ExploratoryDataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtNBZFHO3M7n"
   },
   "source": [
    "# **Course 3 Automatidata project**\n",
    "**Course 3 - Go Beyond the Numbers: Translate Data into Insights**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g-E8SNtmRUkN"
   },
   "source": [
    "You are 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",
    "\n",
    "Luana Rodriquez, the senior data analyst at Automatidata, is pleased with the work you have already completed and requests your 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, include a box plot of the ride durations and some time series plots, like a breakdown by quarter or month. \n",
    "\n",
    "Additionally, the management team has recently asked all EDA to include Tableau visualizations. For this taxi data, create a Tableau dashboard showing a New York City map of taxi/limo trips by month. Make sure it is easy to understand to 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",
    "\n",
    "A notebook was structured and prepared to help you in this project. Please complete the following questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgSbVJvomcVa"
   },
   "source": [
    "# Course 3 End-of-course project: Exploratory data analysis\n",
    "\n",
    "In this activity, you will examine data provided and prepare it for analysis. You will also design a professional data visualization that tells a story, and will help data-driven decisions for business needs. \n",
    "\n",
    "Please note that the Tableau visualization activity is optional, and will not affect your completion of the course. Completing the Tableau activity will help you practice planning out and plotting a data visualization based on a specific business need. The structure of this activity is designed to emulate the proposals you will likely be assigned in your career as a data professional. Completing this activity will help prepare you for those career moments.\n",
    "\n",
    "**The purpose** of this project is to conduct exploratory data analysis on a provided data set. Your mission is to continue the investigation you began in C2 and perform further EDA on this data with the aim of learning more about the variables. \n",
    "  \n",
    "**The goal** is to clean data set and create a visualization.\n",
    "<br/>  \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",
    "\n",
    "**Part 3:** Building visualizations\n",
    "\n",
    "**Part 4:** Evaluate and share results\n",
    "\n",
    "<br/> \n",
    "Follow the instructions and answer the questions below to complete the activity. Then, you will complete an Executive Summary using the questions listed on the PACE Strategy Document.\n",
    "\n",
    "Be sure to complete this activity before moving on. The next course item will provide you with a completed exemplar to compare to your own work. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p5CHWd9rxIyp"
   },
   "source": [
    "# **Visualize a story in Tableau and Python**"
   ]
  },
  {
   "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](#scrollTo=psz51YkZVwtN&line=3&uniqifier=1)\n",
    "   *        [Analyze](#scrollTo=mA7Mz_SnI8km&line=4&uniqifier=1)\n",
    "   *        [Construct](#scrollTo=Lca9c8XON8lc&line=2&uniqifier=1)\n",
    "   *        [Execute](#scrollTo=401PgchTPr4E&line=2&uniqifier=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout these project notebooks, you'll see references to the problem-solving framework PACE. The following notebook components are labeled with the respective PACE stage: Plan, Analyze, Construct, and Execute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xKLZJUqMx8GE"
   },
   "source": [
    "<img src=\"images/Plan.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "\n",
    "## PACE: Plan \n",
    "\n",
    "In this stage, consider the following questions where applicable to complete your code response:\n",
    "1. Identify any outliers: \n",
    "\n",
    "\n",
    "*   What methods are best for identifying outliers?\n",
    "*   How do you make the decision to keep or exclude outliers from any future models?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outliers can be identified using IQR\n",
    "Lower limit and upper limit are 25th percentile - 1.5 * IQR and 75th percentile + 1.5 * IQR\n",
    "\n",
    "The decisions could be\n",
    "1. delete them\n",
    "2. reassign them to lower and upper limits\n",
    "3. impute them with median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SKur6gTPRcvy"
   },
   "source": [
    "### Task 1. Imports, links, and loading\n",
    "Go to Tableau Public\n",
    "The following link will help you complete this activity. Keep Tableau Public open as you proceed to the next steps. \n",
    "\n",
    "Link to supporting materials: \n",
    "Tableau Public: https://public.tableau.com/s/ \n",
    "\n",
    "For EDA of the data, import the data and packages that would be most helpful, such as pandas, numpy and matplotlib. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "EO8vKZI8x8GF"
   },
   "outputs": [],
   "source": [
    "# Import packages and libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** As shown in this cell, the dataset has been automatically loaded in for you. You do not need to download the .csv file, or provide more code, in order to access the dataset and proceed with this lab. Please continue with this activity by completing the following instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "w7XSDxoqyF9D"
   },
   "outputs": [],
   "source": [
    "# Load dataset into dataframe\n",
    "df = pd.read_csv('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 the 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",
    "Decide which columns are applicable\n",
    "\n",
    "The first step is to assess your data. Check the Data Source page on Tableau Public to get a sense of the size, shape and makeup of the data set. Then answer these questions to yourself: \n",
    "\n",
    "Given our scenario, which data columns are most applicable? \n",
    "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",
    "What do you do about missing data (if any)? \n",
    "\n",
    "Are there data outliers? What are they and how might you handle them? \n",
    "\n",
    "What do the distributions of your variables tell you about the question you're asking or the problem you're trying to solve?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Columns that could be relevant\n",
    "pickup time, dropoff time, trip_distance, PU location, DO location, RateCodeID, storeandfwd flag, and related to fare amount\n",
    "\n",
    "#### Missing Data\n",
    "There are no missing values in the data, if there were missing values then,\n",
    "* delete rows if few were there\n",
    "* create a NaN category\n",
    "* derive representative values based on median or average of non missing values.\n",
    "\n",
    "#### Outliers\n",
    "The data for the total amount have outliers with maximum value of 1200 where as mean and std are both at 16. These outliers can be handeled using IQR upper and lower limit rules. Tip column also has outliers.\n",
    "\n",
    "#### Distributions\n",
    "The distribution of total amount shows negative minimum values. Also they have outlier max value of about 1200. The mean and std are both at ~16. 75th percentile is at 17.8.\n",
    "Most rides were of 3 miles or less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uk6zuv_mUU2k"
   },
   "source": [
    "Start by discovering, using head and size. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "cBOUo5p-tbib"
   },
   "outputs": [
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       "      <th>VendorID</th>\n",
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       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
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       "      <td>05/07/2017 1:17:59 PM</td>\n",
       "      <td>05/07/2017 1:48:14 PM</td>\n",
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       "      <td>3.70</td>\n",
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      "text/plain": [
       "   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",
       "\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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "GvT82D1qyF9F"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size-df.shape[0]*df.shape[1]\n",
    "df.isnull().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WulP4lZ9UVgy"
   },
   "source": [
    "Use describe... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "xMpkdNQ0UPmW"
   },
   "outputs": [
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       "      <td>2.269900e+04</td>\n",
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       "      <td>1.336887</td>\n",
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       "      <td>0.333275</td>\n",
       "      <td>0.497445</td>\n",
       "      <td>1.835781</td>\n",
       "      <td>0.312542</td>\n",
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       "      <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",
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       "      <td>2.800626</td>\n",
       "      <td>1.399212</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>16.097295</td>\n",
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       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "psmn0VD0UWBk"
   },
   "source": [
    "And info. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Thlb8oiyUPwe"
   },
   "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": "vCGi3U9nw4Er"
   },
   "source": [
    "### Task 2b. Assess whether dimensions and measures are correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aNBRa33TR_Is"
   },
   "source": [
    "On the data source page in Tableau, double check the data types for the applicable columns you selected on the previous step. Pay close attention to the dimensions and measures to assure they are correct. \n",
    "\n",
    "In Python, consider the data types of the columns. *Consider:* Do they make sense? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ywUoKjQRyF9I"
   },
   "source": [
    "Review the link provided in the previous activity instructions to create the required Tableau visualization. "
   ]
  },
  {
   "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oYuUVTY-cD3y"
   },
   "source": [
    "Plots like scatter plot, bar graph, histogram, box Plot would be helpful in performing EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f0sHpfkhx8GM"
   },
   "source": [
    "<img src=\"images/Construct.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: Construct \n",
    "\n",
    "Consider the questions in your PACE Strategy Document to reflect on the Construct stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BYWkZ4i3wgv4"
   },
   "source": [
    "### Task 3. Data visualization\n",
    "\n",
    "You’ve assessed your data, and decided on which data variables are most applicable. It’s time to plot your visualization(s)!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oMOs4lb1crry"
   },
   "source": [
    "### Boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a check for outliers on relevant columns such as trip distance and trip duration. Remember, some of the best ways to identify the presence of outliers in data are box plots and histograms. \n",
    "\n",
    "**Note:** Remember to convert your date columns to datetime in order to derive total trip duration.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "9K6Alb7uc9my"
   },
   "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  datetime64[ns]\n",
      " 3   tpep_dropoff_datetime  22699 non-null  datetime64[ns]\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: datetime64[ns](2), float64(8), int64(7), object(1)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Convert data columns to datetime\n",
    "df.tpep_dropoff_datetime = pd.to_datetime(df.tpep_dropoff_datetime)\n",
    "df.tpep_pickup_datetime = pd.to_datetime(df.tpep_pickup_datetime)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**trip distance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a',\n",
    "                  '#f781bf', '#a65628', '#984ea3',\n",
    "                  '#999999', '#e41a1c', '#dede00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create box plot of trip_distance\n",
    "plt.figure(figsize=(5,8))\n",
    "plt.boxplot(x=df['trip_distance'], showfliers=True, flierprops=dict(markersize=1), labels=[''], patch_artist=True,boxprops=dict(facecolor='orange'),medianprops=dict(color='black'))\n",
    "plt.ylabel('Trip Distance (miles)')\n",
    "plt.title('Variation in Trip Distances: A Box Plot Analysis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** Most of the trips are less then 3 miles, with 6 miles as the upper limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSpp0Ya1JJsl+TzSX6U5MIkj0uyQ5JlSVa0v9u3dZPkfUlWJjkvyZ4D9Sxt669IsnSUbZYkSeqLUfeovRf4alU9BHgEcCFwBHB6Ve0BnN7mAZ4O7NFehwFHAyTZATgS2BvYCzhyItxJkiRtzEYW1JJsC/wxcCxAVf2mqn4FHAgc31Y7HjioTR8IfLw6ZwDbJdkF2A9YVlXrquoqYBmw/6jaLUmS1Bej7FHbHVgLfDTJ95J8JMk2wE5VdVlb53Jgpza9K3DpwParWtlU5ZIkSRu1UQa1zYE9gaOr6lHA9fzuNCcAVVVAzcbOkhyWZHmS5WvXrp2NKiVJksZqlEFtFbCqqs5s85+nC25r2ilN2t8r2vLVwMKB7XdrZVOV305VHVNVi6tq8YIFC2b1jUiSJI3DyIJaVV0OXJrkwa1oH+AC4BRg4s7NpcDJbfoU4EXt7s/HAle3U6SnAfsm2b7dRLBvK5MkSdqobT7i+v8SOCHJlsBPgZfQhcMTkxwKXAIc3NY9FTgAWAnc0NalqtYlOQr4blvvzVW1bsTtliRJGruRBrWqOhdYPMmifSZZt4DDp6jnOOC4WW2cJElSzzkygSRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQuwsWLdyZJLP2kiRJGjTqkQk2apesWkOdMHv1Zcns1SVJkuY/e9QkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqadmDGpJnpfkXm369UlOSrLn6JsmSZK0aRumR+0fq+raJE8AngocCxw92mZJkiRpmKB2S/v7DOCYqvoKsOUwlSe5OMkPkpybZHkr2yHJsiQr2t/tW3mSvC/JyiTnDfbaJVna1l+RZOmGvUVJkqT5aZigtjrJvwLPB05NstWQ2034k6p6ZFUtbvNHAKdX1R7A6W0e4OnAHu11GK3XLskOwJHA3sBewJET4U6SJGljNkzgOhg4Ddivqn4F7AD83V3Y54HA8W36eOCggfKPV+cMYLskuwD7Acuqal1VXQUsA/a/C/uXJEmaF2YMalV1A3AF8IRWdDOwYsj6C/hakrOTHNbKdqqqy9r05cBObXpX4NKBbVe1sqnKbyfJYUmWJ1m+du3aIZsnSZLUX5vPtEKSI4HFwIOBjwJbAJ8EHj9E/U+oqtVJfg9YluRHgwurqpLUhjf7jqrqGOAYgMWLF89KnZIkSeM0zKnP5wDPBq4HqKpfAPcapvKqWt3+XgF8ke4aszXtlCbt7xVt9dXAwoHNd2tlU5VLkiRt1IYJar+pqqI7jUmSbYapOMk2A89f2wbYF/ghcAowcefmUuDkNn0K8KJ29+djgavbKdLTgH2TbN9uIti3lUmSJG3UZjz1CZzY7vrcLsnLgZcCHx5iu52ALyaZ2M+nquqrSb7b6jwUuITuZgWAU4EDgJXADcBLAKpqXZKjgO+29d5cVeuGeneSJEnz2IxBraremeRpwDV016m9oaqWDbHdT4FHTFJ+JbDPJOUFHD5FXccBx820T0mSpI3JMD1qtGA2YziTJEnS7JkyqCW5lnZd2mSq6t4jaZEkSZKAaYJaVU3cCHAUcBnwCSDAEmCXOWmdJEnSJmyYuz6fXVUfqqprq+qaqjqabhQBSZIkjdAwQe36JEuSbJbkbkmW0J6pJkmSpNEZJqj9L7pHaKxpr+e1MkmSJI3QMI/nuBhPdUqSJM256e76/PuqekeS9zPJ3Z9V9VcjbZkkSdImbroetQvb3+Vz0RBJkiTd3nSP5/i3JJsBf1hVr57DNkmSJIkZbiaoqluAx89RWyRJkjRgmCGkzk1yCvA5Bh7LUVUnjaxVkiRJGiqobQ1cCTxloKwAg5okSdIIDfN4jpfMRUMkSZJ0e8M88FaSJEljYFCTJEnqqSmDWpK/bn+961OSJGkMputRm7g27f1z0RBJkiTd3rQjEyRZAfx+kvMGygNUVT18tE2TJEnatE03MsELkuwMnAY8e+6aJEmSJJjh8RxVdTnwiCRbAg9qxRdV1W9H3jJJkqRN3IzPUUvyJODjwMV0pz0XJllaVd8ZcdskSZI2acOMTPAuYN+qugggyYOATwOPHmXDJEmSNnXDPEdti4mQBlBVPwa2GF2TJEmSBMP1qC1P8hHgk21+CbB8dE2SJEkSDBfU/hw4HPirNv/vwIdG1iJJkiQBww3KfhPddWrvGn1zJEmSNMGxPiVJknrKoCZJktRTMwa1JH84Fw2RJEnS7Q3To/ahJGcl+Ysk2468RZIkSQKGCGpV9US6R3IsBM5O8qkkTxt2B0k2S/K9JF9u87snOTPJyiSfbcNTkWSrNr+yLV80UMdrW/lFSfbb0DcpSZI0Hw11jVpVrQBeD7wGeBLwviQ/SvL/DbH5XwMXDsy/HXh3VT0QuAo4tJUfClzVyt/d1iPJQ4FDgD8A9qfr4dtsmHZLkiTNZ8Nco/bwJO+mC1tPAZ5VVf+jTb97hm13A54BfKTNp233+bbK8cBBbfrANk9bvk9b/0DgM1V1U1X9DFgJ7DXsG5QkSZqvhulRez9wDvCIqjq8qs4BqKpf0PWyTec9wN8Dt7b5+wC/qqqb2/wqYNc2vStwaav7ZuDqtv5t5ZNsI0mStNEaJqg9A/hUVd0IkORuSe4BUFWfmGqjJM8Erqiqs2elpTNIcliS5UmWr127di52KUmSNFLDBLWvA3cfmL9HK5vJ44FnJ7kY+AzdKc/3AtslmRgRYTdgdZteTXfDAm35tsCVg+WTbHObqjqmqhZX1eIFCxYM0TxJkqR+GyaobV1V103MtOl7zLRRVb22qnarqkV0NwN8o6qWAN8EnttWWwqc3KZPafO05d+oqmrlh7S7QncH9gDOGqLdkiRJ89owQe36JHtOzCR5NHDjXdjna4BXJVlJdw3asa38WOA+rfxVwBEAVXU+cCJwAfBV4PCquuUu7F+SJGlemHFQduBvgM8l+QUQYGfg+Ruyk6r6FvCtNv1TJrlrs6p+DTxviu3fCrx1Q/YpSZI0380Y1Krqu0keAjy4FV1UVb8dbbMkSZI0TI8awGOARW39PZNQVR8fWaskSZI0c1BL8gngAcC5wMS1YQUY1CRJkkZomB61xcBD2x2YkiRJmiPD3PX5Q7obCCRJkjSHhulR2xG4IMlZwE0ThVX17JG1SpIkSUMFtTeOuhGSJEm6o2Eez/HtJPcD9qiqr7dxPjcbfdMkSZI2bTNeo5bk5cDngX9tRbsCXxphmyRJksRwNxMcTjfA+jUAVbUC+L1RNkqSJEnDBbWbquo3EzNJNqd7jpokSZJGaJig9u0krwPunuRpwOeAfxttsyRJkjRMUDsCWAv8APgz4FTg9aNslCRJkoa76/NW4MPtJUmSpDkyzFifP2OSa9Kq6v4jaZEkSZKA4cf6nLA18Dxgh9E0R5IkSRNmvEatqq4ceK2uqvcAzxh903RXbbUFJJnV16KFDvsqSdJcGebU554Ds3ej62EbpidOY3bTb6FOmN06s2TN7FYoSZKmNEzg+r8D0zcDFwMHj6Q1kiRJus0wd33+yVw0RJIkSbc3zKnPV023vKreNXvNkSRJ0oRh7/p8DHBKm38WcBawYlSNkiRJ0nBBbTdgz6q6FiDJG4GvVNWfjrJhkiRJm7phhpDaCfjNwPxvWpkkSZJGaJgetY8DZyX5Yps/CDh+ZC2SJEkSMNxdn29N8v8DT2xFL6mq7422WZIkSRrm1CfAPYBrquq9wKoku4+wTZIkSWKIoJbkSOA1wGtb0RbAJ0fZKEmSJA3Xo/Yc4NnA9QBV9QvgXqNslCRJkoYLar+pqgIKIMk2o22SJEmSYLigdmKSfwW2S/Jy4OvAh0fbLEmSJE1712eSAJ8FHgJcAzwYeENVLZuDtkmSJG3Spu1Ra6c8T62qZVX1d1X16mFDWpKtk5yV5PtJzk/ypla+e5Izk6xM8tkkW7byrdr8yrZ80UBdr23lFyXZ786/XUmSpPljmFOf5yR5zJ2o+ybgKVX1COCRwP5JHgu8HXh3VT0QuAo4tK1/KHBVK393W48kDwUOAf4A2B/4UJLN7kR7JEmS5pVhgtrewBlJfpLkvCQ/SHLeTBtV57o2u0V7FfAU4POt/Hi6kQ4ADuR3Ix58HtinnXo9EPhMVd1UVT8DVgJ7DdFuSZKkeW3Ka9SS3Leqfg7c6VONrefrbOCBwAeBnwC/qqqb2yqrgF3b9K7ApQBVdXOSq4H7tPIzBqod3GZwX4cBhwHc9773vbNNliRJ6o3petS+BFBVlwDvqqpLBl/DVF5Vt1TVI4Hd6HrBHnIX2zvdvo6pqsVVtXjBggWj2o0kSdKcmS6oZWD6/ndlJ1X1K+CbwOPoHvMx0ZO3G7C6Ta8GFgK05dsCVw6WT7KNJEnSRmu6oFZTTA8lyYIk27XpuwNPAy6kC2zPbastBU5u06e0edryb7S7Tk8BDml3he4O7AGctaHtkSRJmm+me47aI5JcQ9ezdvc2TZuvqrr3DHXvAhzfrlO7G3BiVX05yQXAZ5K8BfgecGxb/1jgE0lWAuvo7vSkqs5PciJwAXAzcHhV3bLB71SSJGmemTKoVdVdegRGVZ0HPGqS8p8yyV2bVfVr4HlT1PVW4K13pT2SJEnzzTCP55AkSdIYGNQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST01sqCWZGGSbya5IMn5Sf66le+QZFmSFe3v9q08Sd6XZGWS85LsOVDX0rb+iiRLR9VmSZKkPhllj9rNwN9W1UOBxwKHJ3kocARwelXtAZze5gGeDuzRXocBR0MX7IAjgb2BvYAjJ8KdJEnSxmxkQa2qLquqc9r0tcCFwK7AgcDxbbXjgYPa9IHAx6tzBrBdkl2A/YBlVbWuqq4ClgH7j6rdkiRJfTEn16glWQQ8CjgT2KmqLmuLLgd2atO7ApcObLaqlU1VLkmStFEbeVBLck/gC8DfVNU1g8uqqoCapf0clmR5kuVr166djSolSZLGaqRBLckWdCHthKo6qRWvaac0aX+vaOWrgYUDm+/WyqYqv52qOqaqFlfV4gULFszuG5EkSRqDUd71GeBY4MKqetfAolOAiTs3lwInD5S/qN39+Vjg6naK9DRg3yTbt5sI9m1lkiRJG7XNR1j344EXAj9Icm4rex3wNuDEJIcClwAHt2WnAgcAK4EbgJcAVNW6JEcB323rvbmq1o2w3ZIkSb0wsqBWVf8BZIrF+0yyfgGHT1HXccBxs9c6SZKk/nNkAkmSpJ4yqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgpg2y1RaQZNZeixbuPO63JElSb41yZAJthG76LdQJs1dflqyZvcokSdrI2KMmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPjSyoJTkuyRVJfjhQtkOSZUlWtL/bt/IkeV+SlUnOS7LnwDZL2/orkiwdVXslSZL6ZpQ9ah8D9l+v7Ajg9KraAzi9zQM8HdijvQ4DjoYu2AFHAnsDewFHToQ7SZKkjd3IglpVfQdYt17xgcDxbfp44KCB8o9X5wxguyS7APsBy6pqXVVdBSzjjuFPkiRpozTX16jtVFWXtenLgZ3a9K7ApQPrrWplU5XfQZLDkixPsnzt2rWz22pJkqQxGNvNBFVVQM1ifcdU1eKqWrxgwYLZqlaSJGls5jqorWmnNGl/r2jlq4GFA+vt1sqmKpckSdrozXVQOwWYuHNzKXDyQPmL2t2fjwWubqdITwP2TbJ9u4lg31amjcRWW0CSWXstWrjzuN+SJEmzZvNRVZzk08CTgR2TrKK7e/NtwIlJDgUuAQ5uq58KHACsBG4AXgJQVeuSHAV8t6335qpa/wYFzWM3/RbqhNmrL0vWzF5lkiSN2ciCWlW9YIpF+0yybgGHT1HPccBxs9g0SZKkecGRCSRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVtVBzkXZK0MRnZWJ/SODjIuyRpY2KPmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmjSN2R7pwNEOJEkbwpEJpGnM9kgHAFu/eA1JZq2+++22Exdfevms1SdJ6g+DmjTHZjv8GfwkaeNlUJPmub4HP4B7bHU3brjp1lmrzzApaVNhUJN0O6M43Zslt85qnVmyZvYqk6Qe82YCSZKknjKoSZIk9ZRBTZIkqacMapLmndl+vp3PtpPUV95MIGneme0bHrw5oZ8WLdyZS1bN3rHxbmHNRwY1SZu8iR662WIgmB2XrFrT60fPeJw1F+ZNUEuyP/BeYDPgI1X1tjE3SdJGwh66u262e79GweOs+WheBLUkmwEfBJ4GrAK+m+SUqrpgvC2TpDua7R466P9Dg2e79wsgS2a3vtk2iuNsL53WNy+CGrAXsLKqfgqQ5DPAgYBBTVLvzIeHBo9iBIpNzXwYC7jvAV8zmy9BbVfg0oH5VcDeY2qLJM17s38acPbq2pTN/nHpd8A3+M0sVTXuNswoyXOB/avqZW3+hcDeVfXKgXUOAw5rsw8GLpqDpu0I/HIO9qPheUz6yePSPx6TfvK49M9cHJP7VdWCyRbMlx611cDCgfndWtltquoY4Ji5bFSS5VW1eC73qel5TPrJ49I/HpN+8rj0z7iPyXx54O13gT2S7J5kS+AQ4JQxt0mSJGmk5kWPWlXdnOSVwGl0j+c4rqrOH3OzJEmSRmpeBDWAqjoVOHXc7VjPnJ5q1VA8Jv3kcekfj0k/eVz6Z6zHZF7cTCBJkrQpmi/XqEmSJG1yDGp3QpL9k1yUZGWSI8bdHnWSXJzkB0nOTbJ83O3ZFCU5LskVSX44ULZDkmVJVrS/24+zjZuiKY7LG5Osbr+Xc5McMM42bmqSLEzyzSQXJDk/yV+3cn8vYzLNMRnrb8VTnxuoDWf1YwaGswJe4HBW45fkYmBxVfkMojFJ8sfAdcDHq+phrewdwLqqelv7H5vtq+o142znpmaK4/JG4Lqqeuc427apSrILsEtVnZPkXsDZwEHAi/H3MhbTHJODGeNvxR61DXfbcFZV9RtgYjgraZNXVd8B1q1XfCBwfJs+nu4/fJpDUxwXjVFVXVZV57Tpa4EL6Ubh8fcyJtMck7EyqG24yYazGvuBFAAFfC3J2W2kCvXDTlV1WZu+HNhpnI3R7bwyyXnt1Kin2MYkySLgUcCZ+HvphfWOCYzxt2JQ08bkCVW1J/B04PB2ukc9Ut21Fl5v0Q9HAw8AHglcBvzfsbZmE5XknsAXgL+pqmsGl/l7GY9JjslYfysGtQ0343BWGo+qWt3+XgF8ke40tcZvTbv2Y+IakCvG3B4BVbWmqm6pqluBD+PvZc4l2YIuEJxQVSe1Yn8vYzTZMRn3b8WgtuEczqqHkmzTLv4kyTbAvsAPp99Kc+QUYGmbXgqcPMa2qJkIA81z8Pcyp5IEOBa4sKreNbDI38uYTHVMxv1b8a7PO6Hdmvsefjec1VvH2yIluT9dLxp0I258yuMy95J8GngysCOwBjgS+BJwInBf4BLg4KrywvY5NMVxeTLdqZwCLgb+bODaKI1YkicA/w78ALi1Fb+O7poofy9jMM0xeQFj/K0Y1CRJknrKU5+SJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgJkmS1FMGNUkjleQ+Sc5tr8uTrB6Y37Kt8+w2APWwdS5KcmOS7yW5MMlZSV48sHza+pI8sj1mZyyS7JLkyxu4zZuTPLVNfyvJ4jux31cmeemGbidpfHw8h6Q5k+SNwHVV9c6Bss2r6uYNrGcR8OWqelibvz9wEvDeqvroENu/GFhcVa/ckP3OliT/B/iPqrpTDzNN8i3g1VW1fAO3uwfwn1X1qDuzX0lzzx41SXMuyceS/EuSM4F3JHlxkg+st2x5kh8neeZM9VXVT4FXAX/V6his73lJfpjk+0m+03rx3gw8v/XqPT/JXkn+u/XQ/VeSBw/Uc1KSryZZkeQdA+9h/yTntHpPb2XbtEGbz2p1HThFk/8n8NWBfXwpybIkF7der1e17c9IssPA5/LcST7LfVvbz0nyuTZOIUneluSCNpD0O9vndANwcRKHi5Lmic3H3QBJm6zdgD+qqlsGT1s2i+jG03sA8M0kD6yqX89Q3znAQyYpfwOwX1WtTrJdVf0myRsY6FFLcm/giVV1czu9+E90YQq6J5I/CrgJuCjJ+4Ff043598dV9bOJMAX8A/CNqnppku2As5J8vaqun2hMkt2Bq6rqpoE2PqztY2tgJfCaqnpUkncDL6IbCeUOkuwIvB54alVdn+Q1wKuSfJBuqJuHVFW1tkxYDjwROGuaz1JSTxjUJI3L56rqlimWndgGQF6R5Kd0AezcGerLFOX/CXwsyYl0p0cnsy1wfJI96IaJ2WJg2elVdTVAkguA+wHbA9+pqp8BDAzxsy/w7CSvbvNb0w0FdOFAfbsAa9fb/zer6lrg2iRXA//Wyn8APHyKNgM8Fngo8J9JALYE/hu4mi5MHtuuhRu8Hu4KJg+0knrIoCZpXK6fZtn6F88OczHto7h9IOo2rHpFkr2BZwBnJ3n0JNseRReWntOuf/vWwLLBnq9bmP6/mwH+Z1VdNM06N9IFuEGD+7h1YP7WIfa3rKpecIcF3enNfYDnAq8EntIWbd3aIGke8Bo1SX30vCR3S/IA4P7AdMFn4uaCdwLvn2TZA6rqzKp6A11P1kLgWuBeA6ttC6xu0y8eon1nAH/cTmMycOrzNOAv07q3kkx20f6P6U7tzoYzgMcneWDb3zZJHtSuU9u2qk4F/jfwiIFtHgT8cJb2L2nE7FGT1Ec/p7uG6t7AK6a4Pu0BSb5H10N0LfC+qvrYJOv9n3ZKM8DpwPdb/UckORf4Z+AddKc+Xw98ZabGVdXaJIcBJyW5G93pxKfR9cy9Bzivlf8MeOZ6216f5CfturuVM+1riHa8GPh0kq1a8evpPo+Tk2zd3verBjZ7PPDGu7JfSXPHx3NI6pUkH6N79Mbnx92WUUnyHODRVfX6Od7vo4BXVdUL53K/ku48e9QkaY5V1ReT3GcMu94R+Mcx7FfSnWSPmiRJUk95M4EkSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqaf+H/uAYSFb3aDCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of trip_distance\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(x=df['trip_distance'],bins=range(0,26,1),color='orange', edgecolor='black')\n",
    "plt.xlabel('Trip Distance (miles)')\n",
    "plt.ylabel('Frequency of rides')\n",
    "plt.title('Exploring the Trip Distance Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** Most of the trips are less then 3 miles, with 6 miles as the upper limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**total amount**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create box plot of total_amount\n",
    "plt.figure(figsize=(5,8))\n",
    "plt.boxplot(x=df['total_amount'],showfliers=True,flierprops=dict(markersize=1),labels=[''],patch_artist=True,boxprops=dict(facecolor='#006BA4'),medianprops=dict(color='orange'))\n",
    "plt.ylabel('Fare Amount ($)')\n",
    "plt.title('Variation in Total Fare Amount: A Box Plot Analysis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of total_amount\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(x=df['total_amount'],bins=range(-10,101,5),color='#006BA4', edgecolor='black')\n",
    "plt.xticks(range(-10,101,5))\n",
    "plt.xlabel('Total Amount ($)')\n",
    "plt.ylabel('Frequency of rides')\n",
    "plt.title('Exploring the Total Fare Amount Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** Some trips shows negative fares, most rides cost less than $20, with a peak between $5-$10. The maximum total amount for a ride were $1200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**tip amount**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create box plot of tip_amount\n",
    "plt.figure(figsize=(5,8))\n",
    "plt.boxplot(x=df['tip_amount'],showfliers=True,flierprops=dict(markersize=1),labels=[''],patch_artist=True,boxprops=dict(facecolor='#ABABAB'),medianprops=dict(color='black'))\n",
    "plt.ylabel('Tip Amount ($)')\n",
    "plt.title('Variation in Tip Amount: A Box Plot Analysis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of tip_amount\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(x=df['tip_amount'],bins=range(0,21,1),color='#ABABAB', edgecolor='black')\n",
    "plt.xticks(range(0,21,2))\n",
    "plt.xlabel('Tip Amount ($)')\n",
    "plt.ylabel('Frequency of rides')\n",
    "plt.title('Exploring the Tip Amount Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Anaysis:** Most rides recieved tip amount below $3, with a peak at $1. The maximum tip amount was $200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**tip_amount by vendor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of tip_amount by vendor\n",
    "plt.figure(figsize=(12,6))\n",
    "vendor_names = {1: 'Creative Mobile Technologies, LLC', 2: 'VeriFone Inc.'}\n",
    "for VendorID in df['VendorID'].unique():\n",
    "    plt.hist(x=df['tip_amount'][df['VendorID']==VendorID], bins=range(0,21,1), label=vendor_names[VendorID],edgecolor='black')\n",
    "\n",
    "plt.xticks(range(0,21,1))\n",
    "plt.xlabel('Tip Amount ($)')\n",
    "plt.ylabel('Frequency of rides')\n",
    "plt.title('Exploring the Tip Amount Distribution by Vendors')\n",
    "plt.legend(title='Vendor')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, zoom in on the upper end of the range of tips to check whether vendor one gets noticeably more of the most generous tips."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Anaysis:** Verifone Inc. got more rides than CMT LLC, but the tip amounts recieved are consistent for both vendors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of tip_amount by vendor for tips > $10 \n",
    "plt.figure(figsize=(10,6))\n",
    "vendor_names = {1: 'Creative Mobile Technologies, LLC', 2: 'VeriFone Inc.'}\n",
    "for VendorID in df['VendorID'].unique():\n",
    "    plt.hist(x=df['tip_amount'][(df['VendorID']==VendorID) & (df['tip_amount']>10)], bins=range(10,21,1), label=vendor_names[VendorID],edgecolor='black')\n",
    "\n",
    "plt.xticks(range(10,21,1))\n",
    "plt.xlabel('Tip Amount ($)')\n",
    "plt.ylabel('Frequency of rides')\n",
    "plt.title('Exploring the Tip Amount Distribution by Vendors for tips > $10')\n",
    "plt.legend(title='Vendor')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** Tip amounts are still consistent for both vendors, except for the highest tip of $20. Occurances of these events are stil less compared to the whole data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean tips by passenger count**\n",
    "\n",
    "Examine the unique values in the `passenger_count` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    16117\n",
       "2     3305\n",
       "5     1143\n",
       "3      953\n",
       "6      693\n",
       "4      455\n",
       "0       33\n",
       "Name: passenger_count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['passenger_count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate mean tips by passenger_count\n",
    "tip_new_df = df['tip_amount'].groupby(df['passenger_count']).mean().reset_index()\n",
    "tip_new_df\n",
    "tip_new_df=tip_new_df[tip_new_df['passenger_count']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar plot for mean tips by passenger count\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(x=tip_new_df['passenger_count'], height=tip_new_df['tip_amount'],color=CB_color_cycle[0], edgecolor='black')\n",
    "plt.hlines(df['tip_amount'].mean(),-1,8, ls='--', color='red', label='global mean')\n",
    "plt.xticks(range(0,7,1))\n",
    "plt.xlim(0.5, 6.5); plt.ylim(0, 2)\n",
    "plt.xlabel('Number of passengers')\n",
    "plt.ylabel('Mean tip amount ($)')\n",
    "plt.title('Mean Tip amount from rides with different passenger count')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** The rides with 0 passengers are not included as they are illogical and also they are very less compared to other data.\n",
    "The mean tip of all rides are consistent with the passenger count except for the passenger count of 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create month and day columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 22 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  datetime64[ns]\n",
      " 3   tpep_dropoff_datetime  22699 non-null  datetime64[ns]\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",
      " 18  month                  22699 non-null  category      \n",
      " 19  month_num              22699 non-null  int64         \n",
      " 20  day                    22699 non-null  category      \n",
      " 21  day_num                22699 non-null  int64         \n",
      "dtypes: category(2), datetime64[ns](2), float64(8), int64(9), object(1)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Create a month column\n",
    "df['month'] = df['tpep_pickup_datetime'].dt.month_name().str[:3]\n",
    "df['month_num'] = df['tpep_pickup_datetime'].dt.month\n",
    "# create a ordered list of values for making categorical values\n",
    "sort_month = df[['month','month_num']].drop_duplicates().sort_values('month_num').reset_index(drop=True)\n",
    "sort_month\n",
    "# creating months as categorical values so that they can be sorted\n",
    "df.month = pd.Categorical(df.month, categories=sort_month['month'],ordered=True)\n",
    "\n",
    "# Create a day column\n",
    "df['day'] = df['tpep_pickup_datetime'].dt.day_name()\n",
    "df['day_num'] = df['tpep_pickup_datetime'].dt.dayofweek\n",
    "# create a ordered list of values for making categorical values\n",
    "sort_day = df[['day','day_num']].drop_duplicates().sort_values('day_num').reset_index(drop=True)\n",
    "sort_day\n",
    "# creating weekdays as categorical values so that they can be sorted\n",
    "df.day = pd.Categorical(df.day, categories=sort_day['day'],ordered=True)\n",
    "df.info()"
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>month</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>1769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>2049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>1964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>1697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>1724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>1734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>2027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>1843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  Unnamed: 0\n",
       "0    Jan        1997\n",
       "1    Feb        1769\n",
       "2    Mar        2049\n",
       "3    Apr        2019\n",
       "4    May        2013\n",
       "5    Jun        1964\n",
       "6    Jul        1697\n",
       "7    Aug        1724\n",
       "8    Sep        1734\n",
       "9    Oct        2027\n",
       "10   Nov        1843\n",
       "11   Dec        1863"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get total number of rides for each month\n",
    "temp_df = df.groupby('month')['Unnamed: 0'].count().reset_index()\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reorder the results to put the months in calendar order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reorder the monthly ride list so months go in order\n",
    "# ALready done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show the index\n",
    "# Already done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of total rides per month\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(x=temp_df['month'], height=temp_df['Unnamed: 0'],color=CB_color_cycle[1], edgecolor='black')\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Frequency of rides')\n",
    "plt.title('Total Rides per Month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** The rides were booked consistentlly across the months. They were least for the months July, August, September and 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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>2931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>3198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>3390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>3402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>3413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>3367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>2998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  Unnamed: 0\n",
       "0     Monday        2931\n",
       "1    Tuesday        3198\n",
       "2  Wednesday        3390\n",
       "3   Thursday        3402\n",
       "4     Friday        3413\n",
       "5   Saturday        3367\n",
       "6     Sunday        2998"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat the above process, this time for rides by day\n",
    "temp_df = df.groupby('day')['Unnamed: 0'].count().reset_index()\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar plot for ride count by day\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(x=temp_df['day'], height=temp_df['Unnamed: 0'],color=CB_color_cycle[2], edgecolor='black')\n",
    "plt.xlabel('Weekday')\n",
    "plt.ylabel('Frequency of rides')\n",
    "plt.title('Ride Count by Weekday')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** Most of the rides were booked on Wednesday, Thursday, friday and saturday. The least rides were booked on Sunday and Monday"
   ]
  },
  {
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>49574.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>52527.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>55310.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>57181.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>55818.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>51195.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>48624.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  total_amount\n",
       "0     Monday      49574.37\n",
       "1    Tuesday      52527.14\n",
       "2  Wednesday      55310.47\n",
       "3   Thursday      57181.91\n",
       "4     Friday      55818.74\n",
       "5   Saturday      51195.40\n",
       "6     Sunday      48624.06"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat the process, this time for total revenue by day\n",
    "temp_df = df.groupby('day')['total_amount'].sum().reset_index()\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar plot of total revenue by day\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(x=temp_df['day'], height=temp_df['total_amount'],color=CB_color_cycle[3], edgecolor='black')\n",
    "plt.xlabel('Weekday')\n",
    "plt.ylabel('Total Revenue ($)')\n",
    "plt.title('Total Revenue by Weekday')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** Most revenue was generated on Thursady, then Friday and Wednesday, and least on Sunday and Monday which is consistent with the number of rides booked on these days. But the rides on saturdays generated less revenue with the number of rides booked almost same as friday."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot total revenue by month**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>31735.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>28937.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>33085.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>32012.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>33828.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>32920.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>26617.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>27759.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>28206.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>33065.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>30800.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>31261.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  total_amount\n",
       "0    Jan      31735.25\n",
       "1    Feb      28937.89\n",
       "2    Mar      33085.89\n",
       "3    Apr      32012.54\n",
       "4    May      33828.58\n",
       "5    Jun      32920.52\n",
       "6    Jul      26617.64\n",
       "7    Aug      27759.56\n",
       "8    Sep      28206.38\n",
       "9    Oct      33065.83\n",
       "10   Nov      30800.44\n",
       "11   Dec      31261.57"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat the process, this time for total revenue by month\n",
    "temp_df = df.groupby('month')['total_amount'].sum().reset_index()\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of total revenue by month\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(x=temp_df['month'], height=temp_df['total_amount'],color=CB_color_cycle[4], edgecolor='black')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Total Revenue ($)')\n",
    "plt.title('Total Revenue by Month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** The revenue generated monthwise is consistent with the number of rides booked in these months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Lx-vikocvoy"
   },
   "source": [
    "#### Scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create a scatterplot in Tableau Public, which can be easier to manipulate and present. If you'd like step by step instructions, you 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 your executive summary, and adding key insights from your findings on those two variables.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tableau link:** https://public.tableau.com/app/profile/nitin.luhadiya/viz/NYCTLC_17146140834130/Sheet1 <br>\n",
    "**Analysis:** The analysis of the scatter plot shows that there were few rides that had zero trip distance but showed, non-zero trip fare amount."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8TQjML4-0_35"
   },
   "source": [
    "[Tableau visualization guidelines](https://docs.google.com/document/d/1pcfUlttD2Y_a9A4VrKPzikZWCAfFLsBAhuKuomjcUjA/template/preview)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot mean trip distance by drop-off location**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get number of unique drop-off location IDs\n",
    "len(df['DOLocationID'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>mean trip_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>24.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>21.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>210</td>\n",
       "      <td>20.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>17.945000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51</td>\n",
       "      <td>17.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>137</td>\n",
       "      <td>1.818852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>234</td>\n",
       "      <td>1.727806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>237</td>\n",
       "      <td>1.555494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>193</td>\n",
       "      <td>1.390556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>207</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    DOLocationID  mean trip_distance\n",
       "0             23           24.275000\n",
       "1             29           21.650000\n",
       "2            210           20.500000\n",
       "3             11           17.945000\n",
       "4             51           17.310000\n",
       "..           ...                 ...\n",
       "211          137            1.818852\n",
       "212          234            1.727806\n",
       "213          237            1.555494\n",
       "214          193            1.390556\n",
       "215          207            1.200000\n",
       "\n",
       "[216 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean trip distance for each drop-off location\n",
    "temp_df = df.groupby(df['DOLocationID'])['trip_distance'].mean().reset_index(name='mean trip_distance')\n",
    "temp_df['DOLocationID']=temp_df['DOLocationID'].astype(str)\n",
    "# Sort the results in descending order by mean trip distance\n",
    "temp_df = temp_df.sort_values(by='mean trip_distance',ascending=False).reset_index(drop=True)\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of mean trip distances by drop-off location in ascending order by distance\n",
    "temp_df = temp_df.sort_values(by='mean trip_distance',ascending=True).reset_index(drop=True)\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.bar(temp_df['DOLocationID'],temp_df['mean trip_distance'],color=CB_color_cycle[5], edgecolor='black')\n",
    "plt.xlabel('Drop Off Location')\n",
    "plt.ylabel('Mean Trip Distance (miles)')\n",
    "plt.title('Mean Trip Distances by Drop-off Location', fontsize=16)\n",
    "plt.xticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BONUS CONTENT\n",
    "\n",
    "To confirm your conclusion, consider the following experiment:\n",
    "1. Create a sample of coordinates from a normal distribution&mdash;in this case 1,500 pairs of points from a normal distribution with a mean of 10 and a standard deviation of 5\n",
    "2. Calculate the distance between each pair of coordinates \n",
    "3. Group the coordinates by endpoint and calculate the mean distance between that endpoint and all other points it was paired with\n",
    "4. Plot the mean distance for each unique endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.92222634,  5.05907133],\n",
       "       [ 3.34253168,  6.41767126],\n",
       "       [11.1495234 , 10.4547616 ],\n",
       "       ...,\n",
       "       [ 3.29780618,  8.48696075],\n",
       "       [15.60081367,  3.52878971],\n",
       "       [11.28169263, 11.06199979]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#BONUS CONTENT\n",
    "\n",
    "#1. Generate random points on a 2D plane from a normal distribution\n",
    "mean = 10\n",
    "sd = 5\n",
    "size = (1500,2)\n",
    "np.random.normal(mean, sd, size)\n",
    "\n",
    "# 2. Calculate Euclidean distances between points in first half and second half of array\n",
    "#==> ENTER YOUR CODE HERE\n",
    "\n",
    "# 3. Group the coordinates by \"drop-off location\", compute mean distance\n",
    "#==> ENTER YOUR CODE HERE\n",
    "\n",
    "# 4. Plot the mean distance between each endpoint (\"drop-off location\") and all points it connected to\n",
    "#==> ENTER YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram of rides by drop-off location**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, check to 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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if all drop-off locations are consecutively numbered\n",
    "df['DOLocationID'].max() - len(df['DOLocationID'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# There are 49 numbers missing\n",
    "\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",
    "plt.figure(figsize=(16,4))\n",
    "ax = 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);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** Some drop-off locations are more active than other, these could be tourist attraction, transportation places (railways, bus station, airports)"
   ]
  },
  {
   "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 questions in your PACE Strategy Document to reflect on the Execute stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "csHAfM-RSO7l"
   },
   "source": [
    "### Task 4a. Results and evaluation\n",
    "\n",
    "Having built visualizations in Tableau and in Python, what have you learned about the dataset? What other questions have your visualizations uncovered that you should pursue? \n",
    "\n",
    "***Pro tip:*** Put yourself in your client's perspective, what would they want to know? \n",
    "\n",
    "Use the following code fields to pursue any additional EDA based on the visualizations you've already plotted. Also use the space to make sure your visualizations are clean, easily understandable, and accessible. \n",
    "\n",
    "***Ask yourself:*** Did you consider color, contrast, emphasis, and labeling?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pOp0vmf1zOHO"
   },
   "source": [
    "Yes, i had considered the color contrast and labeling\n",
    "\n",
    "I have learned that most rides are below 6 miles and the maximum trip distance is of 35 miles. Also, most rides costs belo $20, but the maximum value is of $1200.\n",
    "\n",
    "My other questions are why few trips shows 0 trip distance and still charge non zero fare\n",
    "\n",
    "My client would likely want to know about the trip durations also. Do they affect the fare prices. when the trip durations are highest chronologically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "puYiPmvGdTJH"
   },
   "outputs": [],
   "source": [
    "df['trip_duration'] = (df['tpep_dropoff_datetime']-df['tpep_pickup_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "iEv7pHw-dTRP"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>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>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>month_num</th>\n",
       "      <th>day</th>\n",
       "      <th>day_num</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <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>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>Mar</td>\n",
       "      <td>3</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>5</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>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>Apr</td>\n",
       "      <td>4</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</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>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>Dec</td>\n",
       "      <td>12</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</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>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>5</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>6</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>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>Apr</td>\n",
       "      <td>4</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>5</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>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>Mar</td>\n",
       "      <td>3</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>5</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>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>5</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2</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>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>Aug</td>\n",
       "      <td>8</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</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>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>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>5</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>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>Nov</td>\n",
       "      <td>11</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>0 days 00:20:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 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  ...  mta_tax  tip_amount  tolls_amount  \\\n",
       "0           100           231  ...      0.5        2.76           0.0   \n",
       "1           186            43  ...      0.5        4.00           0.0   \n",
       "2           262           236  ...      0.5        1.45           0.0   \n",
       "3           188            97  ...      0.5        6.39           0.0   \n",
       "4             4           112  ...      0.5        0.00           0.0   \n",
       "5           161           236  ...      0.5        2.06           0.0   \n",
       "6            79           241  ...      0.5        9.86           0.0   \n",
       "7           237           114  ...      0.5        1.78           0.0   \n",
       "8           234           249  ...      0.5        0.00           0.0   \n",
       "9           239           237  ...      0.5        2.75           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  month  month_num        day day_num  \\\n",
       "0                    0.3         16.56    Mar          3   Saturday       5   \n",
       "1                    0.3         20.80    Apr          4    Tuesday       1   \n",
       "2                    0.3          8.75    Dec         12     Friday       4   \n",
       "3                    0.3         27.69    May          5     Sunday       6   \n",
       "4                    0.3         17.80    Apr          4   Saturday       5   \n",
       "5                    0.3         12.36    Mar          3   Saturday       5   \n",
       "6                    0.3         59.16    May          5  Wednesday       2   \n",
       "7                    0.3         19.58    Aug          8    Tuesday       1   \n",
       "8                    0.3          9.80    Feb          2   Saturday       5   \n",
       "9                    0.3         16.55    Nov         11     Friday       4   \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 23 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U4HeITeOwXfo"
   },
   "source": [
    "### Task 4b. Conclusion\n",
    "*Make it professional and presentable*\n",
    "\n",
    "You have visualized the data you 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? Why are the data visualizations provided in this notebook useful?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5o3dV6NYzNSs"
   },
   "source": [
    "* EDA is important because it 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 that this dataset has some outliers that we will need to make decisions on prior to designing a model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8JabFSqwwLrM"
   },
   "source": [
    "You’ve now completed professional data visualizations according to a business need. Well done! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Congratulations!** You've completed this lab. However, you may not notice a green check mark next to this item on Coursera's platform. Please continue your progress regardless of the check mark. Just click on the \"save\" icon at the top of this notebook to ensure your work has been logged."
   ]
  }
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