02 Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtNBZFHO3M7n"
   },
   "source": [
    "# **Taxi Cab Fares Prediction Project**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g-E8SNtmRUkN"
   },
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "I am the newest data professional in a fictional data consulting firm: Automatidata. The team is still early into the project, having only just completed an initial plan of action and some early Python coding work.\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "Luana Rodriquez, the senior data analyst at Automatidata, is pleased with the work I have already completed and requests my assistance with some EDA and data visualization work for the New York City Taxi and Limousine Commission project (New York City TLC) to get a general understanding of what taxi ridership looks like. The management team is asking for a Python notebook showing data structuring and cleaning, as well as any matplotlib/seaborn visualizations plotted to help understand the data. At the very least, I should include a box plot of the ride durations and some time series plots, like a breakdown by quarter or month.\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "Additionally, the management team has recently asked all EDA to include Tableau visualizations. For this taxi data, I need to create a Tableau dashboard showing a New York City map of taxi/limo trips by month. I should make sure it is easy to understand for someone who isn’t data savvy, and remember that the assistant director at the New York City TLC is a person with visual impairments.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgSbVJvomcVa"
   },
   "source": [
    "# **Exploratory Data Analysis**\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "In this task, we will examine data provided and prepare it for analysis. We will also design a professional data visualization that tells a story, and will help data-driven decisions for business needs.\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**The purpose** of this project is to conduct exploratory data analysis on a provided data set. Our mission is to continue the investigation we began in the previous task and perform further EDA on this data with the aim of learning more about the variables.\n",
    "</p>\n",
    "\n",
    "**The goal** is to clean data set and create a visualization.\n",
    "\n",
    "*This activity has 4 parts:*\n",
    "\n",
    "**Part 1:** Imports, links, and loading\n",
    "\n",
    "**Part 2:** Data Exploration\n",
    "*   Data cleaning\n",
    "\n",
    "**Part 3:** Building visualizations\n",
    "\n",
    "**Part 4:** Evaluate and share results\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "After completing this task and and swering the questions arised, then we will complete an Executive Summary using the questions listed on the PACE Strategy Document.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rdR5eWbYx8GE"
   },
   "source": [
    "# **PACE stages** \n",
    "\n",
    "\n",
    "<img src=\"images/Pace.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "   *        [Plan]\n",
    "   *        [Analyze]\n",
    "   *        [Construct]\n",
    "   *        [Execute]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "Throughout these project notebooks, we'll see references to the problem-solving framework PACE. This notebook components are labeled with the respective PACE stage: Plan, Analyze, Construct, and Execute.\n",
    "</p>"
   ]
  },
  {
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   "source": [
    "<img src=\"images/Plan.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Plan** \n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "In this stage, we'll consider the following questions where applicable to complete our code response:\n",
    "\n",
    "**Exemplar response:**\n",
    "\n",
    "1. Identify any outliers:\n",
    "\n",
    "\n",
    "*   What methods are best for identifying outliers?\n",
    "  * Use numpy functions to investigate the `mean()` and `median()` of the data and understand range of data values\n",
    "  * Use a boxplot to visualize the distribution of the data\n",
    "  * Use histograms to visualize the distribution of the data\n",
    "*   How do you make the decision to keep or exclude outliers from any future models?\n",
    "  * There are three main options for dealing with outliers: keeping them as they are, deleting them, or reassigning them. Whether you keep outliers as they are, delete them, or reassign values is a decision that you make taking into account the nature of the outlying data and the assumptions of the model you are building. To help you make the decision, you can start with these general guidelines:\n",
    "\n",
    "      * Delete them: If you are sure the outliers are mistakes, typos, or errors and the dataset will be used for modeling or machine learning, then you are more likely to decide to delete outliers. Of the three choices, you’ll use this one the least.\n",
    "\n",
    "      * Reassign them: If the dataset is small and/or the data will be used for modeling or machine learning, you are more likely to choose a path of deriving new values to replace the outlier values.\n",
    "      \n",
    "      * Leave them: For a dataset that you plan to do EDA/analysis on and nothing else, or for a dataset you are preparing for a model that is resistant to outliers, it is most likely that you are going to leave them in.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SKur6gTPRcvy"
   },
   "source": [
    "### **Task 1. Imports, links, and loading**\n",
    "\n",
    "For EDA of the data, import the data and packages that would be most helpful, such as pandas, numpy and matplotlib.\n",
    "\n",
    "Then, import the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "EO8vKZI8x8GF"
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "w7XSDxoqyF9D"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'Datasets/2017_Yellow_Taxi_Trip_Data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3mipEiyZx8GI"
   },
   "source": [
    "<img src=\"images/Analyze.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Analyze**\n",
    "\n",
    "Consider these questions in your PACE Strategy Document to reflect on the Analyze stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n09krlKWRw_2"
   },
   "source": [
    "### **Task 2a. Data Exploration and Cleaning**\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "Decide which columns are applicable\n",
    "\n",
    "The first step is to assess data. We could check the Data Source page on Tableau Public to get a sense of the size, shape and makeup of the data set. Then we answer these questions:\n",
    "\n",
    "Given our scenario, which data columns are most applicable? Which data columns can I eliminate, knowing they won’t solve our problem scenario?\n",
    "\n",
    "Consider functions that help you understand and structure the data. \n",
    "\n",
    "*    `head()`\n",
    "*    `describe()`\n",
    "*    `info()`\n",
    "*    `groupby()`\n",
    "*    `sortby()`\n",
    "\n",
    "Considering these questions as we work:\n",
    "\n",
    "What do we do about missing data (if any)?\n",
    "\n",
    "Are there data outliers?\n",
    "\n",
    "What do the distributions of variables tell us about the question we're asking or the problem we're trying to solve?\n",
    "\n",
    "We will find these answers later in the notebook.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uk6zuv_mUU2k"
   },
   "source": [
    "Start by discovering, using head and size. "
   ]
  },
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       "   Unnamed: 0  VendorID    tpep_pickup_datetime   tpep_dropoff_datetime  \\\n",
       "0    24870114         2   03/25/2017 8:55:43 AM   03/25/2017 9:09:47 AM   \n",
       "1    35634249         1   04/11/2017 2:53:28 PM   04/11/2017 3:19:58 PM   \n",
       "2   106203690         1   12/15/2017 7:26:56 AM   12/15/2017 7:34:08 AM   \n",
       "3    38942136         2   05/07/2017 1:17:59 PM   05/07/2017 1:48:14 PM   \n",
       "4    30841670         2  04/15/2017 11:32:20 PM  04/15/2017 11:49:03 PM   \n",
       "5    23345809         2   03/25/2017 8:34:11 PM   03/25/2017 8:42:11 PM   \n",
       "6    37660487         2   05/03/2017 7:04:09 PM   05/03/2017 8:03:47 PM   \n",
       "7    69059411         2   08/15/2017 5:41:06 PM   08/15/2017 6:03:05 PM   \n",
       "8     8433159         2   02/04/2017 4:17:07 PM   02/04/2017 4:29:14 PM   \n",
       "9    95294817         1   11/10/2017 3:20:29 PM   11/10/2017 3:40:55 PM   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "4                1           4.37           1                  N   \n",
       "5                6           2.30           1                  N   \n",
       "6                1          12.83           1                  N   \n",
       "7                1           2.98           1                  N   \n",
       "8                1           1.20           1                  N   \n",
       "9                1           1.60           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "4             4           112             2         16.5    0.5      0.5   \n",
       "5           161           236             1          9.0    0.5      0.5   \n",
       "6            79           241             1         47.5    1.0      0.5   \n",
       "7           237           114             1         16.0    1.0      0.5   \n",
       "8           234           249             2          9.0    0.0      0.5   \n",
       "9           239           237             1         13.0    0.0      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \n",
       "0        2.76           0.0                    0.3         16.56  \n",
       "1        4.00           0.0                    0.3         20.80  \n",
       "2        1.45           0.0                    0.3          8.75  \n",
       "3        6.39           0.0                    0.3         27.69  \n",
       "4        0.00           0.0                    0.3         17.80  \n",
       "5        2.06           0.0                    0.3         12.36  \n",
       "6        9.86           0.0                    0.3         59.16  \n",
       "7        1.78           0.0                    0.3         19.58  \n",
       "8        0.00           0.0                    0.3          9.80  \n",
       "9        2.75           0.0                    0.3         16.55  "
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   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GvT82D1qyF9F",
    "outputId": "b9a42ee6-0573-4949-859b-32639e7f4bb9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "408582"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WulP4lZ9UVgy"
   },
   "source": [
    "Use describe... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "id": "xMpkdNQ0UPmW",
    "outputId": "f873b702-8962-4715-9198-023dd9951849"
   },
   "outputs": [
    {
     "data": {
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       "<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>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.269900e+04</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "      <td>22699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.675849e+07</td>\n",
       "      <td>1.556236</td>\n",
       "      <td>1.642319</td>\n",
       "      <td>2.913313</td>\n",
       "      <td>1.043394</td>\n",
       "      <td>162.412353</td>\n",
       "      <td>161.527997</td>\n",
       "      <td>1.336887</td>\n",
       "      <td>13.026629</td>\n",
       "      <td>0.333275</td>\n",
       "      <td>0.497445</td>\n",
       "      <td>1.835781</td>\n",
       "      <td>0.312542</td>\n",
       "      <td>0.299551</td>\n",
       "      <td>16.310502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.274493e+07</td>\n",
       "      <td>0.496838</td>\n",
       "      <td>1.285231</td>\n",
       "      <td>3.653171</td>\n",
       "      <td>0.708391</td>\n",
       "      <td>66.633373</td>\n",
       "      <td>70.139691</td>\n",
       "      <td>0.496211</td>\n",
       "      <td>13.243791</td>\n",
       "      <td>0.463097</td>\n",
       "      <td>0.039465</td>\n",
       "      <td>2.800626</td>\n",
       "      <td>1.399212</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>16.097295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.212700e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-120.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>-120.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.852056e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>8.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.673150e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.610000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>11.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.537452e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.060000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.134863e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>33.960000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>999.990000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1200.290000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0      VendorID  passenger_count  trip_distance  \\\n",
       "count  2.269900e+04  22699.000000     22699.000000   22699.000000   \n",
       "mean   5.675849e+07      1.556236         1.642319       2.913313   \n",
       "std    3.274493e+07      0.496838         1.285231       3.653171   \n",
       "min    1.212700e+04      1.000000         0.000000       0.000000   \n",
       "25%    2.852056e+07      1.000000         1.000000       0.990000   \n",
       "50%    5.673150e+07      2.000000         1.000000       1.610000   \n",
       "75%    8.537452e+07      2.000000         2.000000       3.060000   \n",
       "max    1.134863e+08      2.000000         6.000000      33.960000   \n",
       "\n",
       "         RatecodeID  PULocationID  DOLocationID  payment_type   fare_amount  \\\n",
       "count  22699.000000  22699.000000  22699.000000  22699.000000  22699.000000   \n",
       "mean       1.043394    162.412353    161.527997      1.336887     13.026629   \n",
       "std        0.708391     66.633373     70.139691      0.496211     13.243791   \n",
       "min        1.000000      1.000000      1.000000      1.000000   -120.000000   \n",
       "25%        1.000000    114.000000    112.000000      1.000000      6.500000   \n",
       "50%        1.000000    162.000000    162.000000      1.000000      9.500000   \n",
       "75%        1.000000    233.000000    233.000000      2.000000     14.500000   \n",
       "max       99.000000    265.000000    265.000000      4.000000    999.990000   \n",
       "\n",
       "              extra       mta_tax    tip_amount  tolls_amount  \\\n",
       "count  22699.000000  22699.000000  22699.000000  22699.000000   \n",
       "mean       0.333275      0.497445      1.835781      0.312542   \n",
       "std        0.463097      0.039465      2.800626      1.399212   \n",
       "min       -1.000000     -0.500000      0.000000      0.000000   \n",
       "25%        0.000000      0.500000      0.000000      0.000000   \n",
       "50%        0.000000      0.500000      1.350000      0.000000   \n",
       "75%        0.500000      0.500000      2.450000      0.000000   \n",
       "max        4.500000      0.500000    200.000000     19.100000   \n",
       "\n",
       "       improvement_surcharge  total_amount  \n",
       "count           22699.000000  22699.000000  \n",
       "mean                0.299551     16.310502  \n",
       "std                 0.015673     16.097295  \n",
       "min                -0.300000   -120.300000  \n",
       "25%                 0.300000      8.750000  \n",
       "50%                 0.300000     11.800000  \n",
       "75%                 0.300000     17.800000  \n",
       "max                 0.300000   1200.290000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "psmn0VD0UWBk"
   },
   "source": [
    "And info. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Thlb8oiyUPwe",
    "outputId": "e578639c-683e-449c-838b-5c218674377b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 18 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Unnamed: 0             22699 non-null  int64  \n",
      " 1   VendorID               22699 non-null  int64  \n",
      " 2   tpep_pickup_datetime   22699 non-null  object \n",
      " 3   tpep_dropoff_datetime  22699 non-null  object \n",
      " 4   passenger_count        22699 non-null  int64  \n",
      " 5   trip_distance          22699 non-null  float64\n",
      " 6   RatecodeID             22699 non-null  int64  \n",
      " 7   store_and_fwd_flag     22699 non-null  object \n",
      " 8   PULocationID           22699 non-null  int64  \n",
      " 9   DOLocationID           22699 non-null  int64  \n",
      " 10  payment_type           22699 non-null  int64  \n",
      " 11  fare_amount            22699 non-null  float64\n",
      " 12  extra                  22699 non-null  float64\n",
      " 13  mta_tax                22699 non-null  float64\n",
      " 14  tip_amount             22699 non-null  float64\n",
      " 15  tolls_amount           22699 non-null  float64\n",
      " 16  improvement_surcharge  22699 non-null  float64\n",
      " 17  total_amount           22699 non-null  float64\n",
      "dtypes: float64(8), int64(7), object(3)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wa-IoAHLUWi8"
   },
   "source": [
    "**Note:** There is no missing data according to the results from the `info()` function. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vCGi3U9nw4Er"
   },
   "source": [
    "### **Task 2b. Assess Whether Dimensions and Measures are Correct**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aNBRa33TR_Is"
   },
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "On the data source page in Tableau, double check the data types for the applicable columns we selected on the previous step. Pay close attention to the dimensions and measures to ensure they are correct. In Python, consider the data types of the columns. *Consider:* Do they make sense? \n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sPlm615Ywifi"
   },
   "source": [
    "### **Task 2c. Select Visualization Type(s)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2YdC51QBSG2v"
   },
   "source": [
    "Select data visualization types that will help you understand and explain the data.\n",
    "\n",
    "Now that you know which data columns you’ll use, it is time to decide which data visualization makes the most sense for EDA of the TLC dataset. What type of data visualization(s) would be most helpful? \n",
    "\n",
    "* Line graph\n",
    "* Bar chart\n",
    "* Box plot\n",
    "* Histogram\n",
    "* Heat map\n",
    "* Scatter plot\n",
    "* A geographic map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Response:** \n",
    "\n",
    "As we'll see below, a bar chart, box plot and scatter plot will be most helpful in understanding of this data.\n",
    "\n",
    "*A box plot will be helpful to determine outliers and where the bulk of the data points reside in terms of `trip_distance`, `duration`, and `total_amount`*\n",
    "\n",
    "*A scatter plot will be helpful to visualize the trends and patters and outliers of critical variables, such as `trip_distance` and `total_amount`*\n",
    "\n",
    "*A bar chart will help determine average number of trips per month, weekday, weekend, etc.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f0sHpfkhx8GM"
   },
   "source": [
    "<img src=\"images/Construct.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Construct**\n",
    "\n",
    "Considering these questions in the PACE Strategy Document to reflect on the Construct stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BYWkZ4i3wgv4"
   },
   "source": [
    "### **Task 3. Data visualization**\n",
    "\n",
    "We’ve assessed our data, and decided on which data variables are most applicable. It’s time to plot visualization(s)!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oMOs4lb1crry"
   },
   "source": [
    "### **Boxplots**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "Performing a check for outliers on relevant columns such as trip distance and trip duration. Some of the best ways to identify the presence of outliers in data are box plots and histograms.\n",
    "\n",
    "**Note:** Remember to convert date columns to datetime in order to derive total trip duration.\n",
    "</p>  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Ts_lcV6qUQHq"
   },
   "outputs": [],
   "source": [
    "# Convert data columns to datetime\n",
    "df['tpep_pickup_datetime']=pd.to_datetime(df['tpep_pickup_datetime'])\n",
    "df['tpep_dropoff_datetime']=pd.to_datetime(df['tpep_dropoff_datetime'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**trip_distance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "qlUccI91a4NH",
    "outputId": "787a5adc-9de1-4e7c-b835-3bed4f029daf"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create box plot of trip_distance\n",
    "plt.figure(figsize=(7,2))\n",
    "plt.title('trip_distance')\n",
    "sns.boxplot(data=None, x=df['trip_distance'], fliersize=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of trip_distance\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.histplot(df['trip_distance'], bins=range(0,26,1))\n",
    "plt.title('Trip distance histogram');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** The majority of trips were journeys of less than two miles. The number of trips falls away steeply as the distance traveled increases beyond two miles.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**total_amount**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "uLvs3_hMyF9H",
    "outputId": "bc0ba9a1-d749-4527-e332-b387f622408d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAADtCAYAAABDElTOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdXElEQVR4nO3deVjWVf7/8dcHub1BBRRcCUTNQg0tc8vlm5aOuWWN02Jm4mSLOppraVZu5dhmV05X2uQ4NtWUOleYtmjhgtZgaiqpuaUDYkKjkoqNG3Kf3x/9+OQtqIggeHw+rotL7vM553zO5w3Cy8/i7RhjjAAAACwRUNoLAAAAKE6EGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQboAxLTk7WxIkTdfjw4SKN/+CDD/T6669f0ho6dOigDh06XNIcNjp27JgmTpyopKSk0l4KgLMQboAyLDk5WZMmTSrVcIOCHTt2TJMmTSLcAGUQ4QYAAFiFcAOUURMnTtSTTz4pSapbt64cx5HjOEpKSpLP59PLL7+sBg0ayOv1qnr16urXr59+/PFHd3yHDh302Wefac+ePe5Yx3Hc7ZMmTVKrVq0UHh6u0NBQ3XzzzZo9e7aK4710ExMTdddddykqKkpBQUGqX7++Hn/8cR08eDDfMTqOo02bNunee+9VWFiYwsPDNXLkSJ0+fVo7duxQly5dFBISojp16ujll1/Ot6/09HT17dtX1atXl9frVcOGDTVt2jT5fD63T1JSklu7M6WlpclxHL3zzjtuW//+/VWpUiXt2rVL3bp1U6VKlRQdHa1Ro0bp5MmT7rhq1aq5dcyrbf/+/S+5dgAuXWBpLwBAwR555BH9/PPPeuONN5SQkKBatWpJkho1aqRBgwbp7bff1pAhQ9SjRw+lpaXpueeeU1JSkjZs2KCqVatqxowZeuyxx7R7924tWLAg3/xpaWl6/PHHVbt2bUnSN998o6FDh2rfvn0aP378Ja199+7dat26tR555BGFhYUpLS1Nr732mtq1a6fNmzfL4/H49b/vvvvUt29fPf7440pMTNTLL7+snJwcLV26VIMHD9bo0aP1wQcfaMyYMapfv7569eolSTpw4IDatGmjU6dO6fnnn1edOnX06aefavTo0dq9e7dmzJhRpPXn5OSoZ8+eGjBggEaNGqVVq1bp+eefV1hYmMaPH69atWppyZIl6tKliwYMGKBHHnlEktzAA6CUGQBl1iuvvGIkmdTUVLdt27ZtRpIZPHiwX981a9YYSWbcuHFuW/fu3U1MTMwF95Obm2tycnLM5MmTTUREhPH5fO629u3bm/bt2xf5GHw+n8nJyTF79uwxkszChQvdbRMmTDCSzLRp0/zG3HTTTUaSSUhIcNtycnJMtWrVTK9evdy2sWPHGklmzZo1fuMHDRpkHMcxO3bsMMYYs2LFCiPJrFixwq9famqqkWTmzJnjtsXHxxtJZv78+X59u3XrZmJjY93XBw4cMJLMhAkTLqoeAEoel6WAK8yKFSskKd8lkJYtW6phw4ZatmxZoeZZvny5OnXqpLCwMJUrV04ej0fjx49XVlaW9u/ff0lr3L9/vwYOHKjo6GgFBgbK4/EoJiZGkrRt27Z8/Xv06OH3umHDhnIcR127dnXbAgMDVb9+fe3Zs8fvGBo1aqSWLVv6je/fv7+MMVq+fHmR1u84ju68806/tiZNmvjtG0DZxWUp4AqTlZUlSe5lqjNFRkYW6hfw2rVr1blzZ3Xo0EGzZs1SVFSUypcvr48//lhTpkzR8ePHi7w+n8+nzp07KyMjQ88995waN26sihUryufz6ZZbbilw7vDwcL/X5cuXV4UKFRQUFJSvPTs7232dlZWlOnXq5JsvMjLS3V4UBe3b6/XqxIkTRZoPwOVFuAGuMBEREZKkzMxMRUVF+W3LyMhQ1apVLzjH3Llz5fF49Omnn/r9Ev/4448veX1btmzRd999p3feeUfx8fFu+65duy557rNFREQoMzMzX3tGRoYkubXIO8a8G4LznH2DMwA7cFkKKMO8Xq8k+Z3tuP322yVJ77//vl/fdevWadu2berYsaPf+ILOlDiOo8DAQJUrV85tO378uN57771LXnPeE1l5a8/z17/+9ZLnPlvHjh21detWbdiwwa/93XffleM4uu222yTJPbuzadMmv36LFi0q8r4L+toAKBs4cwOUYY0bN5YkTZ8+XfHx8fJ4PIqNjdVjjz2mN954QwEBAeratav7tFR0dLRGjBjhNz4hIUEzZ85Us2bNFBAQoObNm6t79+567bXX1KdPHz322GPKysrSq6++mi+QFEWDBg107bXXauzYsTLGKDw8XJ988okSExMvee6zjRgxQu+++666d++uyZMnKyYmRp999plmzJihQYMG6frrr5ck1axZU506ddLUqVNVpUoVxcTEaNmyZUpISCjyvkNCQhQTE6OFCxeqY8eOCg8PV9WqVQu8TAbgMivtO5oBnN/TTz9tIiMjTUBAgPvET25urnnppZfM9ddfbzwej6latarp27ev2bt3r9/Yn3/+2dxzzz2mcuXKxnEcc+Zf+b///e8mNjbWeL1eU69ePTN16lQze/bsfE9nFeVpqa1bt5rf/e53JiQkxFSpUsXce++9Jj09Pd/TRXlPSx04cMBvfHx8vKlYsWK+edu3b29uuOEGv7Y9e/aYPn36mIiICOPxeExsbKx55ZVXTG5url+/zMxMc88995jw8HATFhZm+vbta7799tsCn5YqaN95az3T0qVLTdOmTY3X6zWSTHx8fCErBKAkOcYUw//YBQAAUEZwzw0AALAK99wAKLTc3Nzzvj2D4zh+NykDQGngzA2AQuvYsaM8Hs85P6699trSXiIAiHtuABTajh07dPTo0XNu93q97hNeAFBaCDcAAMAqXJYCAABWKfINxT6fTxkZGQoJCXH/R1IAAICSYIzR0aNHFRkZqYCA85+bKXK4ycjIUHR0dFGHAwAAXLS9e/fme1+9sxU53ISEhLg7CQ0NLeo0AAAAF5Sdna3o6Gg3f5xPkcNN3qWo0NBQwg0AALgsCnMrDDcUAwAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVivz2Czg/Y4yOHz+ukydPSpK8Xq8cx1FQUBDvog4AQAki3JSQEydOqFu3bvnaFy9erODg4FJYEQAAVwcuSwEAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsEpgaS/ABsYYnThxQpIUFBQkx3FKZAwAALgwztwUgxMnTqhr167q2rWrG1hKYgwAALgwwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsElvYCbGCMcT8/ceKE359nK2j7meMBAMClcUwhf7OePHlSJ0+edF9nZ2crOjpaR44cUWhoaIksLjk5WRMmTNCkSZPUpk2bEtlHcRg0aJC2bdtW2su4aLVr19a+ffvk8/lkjFFcXJx27Nih3r17a+7cue6fZ9Z/4sSJSkpKcvue62tTlK/dhcYUZs4r5XumtFAfACWlpH++ZGdnKywsrFC5o9CXpaZOnaqwsDD3Izo6+pIXeiFz585VTk6O5s2bV+L7uhRXYrCRpPT0dOXm5rpnjrZs2aKcnBwlJCT4/Xlm/VetWuXX91xfm6J87S40pjBzXinfM6WF+gAoKWXp50uhw83TTz+tI0eOuB979+4tyXVJknr37i2Px6P777+/xPd1KRo1auR+vmDBAi1evFgLFiwosO+Ftl9OtWvXVrly5eQ4jiQpLi5OHo9HvXr18vvzzPrfeuutfn3P9bUpytfuQmMKM+eV8j1TWqgPgJJSln6+FPqy1Nku5vSQ7Y4fP66uXbtKkhYvXqzg4GC/tjMVtD2vDQAAFKxELksBAABcCQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwSmBpL8AGQUFBWrx4sft5SY0BAAAXRrgpBo7jKDg4uMTHAACAC+OyFAAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqwSW9gJsFRQUpM8//1wnT56UJHm9XjmOo6CgoFJeGQAAdiPclBDHcVShQgVVqFChtJcCAMBVhctSAADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFilyG+caYyRJGVnZxfbYgAAAAqSlzfy8sf5FDncHD16VJIUHR1d1CkAAAAuytGjRxUWFnbePo4pTAQqgM/nU0ZGhkJCQuQ4TpEWaJvs7GxFR0dr7969Cg0NLe3llGnUqvCoVeFRq8KhToVHrQqvpGtljNHRo0cVGRmpgIDz31VT5DM3AQEBioqKKupwq4WGhvKXoJCoVeFRq8KjVoVDnQqPWhVeSdbqQmds8nBDMQAAsArhBgAAWIVwU4y8Xq8mTJggr9db2ksp86hV4VGrwqNWhUOdCo9aFV5ZqlWRbygGAAAoizhzAwAArEK4AQAAViHcAAAAqxBuAACAVQg3xWjGjBmqW7eugoKC1KxZM3311VelvaTLaurUqWrRooVCQkJUvXp13X333dqxY4dfH2OMJk6cqMjISAUHB6tDhw76/vvv/fqcPHlSQ4cOVdWqVVWxYkX17NlTP/744+U8lMtq6tSpchxHw4cPd9uo02/27dunvn37KiIiQhUqVNBNN92k9evXu9up1a9Onz6tZ599VnXr1lVwcLDq1aunyZMny+fzuX2u1lqtWrVKd955pyIjI+U4jj7++GO/7cVVl0OHDumhhx5SWFiYwsLC9NBDD+nw4cMlfHTF53x1ysnJ0ZgxY9S4cWNVrFhRkZGR6tevnzIyMvzmKDN1MigWc+fONR6Px8yaNcts3brVDBs2zFSsWNHs2bOntJd22dxxxx1mzpw5ZsuWLSYlJcV0797d1K5d2/zyyy9unxdffNGEhISYjz76yGzevNncf//9platWiY7O9vtM3DgQHPNNdeYxMREs2HDBnPbbbeZG2+80Zw+fbo0DqtErV271tSpU8c0adLEDBs2zG2nTr/6+eefTUxMjOnfv79Zs2aNSU1NNUuXLjW7du1y+1CrX73wwgsmIiLCfPrppyY1NdX861//MpUqVTKvv/662+dqrdXnn39unnnmGfPRRx8ZSWbBggV+24urLl26dDFxcXEmOTnZJCcnm7i4ONOjR4/LdZiX7Hx1Onz4sOnUqZOZN2+e2b59u1m9erVp1aqVadasmd8cZaVOhJti0rJlSzNw4EC/tgYNGpixY8eW0opK3/79+40ks3LlSmOMMT6fz9SsWdO8+OKLbp8TJ06YsLAw89Zbbxljfv0L5PF4zNy5c90++/btMwEBAWbJkiWX9wBK2NGjR811111nEhMTTfv27d1wQ51+M2bMGNOuXbtzbqdWv+nevbt5+OGH/dp69epl+vbta4yhVnnO/qVdXHXZunWrkWS++eYbt8/q1auNJLN9+/YSPqriV1AIPNvatWuNJPcf8WWpTlyWKganTp3S+vXr1blzZ7/2zp07Kzk5uZRWVfqOHDkiSQoPD5ckpaam6qeffvKrk9frVfv27d06rV+/Xjk5OX59IiMjFRcXZ10t//SnP6l79+7q1KmTXzt1+s2iRYvUvHlz3XvvvapevbqaNm2qWbNmudup1W/atWunZcuWaefOnZKk7777Tl9//bW6desmiVqdS3HVZfXq1QoLC1OrVq3cPrfccovCwsKsrd2RI0fkOI4qV64sqWzVqchvnInfHDx4ULm5uapRo4Zfe40aNfTTTz+V0qpKlzFGI0eOVLt27RQXFydJbi0KqtOePXvcPuXLl1eVKlXy9bGplnPnztWGDRu0bt26fNuo02/+85//aObMmRo5cqTGjRuntWvX6oknnpDX61W/fv2o1RnGjBmjI0eOqEGDBipXrpxyc3M1ZcoUPfDAA5L4vjqX4qrLTz/9pOrVq+ebv3r16lbW7sSJExo7dqz69OnjvklmWaoT4aYYOY7j99oYk6/tajFkyBBt2rRJX3/9db5tRamTTbXcu3evhg0bpi+//FJBQUHn7He110mSfD6fmjdvrj//+c+SpKZNm+r777/XzJkz1a9fP7cftZLmzZun999/Xx988IFuuOEGpaSkaPjw4YqMjFR8fLzbj1oVrDjqUlB/G2uXk5Oj3r17y+fzacaMGRfsXxp14rJUMahatarKlSuXL3Xu378/378GrgZDhw7VokWLtGLFCkVFRbntNWvWlKTz1qlmzZo6deqUDh06dM4+V7r169dr//79atasmQIDAxUYGKiVK1fqL3/5iwIDA93jvNrrJEm1atVSo0aN/NoaNmyo9PR0SXxPnenJJ5/U2LFj1bt3bzVu3FgPPfSQRowYoalTp0qiVudSXHWpWbOm/vvf/+ab/8CBA1bVLicnR/fdd59SU1OVmJjonrWRyladCDfFoHz58mrWrJkSExP92hMTE9WmTZtSWtXlZ4zRkCFDlJCQoOXLl6tu3bp+2+vWrauaNWv61enUqVNauXKlW6dmzZrJ4/H49cnMzNSWLVusqWXHjh21efNmpaSkuB/NmzfXgw8+qJSUFNWrV486/X9t27bN998J7Ny5UzExMZL4njrTsWPHFBDg/yO9XLly7qPg1KpgxVWX1q1b68iRI1q7dq3bZ82aNTpy5Ig1tcsLNj/88IOWLl2qiIgIv+1lqk7FdmvyVS7vUfDZs2ebrVu3muHDh5uKFSuatLS00l7aZTNo0CATFhZmkpKSTGZmpvtx7Ngxt8+LL75owsLCTEJCgtm8ebN54IEHCnzkMioqyixdutRs2LDB3H777Vf8o6gXcubTUsZQpzxr1641gYGBZsqUKeaHH34w//znP02FChXM+++/7/ahVr+Kj48311xzjfsoeEJCgqlatap56qmn3D5Xa62OHj1qNm7caDZu3Ggkmddee81s3LjRfcqnuOrSpUsX06RJE7N69WqzevVq07hx4yvqUfDz1SknJ8f07NnTREVFmZSUFL+f8SdPnnTnKCt1ItwUozfffNPExMSY8uXLm5tvvtl9BPpqIanAjzlz5rh9fD6fmTBhgqlZs6bxer3m1ltvNZs3b/ab5/jx42bIkCEmPDzcBAcHmx49epj09PTLfDSX19nhhjr95pNPPjFxcXHG6/WaBg0amLfffttvO7X6VXZ2thk2bJipXbu2CQoKMvXq1TPPPPOM3y+eq7VWK1asKPBnU3x8vDGm+OqSlZVlHnzwQRMSEmJCQkLMgw8+aA4dOnSZjvLSna9Oqamp5/wZv2LFCneOslInxxhjiu88EAAAQOninhsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwCF1r9/f919992lvQwAOC/CDXCF69Chg4YPH17iY642BDngykW4AQAAViHcAFew/v37a+XKlZo+fbocx5HjOEpLS9PKlSvVsmVLeb1e1apVS2PHjtXp06fPOyY3N1cDBgxQ3bp1FRwcrNjYWE2fPr3Ia1uyZInatWunypUrKyIiQj169NDu3bvd7WlpaXIcR/Pnz9f//d//KTg4WC1atNDOnTu1bt06NW/eXJUqVVKXLl104MABd5zP59PkyZMVFRUlr9erm266SUuWLHG3JyUlyXEcHT582G1LSUlxj1OS3nnnHVWuXFlffPGFGjZs6O4nMzNTkjRx4kT94x//0MKFC90aJSUlFbkWAC4vwg1wBZs+fbpat26tRx99VJmZmcrMzJTH41G3bt3UokULfffdd5o5c6Zmz56tF1544ZxjoqOj5fP5FBUVpfnz52vr1q0aP368xo0bp/nz5xdpbf/73/80cuRIrVu3TsuWLVNAQIB+//vfy+fz+fWbMGGCnn32WW3YsEGBgYF64IEH9NRTT2n69On66quvtHv3bo0fP97vmKdNm6ZXX31VmzZt0h133KGePXvqhx9+uKj1HTt2TK+++qree+89rVq1Sunp6Ro9erQkafTo0brvvvvcwJOZmak2bdoUqQ4ASkGxvsc4gMuuffv2ZtiwYe7rcePGmdjYWOPz+dy2N99801SqVMnk5uYWOOZcBg8ebP7whz+4r+Pj481dd91VpHXu37/fSDKbN282xhiTmppqJJm//e1vbp8PP/zQSDLLli1z26ZOnWpiY2Pd15GRkWbKlCl+c7do0cIMHjzYGGPMihUrjCRz6NAhd/vGjRuNJJOammqMMWbOnDlGktm1a5fb58033zQ1atQolmMFULo4cwNYZtu2bWrdurUcx3Hb2rZtq19++UU//vjjece+9dZbat68uapVq6ZKlSpp1qxZSk9PL9I6du/erT59+qhevXoKDQ1V3bp1JSnffE2aNHE/r1GjhiSpcePGfm379++XJGVnZysjI0Nt27b1m6Nt27batm3bRa2vQoUKuvbaa93XtWrVcvcD4MpGuAEsY4zxCzZ5bZLytZ9p/vz5GjFihB5++GF9+eWXSklJ0R//+EedOnWqSOu48847lZWVpVmzZmnNmjVas2aNJOWbz+PxuJ/nre/strMvZRV0fHltAQEBbluenJycfOs7cx95c545BsCVi3ADXOHKly+v3Nxc93WjRo2UnJzs94s6OTlZISEhuuaaawocI0lfffWV2rRpo8GDB6tp06aqX7++3w3AFyMrK0vbtm3Ts88+q44dO6phw4Y6dOhQkeY6U2hoqCIjI/X111/7tScnJ6thw4aSpGrVqkmSe3Ow9OsNxReroBoBuDIQboArXJ06dbRmzRqlpaXp4MGDGjx4sPbu3auhQ4dq+/btWrhwoSZMmKCRI0e6ZzXOHuPz+VS/fn19++23+uKLL7Rz504999xzWrduXZHWVKVKFUVEROjtt9/Wrl27tHz5co0cObJYjvfJJ5/USy+9pHnz5mnHjh0aO3asUlJSNGzYMElS/fr1FR0drYkTJ2rnzp367LPPNG3atIveT506dbRp0ybt2LFDBw8eLPDsD4CyiXADXOFGjx6tcuXKqVGjRqpWrZpycnL0+eefa+3atbrxxhs1cOBADRgwQM8+++w5x6Snp2vgwIHq1auX7r//frVq1UpZWVkaPHhwkdYUEBCguXPnav369YqLi9OIESP0yiuvFMvxPvHEExo1apRGjRqlxo0ba8mSJVq0aJGuu+46Sb9ebvrwww+1fft23XjjjXrppZfcJ8UuxqOPPqrY2Fj3HqR///vfxbJ+ACXPMVxkBgAAFuHMDQAAsArhBsBFS09PV6VKlc75UdTHxwGgOHBZCsBFO336tPtWBgWpU6eOAgMDL9+CAOAMhBsAAGAVLksBAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKzy/wDIS3SDnUQelgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create box plot of total_amount\n",
    "plt.figure(figsize=(7,2))\n",
    "plt.title('total_amount')\n",
    "sns.boxplot(x=df['total_amount'], fliersize=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of total_amount\n",
    "plt.figure(figsize=(12,6))\n",
    "ax = sns.histplot(df['total_amount'], bins=range(-10,101,5))\n",
    "ax.set_xticks(range(-10,101,5))\n",
    "ax.set_xticklabels(range(-10,101,5))\n",
    "plt.title('Total amount histogram');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** The total cost of each trip also has a distribution that skews right, with most costs falling in the $5-15 range.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**tip_amount**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create box plot of tip_amount\n",
    "plt.figure(figsize=(7,2))\n",
    "plt.title('tip_amount')\n",
    "sns.boxplot(x=df['tip_amount'], fliersize=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of tip_amount\n",
    "plt.figure(figsize=(12,6))\n",
    "ax = sns.histplot(df['tip_amount'], bins=range(0,21,1))\n",
    "ax.set_xticks(range(0,21,2))\n",
    "ax.set_xticklabels(range(0,21,2))\n",
    "plt.title('Tip amount histogram');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The distribution for tip amount is right-skewed, with nearly all the tips in the $0-3 range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**tip_amount by vendor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of tip_amount by vendor\n",
    "plt.figure(figsize=(12,7))\n",
    "ax = sns.histplot(data=df, x='tip_amount', bins=range(0,21,1), \n",
    "                  hue='VendorID', \n",
    "                  multiple='stack',\n",
    "                  palette='pastel')\n",
    "ax.set_xticks(range(0,21,1))\n",
    "ax.set_xticklabels(range(0,21,1))\n",
    "plt.title('Tip amount by vendor histogram');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** Separating the tip amount by vendor reveals that there are no noticeable aberrations in the distribution of tips between the two vendors in the dataset. Vendor two has a slightly higher share of the rides, and this proportion is approximately maintained for all tip amounts.\n",
    "\n",
    "Next, zooming in on the upper end of the range of tips to check whether vendor one gets noticeably more of the most generous tips.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of tip_amount by vendor for tips > $10 \n",
    "tips_over_ten = df[df['tip_amount'] > 10]\n",
    "plt.figure(figsize=(12,7))\n",
    "ax = sns.histplot(data=tips_over_ten, x='tip_amount', bins=range(10,21,1), \n",
    "                  hue='VendorID', \n",
    "                  multiple='stack',\n",
    "                  palette='pastel')\n",
    "ax.set_xticks(range(10,21,1))\n",
    "ax.set_xticklabels(range(10,21,1))\n",
    "plt.title('Tip amount by vendor histogram');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** The proportions are maintained even at these higher tip amounts, with the exception being at highest extremity, but this is not noteworthy due to the low sample size at these tip amounts.\n",
    "\n",
    "<p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean tips by passenger count**\n",
    "\n",
    "Examine the unique values in the `passenger_count` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    16117\n",
       "2     3305\n",
       "5     1143\n",
       "3      953\n",
       "6      693\n",
       "4      455\n",
       "0       33\n",
       "Name: passenger_count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['passenger_count'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** Nearly two thirds of the rides were single occupancy, though there were still nearly 700 rides with as many as six passengers. Also, there are 33 rides with an occupancy count of zero, which doesn't make sense. These would likely be dropped unless a reasonable explanation can be found for them.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kevin Nathanael\\AppData\\Local\\Temp\\ipykernel_21096\\1943984880.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  mean_tips_by_passenger_count = df.groupby(['passenger_count']).mean()[['tip_amount']]\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>tip_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.135758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.848920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.856378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.716768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.530264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.873185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.720260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tip_amount\n",
       "passenger_count            \n",
       "0                  2.135758\n",
       "1                  1.848920\n",
       "2                  1.856378\n",
       "3                  1.716768\n",
       "4                  1.530264\n",
       "5                  1.873185\n",
       "6                  1.720260"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate mean tips by passenger_count\n",
    "mean_tips_by_passenger_count = df.groupby(['passenger_count']).mean()[['tip_amount']]\n",
    "mean_tips_by_passenger_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar plot for mean tips by passenger count\n",
    "data = mean_tips_by_passenger_count.tail(-1)\n",
    "pal = sns.color_palette(\"Greens_d\", len(data))\n",
    "rank = data['tip_amount'].argsort().argsort()\n",
    "plt.figure(figsize=(12,7))\n",
    "ax = sns.barplot(x=data.index,\n",
    "            y=data['tip_amount'],\n",
    "            palette=np.array(pal[::-1])[rank])\n",
    "ax.axhline(df['tip_amount'].mean(), ls='--', color='red', label='global mean')\n",
    "ax.legend()\n",
    "plt.title('Mean tip amount by passenger count', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** Mean tip amount varies very little by passenger count. Although it does drop noticeably for four-passenger rides, it's expected that there would be a higher degree of fluctuation because rides with four passengers were the least plentiful in the dataset (aside from rides with zero passengers).\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create month and day columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create a month column\n",
    "df['month'] = df['tpep_pickup_datetime'].dt.month_name()\n",
    "\n",
    "# Create a day column\n",
    "df['day'] = df['tpep_pickup_datetime'].dt.day_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot total ride count by month**\n",
    "\n",
    "Begin by calculating total ride count by month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "March        2049\n",
       "October      2027\n",
       "April        2019\n",
       "May          2013\n",
       "January      1997\n",
       "June         1964\n",
       "December     1863\n",
       "November     1843\n",
       "February     1769\n",
       "September    1734\n",
       "August       1724\n",
       "July         1697\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get total number of rides for each month\n",
    "monthly_rides = df['month'].value_counts()\n",
    "monthly_rides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The months are out of order.\n",
    "\n",
    "Reorder the results to put the months in calendar order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "January      1997\n",
       "February     1769\n",
       "March        2049\n",
       "April        2019\n",
       "May          2013\n",
       "June         1964\n",
       "July         1697\n",
       "August       1724\n",
       "September    1734\n",
       "October      2027\n",
       "November     1843\n",
       "December     1863\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reorder the monthly ride list so months go in order\n",
    "month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July',\n",
    "         'August', 'September', 'October', 'November', 'December']\n",
    "\n",
    "monthly_rides = monthly_rides.reindex(index=month_order)\n",
    "monthly_rides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['January', 'February', 'March', 'April', 'May', 'June', 'July',\n",
       "       'August', 'September', 'October', 'November', 'December'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the index\n",
    "monthly_rides.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of total rides per month\n",
    "plt.figure(figsize=(12,7))\n",
    "ax = sns.barplot(x=monthly_rides.index, y=monthly_rides)\n",
    "ax.set_xticklabels(month_order)\n",
    "plt.title('Ride count by month', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Monthly rides are fairly consistent, with notable dips in the summer months of July, August, and September, and also in February."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot total ride count by day**\n",
    "\n",
    "Repeat the above process, but now calculate the total rides by day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Monday       2931\n",
       "Tuesday      3198\n",
       "Wednesday    3390\n",
       "Thursday     3402\n",
       "Friday       3413\n",
       "Saturday     3367\n",
       "Sunday       2998\n",
       "Name: day, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat the above process, this time for rides by day\n",
    "daily_rides = df['day'].value_counts()\n",
    "day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "daily_rides = daily_rides.reindex(index=day_order)\n",
    "daily_rides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar plot for ride count by day\n",
    "plt.figure(figsize=(12,7))\n",
    "ax = sns.barplot(x=daily_rides.index, y=daily_rides)\n",
    "ax.set_xticklabels(day_order)\n",
    "ax.set_ylabel('Count')\n",
    "plt.title('Ride count by day', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Suprisingly, Wednesday through Saturday had the highest number of daily rides, while Sunday and Monday had the least."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot total revenue by day of the week**\n",
    "\n",
    "Repeat the above process, but now calculate the total revenue by day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kevin Nathanael\\AppData\\Local\\Temp\\ipykernel_21096\\1229670924.py:4: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  total_amount_day = df.groupby('day').sum()[['total_amount']]\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>total_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>49574.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>52527.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>55310.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>57181.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>55818.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>51195.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>48624.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           total_amount\n",
       "day                    \n",
       "Monday         49574.37\n",
       "Tuesday        52527.14\n",
       "Wednesday      55310.47\n",
       "Thursday       57181.91\n",
       "Friday         55818.74\n",
       "Saturday       51195.40\n",
       "Sunday         48624.06"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat the process, this time for total revenue by day\n",
    "day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "total_amount_day = df.groupby('day').sum()[['total_amount']]\n",
    "total_amount_day = total_amount_day.reindex(index=day_order)\n",
    "total_amount_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar plot of total revenue by day\n",
    "plt.figure(figsize=(12,7))\n",
    "ax = sns.barplot(x=total_amount_day.index, y=total_amount_day['total_amount'])\n",
    "ax.set_xticklabels(day_order)\n",
    "ax.set_ylabel('Revenue (USD)')\n",
    "plt.title('Total revenue by day', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** Thursday had the highest gross revenue of all days, and Sunday and Monday had the least. Interestingly, although Saturday had only 35 fewer rides than Thursday, its gross revenue was ~$6,000 less than Thursday's&mdash;more than a 10% drop.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot total revenue by month**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kevin Nathanael\\AppData\\Local\\Temp\\ipykernel_21096\\2714539061.py:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  total_amount_month = df.groupby('month').sum()[['total_amount']]\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>total_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>31735.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>28937.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>33085.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>32012.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>33828.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>32920.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>26617.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>27759.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>28206.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>33065.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>30800.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>31261.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           total_amount\n",
       "month                  \n",
       "January        31735.25\n",
       "February       28937.89\n",
       "March          33085.89\n",
       "April          32012.54\n",
       "May            33828.58\n",
       "June           32920.52\n",
       "July           26617.64\n",
       "August         27759.56\n",
       "September      28206.38\n",
       "October        33065.83\n",
       "November       30800.44\n",
       "December       31261.57"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat the process, this time for total revenue by month\n",
    "total_amount_month = df.groupby('month').sum()[['total_amount']]\n",
    "total_amount_month = total_amount_month.reindex(index=month_order)\n",
    "total_amount_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of total revenue by month\n",
    "plt.figure(figsize=(12,7))\n",
    "ax = sns.barplot(x=total_amount_month.index, y=total_amount_month['total_amount'])\n",
    "plt.title('Total revenue by month', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** Monthly revenue generally follows the pattern of monthly rides, with noticeable dips in the summer months of July, August, and September, and also one in February.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j3AW_1yH0trT"
   },
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "We can create a scatterplot in Tableau Public, which can be easier to manipulate and present. If we'd like step by step instructions, we can review the following link. Those instructions create a scatterplot showing the relationship between total_amount and trip_distance. Consider adding the Tableau visualization to the executive summary, and adding key insights from our findings on those two variables.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot mean trip distance by drop-off location**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get number of unique drop-off location IDs\n",
    "df['DOLocationID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kevin Nathanael\\AppData\\Local\\Temp\\ipykernel_21096\\1463569489.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  distance_by_dropoff = df.groupby('DOLocationID').mean()[['trip_distance']]\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>trip_distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOLocationID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1.390556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>1.555494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1.727806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1.818852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>17.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17.945000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>20.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>21.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.275000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              trip_distance\n",
       "DOLocationID               \n",
       "207                1.200000\n",
       "193                1.390556\n",
       "237                1.555494\n",
       "234                1.727806\n",
       "137                1.818852\n",
       "...                     ...\n",
       "51                17.310000\n",
       "11                17.945000\n",
       "210               20.500000\n",
       "29                21.650000\n",
       "23                24.275000\n",
       "\n",
       "[216 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean trip distance for each drop-off location\n",
    "distance_by_dropoff = df.groupby('DOLocationID').mean()[['trip_distance']]\n",
    "\n",
    "# Sort the results in descending order by mean trip distance\n",
    "distance_by_dropoff = distance_by_dropoff.sort_values(by='trip_distance')\n",
    "distance_by_dropoff "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of mean trip distances by drop-off location in ascending order by distance\n",
    "plt.figure(figsize=(14,6))\n",
    "ax = sns.barplot(x=distance_by_dropoff.index, \n",
    "                 y=distance_by_dropoff['trip_distance'],\n",
    "                 order=distance_by_dropoff.index)\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xticks([])\n",
    "plt.title('Mean trip distance by drop-off location', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** This plot presents a characteristic curve related to the cumulative density function of a normal distribution. In other words, it indicates that the drop-off points are relatively evenly distributed over the terrain. This is good to know, because geographic coordinates were not included in this dataset, so there was no obvious way to test for the distibution of locations. \n",
    "\n",
    "To confirm this conclusion, consider the following experiment:\n",
    "\n",
    "1. We will create a sample of coordinates from a normal distribution—in this case 1,500 pairs of points from a normal distribution with a mean of 10 and a standard deviation of 5\n",
    "\n",
    "2. We 'll calculate the distance between each pair of coordinates \n",
    "\n",
    "3. Then group the coordinates by endpoint and calculate the mean distance between that endpoint and all other points it was paired with\n",
    "\n",
    "4. Plotting the mean distance for each unique endpoint\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Generate random points on a 2D plane from a normal distribution\n",
    "test = np.round(np.random.normal(10, 5, (3000, 2)), 1)\n",
    "midway = int(len(test)/2)  # Calculate midpoint of the array of coordinates\n",
    "start = test[:midway]      # Isolate first half of array (\"pick-up locations\")\n",
    "end = test[midway:]        # Isolate second half of array (\"drop-off locations\")\n",
    "\n",
    "# 2. Calculate Euclidean distances between points in first half and second half of array\n",
    "distances = (start - end)**2           \n",
    "distances = distances.sum(axis=-1)\n",
    "distances = np.sqrt(distances)\n",
    "\n",
    "# 3. Group the coordinates by \"drop-off location\", compute mean distance\n",
    "test_df = pd.DataFrame({'start': [tuple(x) for x in start.tolist()],\n",
    "                   'end': [tuple(x) for x in end.tolist()],\n",
    "                   'distance': distances})\n",
    "data = test_df[['end', 'distance']].groupby('end').mean()\n",
    "data = data.sort_values(by='distance')\n",
    "\n",
    "# 4. Plot the mean distance between each endpoint (\"drop-off location\") and all points it connected to\n",
    "plt.figure(figsize=(14,6))\n",
    "ax = sns.barplot(x=data.index,\n",
    "                 y=data['distance'],\n",
    "                 order=data.index)\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xticks([])\n",
    "ax.set_xlabel('Endpoint')\n",
    "ax.set_ylabel('Mean distance to all other points')\n",
    "ax.set_title('Mean distance between points taken randomly from normal distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** The curve described by this graph is nearly identical to that of the mean distance traveled by each taxi ride to each drop-off location. This reveals that the drop-off locations in the taxi dataset are evenly distributed geographically. Note, however, that this does *not* mean that there was an even distrubtion of *rides* to each drop-off point. Examine this next.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram of rides by drop-off location**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "First, check whether the drop-off locations IDs are consecutively numbered. For instance, does it go 1, 2, 3, 4..., or are some numbers missing (e.g., 1, 3, 4...). If numbers aren't all consecutive, the histogram will look like some locations have very few or no rides when in reality there's no bar because there's no location. \n",
    "\n",
    "There are many ways to do this.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if all drop-off locations are consecutively numbered\n",
    "df['DOLocationID'].max() - len(set(df['DOLocationID'])) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** There are 49 numbers that do not represent a drop-off location. \n",
    "\n",
    "To eliminate the spaces in the historgram that these missing numbers would create, sort the unique drop-off location values, then convert them to strings. This will make the histplot function display all bars directly next to each other.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "# DOLocationID column is numeric, so sort in ascending order\n",
    "sorted_dropoffs = df['DOLocationID'].sort_values()\n",
    "\n",
    "# Convert to string\n",
    "sorted_dropoffs = sorted_dropoffs.astype('str')\n",
    "\n",
    "# Plot\n",
    "sns.histplot(sorted_dropoffs, bins=range(0, df['DOLocationID'].max()+1, 1))\n",
    "plt.xticks([])\n",
    "plt.xlabel('Drop-off locations')\n",
    "plt.title('Histogram of rides by drop-off location', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "**Note:** Notice that out of the 200+ drop-off locations, a disproportionate number of locations receive the majority of the traffic, while all the rest get relatively few trips. It's likely that these high-traffic locations are near popular tourist attractions like the Empire State Building or Times Square, airports, and train and bus terminals. However, it would be helpful to know the location that each ID corresponds with. Unfortunately, this is not in the data.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NsvBtco1x8GN"
   },
   "source": [
    "<img src=\"images/Execute.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Execute**\n",
    "\n",
    "Consider the PACE Strategy Document to reflect on the Execute stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "csHAfM-RSO7l"
   },
   "source": [
    "### **Task 4a. Results and evaluation**\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "Having built visualizations in Tableau and in Python, what have we learned about the dataset? What other questions have our visualizations uncovered that we should pursue?\n",
    "\n",
    "I have learned that the highest distribution of trip distances are below 5 miles, but there are outliers all the way out to 35 miles. There are no missing values. \n",
    "\n",
    "My other questions are there are several trips that have a trip distance of \"0.0.\" What might those trips be? Will they impact our model? \n",
    "\n",
    "My client would likely want to know if the data includes dropoff and pickup times. We can use that information to derive a trip duration for each line of data. This would likely be something that will help the client with their model. \n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 4a. Results and evaluation**\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "Having built visualizations in Tableau and in Python, what have we learned about the dataset? What other questions have our visualizations uncovered that we should pursue?\n",
    "\n",
    "I have learned that the highest distribution of trip distances are below 5 miles, but there are outliers all the way out to 35 miles. There are no missing values. \n",
    "\n",
    "My other questions are there are several trips that have a trip distance of \"0.0.\" What might those trips be? Will they impact our model? \n",
    "\n",
    "My client would likely want to know if the data includes dropoff and pickup times. We can use that information to derive a trip duration for each line of data. This would likely be something that will help the client with their model. \n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "puYiPmvGdTJH"
   },
   "outputs": [],
   "source": [
    "df['trip_duration'] = (df['tpep_dropoff_datetime']-df['tpep_pickup_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "iEv7pHw-dTRP",
    "outputId": "c4af2841-0ade-4f20-e082-675e6abe077e"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>...</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
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       "      <td>2017-03-25 08:55:43</td>\n",
       "      <td>2017-03-25 09:09:47</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>231</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "      <td>March</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0 days 00:14:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-11 14:53:28</td>\n",
       "      <td>2017-04-11 15:19:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "      <td>April</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0 days 00:26:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-15 07:26:56</td>\n",
       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0 days 00:07:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-07 13:17:59</td>\n",
       "      <td>2017-05-07 13:48:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>May</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>0 days 00:30:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30841670</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-15 23:32:20</td>\n",
       "      <td>2017-04-15 23:49:03</td>\n",
       "      <td>1</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "      <td>April</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0 days 00:16:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23345809</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 20:34:11</td>\n",
       "      <td>2017-03-25 20:42:11</td>\n",
       "      <td>6</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>161</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.36</td>\n",
       "      <td>March</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0 days 00:08:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37660487</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-03 19:04:09</td>\n",
       "      <td>2017-05-03 20:03:47</td>\n",
       "      <td>1</td>\n",
       "      <td>12.83</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>241</td>\n",
       "      <td>...</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>59.16</td>\n",
       "      <td>May</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0 days 00:59:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>69059411</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-15 17:41:06</td>\n",
       "      <td>2017-08-15 18:03:05</td>\n",
       "      <td>1</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>237</td>\n",
       "      <td>114</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>19.58</td>\n",
       "      <td>August</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0 days 00:21:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8433159</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-04 16:17:07</td>\n",
       "      <td>2017-02-04 16:29:14</td>\n",
       "      <td>1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>234</td>\n",
       "      <td>249</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.80</td>\n",
       "      <td>February</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0 days 00:12:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>95294817</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-10 15:20:29</td>\n",
       "      <td>2017-11-10 15:40:55</td>\n",
       "      <td>1</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>237</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.55</td>\n",
       "      <td>November</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0 days 00:20:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "4    30841670         2  2017-04-15 23:32:20   2017-04-15 23:49:03   \n",
       "5    23345809         2  2017-03-25 20:34:11   2017-03-25 20:42:11   \n",
       "6    37660487         2  2017-05-03 19:04:09   2017-05-03 20:03:47   \n",
       "7    69059411         2  2017-08-15 17:41:06   2017-08-15 18:03:05   \n",
       "8     8433159         2  2017-02-04 16:17:07   2017-02-04 16:29:14   \n",
       "9    95294817         1  2017-11-10 15:20:29   2017-11-10 15:40:55   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "4                1           4.37           1                  N   \n",
       "5                6           2.30           1                  N   \n",
       "6                1          12.83           1                  N   \n",
       "7                1           2.98           1                  N   \n",
       "8                1           1.20           1                  N   \n",
       "9                1           1.60           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  ...  fare_amount  extra  mta_tax  tip_amount  \\\n",
       "0           100           231  ...         13.0    0.0      0.5        2.76   \n",
       "1           186            43  ...         16.0    0.0      0.5        4.00   \n",
       "2           262           236  ...          6.5    0.0      0.5        1.45   \n",
       "3           188            97  ...         20.5    0.0      0.5        6.39   \n",
       "4             4           112  ...         16.5    0.5      0.5        0.00   \n",
       "5           161           236  ...          9.0    0.5      0.5        2.06   \n",
       "6            79           241  ...         47.5    1.0      0.5        9.86   \n",
       "7           237           114  ...         16.0    1.0      0.5        1.78   \n",
       "8           234           249  ...          9.0    0.0      0.5        0.00   \n",
       "9           239           237  ...         13.0    0.0      0.5        2.75   \n",
       "\n",
       "   tolls_amount  improvement_surcharge  total_amount     month        day  \\\n",
       "0           0.0                    0.3         16.56     March   Saturday   \n",
       "1           0.0                    0.3         20.80     April    Tuesday   \n",
       "2           0.0                    0.3          8.75  December     Friday   \n",
       "3           0.0                    0.3         27.69       May     Sunday   \n",
       "4           0.0                    0.3         17.80     April   Saturday   \n",
       "5           0.0                    0.3         12.36     March   Saturday   \n",
       "6           0.0                    0.3         59.16       May  Wednesday   \n",
       "7           0.0                    0.3         19.58    August    Tuesday   \n",
       "8           0.0                    0.3          9.80  February   Saturday   \n",
       "9           0.0                    0.3         16.55  November     Friday   \n",
       "\n",
       "    trip_duration  \n",
       "0 0 days 00:14:04  \n",
       "1 0 days 00:26:30  \n",
       "2 0 days 00:07:12  \n",
       "3 0 days 00:30:15  \n",
       "4 0 days 00:16:43  \n",
       "5 0 days 00:08:00  \n",
       "6 0 days 00:59:38  \n",
       "7 0 days 00:21:59  \n",
       "8 0 days 00:12:07  \n",
       "9 0 days 00:20:26  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U4HeITeOwXfo"
   },
   "source": [
    "### **Task 4b. Conclusion**\n",
    "\n",
    "<p style=\"text-align: justify;\">\n",
    "\n",
    "*Make it professional and presentable*\n",
    "\n",
    "We have visualized the data we need to share with the director now. Remember, the goal of a data visualization is for an audience member to glean the information on the chart in mere seconds.\n",
    "\n",
    "*Questions to ask yourself for reflection:*\n",
    "* Why is it important to conduct Exploratory Data Analysis? \n",
    "* Why are the data visualizations provided in this notebook useful?\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5o3dV6NYzNSs"
   },
   "source": [
    "**Response:**\n",
    "\n",
    "EDA is important because ... \n",
    "\n",
    "* *EDA helps a data professional to get to know the data, understand its outliers, clean its missing values, and prepare it for future modeling.* \n",
    "\n",
    "Visualizations helped me understand ..\n",
    "\n",
    "* *That this dataset has some outliers that we will need to make decisions on prior to designing a model.* \n"
   ]
  }
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