '40.799141', '40.728764', '40.743042', '40.720154', '40.780544', '40.759911', '40.751549', '40.740463'],\n",
      "\n",
      "dropoff_longitude: ['-73.984421', '-73.990303', '-73.972168', '-74.007118', '-74.010284', '-73.959747', '-73.971489', '-74.003899', '-73.976425', '-73.985565'],\n",
      "\n",
      "dropoff_latitude: ['40.718903', '40.737347', '40.786446', '40.705399', '40.703964', '40.80854', '40.78241', '40.715969', '40.74905', '40.773209'],\n",
      "\n",
      "CPU times: user 17.6 s, sys: 494 ms, total: 18.1 s\n",
      "Wall time: 18.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "f = open('trip_data_2.csv', 'r')\n",
    "reader = csv.reader(f)\n",
    "sample_data = {}\n",
    "c = 0\n",
    "\n",
    "for row in reader:\n",
    "    if c == 0:\n",
    "        for col_name in row:\n",
    "            sample_data[col_name] = []\n",
    "    elif c>0 and c<11:\n",
    "        for data, col_name in zip(row, sample_data):\n",
    "            sample_data[col_name].append(data)\n",
    "    c+=1\n",
    "\n",
    "print('''Give some sample data for each field.''')\n",
    "for i in sample_data:\n",
    "    print(f'{i}: {sample_data[i]},\\n')\n",
    "\n",
    "# c = 0\n",
    "# for row in reader:\n",
    "#     print(*row, sep=' | ')\n",
    "#     if c>5:\n",
    "#         break\n",
    "#     c+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ### 4. What MySQL data types / len would you need to store each of the fields?\n",
    "        a. int(xx), varchar(xx),date,datetime,bool, decimal(m,d) -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. What MySQL data types / len would you need to store each of the fields? int(xx), varchar(xx),date,datetime,bool, decimal(m,d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What MySQL data types / len would you need to store each of the fields?\n",
      "\n",
      "Columns             | Datatype(len)\n",
      "--------------------|-------------\n",
      "medallion           | varchar(100)\n",
      "hack_license        | varchar(100)\n",
      "vendor_id           | varchar(5)\n",
      "rate_code           | int\n",
      "store_and_fwd_flag  | varchar(10)\n",
      "pickup_datetime     | datetime\n",
      "dropoff_datetime    | datetime\n",
      "passenger_count     | int\n",
      "trip_time_in_secs   | decimal(10,6)\n",
      "trip_distance       | decimal(6,2)\n",
      "pickup_longitude    | decimal(4, 10)\n",
      "pickup_latitude     | decimal(4, 10)\n",
      "dropoff_longitude   | decimal(4, 10)\n",
      "dropoff_latitude    | decimal(4, 10)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('''What MySQL data types / len would you need to store each of the fields?''')\n",
    "print(f'''\n",
    "{'Columns':<20}| Datatype(len)\n",
    "{'--------------------|-------------'}\n",
    "{'medallion':<20}| varchar(100)\n",
    "{'hack_license':<20}| varchar(100)\n",
    "{'vendor_id':<20}| varchar(5)\n",
    "{'rate_code':<20}| int\n",
    "{'store_and_fwd_flag':<20}| varchar(10)\n",
    "{'pickup_datetime':<20}| datetime\n",
    "{'dropoff_datetime':<20}| datetime\n",
    "{'passenger_count':<20}| int\n",
    "{'trip_time_in_secs':<20}| decimal(10,6)\n",
    "{'trip_distance':<20}| decimal(6,2)\n",
    "{'pickup_longitude':<20}| decimal(4, 10)\n",
    "{'pickup_latitude':<20}| decimal(4, 10)\n",
    "{'dropoff_longitude':<20}| decimal(4, 10)\n",
    "{'dropoff_latitude':<20}| decimal(4, 10)\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. What is the geographic range of your data (min/max - X/Y)?\n",
    "        a. Plot this (approximately on a map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the geographic range of your data (min/max - X/Y)?\n",
      "a. Plot this (approximately on a map)\n",
      "[(-71.099113, 47.721272), (-71.099113, 32.793495), (-84.873749, 32.793495), (-84.873749, 47.721272)]\n"
     ]
    }
   ],
   "source": [
    "print('''What is the geographic range of your data (min/max - X/Y)?\n",
    "a. Plot this (approximately on a map)''')\n",
    "\n",
    "f = open('trip_data_2.csv', 'r')\n",
    "reader = csv.reader(f)\n",
    "\n",
    "# Initialize variables for extreme points\n",
    "eastmost_longitude = -float('inf')\n",
    "westmost_longitude = float('inf')\n",
    "northmost_latitude = -float('inf')\n",
    "southmost_latitude = float('inf')\n",
    "\n",
    "def haversine(lon1, lat1, lon2, lat2):\n",
    "    R = 3959.87433 # this is in miles.  For Earth radius in kilometers use 6372.8 km\n",
    "    \n",
    "    dLat = radians(lat2 - lat1)\n",
    "    dLon = radians(lon2 - lon1)\n",
    "    lat1 = radians(lat1)\n",
    "    lat2 = radians(lat2)\n",
    "    \n",
    "    a = sin(dLat/2)**2 + cos(lat1)*cos(lat2)*sin(dLon/2)**2\n",
    "    c = 2*asin(sqrt(a))\n",
    "    d = R*c\n",
    "    return d\n",
    "    \n",
    "c = 0  \n",
    "for row in reader:\n",
    "    if c == 0:  \n",
    "       c += 1\n",
    "       continue\n",
    "        \n",
    "    pickup_longitude = float(row[10])\n",
    "    pickup_latitude = float(row[11])\n",
    "    # dropoff_longitude = float(row[12])  # Handle missing values if necessary (e.g., using a try-except block)\n",
    "    try:\n",
    "        dropoff_longitude = float(row[12])\n",
    "    except ValueError:\n",
    "        continue  \n",
    "\n",
    "    dropoff_latitude = float(row[13])\n",
    "        \n",
    "    if c > 0 and row[10] not in ['', '-', None] and row[11] not in ['', '-', None] and row[12] not in ['', '-', None] and row[13] not in ['', '-', None]:\n",
    "        lon1, lat1, lon2, lat2 = float(row[10]), float(row[11]), float(row[12]), float(row[13])\n",
    "        trip_dist_h = haversine(lon1, lat1, lon2, lat2)\n",
    "        trip_dist = float(row[9])\n",
    "    \n",
    "    if abs(trip_dist - trip_dist_h) < 2:\n",
    "        if (pickup_latitude >= 24.521208 and pickup_latitude <= 49.382808) and \\\n",
    "        (dropoff_latitude >= 24.521208 and dropoff_latitude <= 49.382808) and \\\n",
    "        (pickup_longitude >= -124.736342 and pickup_longitude <= -66.945392) and \\\n",
    "        (dropoff_longitude >= -124.736342 and dropoff_longitude <= -66.945392):\n",
    "            # Update extreme points\n",
    "            eastmost_longitude = max(eastmost_longitude, pickup_longitude, dropoff_longitude)\n",
    "            westmost_longitude = min(westmost_longitude, pickup_longitude, dropoff_longitude)\n",
    "            northmost_latitude = max(northmost_latitude, pickup_latitude, dropoff_latitude)\n",
    "            southmost_latitude = min(southmost_latitude, pickup_latitude, dropoff_latitude)\n",
    "\n",
    "    c += 1\n",
    "\n",
    "# Create the four-sided shape coordinates\n",
    "shape_coordinates = [(eastmost_longitude, northmost_latitude),\n",
    "                    (eastmost_longitude, southmost_latitude),\n",
    "                    (westmost_longitude, southmost_latitude),\n",
    "                    (westmost_longitude, northmost_latitude)]\n",
    "\n",
    "print(shape_coordinates)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2228.7683"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(eastmost_longitude, pickup_longitude, dropoff_longitude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. What is the average overall computed trip distance? (You should use Haversine Distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the average overall computed trip distance? (You should use Haversine Distance)\n",
      "Average distance using Haversine distance: 1.96 Miles\n",
      "Average distance using trip_distance column: 2.74 Miles\n",
      "CPU times: user 31.7 s, sys: 1.02 s, total: 32.7 s\n",
      "Wall time: 33.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "f = open('trip_data_2.csv', 'r')\n",
    "reader = csv.reader(f)\n",
    "\n",
    "c = 0\n",
    "distances = []\n",
    "distances2 = []\n",
    "\n",
    "def haversine(lon1, lat1, lon2, lat2):\n",
    "    R = 3959.87433 # this is in miles.  For Earth radius in kilometers use 6372.8 km\n",
    "    \n",
    "    dLat = radians(lat2 - lat1)\n",
    "    dLon = radians(lon2 - lon1)\n",
    "    lat1 = radians(lat1)\n",
    "    lat2 = radians(lat2)\n",
    "    \n",
    "    a = sin(dLat/2)**2 + cos(lat1)*cos(lat2)*sin(dLon/2)**2\n",
    "    c = 2*asin(sqrt(a))\n",
    "    d = R*c\n",
    "    return d\n",
    "\n",
    "\n",
    "for row in reader:\n",
    "    if c > 0 and row[10] not in ['', '-', None] and row[11] not in ['', '-', None] and row[12] not in ['', '-', None] and row[13] not in ['', '-', None]:\n",
    "        lon1, lat1, lon2, lat2 = float(row[10]), float(row[11]), float(row[12]), float(row[13])\n",
    "        distances.append(haversine(lon1, lat1, lon2, lat2))\n",
    "        distances2.append(float(row[9]))\n",
    "    c+=1\n",
    "\n",
    "print('''What is the average overall computed trip distance? (You should use Haversine Distance)''')\n",
    "# print(min(distances), max(distances), np.median(distances), np.mean(distances))\n",
    "# print(min(distances2), max(distances2), np.median(distances2), np.mean(distances2))\n",
    "\n",
    "distances = np.array(distances)\n",
    "distances2 = np.array(distances2)\n",
    "\n",
    "dt = np.where(distances > np.max(distances2), np.mean(distances2), distances)\n",
    "print(f'Average distance using Haversine distance: {np.mean(dt):.2f} Miles')\n",
    "print(f'Average distance using trip_distance column: {np.mean(distances2):.2f} Miles')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 6.a Draw a histogram of the trip distances binned anyway you see fit.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 30.4 s, sys: 3.41 s, total: 33.8 s\n",
      "Wall time: 34.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "f = open('trip_data_2.csv', 'r')\n",
    "reader = csv.reader(f)\n",
    "\n",
    "c = 0\n",
    "trip_dist = []\n",
    "\n",
    "for row in reader:\n",
    "    if c > 0:\n",
    "        trip_dist.append(float(row[9]))\n",
    "    c+=1\n",
    "\n",
    "trip_dist = list(filter(lambda x: x > 0, trip_dist))\n",
    "plt.hist(trip_dist, 20)\n",
    "plt.title(\"distance calculated using actual_trip_distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73844"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(min(trip_dist), max(trip_dist), np.median(trip_dist), np.percentile(trip_dist, [25, 50 ,75, 99]))\n",
    "# np.where(trip_dist > 20, )\n",
    "# len(list(filter(lambda x: x == 0, trip_dist)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram on distance calculated using haversine distance\n",
    "\n",
    "plt.hist(list(filter(lambda x: x > 0, dt)), 20)\n",
    "plt.title(\"distance calculated using haversine distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. What are the distinct values for each field? (If applicable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "7. What are the distinct values for each field? (If applicable)\n",
      "medallion: ['00005007A9F30E289E760362F69E4EAD' '000318C2E3E6381580E5C99910A60668'\n",
      " '000351EDC735C079246435340A54C7C1' ... 'FFF010F904EF7B60DAF12560AFE5127C'\n",
      " 'FFF9D2834D24079962E68642F14BAC82' 'FFFECF75AB6CC4FF9E8A8B633AB81C26']\n",
      "hack_license: ['0002555BBE359440D6CEB34B699D3932' '0008B3E338CE8C3377E071A4D80D3694'\n",
      " '000A4EBF1CEB9C6BD9978D4362493C6E' ... 'FFFBCEA3D4E21E05902EE67AD556F67C'\n",
      " 'FFFC8985B6B37B60AE71741C01AC423A' 'FFFF521546CE00599FF7D09CABC0502B']\n",
      "vendor_id: ['CMT' 'VTS']\n",
      "rate_code: [  0   1   2   3   4   5   6   7  28  79 210 221]\n",
      "store_and_fwd_flag: ['' 'N' 'Y']\n",
      "passenger_count: [  0   1   2   3   4   5   6   7   8   9 129 208]\n",
      "CPU times: user 39.4 s, sys: 8.77 s, total: 48.1 s\n",
      "Wall time: 51.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "f = open('trip_data_2.csv', 'r')\n",
    "reader = csv.reader(f)\n",
    "c = 0\n",
    "\n",
    "medallion = []\n",
    "hack_license = []\n",
    "vendor_id = []\n",
    "rate_code = []\n",
    "store_and_fwd_flag = []\n",
    "passenger_count = []\n",
    "trip_time_in_secs = []\n",
    "trip_distance = []\n",
    "\n",
    "for row in reader:\n",
    "    if c>0:\n",
    "        try:\n",
    "            medallion.append(row[0])\n",
    "            hack_license.append(row[1])\n",
    "            vendor_id.append(row[2])\n",
    "            rate_code.append(int(row[3]))\n",
    "            store_and_fwd_flag.append(row[4])\n",
    "            passenger_count.append(int(row[7]))\n",
    "            trip_time_in_secs.append(float(row[8]))\n",
    "            trip_distance.append(float(row[9]))\n",
    "        except Exception as e:\n",
    "            print(row)\n",
    "            print(e)\n",
    "            break\n",
    "    c+=1\n",
    "\n",
    "print('''\\n7. What are the distinct values for each field? (If applicable)''')\n",
    "print(f'medallion: {np.unique(medallion)}')\n",
    "print(f'hack_license: {np.unique(hack_license)}')\n",
    "print(f'vendor_id: {np.unique(vendor_id)}')\n",
    "print(f'rate_code: {np.unique(rate_code)}')\n",
    "print(f'store_and_fwd_flag: {np.unique(store_and_fwd_flag)}')\n",
    "print(f'passenger_count: {np.unique(passenger_count)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. For other numeric types besides lat and lon, what are the min and max values?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# trips with no customers\n",
    "pc_zero = list(filter(lambda x:x==0, passenger_count))\n",
    "len(pc_zero)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# trips with more 9 customers in the vehicle\n",
    "pc_outliers = list(filter(lambda x:x>9, passenger_count))\n",
    "len(pc_outliers)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(len(trip_time_in_secs), len(list(filter(lambda x:x == 0, trip_time_in_secs))))\n",
    "print(len(list(filter(lambda x:x == 0, trip_time_in_secs)))/len(trip_time_in_secs))\n",
    "print(np.min(trip_time_in_secs), np.percentile(trip_time_in_secs, [25,50,75, 90]), max(trip_time_in_secs))\n",
    "\n",
    "print(len(list(filter(lambda x:x < 10, trip_time_in_secs))))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "lt = np.where(np.array(trip_time_in_secs, dtype = int) <= 10)[0]\n",
    "lt, np.array(trip_distance, dtype=float)[lt][:5]\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "np.where(np.array(trip_distance, dtype=float)[lt] > 0)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "for i in np.arange(1, 1000):\n",
    "    if trip_time_in_secs[i] <= 10:\n",
    "        print(i, trip_time_in_secs[i], trip_distance[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8. For other numeric types besides lat and lon, what are the min and max values?\n",
      "    columns      |    min    |    max     \n",
      "-----------------|-----------|-------------\n",
      "passenger_count  |     1     |    208    \n",
      "trip_time_in_secs|    1.0    |  10800.0  \n",
      "trip_distance    |    0.01   |   100.0   \n",
      "\n",
      "    columns      |    min    |    max     \n",
      "-----------------|-----------|-------------\n",
      "passenger_count  |    1.0    |    9.0    \n",
      "trip_time_in_secs|    1.0    |  10800.0  \n",
      "trip_distance    |    0.01   |   100.0   \n"
     ]
    }
   ],
   "source": [
    "## pc_median = np.median(list(map(int, np.unique(passenger_count))))\n",
    "## passenger_count = np.array(passenger_count, dtype = int)\n",
    "\n",
    "passenger_count = list(filter(lambda x:x>0, passenger_count))\n",
    "trip_time_in_secs = list(filter(lambda x:x>0, trip_time_in_secs))\n",
    "trip_distance = list(filter(lambda x:x>0, trip_distance))\n",
    "\n",
    "\n",
    "min_pc, max_pc = min(passenger_count), max(passenger_count)\n",
    "min_tt, max_tt = min(trip_time_in_secs), max(trip_time_in_secs)\n",
    "min_td, max_td = min(trip_distance), max(trip_distance)\n",
    "\n",
    "print('''8. For other numeric types besides lat and lon, what are the min and max values?''')\n",
    "print('    columns      |    min    |    max     ')\n",
    "print('-----------------|-----------|-------------')\n",
    "print(f'passenger_count  | {min_pc:^10}| {max_pc:^10}')\n",
    "print(f'trip_time_in_secs| {min_tt:^10}| {max_tt:^10}')\n",
    "print(f'trip_distance    | {min_td:^10}| {max_td:^10}')\n",
    "\n",
    "pc_median = np.median(passenger_count)\n",
    "# print(pc_median)\n",
    "\n",
    "passenger_count_updated = np.where((np.array(passenger_count) > 9) | (np.array(passenger_count) == 0), pc_median, np.array(passenger_count))\n",
    "\n",
    "min_pc, max_pc = min(passenger_count_updated), max(passenger_count_updated)\n",
    "print('\\n    columns      |    min    |    max     ')\n",
    "print('-----------------|-----------|-------------')\n",
    "print(f'passenger_count  | {min_pc:^10}| {max_pc:^10}')\n",
    "print(f'trip_time_in_secs| {min_tt:^10}| {max_tt:^10}')\n",
    "print(f'trip_distance    | {min_td:^10}| {max_td:^10}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Create a chart which shows the average number of passengers each hour of the day. (X axis should have 24 hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 2s, sys: 638 ms, total: 1min 3s\n",
      "Wall time: 1min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "f = open('trip_data_2.csv', 'r')\n",
    "reader = csv.reader(f)\n",
    "c = 0\n",
    "\n",
    "hours_dict = {}\n",
    "\n",
    "for row in reader:\n",
    "    if c > 0:\n",
    "        h = datetime.strptime(row[5], \"%Y-%m-%d %H:%M:%S\").hour\n",
    "        pc = int(row[7])\n",
    "        if not h in hours_dict.keys():\n",
    "            hours_dict[h] = pc\n",
    "        else:\n",
    "            hours_dict[h] = hours_dict[h] + pc\n",
    "    c+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_pc_hour = dict(sorted({k: int(v/len(hours_dict)) for k, v in hours_dict.items()}.items()))\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.plot(avg_pc_hour.keys(), avg_pc_hour.values())\n",
    "plt.title('Average number of passengers each hour of the day')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Avg passenger count')\n",
    "plt.xticks(range(0, 24))\n",
    "plt.grid(axis = 'y')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Create a new CSV file which has only one out of every thousand rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10. Create a new CSV file which has only one out of every thousand rows\n",
      "[['medallion', 'hack_license', 'vendor_id', 'rate_code', 'store_and_fwd_flag', 'pickup_datetime', 'dropoff_datetime', 'passenger_count', 'trip_time_in_secs', 'trip_distance', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude'], ['1B5C0970F2AE8CFFBA8AE4584BEAED29', 'D961332334524990D1BBD462E2EFB8A4', 'CMT', '1', 'N', '2013-02-08 23:35:14', '2013-02-08 23:42:58', '1', '463', '.80', '-73.992439', '40.724487', '-73.984421', '40.718903'], ['AC77B897B23612F6857D90D4195E9292', '18F658FA8369EC322B56527C6EBA55EC', 'VTS', '1', '', '2013-02-01 16:01:00', '2013-02-01 16:05:00', '2', '240', '.02', '-73.979729', '40.755161', '-73.980225', '40.754196'], ['2D2792269580F25AE3C893CBB2FEF94C', 'E62CE1A5CC40099E4896353F50F0C883', 'VTS', '1', '', '2013-02-01 15:46:00', '2013-02-01 15:51:00', '5', '300', '.64', '-73.953995', '40.741585', '-73.953995', '40.741585'], ['45E64013C02CAB97F5651AD6873975EF', 'F41DB2402AF1B1456BA9EB0FC5116706', 'VTS', '1', '', '2013-02-01 13:47:00', '2013-02-01 14:01:00', '1', '840', '4.73', '-73.950005', '40.802246', '-73.966507', '40.756611'], ['99CB433A506A7B0DBA57BA3B63D43D17', '1142579CC34712EADB164F802ABD5F9F', 'VTS', '1', '', '2013-02-01 16:11:00', '2013-02-01 16:16:00', '1', '300', '.52', '-73.986465', '40.722454', '-73.987625', '40.727798'], ['A614810E4C8DB5D24EF62F55995648BD', 'BCF6A9D98B89D59FC37DE105CB64CD37', 'VTS', '1', '', '2013-02-01 14:29:00', '2013-02-01 14:51:00', '5', '1320', '2.08', '-74.00293', '40.740097', '-73.981712', '40.758675'], ['101C9958C7D6C5E996CEBBD04C965936', '91927349DF07550D9D15BDF632DD297A', 'VTS', '1', '', '2013-02-01 14:03:00', '2013-02-01 14:28:00', '2', '1500', '5.72', '-73.978943', '40.756172', '-73.995911', '40.716335'], ['8248744EE9340514601EB749DFEC9FFB', '386B2D9C2078CCD3358DC5DEF4C0DE78', 'VTS', '1', '', '2013-02-01 15:52:00', '2013-02-01 15:55:00', '1', '180', '.52', '-73.966225', '40.803932', '-73.965477', '40.808643'], ['799153A138F4E8334A1A95AE25040B83', '5438183A1B54B34BFB3E21E5D316A317', 'VTS', '1', '', '2013-02-01 15:41:00', '2013-02-01 15:47:00', '2', '360', '1.17', '-73.960327', '40.776073', '-73.975082', '40.785118']]\n",
      "CPU times: user 19.3 s, sys: 3.94 s, total: 23.3 s\n",
      "Wall time: 25.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "f = open('trip_data_2.csv', 'r')\n",
    "reader = csv.reader(f)\n",
    "c = 0\n",
    "sample_rows = []\n",
    "\n",
    "for row in reader:\n",
    "    if c == 0:\n",
    "        sample_rows.append(row)\n",
    "    if c % 1000 == 1:\n",
    "        sample_rows.append(row)\n",
    "    c+=1\n",
    "\n",
    "print('''10. Create a new CSV file which has only one out of every thousand rows''')\n",
    "print(sample_rows[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f2=open(\"one_outof_1000.csv\",'w')\n",
    "f2.write('')\n",
    "f2.close()\n",
    "f2=open('one_outof_1000.csv','a')\n",
    "w=csv.writer(f2,delimiter=',',lineterminator='\\n')\n",
    "\n",
    "\n",
    "for row in sample_rows:\n",
    "    w.writerow(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Repeat step 9 with the reduced dataset and compare the two charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_hours_dict = {}\n",
    "c = 0 \n",
    "for row in sample_rows:\n",
    "    if c > 0:\n",
    "        h = datetime.strptime(row[5], \"%Y-%m-%d %H:%M:%S\").hour\n",
    "        pc = int(row[7])\n",
    "        if not h in sample_hours_dict.keys():\n",
    "            sample_hours_dict[h] = pc\n",
    "        else:\n",
    "            sample_hours_dict[h] = sample_hours_dict[h] + pc\n",
    "    c+=1\n",
    "    \n",
    "sample_avg_pc_hour = dict(sorted({k: int(v/len(sample_hours_dict)) for k, v in sample_hours_dict.items()}.items()))\n",
    "plt.figure(figsize = (10,5))\n",
    "# plt.bar(avg_pc_hour.keys(), avg_pc_hour.values())\n",
    "plt.plot(sample_avg_pc_hour.keys(), sample_avg_pc_hour.values())\n",
    "plt.title('Average number of passengers each hour of the day on sample data')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Avg passenger count')\n",
    "plt.xticks(range(0, 24))\n",
    "plt.grid(axis = 'y')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize = (10,5))\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Hours')\n",
    "ax1.set_ylabel('Average passenger count', color=color)\n",
    "ax1.plot(avg_pc_hour.keys(), avg_pc_hour.values(), color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  \n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Average passenger count of sample', color=color)  \n",
    "ax2.plot(sample_avg_pc_hour.keys(), sample_avg_pc_hour.values(), color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "plt.xticks(range(0, 24))\n",
    "fig.tight_layout()  \n",
    "plt.title('Population vs Sample data distribution of avg passengers each hour of the day')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}