IreneCrisologo/amtrak-delays.ipynb Secret

## amtrak-delays.ipynb
{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/IreneCrisologo/1f263419263d905e763c029df26ad2ef/amtrak-delays.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "69f8137d",
      "metadata": {
        "id": "69f8137d"
      },
      "source": [
        "# Are Amtrak trains always late?\n",
        "\n",
        "Short answer: no!\n",
        "\n",
        "I took the train from Chicago going to and from New Orleans to attend AGU 2021, and experienced a 6-hour and a 3-hour delay, respectively. Given that there was a tornado that ploughed through the train route, I wasn't too bothered about it. But I've heard several complaints about Amtrak being always late, so I wanted to check the data and see for myself. This analysis is also inspired by a tweet from @douglas_rao https://twitter.com/douglas_rao/status/1469863061494272001\n",
        "\n",
        "Data from https://juckins.net/amtrak_status/archive/html/home.php\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ccbeab73",
      "metadata": {
        "id": "ccbeab73"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d5afa0f9",
      "metadata": {
        "id": "d5afa0f9"
      },
      "source": [
        "## Part 1: Chicago to New Orleans"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e06d0be",
      "metadata": {
        "id": "9e06d0be"
      },
      "outputs": [],
      "source": [
        "df = pd.read_excel('AmtrakDelays_CHI_NOL.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "55f5ceb1",
      "metadata": {
        "id": "55f5ceb1",
        "outputId": "6742dbf2-bea4-4580-fa1b-b4cae3714db1"
      },
      "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>Origin Date</th>\n",
              "      <th>Station</th>\n",
              "      <th>Sch Ar</th>\n",
              "      <th>Act Ar</th>\n",
              "      <th>Comments</th>\n",
              "      <th>Ar Delay (mins)</th>\n",
              "      <th>Service Disruption</th>\n",
              "      <th>Cancellations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>06/27/2015 (Sa)</td>\n",
              "      <td>NOL</td>\n",
              "      <td>06/28/2015 3:32 PM (Su)</td>\n",
              "      <td>3:12AM</td>\n",
              "      <td>Ar: 11 hr, 40 min late.</td>\n",
              "      <td>700</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>06/27/2015 (Sa)</td>\n",
              "      <td>GWD</td>\n",
              "      <td>06/28/2015 9:00 AM (Su)</td>\n",
              "      <td>7:10PM</td>\n",
              "      <td>Ar: 10 hr, 10 min late | Dp: 10 hr, 17 min late.</td>\n",
              "      <td>610</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>06/27/2015 (Sa)</td>\n",
              "      <td>JAN</td>\n",
              "      <td>06/28/2015 11:12 AM (Su)</td>\n",
              "      <td>9:11PM</td>\n",
              "      <td>Ar: 9 hr, 59 min late. | Dp: 10 hr, 41 min late.</td>\n",
              "      <td>599</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>04/29/2017 (Sa)</td>\n",
              "      <td>NOL</td>\n",
              "      <td>04/30/2017 3:32 PM (Su)</td>\n",
              "      <td>1:19AM</td>\n",
              "      <td>Ar: 9 hr, 47 min late.</td>\n",
              "      <td>587</td>\n",
              "      <td>SD</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>06/27/2015 (Sa)</td>\n",
              "      <td>MEM</td>\n",
              "      <td>06/28/2015 6:27 AM (Su)</td>\n",
              "      <td>4:05PM</td>\n",
              "      <td>Ar: 9 hr, 38 min late. | Dp: 9 hr, 48 min late.</td>\n",
              "      <td>578</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Origin Date Station                    Sch Ar  Act Ar  \\\n",
              "0  06/27/2015 (Sa)     NOL   06/28/2015 3:32 PM (Su)  3:12AM   \n",
              "1  06/27/2015 (Sa)     GWD   06/28/2015 9:00 AM (Su)  7:10PM   \n",
              "2  06/27/2015 (Sa)     JAN  06/28/2015 11:12 AM (Su)  9:11PM   \n",
              "3  04/29/2017 (Sa)     NOL   04/30/2017 3:32 PM (Su)  1:19AM   \n",
              "4  06/27/2015 (Sa)     MEM   06/28/2015 6:27 AM (Su)  4:05PM   \n",
              "\n",
              "                                           Comments  Ar Delay (mins)  \\\n",
              "0                           Ar: 11 hr, 40 min late.              700   \n",
              "1  Ar: 10 hr, 10 min late | Dp: 10 hr, 17 min late.              610   \n",
              "2  Ar: 9 hr, 59 min late. | Dp: 10 hr, 41 min late.              599   \n",
              "3                            Ar: 9 hr, 47 min late.              587   \n",
              "4   Ar: 9 hr, 38 min late. | Dp: 9 hr, 48 min late.              578   \n",
              "\n",
              "  Service Disruption Cancellations  \n",
              "0                NaN           NaN  \n",
              "1                NaN           NaN  \n",
              "2                NaN           NaN  \n",
              "3                 SD           NaN  \n",
              "4                NaN           NaN  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cc29268c",
      "metadata": {
        "id": "cc29268c"
      },
      "source": [
        "Rename column for easier referencing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ac198ddb",
      "metadata": {
        "id": "ac198ddb"
      },
      "outputs": [],
      "source": [
        "df = df.rename(columns={'Ar Delay (mins)':'Ar_Delay_mins'})"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4f917269",
      "metadata": {
        "id": "4f917269"
      },
      "source": [
        "Checking for NaN values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "93a4272c",
      "metadata": {
        "id": "93a4272c",
        "outputId": "38bf64bb-ddb9-4115-df2f-2a550deb3bc0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Ar_Delay_mins'].isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f3f9c05c",
      "metadata": {
        "id": "f3f9c05c"
      },
      "source": [
        "Counting how many datapoints are recorded for each station."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bd5d3dc7",
      "metadata": {
        "id": "bd5d3dc7",
        "outputId": "9e5b7c13-2959-413a-a630-c952ab3df124"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "NOL    3533\n",
              "CDL    2207\n",
              "GWD    2195\n",
              "MEM    2188\n",
              "JAN    2171\n",
              "MCB     367\n",
              "HAZ      76\n",
              "CEN      64\n",
              "YAZ      34\n",
              "NBN      31\n",
              "HMW      30\n",
              "BRH      27\n",
              "MKS      25\n",
              "FTN      25\n",
              "EFG      24\n",
              "MAT      22\n",
              "KKI      21\n",
              "CHM      20\n",
              "HMD      12\n",
              "Name: Station, dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Station'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9c480ef",
      "metadata": {
        "id": "f9c480ef"
      },
      "source": [
        "Looks like most of the reports come from 5 stations (New Orleans LA, Carbondale IL, Greenwood MI, Memphis TN, Jackson MI) so i'm only going to process those."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c816ddc",
      "metadata": {
        "id": "3c816ddc"
      },
      "outputs": [],
      "source": [
        "df = df[df['Station'].isin(['NOL','CDL','MEM','GWD','JAN'])]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cbf96d04",
      "metadata": {
        "id": "cbf96d04"
      },
      "source": [
        "Let's look at the histogram of delays, converting minutes to hours."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4d411fbe",
      "metadata": {
        "id": "4d411fbe",
        "outputId": "8043a1b2-b249-4ed7-e0b9-f70247937197"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.0, 1.0, 'Amtrak City of New Orleans (Southbound) is not always late')"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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2B67IzIt6/K2ARy54P6K0BD2fR19wd6QMPj8/M38/TlmOpbSUHNF911Gd/lfKh82xw+5kZt5LCS53oIwnWWV/atbHUVrSHuhaxb7DbrNHGbJ+EL+vJv3pGNkXUy7eT28mRsSGve7IiognULoxO0HFeZSWk727sr6B8gXyR3X6lpqvuyyv6FGmFQzZGtTHX3ZN70U51jtDJQY99n9V/z9S9hogvHSC5bqUMuC6u3zdddi0BWUM0YRk5oOZeR6l5XYNYJuJrqth0PrrfPEc5D0d9hr8iAleH9Zh1XPwbX3Wfw5lHOjhlJuc+l5fa8vyNyjd0mOdf72sQxkf+sgXwCgPA+/ulu9Xr6dTWtLW63M9NSCbJDO9heyVlGDk/Zl5bvfMiDia0sqzK+N3Ow3iSuAVEXE65QN46TgH40+Bu4DPRsRHKOM8PkT51rVev4Uy88CIeAg4ISLWqCdiv7w/joj3AYdHxDaUAZ2/pnxzfzGlZeuvKBftofYpyzOLFlHucrssM386xr52fJ/SpP/5KI/NuIJyd9A7KDcfDP2tvJfMXBERH6OMxej2YcrdgT+OiCMpQcL6lAvZ0zPzrxt5z6EEwA9RAh8oD3i9ixLU9+sWbZbltxHxRspdWD+LiM9Q7iibT6m7Z1IG9E60dfB9wI+BMyLii5TgZUPK3ZdrZuZBmfm7iDgPeH9ELKMcY3/Nyu60odQg/gjKt/HrKcHevpRv2n3H1GXm/RFxPqWrpmlX4HP1ePovyjfzp1Hu1tqA8qFEZt4e5dl5B0fEvZSulm0ogfdPqOOQMjMj4hvA2yPiWkpw8Yq6nW5XAs+PiFdSur9+m5mLB62Lhu0i4kuUsVlbAYcBP8rMs+v8QY/9Cyl3QH6qdjd3HnkyXhdaT5l5Zz3mPhgRd1PG9uxM6Z5aRR3WsRXljsyB1frbD/gu5fh+AuXmp7tZGZSujkHrr9OKtH9EHE8JgC7talXrmNA1uGHY68PpwAci4hDKNWg3yk0Y/Xyecp79FnhUj0hEHMPKul1Oec/ewsqxW4M6nfLonUX1+N2KEkjf3JWvX72eG+VRR9+q5+YFlFb7+ZT35wOZOd5DvTWIQUb+T9c/Sh/8XcA6feavR7lNeVGdPpTyLWGtrnyL6boLi5V3Bu3eSHse8HNKC8Ajd+RQgqAlfcqwG/ALypiN/6FcwA6l991UH+9K+yTlA/CNA9TFcymDjJdRTqTbKSfum1n5qIB9WfUuy5771Jj/nJq+/xDvy7qUO4SWUb7ZXksJTJp3bE7oLsuu9LXqunuVezNKi9TNtQzLKGNJ3tyVb5u6/Hld6acwziM+epRzG8og+qWNbX4N2LZH3nOBn/RZT6/92YYSCCynfIAvoTz64uWNPPMpH2p313xHUoKUR+1Hv21TzoPOubIRZbzgtZRz6HZKa8HLBqiHd1PGmzTvINuMMhbt/Fq2BygDy0+jPEy3uXzU4+WaRj1+lsZd0DXfk2t9/7aW7/N99veZlA/T39d5410PFtG4S42V14LX1nl3srLleMNhj/2ab7v6PtxD+QL1Pga/Lsyn625ESsD8cUrAeV9d97Z9jqU3Uc73R92F3ON9fNR2KAPAv0EJxv7QeP/+fJz1dOpv9670fVn1ejRo/X2Ecm4/1L2OHtsf5hrc67wb+PpAaV36XK2buyk3em3Ra901/yZ13qd6zNuHlY/HWVHr/TN0nQc9llvEqo+9OKAufx/lC8Hudd3nDlKvlJbL91LGnf2BcofoLyk3Oaw33jXBv8H+OrcbSz1FxGGUE/GP89F3oUo9RbmLdQnwt5nZfbekWhYR36e0Er6l7bLMdRHxTso4sa0y8/q2y6N2zfQuS41IROxI+Ub8XsqDHw3GNJDMvCsiPgn8Y0R8Lf3WN21ExAJKV9uw45A0iSJiW8pjhT5KeZagwZgMyNTXyZS7tM6gNGNLwzic0o22Cd6FNZ38EeVBuAYA7TqKMszkp5S7lyW7LCVJkto2ox97IUmSNBsYkEmSJLVsRo8h23DDDXP+/PltF0OSJGlcP//5z3+bmfN6zZvRAdn8+fO56KKLxs8oSZLUsoj4Vb95dllKkiS1zIBMkiSpZQZkkiRJLTMgkyRJapkBmSRJUssMyCRJklpmQCZJktQyAzJJkqSWGZBJkiS1zIBMkiSpZQZkkiRJLTMgm8XmH/S9tosgSZIGYEAmSZLUMgMySZKklhmQSZIktcyATJIkqWUGZJIkSS0zIJMkSWqZAZkkSVLLDMgkSZJaZkAmSZLUMgMySZKklhmQSZIktcyATJIkqWUGZJIkSS0zIJMkSWqZAZkkSVLLRhaQRcTmEfHDiLgqIq6IiPfW9A0i4qyIuK7+X7+xzMERcX1EXBMRLxtV2SRJkqaTUbaQPQi8PzO3AZ4N7B8R2wIHAWdn5pbA2XWaOm9vYDtgD+CoiFhzhOWTJEmaFkYWkGXmssy8uL6+G7gK2BTYEzi+ZjseeHV9vSdwYmauyMwbgeuBXUZVvtls/kHfa7sIkiRpCFMyhiwi5gM7AucDG2fmMihBG7BRzbYpcFNjsSU1TZIkaVYbeUAWEU8Evg0cmJl3jZW1R1r2WN9+EXFRRFx06623TlYxJUmSWjPSgCwiHkMJxr6Wmd+pybdExCZ1/ibA8pq+BNi8sfhmwNLudWbmMZm5MDMXzps3b3SFlyRJmiKjvMsygC8CV2Xm4Y1ZpwL71Nf7AKc00veOiMdFxBbAlsAFoyqfJEnSdLHWCNf9POAtwGURcUlNOwT4BHBSRLwd+DWwF0BmXhERJwFXUu7Q3D8zHxph+SRJkqaFkQVkmfkTeo8LA3hxn2UOAw4bVZkkSZKmI5/UL0mS1DIDMkmSpJYZkEmSJLXMgEySJKllBmRzkD+tJEnS9GJAJkmS1DIDMkmSpJYZkEmSJLXMgEySJKllBmSSJEktMyCTJElqmQGZJElSywzIJEmSWmZAJkmS1DIDMkmSpJYZkEmSJLXMgGwO8LcrJUma3gzIJEmSWmZAJkmS1DIDMkmSpJYZkEmSJLXMgEySJKllBmSSJEktMyCTJElqmQHZHOGzyCRJmr4MyCRJklpmQDbL2TImSdL0Z0AmSZLUMgMySZKklhmQSZIktcyATJIkqWUGZLOIA/glSZqZDMhmqX7BmUGbJEnTjwGZJElSywzIJEmSWmZAJkmS1DIDMkmSpJYZkEmSJLXMgEySJKllBmSSJEktMyCTJElqmQGZJElSywzIJEmSWmZAJkmS1DIDMkmSpJYZkM0h/rC4JEnTkwGZJElSywzIZgFbviRJmtkMyCRJklpmQCZJktQyAzJJkqSWjSwgi4jjImJ5RFzeSDs0Im6OiEvq38sb8w6OiOsj4pqIeNmoyiVJkjTdjLKFbBGwR4/0z2Tmgvp3GkBEbAvsDWxXlzkqItYcYdkkSZKmjZEFZJn5Y+D2AbPvCZyYmSsy80bgemCXUZVNkiRpOmljDNl7IuLS2qW5fk3bFLipkWdJTZMkSZr1pjog+xzwDGABsAz4dE2PHnmz1woiYr+IuCgiLrr11ltHUsiZyGeRSZI0c01pQJaZt2TmQ5n5MPAFVnZLLgE2b2TdDFjaZx3HZObCzFw4b9680RZYkiRpCkxpQBYRmzQmXwN07sA8Fdg7Ih4XEVsAWwIXTGXZJEmS2rLWqFYcEV8HdgU2jIglwEeAXSNiAaU7cjHwNwCZeUVEnARcCTwI7J+ZD42qbJIkSdPJyAKyzHxjj+QvjpH/MOCwUZVHkiRpuvJJ/XNU5yYAbwaQJKl9BmRzmMGYJEnTgwHZLGOQJUnSzGNAJkmS1DIDMkmSpJYZkEmSJLXMgEySJKllBmSSJEktMyCTJElqmQGZJElSywzIJEmSWmZAJkmS1DIDMkmSpJYZkEmSJLVsoIAsIv501AWRJEmaqwZtIft8RFwQEX8bEU8eZYHUDn+UXJKk9gwUkGXm/wLeBGwOXBQRJ0TES0ZaMkmSpDli4DFkmXkd8CHgA8ALgX+LiKsj4rWjKpwkSdJcMOgYsu0j4jPAVcBuwF9k5jb19WdGWD5JkqRZb60B8x0JfAE4JDPv6yRm5tKI+NBISiZJkjRHDBqQvRy4LzMfAoiINYC1M/P3mfmVkZVOkiRpDhh0DNkPgMc3ptepaZok3uUoSdLcNWhAtnZm3tOZqK/XGU2RJEmS5pZBA7J7I2KnzkREPAu4b4z8kiRJGtCgY8gOBL4ZEUvr9CbAG0ZSIkmSpDlmoIAsMy+MiGcCWwMBXJ2ZD4y0ZJoyjl+TJKldg7aQAewMzK/L7BgRZOaXR1IqSZKkOWSggCwivgI8A7gEeKgmJ2BAJkmStJoGbSFbCGybmTnKwkiSJM1Fg95leTnwR6MsiCRJ0lw1aAvZhsCVEXEBsKKTmJmvGkmpJEmS5pBBA7JDR1kISZKkuWygLsvM/BGwGHhMfX0hcPEIyzXn+SgKSZLmjoECsoh4J/At4OiatCnw3RGVSS0yEJQkaeoNOqh/f+B5wF0AmXkdsNGoCjXXGARJkjS3DRqQrcjM+zsTEbEW5TlkkiRJWk2DBmQ/iohDgMdHxEuAbwL/MbpiSZIkzR2DBmQHAbcClwF/A5wGfGhUhZIkSZpLBr3L8uHM/EJm7pWZr6+v7bKcpRzTJknS1Br0tyxvpMeYscx8+qSXSJIkaY4Z5rcsO9YG9gI2mPziSJIkzT2Ddlne1vi7OTP/FdhttEWTJEmaGwbtstypMbkGpcXsSSMpkSRJ0hwzaJflpxuvH6T8jNJfTnppxPyDvsfiT7yi7WJIkqQpNFBAlpkvGnVBJEmS5qpBuyzfN9b8zDx8coqjNvm4C0mS2jHog2EXAu+m/Kj4psC7gG0p48gcSzYF2gqWDNIkSRq9QceQbQjslJl3A0TEocA3M/MdoyqY+nOcmSRJs8ugLWRPBe5vTN8PzJ/00kiSJM1Bg7aQfQW4ICJOpjyx/zXAl0dWKkmSpDlk0LssD4uI7wPPr0lvy8xfjK5YkiRJc8egXZYA6wB3ZeYRwJKI2GKszBFxXEQsj4jLG2kbRMRZEXFd/b9+Y97BEXF9RFwTES8bek8kSZJmqIECsoj4CPAB4OCa9Bjgq+MstgjYoyvtIODszNwSOLtOExHbAnsD29VljoqINQcpmyRJ0kw3aAvZa4BXAfcCZOZSxnncRWb+GLi9K3lP4Pj6+njg1Y30EzNzRWbeCFwP7DJg2SRJkma0QQOy+zMzKQP6iYgnTHB7G2fmMoD6f6OavilwUyPfkpomSZI06w0akJ0UEUcDT46IdwI/AL4wieWIHmnZM2PEfhFxUURcdOutt05iESRJktoxbkAWEQF8A/gW8G1ga+DDmfnvE9jeLRGxSV3vJsDymr4E2LyRbzNgaa8VZOYxmbkwMxfOmzdvAkWYHmbCE/BnQhklSZoNxn3sRWZmRHw3M58FnLWa2zsV2Af4RP1/SiP9hIg4HPhjYEvggtXc1pxg0CRJ0sw3aJfleRGx8zArjoivAz8Dto6IJRHxdkog9pKIuA54SZ0mM68ATgKuBE4H9s/Mh4bZniaXgZ4kSVNn0Cf1vwh4V0QsptxpGZTGs+37LZCZb+wz68V98h8GHDZgeWaFfr9JaTAkSdLcMmZAFhFPzcxfA/97isojSZI054zXQvZdYKfM/FVEfDszXzcFZZIkSZpTxhtD1nwcxdNHWRCtyq5LSZLmhvECsuzzWnOIgaEkSaM1XkC2Q0TcFRF3A9vX13dFxN0RcddUFHCuawZDBkaSJM1OY44hy0x/4FuSJGnEBn0OmabAsC1gtphJkjQ7GJBJkiS1zIBskthaJUmSJsqATJIkqWUGZBqILYCSJI2OAZkkSVLLDMgkSZJaZkAmSZLUMgOyGcDxW5IkzW4GZC0z2JIkSQZkkiRJLTMgkyRJapkB2Wqyy1GSJK0uAzJJkqSWGZDNULbMSZI0exiQSZIktcyATJIkqWVrtV2A2WAquw/b7KpsbnvxJ17RWjkkSZptbCEbsU4Q45gvSZLUjwGZJElSywzIJEmSWmZAthrG64ac7d2Us33/JEmaKgZkkiRJLTMg02qxlUySpNVnQKYJMRCTJGnyGJBNAYMXSZI0FgMySZKklhmQTSJbwiRJ0kQYkGnSGZhKkjQcA7IWGLBIkqQmA7IRMOCSJEnDMCCbZAZjkiRpWAZkkiRJLTMgG5G52FLWa5/nYj1IkjQsA7IpZHAiSZJ6MSCTJElqmQHZBNnaJUmSJosBmSRJUssMyCRJklpmQDYBdldKkqTJZECmSWGQKknSxBmQabUZjEmStHoMyKaYwYskSepmQKZJZcApSdLw1mpjoxGxGLgbeAh4MDMXRsQGwDeA+cBi4C8z8442yidJkjSV2mwhe1FmLsjMhXX6IODszNwSOLtOS5IkzXrTqctyT+D4+vp44NXtFUWSJGnqtBWQJXBmRPw8IvaraRtn5jKA+n+jlsomSZI0pVoZQwY8LzOXRsRGwFkRcfWgC9YAbj+Apz71qaMqnyRJ0pRppYUsM5fW/8uBk4FdgFsiYhOA+n95n2WPycyFmblw3rx5U1VkrQbvvJQkaWxTHpBFxBMi4kmd18BLgcuBU4F9arZ9gFOmumySJEltaKPLcmPg5IjobP+EzDw9Ii4EToqItwO/BvZqoWySJElTbsoDssy8AdihR/ptwIunujzDsvtNkiRNtun02Itpz2BscliPkiQ9mgGZJElSywzINDK2hEmSNBgDMkmSpJYZkEmSJLXMgEytsUtTkqTCgEySJKllBmQaCVu/JEkanAGZJElSywzINCVsMZMkqT8DMkmSpJYZkKkVtphJkrSSAZkkSVLLDMgkSZJaZkA2ILvYJEnSqBiQaUoZ2EqStCoDMkmSpJYZkGlasQVNkjQXGZBpWjNAkyTNBQZkmjIGV5Ik9WZANgADidGyfiVJc50BmWYUgzdJ0mxkQCZJktQyAzK1yhYvSZIMyDRNNAOzzmuDNUnSXGFApmmpOxgbJjgzkJMkzTQGZJo2DLokSXOVAZkkSVLLDMgkSZJaZkCmGWPU3ZR2g0qS2mJAJkmS1DIDMs04tmRJkmYbAzLNCgZpkqSZzIBMM9bqPKtMkqTpxIBMkiSpZQZkmvEm2jJmi5okabowINO0Z+AkSZrtDMg0q/T6YXIDOknSdGdAplmrV1A2XnA2mcGbgaAkaVAGZFIPBlOSpKlkQKY5Y6xWsn4BmIGZJGkqGJBJkiS1zIBMM9pEx4SNNeh/2Olh2OImSerFgEwawmQHVMN0n0qSZi8DMmkAwwZOEwmqBl3GgE2SZh8DMmlAw3RzjrW8AZUkqZsBmea01W3lmowH0E5FgGYQKEnTmwGZJElSywzIpBGaf9D3ZkR3pi1oktQuAzJpBhj28R6T1X1qd6okTQ0DMmk1dY8jm4y7JfutZ5gxb8OOgRs0bxtBmyTNdtMuIIuIPSLimoi4PiIOars80mSY7CCmE7AN0nI2ld2hk7EtH/8haS6aVgFZRKwJfBb438C2wBsjYtt2SyW1a9jAY9g7R7uDqF6B3uq0pA3S2jfRX1xozp8LAVrb+zjR5+tN5PgZRVkmS9vvg2anaRWQAbsA12fmDZl5P3AisGfLZZIkSRqp6RaQbQrc1JheUtOkOWmQVqRhn4U2aHfnWOvvtY5BW8HG6kYdr3Wt1/xhy9+v7P3KP2gZx5vuV97xWiSHGUs4Gb8eMZljD1enFXQirZ5jHSvd6+z3eqKGHfM5m4xiSMYgaRPJM4plJ0tkZttleERE7AW8LDPfUaffAuySmQc08uwH7FcntwaumfKCzgwbAr9tuxAzjHU2POtseNbZ8Kyz4Vlnw5uKOntaZs7rNWOtEW94WEuAzRvTmwFLmxky8xjgmKks1EwUERdl5sK2yzGTWGfDs86GZ50NzzobnnU2vLbrbLp1WV4IbBkRW0TEY4G9gVNbLpMkSdJITasWssx8MCLeA5wBrAkcl5lXtFwsSZKkkZpWARlAZp4GnNZ2OWYBu3WHZ50NzzobnnU2POtseNbZ8Fqts2k1qF+SJGkumm5jyCRJkuYcA7JZyJ+f6i0ijouI5RFxeSNtg4g4KyKuq//Xb8w7uNbhNRHxsnZK3a6I2DwifhgRV0XEFRHx3ppuvfUREWtHxAUR8ctaZx+t6dbZGCJizYj4RUT8Z522vsYREYsj4rKIuCQiLqpp1tsYIuLJEfGtiLi6XteeM13qzIBslvHnp8a0CNijK+0g4OzM3BI4u05T62xvYLu6zFG1bueaB4H3Z+Y2wLOB/WvdWG/9rQB2y8wdgAXAHhHxbKyz8bwXuKoxbX0N5kWZuaDxuAbrbWxHAKdn5jOBHSjH3LSoMwOy2cefn+ojM38M3N6VvCdwfH19PPDqRvqJmbkiM28ErqfU7ZySmcsy8+L6+m7KxWtTrLe+srinTj6m/iXWWV8RsRnwCuDYRrL1NTHWWx8RsS7wAuCLAJl5f2beyTSpMwOy2cefnxrOxpm5DErwAWxU063HLhExH9gROB/rbUy1++0SYDlwVmZaZ2P7V+AfgYcbadbX+BI4MyJ+Xn/FBqy3sTwduBX4Uu0ePzYinsA0qTMDstkneqR5K+3wrMeGiHgi8G3gwMy8a6ysPdLmXL1l5kOZuYDyayO7RMSfjpF9TtdZRLwSWJ6ZPx90kR5pc6a+ujwvM3eiDFHZPyJeMEZe66086msn4HOZuSNwL7V7so8prTMDstln3J+f0qPcEhGbANT/y2u69VhFxGMowdjXMvM7Ndl6G0DtDjmXMv7EOuvtecCrImIxZYjFbhHxVayvcWXm0vp/OXAypTvNeutvCbCktlgDfIsSoE2LOjMgm338+anhnArsU1/vA5zSSN87Ih4XEVsAWwIXtFC+VkVEUMZbXJWZhzdmWW99RMS8iHhyff14YHfgaqyznjLz4MzcLDPnU65X52Tmm7G+xhQRT4iIJ3VeAy8FLsd66yszfwPcFBFb16QXA1cyTeps2j2pX6vHn5/qLyK+DuwKbBgRS4CPAJ8AToqItwO/BvYCyMwrIuIkysn6ILB/Zj7USsHb9TzgLcBldUwUwCFYb2PZBDi+3o21BnBSZv5nRPwM62wYHmNj2xg4uXxnYi3ghMw8PSIuxHobywHA12qDxQ3A26jnadt15pP6JUmSWmaXpSRJUssMyCRJklpmQCZJktQyAzJJkqSWGZBJkiS1zIBM0shFxEMRcUlEXBERv4yI90XEmNefiJgfEZePsEyndZ4XNl3WGxELIuLlk1wkSTOAzyGTNBXuqz8lRERsBJwArEd5FlwrMnMkgc9qrncBsBA4bXJKI2mmsIVM0pSqP/OyH/CeKNaMiE9FxIURcWlE/E33MrW17L8i4uL699ya/pWI2LOR72sR8aqI2C4iLqitcpdGxJY91rk4Ijas674qIr5QW/DOrE/Y786/KCI+FxE/jIgbIuKFEXFcXXbRMOuNiHMjYmF9vWFd5rHAx4A31HK/oT6N/bhaN7/o7Osg+ydpZjEgkzTlMvMGyvVnI+DtwO8yc2dgZ+Cd9WdKmpYDL6k/pPwG4N9q+rGUJ20TEesBz6W0Lr0LOKK2yi2k/CbdWLYEPpuZ2wF3Aq/rk299YDfg74H/AD4DbAf8WUQsWI31kpn3Ax8GvpGZCzLzG8AHKT8ltDPwIuBT9Wdyht0/SdOcXZaS2hL1/0uB7SPi9XV6PUogc20j72OAI2vQ8xCwFUBm/igiPlu7QV8LfLv+fNjPgA9GxGbAdzLzunHKcmNmXlJf/xyY3yfff2RmRsRlwC2ZeRlARFxRl7mkK/+g6+3npZQf3v6HOr028FRg2P2TNM0ZkEmachHxdEpgtZwSmB2QmWd05ZnfmPx74BZgB0rL2h8a874CvInyw9R/DZCZJ0TE+cArgDMi4h2Zec4YRVrReP0QsEqXZVe+h7uWeZje19N+632QlT0Ua49RrgBel5nXdKVfNeT+SZrm7LKUNKUiYh7weeDILD+mewbw7oh4TJ2/Ve2Wa1oPWJaZD1N+7HzNxrxFwIFQfgy4ruPpwA2Z+W/AqcD2I9uhiVkMPKu+fn0j/W7gSY3pM4ADov6CdETsWP9P9/2TNCQDMklT4fGdx14APwDOBD5a5x0LXAlcXB9zcTSrtjYdBewTEedRuivv7czIzFuAq4AvNfK/Abg8Ii4Bngl8edL3aPX8P0oQ+lNgw0b6D4FtO4P6gX+idNdeWuvmn2q+6b5/koYU5QuqJM1MEbEOcBmwU2b+ru3ySNJE2EImacaKiN2Bq4F/NxiTNJPZQiZJktQyW8gkSZJaZkAmSZLUMgMySZKklhmQSZIktcyATJIkqWUGZJIkSS37/yC3WN9AJMNUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "counts, labels = np.histogram(df['Ar_Delay_mins'],bins=np.arange(-60,600,1))\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "plt.bar(labels[1:], counts, align='center',width=1)\n",
        "plt.xlabel('Delays in minutes')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Amtrak City of New Orleans (Southbound) is not always late',loc='left',fontsize=16)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "57dc400e",
      "metadata": {
        "id": "57dc400e",
        "outputId": "2a6ee686-cb05-468b-f4c5-7eb7a4ff1c85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Median delay in minutes:  2.0\n",
            "Mean delay in minutes:  10.485846754514398\n"
          ]
        }
      ],
      "source": [
        "print('Median delay in minutes: ', np.median(df['Ar_Delay_mins']))\n",
        "print('Mean delay in minutes: ', np.mean(df['Ar_Delay_mins']))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "09531ff9",
      "metadata": {
        "id": "09531ff9"
      },
      "source": [
        "Delays reaching the destination of Union Passenger Terminal, New Orleans"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "204dff7c",
      "metadata": {
        "id": "204dff7c"
      },
      "outputs": [],
      "source": [
        "df_dest = df.loc[df['Station']=='NOL']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "92731edf",
      "metadata": {
        "id": "92731edf",
        "outputId": "75c57f6d-95f2-4985-ecf5-8994fc5733a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x2729b9c8970>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "counts, labels = np.histogram(df['Ar_Delay_mins'],bins=np.arange(-60,600,1))\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "plt.bar(labels[1:], counts, align='center',width=1,label='All')\n",
        "\n",
        "counts_dest, labels_dest = np.histogram(df_dest['Ar_Delay_mins'],bins=np.arange(-60,600,1))\n",
        "plt.bar(labels_dest[1:], counts_dest, align='center',width=1,label='New Orleans')\n",
        "plt.xlabel('Delays in minutes')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "#plt.suptitle('You are more likely to arrive in New Orleans on-time or earlier via Amtrak',fontsize=14,ha='left',x=1)\n",
        "#plt.title('The train successfully makes up for time despite delays in other stations',loc='left')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4d772e7b",
      "metadata": {
        "id": "4d772e7b"
      },
      "source": [
        "The histogram shows that despite delays arriving at the other stations along the route, the train is almost always able to make up for lost time and arrive in New Orleans on-time or earlier than scheduled."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c4c0b8d3",
      "metadata": {
        "id": "c4c0b8d3",
        "outputId": "26ad9cf2-3005-4552-9cad-78d3f97ead38"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Median delay in minutes:  -25.0\n",
            "Mean delay in minutes:  -7.866685536371356\n"
          ]
        }
      ],
      "source": [
        "print('Median delay in minutes: ', np.median(df_dest['Ar_Delay_mins']))\n",
        "print('Mean delay in minutes: ', np.mean(df_dest['Ar_Delay_mins']))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ce60ba4",
      "metadata": {
        "id": "4ce60ba4"
      },
      "source": [
        "Earliest arrival"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1439a469",
      "metadata": {
        "id": "1439a469",
        "outputId": "b1377aa5-7f87-4f5a-fc36-a35bb9ab0649"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-107 minutes\n"
          ]
        }
      ],
      "source": [
        "print(str(np.min(df_dest['Ar_Delay_mins'])) + ' minutes')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdf77824",
      "metadata": {
        "id": "fdf77824"
      },
      "source": [
        "Latest arrival"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e80a19c2",
      "metadata": {
        "id": "e80a19c2",
        "outputId": "4100d97d-835e-4b01-95bc-208a3cb65b97"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "700 minutes\n"
          ]
        }
      ],
      "source": [
        "print(str(np.max(df_dest['Ar_Delay_mins'])) + ' minutes')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "984131bc",
      "metadata": {
        "id": "984131bc"
      },
      "source": [
        "Percentage arrives on time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d093b607",
      "metadata": {
        "id": "d093b607"
      },
      "outputs": [],
      "source": [
        "df_ontime30 = df.loc[(df['Ar_Delay_mins']<=30)]\n",
        "df_ontime60 = df.loc[(df['Ar_Delay_mins']<=60)]\n",
        "df_ontime = df.loc[(df['Ar_Delay_mins']<=0)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4a9f17ae",
      "metadata": {
        "id": "4a9f17ae",
        "outputId": "65500e6a-0001-4fc6-db02-4e8776f76677"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "88.86% of the time the train arrives within 60 mins of schedule\n",
            "78.14% of the time the train arrives within 30 mins of schedule\n",
            "47.36% of the time the train arrives on time or earlier\n"
          ]
        }
      ],
      "source": [
        "print(str(np.round(len(df_ontime60)*100./len(df),2))+'% of the time the train arrives within 60 mins of schedule')\n",
        "print(str(np.round(len(df_ontime30)*100./len(df),2))+'% of the time the train arrives within 30 mins of schedule')\n",
        "print(str(np.round(len(df_ontime)*100./len(df),2))+'% of the time the train arrives on time or earlier')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4f46835b",
      "metadata": {
        "id": "4f46835b",
        "outputId": "20ca0cc5-e8ec-499d-be73-9c84e5a63978"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "90.01% of the time the train arrives within 60 mins of schedule\n",
            "83.78% of the time the train arrives within 30 mins of schedule\n",
            "72.43% of the time the train arrives on time or earlier\n"
          ]
        }
      ],
      "source": [
        "df_dest_ontime30 = df_dest.loc[(df_dest['Ar_Delay_mins']<=30)]\n",
        "df_dest_ontime60 = df_dest.loc[(df_dest['Ar_Delay_mins']<=60)]\n",
        "df_dest_ontime = df_dest.loc[(df_dest['Ar_Delay_mins']<=0)]\n",
        "\n",
        "print(str(np.round(len(df_dest_ontime60)*100./len(df_dest),2))+'% of the time the train arrives within 60 mins of schedule')\n",
        "print(str(np.round(len(df_dest_ontime30)*100./len(df_dest),2))+'% of the time the train arrives within 30 mins of schedule')\n",
        "print(str(np.round(len(df_dest_ontime)*100./len(df_dest),2))+'% of the time the train arrives on time or earlier')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a60d3de",
      "metadata": {
        "id": "5a60d3de"
      },
      "source": [
        "## Part 2: New Orleans to Chicago"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6c9824de",
      "metadata": {
        "id": "6c9824de",
        "outputId": "b802faa8-2869-4291-b97b-3af741cc09b1"
      },
      "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>Origin_date</th>\n",
              "      <th>Station</th>\n",
              "      <th>Sch_Ar</th>\n",
              "      <th>Act_Ar</th>\n",
              "      <th>Comments</th>\n",
              "      <th>Ar_Delay_mins</th>\n",
              "      <th>ServiceDisruption</th>\n",
              "      <th>Cancellations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>05/27/2017 (Sa)</td>\n",
              "      <td>CHI</td>\n",
              "      <td>05/28/2017 9:00 AM (Su)</td>\n",
              "      <td>10:39PM</td>\n",
              "      <td>Ar: 13 hr, 39 min late.</td>\n",
              "      <td>819</td>\n",
              "      <td>SD</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>06/23/2009 (Tu)</td>\n",
              "      <td>CHI</td>\n",
              "      <td>06/24/2009 9:00 AM (We)</td>\n",
              "      <td>10:28PM</td>\n",
              "      <td>Ar: 13 hr and 28 min late.</td>\n",
              "      <td>808</td>\n",
              "      <td>SD</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>05/27/2017 (Sa)</td>\n",
              "      <td>CDL</td>\n",
              "      <td>05/28/2017 3:11 AM (Su)</td>\n",
              "      <td>4:27PM</td>\n",
              "      <td>Ar: 13 hr, 16 min late | Dp: 13 hr, 34 min late.</td>\n",
              "      <td>796</td>\n",
              "      <td>SD</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>03/30/2015 (Mo)</td>\n",
              "      <td>CHI</td>\n",
              "      <td>03/31/2015 9:00 AM (Tu)</td>\n",
              "      <td>7:30PM</td>\n",
              "      <td>Ar: 10 hr, 30 min late.</td>\n",
              "      <td>630</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11/20/2020 (Fr)</td>\n",
              "      <td>CHI</td>\n",
              "      <td>11/21/2020 9:15 AM (Sa)</td>\n",
              "      <td>7:17PM</td>\n",
              "      <td>Ar: 10 hr, 2 min late.</td>\n",
              "      <td>602</td>\n",
              "      <td>SD</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Origin_date Station                   Sch_Ar   Act_Ar  \\\n",
              "0  05/27/2017 (Sa)     CHI  05/28/2017 9:00 AM (Su)  10:39PM   \n",
              "1  06/23/2009 (Tu)     CHI  06/24/2009 9:00 AM (We)  10:28PM   \n",
              "2  05/27/2017 (Sa)     CDL  05/28/2017 3:11 AM (Su)   4:27PM   \n",
              "3  03/30/2015 (Mo)     CHI  03/31/2015 9:00 AM (Tu)   7:30PM   \n",
              "4  11/20/2020 (Fr)     CHI  11/21/2020 9:15 AM (Sa)   7:17PM   \n",
              "\n",
              "                                           Comments  Ar_Delay_mins  \\\n",
              "0                           Ar: 13 hr, 39 min late.            819   \n",
              "1                        Ar: 13 hr and 28 min late.            808   \n",
              "2  Ar: 13 hr, 16 min late | Dp: 13 hr, 34 min late.            796   \n",
              "3                           Ar: 10 hr, 30 min late.            630   \n",
              "4                            Ar: 10 hr, 2 min late.            602   \n",
              "\n",
              "  ServiceDisruption Cancellations  \n",
              "0                SD           NaN  \n",
              "1                SD           NaN  \n",
              "2                SD           NaN  \n",
              "3               NaN           NaN  \n",
              "4                SD           NaN  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_excel('AmtrakDelays_NOL_CHI.xlsx')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e85dd6a",
      "metadata": {
        "id": "8e85dd6a"
      },
      "source": [
        "Rename column for easier referencing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68695463",
      "metadata": {
        "id": "68695463"
      },
      "outputs": [],
      "source": [
        "df = df.rename(columns={'Ar Delay (mins)':'Ar_Delay_mins'})"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ba297f76",
      "metadata": {
        "id": "ba297f76"
      },
      "source": [
        "Checking for NaN values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3aaf54b5",
      "metadata": {
        "id": "3aaf54b5",
        "outputId": "f631fff1-8ebf-476c-e3d3-54b684762a80"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Ar_Delay_mins'].isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3b433479",
      "metadata": {
        "id": "3b433479"
      },
      "source": [
        "Counting how many datapoints are recorded for each station."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adfb7f08",
      "metadata": {
        "id": "adfb7f08",
        "outputId": "c2457b8a-a74d-4049-b98a-cf129df4a89b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CHI    4534\n",
              "CDL    2265\n",
              "MEM    2229\n",
              "JAN    2079\n",
              "GWD     181\n",
              "HMW      58\n",
              "MAT      52\n",
              "KKI      49\n",
              "FTN      46\n",
              "CEN      43\n",
              "EFG      43\n",
              "NBN      42\n",
              "CHM      21\n",
              "YAZ      17\n",
              "MKS      15\n",
              "MCB      11\n",
              "BRH      10\n",
              "HMD       9\n",
              "HAZ       9\n",
              "NYP       7\n",
              "PHL       4\n",
              "BAL       1\n",
              "NCR       1\n",
              "Name: Station, dtype: int64"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Station'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fa8193d4",
      "metadata": {
        "id": "fa8193d4"
      },
      "source": [
        "Looks like most of the reports come from 4 stations (Chicago IL, Carbondale IL, Memphis TN, Jackson MI) so i'm only going to process those."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f8e9d51f",
      "metadata": {
        "id": "f8e9d51f"
      },
      "outputs": [],
      "source": [
        "df = df[df['Station'].isin(['CHI','CDL','MEM','JAN'])]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a045dc54",
      "metadata": {
        "id": "a045dc54"
      },
      "source": [
        "Let's look at the histogram of delays, converting minutes to hours."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9cd60d52",
      "metadata": {
        "id": "9cd60d52",
        "outputId": "216d3aa0-1c83-414a-c1e2-a4e4b2eab0c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.0, 1.0, 'Amtrak City of New Orleans (Northbound) is not always late')"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "counts, labels = np.histogram(df['Ar_Delay_mins'],bins=np.arange(-60,600,1))\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "plt.bar(labels[1:], counts, align='center',width=1)\n",
        "plt.xlabel('Delays in minutes')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Amtrak City of New Orleans (Northbound) is not always late',loc='left',fontsize=16)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e56d899b",
      "metadata": {
        "id": "e56d899b",
        "outputId": "b888cc1e-0d9f-4baf-81e5-0d7e70aa5260"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Median delay in minutes:  0.0\n",
            "Mean delay in minutes:  12.635004951832178\n"
          ]
        }
      ],
      "source": [
        "print('Median delay in minutes: ', np.median(df['Ar_Delay_mins']))\n",
        "print('Mean delay in minutes: ', np.mean(df['Ar_Delay_mins']))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2174f32",
      "metadata": {
        "id": "b2174f32"
      },
      "source": [
        "Delays reaching the destination of Union Station, Chicago"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94a4dc76",
      "metadata": {
        "id": "94a4dc76"
      },
      "outputs": [],
      "source": [
        "df_dest = df.loc[df['Station']=='CHI']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0a0da9c2",
      "metadata": {
        "id": "0a0da9c2",
        "outputId": "728a8094-1f57-44e9-8bbf-0a924eecddfb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x2729eb6fa60>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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YJ0fECcBy4LTM/H/ALsDPqtbpK9reJCIWAYsA3v72tzehHEmSpHI1elL/fcD/aMLxzqcy0jYbeAr4p6I9aqxbM/xl5gWZOScz50yZMqUJJUmSJJWr0RGyycD9EXEX8Fp/Y2YePZyDZebT/e8j4tvAfxSLfcCuVatOBZ4czr4lSZJGq0YD2dJmHCwidsrMp4rFD1MZeQO4Drg0Ir5K5aT+GcBdzTimJElSp2sokGXmf0XEbsCMzLwpIrYGxtXbJiKWAYcAkyOiD/gycEhEzKYyHdkL/Fmx/1URcTlwP7AeWOwVliPjbSwkSRp9Gr3K8k+pnEg/ico5YLsA3wI+MNg2mXlsjeYL66x/BnBGI/VIkiSNJY2e1L8YOBB4ASAzHwZ2aFVRaj9H1iRJKk+jgey1zHy9fyEixjPIVZCSJEkankYD2X9FxBeBCRHxB8C/Af/eurIkSZK6R6OBbAmwFriXyon4PwT+plVFdbuB04e9PZ8oqRJJktQOjV5l+Qbw7eKXJEmSmqjRqywfo8Y5Y5m5e9MrkiRJ6jLDeZZlvx7go1RugSFJkqTN1NA5ZJm5rurXE5n5NeDQ1pYmSZLUHRqdsty/anELKiNmE1tSkSRJUpdpdMryn6rer6fy2KOPNb0aSZKkLtToVZbvb3UhkiRJ3arRKcu/rPd5Zn61OeWoHaYtuZ7eM4/cZFmSJJVnOFdZzgWuK5b/CLgVeLwVRal1DF+SJHWeRgPZZGD/zHwRICKWAv+WmZ9uVWGSJEndotFHJ70deL1q+XVgWtOrkSRJ6kKNjpBdAtwVEVdTuWP/h4HvtawqSZKkLtLoVZZnRMT/AQ4umv4kM+9pXVmSJEndo9EpS4CtgRcy8+tAX0RMb1FNkiRJXaWhQBYRXwb+Cji9aNoS+NdWFSVJktRNGh0h+zBwNPAyQGY+iY9OkiRJaopGA9nrmZlUTugnIrZpXUlqBe8/JklS52o0kF0eEf8MvDUi/hS4Cfh268qSJEnqHkNeZRkRAfwA2BN4AXgn8LeZeWOLa5MkSeoKQwayzMyIuCYz3w0YwkYxpy0lSepMjU5Z/iwi5ra0EkmSpC7V6J363w/8eUT0UrnSMqgMns1qVWGSJEndom4gi4i3Z+Ya4A/bVI/axOlLSZI6x1AjZNcA+2fmryPiysxc0IaaJEmSuspQ55BF1fvdW1mIJElStxoqkOUg7yVJktQkQ01ZvisiXqAyUjaheA+/O6n/LS2tTpIkqQvUDWSZOa5dhUiSJHWrRu9DJkmSpBYxkGkT3g5DkqT2M5BJkiSVzEAmSZJUMgOZNnK6UpKkchjIJEmSSmYgkyRJKlnLAllEXBQRz0TEfVVtkyLixoh4uHh9W9Vnp0fEIxHxYEQc1qq6JEmSOk0rR8i+Cxw+oG0JcHNmzgBuLpaJiJnAQmDvYpvzIqIrb0rreVySJHWflgWyzLwV+O8BzccAFxfvLwY+VNV+WWa+lpmPAY8A81pV22jQ2/OJskuQJElt0u5zyHbMzKcAitcdivZdgMer1usr2iRJksa8TjmpP2q0Zc0VIxZFxPKIWL527doWl9VZHDWTJGlsancgezoidgIoXp8p2vuAXavWmwo8WWsHmXlBZs7JzDlTpkxpabGSJEnt0O5Adh1wYvH+RODaqvaFEbFVREwHZgB3tbm2juSomCRJY9/4Vu04IpYBhwCTI6IP+DJwJnB5RHwKWAN8FCAzV0XE5cD9wHpgcWZuaFVtkiRJnaRlgSwzjx3kow8Msv4ZwBmtqkeSJKlTdcpJ/arB6UpJkrqDgUxv4s1pJUlqLwOZJElSyQxko4TTl5IkjV0GMkmSpJIZyDqI525JktSdDGSSJEklM5CpJkfrJElqHwPZGOaFAJIkjQ4GMkmSpJIZyCRJkkpmIBPg9KYkSWUykHUxQ5gkSZ3BQKa6vNpSkqTWM5CVzMAjSZIMZB3AUCZJUnczkHWI4YSyZp775XlkkiSVz0AmSZJUMgOZGuK0qiRJrWMgkyRJKpmBTJIkqWQGMg3J6UpJklrLQDbKlH1VpOFMkqTmM5BJkiSVzEAmSZJUMgOZBuX0pCRJ7WEgkyRJKpmBTKVfKCBJUrczkEmSJJXMQCZJklQyA5kkSVLJDGSjmOd+SZI0NhjISuDtJCRJUjUDmRpmkJQkqTUMZBo2g5kkSc1lIJMkSSqZgUySJKlkBrIxyKsvJUkaXQxkJfE8LEmS1M9A1oGGO8JVxoiYgVKSpOYpJZBFRG9E3BsRKyNiedE2KSJujIiHi9e3lVHbaFYdzNoR0gxlkiQ1R5kjZO/PzNmZOadYXgLcnJkzgJuL5TGtGYHG88UkSRr9OmnK8hjg4uL9xcCHyitl7DCwSZLU+coKZAncEBErImJR0bZjZj4FULzuUGvDiFgUEcsjYvnatWvbVG7n6w9eBjBJkkaf8SUd98DMfDIidgBujIgHGt0wMy8ALgCYM2dOtqrATmfwkiRp7ChlhCwznyxenwGuBuYBT0fETgDF6zNl1KaR8QR/SZJGru2BLCK2iYiJ/e+B+cB9wHXAicVqJwLXtru2dhlLo1sGMUmSNl8ZU5Y7AldHRP/xL83M/4yIXwCXR8SngDXAR0uoTZIkqe3aHsgy81HgXTXa1wEfaHc9kiRJZeuk215IkiR1JQNZydpxPtlYOmdNkqSxyEDWYt1w0ns3fEdJklrJQKamGhjODGuSJA3NQCZJklQyA5k24flmkiS1n4GsTTpp6m6w0GUYkySpHAYyNV0nhU9JkkYDA1lJxuIo1VBBrPpzQ5skSb9jIOtSozn4SZI01hjImshRH0mSNBIGsg4yGkatGq1xOOHUICtJ6nYGMtU0GsKhJEljhYGszbp1NKj/e3fr95ckqR4DWRPVGlWqbhsYRkbDKNRoqFGSpNHOQNZsS7cDOu8WD709nzBcSZLUoQxkkiRJJTOQqSFDTcfWM3CEsNaIYb0RxU4YYZQkqZUMZC0yMKyMpunCdtVq0JIkqcJA1gFGU1hrNkfDJEkykLWEoWJk7DdJUrcykLXAWBnxqvc9GvmOzQhYhjRJUjcwkLXBWAlokiSpNQxkbdQJwWwkNXRC3ZIkjWXjyy5grJi25Hp6e0a+vaFnU05VSpK6iSNkGhU67ckHkiQ1k4GshbohOAw1stfIyN9IRgdr3S6jG/pbkjQ2GciaoDJd2VioGKtTk534vQxokqTRwkC2mbrxH/1OCV/d2PeSpLHJQLaZens+MWhA6ZTg0ixj5fsY5CRJncZAplI0K9xtbrgynEmSOoGBTE1Vb8Sw//ORGiw89bf39nxi4/vhPCPTUCZJKpuBTJutP2QN94rLRqZ6hwp4wzVYYBtsveGsY7CTJI2UgUwt0e5RsnrrjyRcSZLUTgYytUUjo2fNDnFDbTPSoDaSUChJUj0GMpVqsGnMocJZM24mO9RxJElqFwOZRrWhAlytwNdoCBtqZKvRc+Ia3Z8kqXsZyDRqDCcA1fpsqNA20sA06Mn9S7ere1wDmiSpn4FsM/gPan2bOx040sdRNTI1Odhy9S00qvc38OKAeldYdsI06Obe5sOfbUlqr44LZBFxeEQ8GBGPRMSSsuuppdY/2hpc2f00nONX38usloHPLR3Oz8JQ+x6OzQlcnT61WvbxJakMHRXIImIc8E3gD4GZwLERMbPcqqos3e53/yAX01Fqr+He62wk6w5nKvRN56QVPxdD3aNssKs3h7pNx8AAWHf94ud1sOPV0/A0bp0/B60IViP9PgO3l6RO01GBDJgHPJKZj2bm68BlwDEl17SJskd71DqDha1Gb3zbyL6rX2uFnoEBr2bgGhCCaj2hoBm1bnzfH+wGCXjVNb5p6nYY/3GpV/vAPhjp92tkertebc1Q6/e0v3+Hc9xGp54bvXXLZn1H/4Oqko2F/2x1WiDbBXi8armvaCvVYH9ZqjO1IzQ3fIxaPzdVbdUXANQaadt4rEEuENgk6A3yM7rJNOvAYxchq+ZfZgNrqGqvDoHV+64bppZu97tf1csD1xmk/rqjY/X+fFYfr8ZrvXMEa33Hejbupyq81jrmcK/Q7d+2kXMVq49fc6RzkO8w2M/fm5YH/AyN6L58A38v6tS1cZuhDOzz/n3W+vM2xHaN9NdgBv4sN1r7wO03eR2in9r9TN+mB/qBBvu9rHOcuj+/g7XV2V8ZIjPLrmGjiPgocFhmfrpYPh6Yl5mfrVpnEbCoWHwn8GDbCx0dJgPPll3EKGOfDZ99Nnz22fDZZ8Nnnw1fO/pst8ycUuuD8S0+8HD1AbtWLU8FnqxeITMvAC5oZ1GjUUQsz8w5Zdcxmthnw2efDZ99Nnz22fDZZ8NXdp912pTlL4AZETE9In4PWAhcV3JNkiRJLdVRI2SZuT4iTgZ+BIwDLsrMVSWXJUmS1FIdFcgAMvOHwA/LrmMMcFp3+Oyz4bPPhs8+Gz77bPjss+Ertc866qR+SZKkbtRp55BJkiR1HQPZGDQaHj9Vhoi4KCKeiYj7qtomRcSNEfFw8fq2qs9OL/rwwYg4rJyqyxURu0bETyJidUSsiohTinb7bRAR0RMRd0XEL4s++7ui3T6rIyLGRcQ9EfEfxbL9NYSI6I2IeyNiZUQsL9rstzoi4q0RcUVEPFD8vfY/O6XPDGRjTMc/fqpc3wUOH9C2BLg5M2cANxfLFH22ENi72Oa8om+7zXrgtMzcCzgAWFz0jf02uNeAQzPzXcBs4PCIOAD7bCinAKurlu2vxrw/M2dX3a7Bfqvv68B/ZuaewLuo/Mx1RJ8ZyMaejn/8VFky81bgvwc0HwNcXLy/GPhQVftlmflaZj4GPEKlb7tKZj6VmXcX71+k8pfXLthvg8qKl4rFLYtfiX02qIiYChwJfKeq2f4aGfttEBHxFuC9wIUAmfl6Zj5Hh/SZgWzs6cjHT3WwHTPzKaiED2CHot1+HCAipgH7AT/HfqurmH5bCTwD3JiZ9ll9XwO+ALxR1WZ/DS2BGyJiRfEUG7Df6tkdWAv8SzE9/p2I2IYO6TMD2dgTNdq8lHb47McqEbEtcCVwama+UG/VGm1d12+ZuSEzZ1N52si8iNinzupd3WcRcRTwTGauaHSTGm1d018DHJiZ+1M5RWVxRLy3zrr2W+VWX/sD52fmfsDLFNOTg2hrnxnIxp4hHz+lTTwdETsBFK/PFO32YyEitqQSxr6fmVcVzfZbA4rpkFuonH9in9V2IHB0RPRSOcXi0Ij4V+yvIWXmk8XrM8DVVKbT7LfB9QF9xYg1wBVUAlpH9JmBbOzx8VPDcx1wYvH+RODaqvaFEbFVREwHZgB3lVBfqSIiqJxvsTozv1r1kf02iIiYEhFvLd5PAD4IPIB9VlNmnp6ZUzNzGpW/r36cmf8L+6uuiNgmIib2vwfmA/dhvw0qM/8v8HhEvLNo+gBwPx3SZx13p35tHh8/NbiIWAYcAkyOiD7gy8CZwOUR8SlgDfBRgMxcFRGXU/nDuh5YnJkbSim8XAcCxwP3FudEAXwR+62enYCLi6uxtgAuz8z/iIg7sc+Gw5+x+nYErq78n4nxwKWZ+Z8R8Qvst3o+C3y/GLB4FPgTij+nZfeZd+qXJEkqmVOWkiRJJTOQSZIklcxAJkmSVDIDmSRJUskMZJIkSSUzkElquYjYEBErI2JVRPwyIv4yIur+/RMR0yLivhbW9MP++4V1yn4jYnZEHNHkkiSNAt6HTFI7vFI8SoiI2AG4FNiOyr3gSpGZLQk+m7nf2cAc4IfNqUbSaOEImaS2Kh7zsgg4OSrGRcQ/RsQvIuJXEfFnA7cpRstui4i7i1+/X7RfEhHHVK33/Yg4OiL2joi7ilG5X0XEjBr77I2IycW+V0fEt4sRvBuKO+wPXP+7EXF+RPwkIh6NiPdFxEXFtt8dzn4j4paImFO8n1xs83vA/wY+XtT98eJu7BcVfXNP/3dt5PtJGl0MZJLaLjMfpfL3zw7Ap4DnM3MuMBf40+IxJdWeAf6geJDyx4FzivbvULnTNhGxHfD7VEaX/hz4ejEqN4fKM+nqmQF8MzP3Bp4DFgyy3tuAQ4HPAf8OnA3sDewbEbM3Y79k5uvA3wI/yMzZmfkD4K+pPEpoLvB+4B+Lx+QM9/tJ6nBOWUoqSxSv84FZEfGRYnk7KkHmoap1twTOLULPBmAPgMz8r4j4ZjEN+sfAlcXjw+4E/joipgJXZebDQ9TyWGauLN6vAKYNst6/Z2ZGxL3A05l5L0BErCq2WTlg/Ub3O5j5VB68/fliuQd4OzDc7yepwxnIJLVdROxOJVg9QyWYfTYzfzRgnWlVi58DngbeRWVk7dWqzy4BjqPyYOpPAmTmpRHxc+BI4EcR8enM/HGdkl6rer8BeNOU5YD13hiwzRvU/vt0sP2u53czFD116gpgQWY+OKB99TC/n6QO55SlpLaKiCnAt4Bzs/Iw3R8Bn4mILYvP9yim5aptBzyVmW9Qedj5uKrPvgucCpWHARf72B14NDPPAa4DZrXsC41ML/Du4v1HqtpfBCZWLf8I+GwUT5COiP2K107/fpKGyUAmqR0m9N/2ArgJuAH4u+Kz7wD3A3cXt7n4Z9482nQecGJE/IzKdOXL/R9k5tPAauBfqtb/OHBfRKwE9gS+1/RvtHnOohJCfwpMrmr/CTCz/6R+4CtUpmt/VfTNV4r1Ov37SRqmqPwHVZJGp4jYGrgX2D8zny+7HkkaCUfIJI1aEfFB4AHgG4YxSaOZI2SSJEklc4RMkiSpZAYySZKkkhnIJEmSSmYgkyRJKpmBTJIkqWQGMkmSpJL9fwIrFcdevMc6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "counts, labels = np.histogram(df['Ar_Delay_mins'],bins=np.arange(-60,600,1))\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "plt.bar(labels[1:], counts, align='center',width=1,label='All')\n",
        "\n",
        "counts_dest, labels_dest = np.histogram(df_dest['Ar_Delay_mins'],bins=np.arange(-60,600,1))\n",
        "plt.bar(labels_dest[1:], counts_dest, align='center',width=1,label='New Orleans')\n",
        "plt.xlabel('Delays in minutes')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a560d894",
      "metadata": {
        "id": "a560d894",
        "outputId": "d83a999c-4074-4916-f2e2-9d965fe99e94"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Median delay in minutes:  -3.0\n",
            "Mean delay in minutes:  12.15968239964711\n"
          ]
        }
      ],
      "source": [
        "print('Median delay in minutes: ', np.median(df_dest['Ar_Delay_mins']))\n",
        "print('Mean delay in minutes: ', np.mean(df_dest['Ar_Delay_mins']))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "908c61d7",
      "metadata": {
        "id": "908c61d7"
      },
      "source": [
        "Earliest arrival"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b281bed3",
      "metadata": {
        "id": "b281bed3",
        "outputId": "fbd33296-5126-460e-a3e5-bbb97e83bfde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-61 minutes\n"
          ]
        }
      ],
      "source": [
        "print(str(np.min(df_dest['Ar_Delay_mins'])) + ' minutes')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f09838ff",
      "metadata": {
        "id": "f09838ff"
      },
      "source": [
        "Latest arrival"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67bf5a95",
      "metadata": {
        "id": "67bf5a95",
        "outputId": "b769aaf1-6c6c-4367-9717-c6137d82bf5f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "819 minutes\n"
          ]
        }
      ],
      "source": [
        "print(str(np.max(df_dest['Ar_Delay_mins'])) + ' minutes')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7697a5d0",
      "metadata": {
        "id": "7697a5d0"
      },
      "source": [
        "Percentage arrives on time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aef9ad03",
      "metadata": {
        "id": "aef9ad03"
      },
      "outputs": [],
      "source": [
        "df_ontime30 = df.loc[(df['Ar_Delay_mins']<=30)]\n",
        "df_ontime60 = df.loc[(df['Ar_Delay_mins']<=60)]\n",
        "df_ontime = df.loc[(df['Ar_Delay_mins']<=0)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "45a64dd4",
      "metadata": {
        "id": "45a64dd4",
        "outputId": "b4d9784f-c4c1-47c3-8e75-225520a7f6a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "90.51% of the time the train arrives within 60 mins of schedule\n",
            "81.4% of the time the train arrives within 30 mins of schedule\n",
            "51.12% of the time the train arrives on time or earlier\n"
          ]
        }
      ],
      "source": [
        "print(str(np.round(len(df_ontime60)*100./len(df),2))+'% of the time the train arrives within 60 mins of schedule')\n",
        "print(str(np.round(len(df_ontime30)*100./len(df),2))+'% of the time the train arrives within 30 mins of schedule')\n",
        "print(str(np.round(len(df_ontime)*100./len(df),2))+'% of the time the train arrives on time or earlier')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0e5f4e15",
      "metadata": {
        "id": "0e5f4e15",
        "outputId": "e4cdb45b-98b7-43d3-f4cf-4038c69a57c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "88.8% of the time the train arrives within 60 mins of schedule\n",
            "80.88% of the time the train arrives within 30 mins of schedule\n",
            "54.34% of the time the train arrives on time or earlier\n"
          ]
        }
      ],
      "source": [
        "df_dest_ontime30 = df_dest.loc[(df_dest['Ar_Delay_mins']<=30)]\n",
        "df_dest_ontime60 = df_dest.loc[(df_dest['Ar_Delay_mins']<=60)]\n",
        "df_dest_ontime = df_dest.loc[(df_dest['Ar_Delay_mins']<=0)]\n",
        "\n",
        "print(str(np.round(len(df_dest_ontime60)*100./len(df_dest),2))+'% of the time the train arrives within 60 mins of schedule')\n",
        "print(str(np.round(len(df_dest_ontime30)*100./len(df_dest),2))+'% of the time the train arrives within 30 mins of schedule')\n",
        "print(str(np.round(len(df_dest_ontime)*100./len(df_dest),2))+'% of the time the train arrives on time or earlier')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ff1cba0f",
      "metadata": {
        "id": "ff1cba0f"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "My experience of 6-hour and 3-hour delays are not the norm. Based on 10 years of data from the most reported stations along the City of New Orleans route, the trains are actually within 30 minutes of schedule most of the time. Half the time it arrives on-time or even earlier at the end stations. "
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "Amtrak Delays.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}