Project1_MTA.ipynb

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      "name": "Project1_MTA.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XjEdDk-zDsQ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime\n",
        "# gdoc notes link: https://docs.google.com/document/d/1cjXIWAZjWP8KMultOzcyQ4vc6-9tzu3A9N6552qh1cU/edit"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gc7IWeeV2oAy"
      },
      "source": [
        "#Challenge 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iroqz9nt0gQk"
      },
      "source": [
        "# pull last month of august through ~sept 22nd each year\n",
        "# exclude 2020 due to COVID\n",
        "\n",
        "weeks_for_2019 = [\"http://web.mta.info/developers/data/nyct/turnstile/turnstile_190921.txt\", \n",
        "                 \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_190914.txt\", \n",
        "                 \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_190907.txt\", \n",
        "                 \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_190831.txt\",\n",
        "                 \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_190824.txt\"]\n",
        "\n",
        "weeks_for_2018 = [\"http://web.mta.info/developers/data/nyct/turnstile/turnstile_180922.txt\",\n",
        "                 \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_180915.txt\",\n",
        "                 \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_180908.txt\",\n",
        "                 \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_180901.txt\",\n",
        "                 \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_180825.txt\"]\n",
        "\n",
        "data_2019 = [pd.read_csv(s) for s in weeks_for_2019]\n",
        "data_2018 = [pd.read_csv(s) for s in weeks_for_2018]\n",
        "all_data = data_2018+data_2019\n",
        "\n",
        "#Initially, we looked at combining data from several years; this caused problems with sorting later, and we pulled back to a single year.\n",
        "\n",
        "#df = pd.concat(all_data, ignore_index=True)\n",
        "#df = pd.concat(data_2018, ignore_index=True)\n",
        "df = pd.concat(data_2019, ignore_index=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgfbC4xN4B19"
      },
      "source": [
        "#Challenge 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4GpZ1SDX00Av"
      },
      "source": [
        "# Preparing the data for further processing.\n",
        "\n",
        "# create a datetime field from date and time fields. \n",
        "df['date_time'] = pd.to_datetime(df['DATE'] + ' ' + df['TIME'])\n",
        "\n",
        "# Create a single turnstile ID field to filter with.\n",
        "df['TURNSTILE_ID'] = df['C/A']+'_'+ df['UNIT']+'_' + df['SCP']+'_' + df['STATION']\n",
        "\n",
        "# Fix the wonky exits column title.\n",
        "df.rename(columns={'EXITS                                                               ': 'EXITS'}, inplace=True)\n",
        "\n",
        "#Sort by the new fields.\n",
        "df = df.sort_values(['TURNSTILE_ID', 'date_time'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5QIlGxkW-p7D",
        "outputId": "5a588ffd-9a31-4ee3-fd9d-2dea84dddeb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "#Identify: What are the duplicated turnstiles/dates in the data?\n",
        "df.groupby([\"TURNSTILE_ID\",\"DATE\",\"TIME\"])[\"ENTRIES\"].count().reset_index().sort_values(by=\"ENTRIES\",ascending=False).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TURNSTILE_ID</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>ENTRIES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>638579</th>\n",
              "      <td>R101_R001_02-00-00_SOUTH FERRY</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>989874</th>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>04:59:02</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>990087</th>\n",
              "      <td>R622_R123_00-00-07_FRANKLIN AV</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>04:59:02</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>639574</th>\n",
              "      <td>R101_R001_02-00-04_SOUTH FERRY</td>\n",
              "      <td>09/17/2019</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180059</th>\n",
              "      <td>H039_R375_00-00-01_NEW LOTS</td>\n",
              "      <td>09/13/2019</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>709838</th>\n",
              "      <td>R161A_R452_01-00-02_72 ST</td>\n",
              "      <td>08/26/2019</td>\n",
              "      <td>21:00:00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179849</th>\n",
              "      <td>H039_R375_00-00-00_NEW LOTS</td>\n",
              "      <td>09/13/2019</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>640204</th>\n",
              "      <td>R101_R001_02-00-07_SOUTH FERRY</td>\n",
              "      <td>09/17/2019</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684126</th>\n",
              "      <td>R138_R293_00-03-01_34 ST-PENN STA</td>\n",
              "      <td>08/24/2019</td>\n",
              "      <td>06:00:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684165</th>\n",
              "      <td>R138_R293_00-03-01_34 ST-PENN STA</td>\n",
              "      <td>08/30/2019</td>\n",
              "      <td>22:00:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             TURNSTILE_ID        DATE      TIME  ENTRIES\n",
              "638579     R101_R001_02-00-00_SOUTH FERRY  08/22/2019  05:00:00        2\n",
              "989874     R622_R123_00-00-06_FRANKLIN AV  08/22/2019  04:59:02        2\n",
              "990087     R622_R123_00-00-07_FRANKLIN AV  08/22/2019  04:59:02        2\n",
              "639574     R101_R001_02-00-04_SOUTH FERRY  09/17/2019  05:00:00        2\n",
              "180059        H039_R375_00-00-01_NEW LOTS  09/13/2019  05:00:00        2\n",
              "709838          R161A_R452_01-00-02_72 ST  08/26/2019  21:00:00        2\n",
              "179849        H039_R375_00-00-00_NEW LOTS  09/13/2019  05:00:00        2\n",
              "640204     R101_R001_02-00-07_SOUTH FERRY  09/17/2019  05:00:00        2\n",
              "684126  R138_R293_00-03-01_34 ST-PENN STA  08/24/2019  06:00:00        1\n",
              "684165  R138_R293_00-03-01_34 ST-PENN STA  08/30/2019  22:00:00        1"
            ]
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          "metadata": {
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          "execution_count": 68
        }
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1026240, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "fs5tvFzF_4Uv",
        "outputId": "1a10aa64-f424-40a0-defa-f9550b5b2b6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "#df_2[df_2[\"TURNSTILE_ID\"] == \"R101_R001_02-00-00_SOUTH FERRY\" , df_2[\"DATE\"] ==\"09/14/2018\" , df_2[\"TIME\"] == \"05:00:00\"]\n",
        "#(turnstiles_df[\"DATE_TIME\"].dt.date == datetime.datetime(2016, 9, 16).date())\n",
        "\n",
        "themask = ((df[\"TURNSTILE_ID\"] == \"R622_R123_00-00-06_FRANKLIN AV\" ) & (df[\"date_time\"].dt.date == datetime.datetime(2019,8,22).date()))\n",
        "\n",
        "df[themask].head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C/A</th>\n",
              "      <th>UNIT</th>\n",
              "      <th>SCP</th>\n",
              "      <th>STATION</th>\n",
              "      <th>LINENAME</th>\n",
              "      <th>DIVISION</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>DESC</th>\n",
              "      <th>ENTRIES</th>\n",
              "      <th>EXITS</th>\n",
              "      <th>date_time</th>\n",
              "      <th>TURNSTILE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1018982</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>10088508</td>\n",
              "      <td>12195136</td>\n",
              "      <td>2019-08-22 00:00:00</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018983</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>04:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>10088520</td>\n",
              "      <td>12195190</td>\n",
              "      <td>2019-08-22 04:00:00</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018984</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>04:59:02</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>10088529</td>\n",
              "      <td>12195193</td>\n",
              "      <td>2019-08-22 04:59:02</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018985</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>04:59:02</td>\n",
              "      <td>RECOVR AUD</td>\n",
              "      <td>10088530</td>\n",
              "      <td>12195193</td>\n",
              "      <td>2019-08-22 04:59:02</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018986</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>05:01:42</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>10088530</td>\n",
              "      <td>12195193</td>\n",
              "      <td>2019-08-22 05:01:42</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018987</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>05:01:54</td>\n",
              "      <td>RECOVR AUD</td>\n",
              "      <td>10088530</td>\n",
              "      <td>12195193</td>\n",
              "      <td>2019-08-22 05:01:54</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018988</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>08:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>10088756</td>\n",
              "      <td>12195232</td>\n",
              "      <td>2019-08-22 08:00:00</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018989</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>10089292</td>\n",
              "      <td>12195348</td>\n",
              "      <td>2019-08-22 12:00:00</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018990</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>10089523</td>\n",
              "      <td>12195550</td>\n",
              "      <td>2019-08-22 16:00:00</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018991</th>\n",
              "      <td>R622</td>\n",
              "      <td>R123</td>\n",
              "      <td>00-00-06</td>\n",
              "      <td>FRANKLIN AV</td>\n",
              "      <td>2345S</td>\n",
              "      <td>IRT</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>20:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>10089733</td>\n",
              "      <td>12196316</td>\n",
              "      <td>2019-08-22 20:00:00</td>\n",
              "      <td>R622_R123_00-00-06_FRANKLIN AV</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          C/A  UNIT  ...           date_time                    TURNSTILE_ID\n",
              "1018982  R622  R123  ... 2019-08-22 00:00:00  R622_R123_00-00-06_FRANKLIN AV\n",
              "1018983  R622  R123  ... 2019-08-22 04:00:00  R622_R123_00-00-06_FRANKLIN AV\n",
              "1018984  R622  R123  ... 2019-08-22 04:59:02  R622_R123_00-00-06_FRANKLIN AV\n",
              "1018985  R622  R123  ... 2019-08-22 04:59:02  R622_R123_00-00-06_FRANKLIN AV\n",
              "1018986  R622  R123  ... 2019-08-22 05:01:42  R622_R123_00-00-06_FRANKLIN AV\n",
              "1018987  R622  R123  ... 2019-08-22 05:01:54  R622_R123_00-00-06_FRANKLIN AV\n",
              "1018988  R622  R123  ... 2019-08-22 08:00:00  R622_R123_00-00-06_FRANKLIN AV\n",
              "1018989  R622  R123  ... 2019-08-22 12:00:00  R622_R123_00-00-06_FRANKLIN AV\n",
              "1018990  R622  R123  ... 2019-08-22 16:00:00  R622_R123_00-00-06_FRANKLIN AV\n",
              "1018991  R622  R123  ... 2019-08-22 20:00:00  R622_R123_00-00-06_FRANKLIN AV\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Puxpm1kC0Pm"
      },
      "source": [
        "# We scanned a few duplicates (see cell above), and chose to drop the second of the two duplicate rows (by turnstile_id and date_time)\n",
        "df = df.drop_duplicates(subset=[\"TURNSTILE_ID\",\"date_time\"],keep=\"first\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDzEpxMNDtbK",
        "outputId": "e252b8fd-b9cc-4361-d722-a79bc0a00a14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1026232, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UArM84Tn8iGg"
      },
      "source": [
        "#Challenge 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qxx9d1H8lBy",
        "outputId": "1b8e7662-cad0-4cbb-f0f9-c30022bb185e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "#First, note that we are using .first for entries, after grouping by date. Because the entries are sorted ascending by date/time, this means that the value assigned to a day\n",
        "#will be just after midnight, and heavily influenced by the previous day.\n",
        "df_events_per_day = df.groupby([\"TURNSTILE_ID\",\"DATE\"],as_index=False)[\"ENTRIES\", \"C/A\", \"UNIT\", \"SCP\", \"STATION\",\"TIME\", \"date_time\"].first()\n",
        "\n",
        "\n",
        "#In order to solve this off-by-one issue, we replace the date with the previous day's date. \n",
        "df_events_per_day[[\"previous_date\",\"previous_entries\"]] = df_events_per_day.groupby([\"TURNSTILE_ID\"])[\"DATE\",\"ENTRIES\"].apply(lambda grp: grp.shift(1))\n",
        "\n",
        "\n",
        "df_events_per_day.drop(columns=['DATE'], axis=1, inplace=True)\n",
        "df_events_per_day.rename({'previous_date' : 'DATE'}, axis=1, inplace=True)\n",
        "df['date_time'] = pd.to_datetime(df['DATE'] + ' ' + df['TIME'])\n",
        "\n",
        "df_events_per_day.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  import sys\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>TURNSTILE_ID</th>\n",
              "      <th>ENTRIES</th>\n",
              "      <th>C/A</th>\n",
              "      <th>UNIT</th>\n",
              "      <th>SCP</th>\n",
              "      <th>STATION</th>\n",
              "      <th>TIME</th>\n",
              "      <th>date_time</th>\n",
              "      <th>DATE</th>\n",
              "      <th>previous_entries</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7170294</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7170943</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-18</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>7170294.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7171363</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>08/18/2019</td>\n",
              "      <td>7170943.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7172478</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-20</td>\n",
              "      <td>08/19/2019</td>\n",
              "      <td>7171363.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7173517</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>08/20/2019</td>\n",
              "      <td>7172478.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               TURNSTILE_ID  ENTRIES  ...        DATE previous_entries\n",
              "0  A002_R051_02-00-00_59 ST  7170294  ...         NaN              NaN\n",
              "1  A002_R051_02-00-00_59 ST  7170943  ...  08/17/2019        7170294.0\n",
              "2  A002_R051_02-00-00_59 ST  7171363  ...  08/18/2019        7170943.0\n",
              "3  A002_R051_02-00-00_59 ST  7172478  ...  08/19/2019        7171363.0\n",
              "4  A002_R051_02-00-00_59 ST  7173517  ...  08/20/2019        7172478.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7G7k5Jfa1SkR",
        "outputId": "c108cbaf-0df9-4115-8001-da009c5f45b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#Next, remove the first day's worth of data, since it will be associated with a nonexistent day.\n",
        "df_events_per_day.dropna(subset=[\"DATE\"],axis=0,inplace=True)\n",
        "df_events_per_day.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TURNSTILE_ID</th>\n",
              "      <th>ENTRIES</th>\n",
              "      <th>C/A</th>\n",
              "      <th>UNIT</th>\n",
              "      <th>SCP</th>\n",
              "      <th>STATION</th>\n",
              "      <th>TIME</th>\n",
              "      <th>date_time</th>\n",
              "      <th>DATE</th>\n",
              "      <th>previous_entries</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7170943</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-18</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>7170294.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7171363</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>08/18/2019</td>\n",
              "      <td>7170943.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7172478</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-20</td>\n",
              "      <td>08/19/2019</td>\n",
              "      <td>7171363.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7173517</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>08/20/2019</td>\n",
              "      <td>7172478.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7174625</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-22</td>\n",
              "      <td>08/21/2019</td>\n",
              "      <td>7173517.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               TURNSTILE_ID  ENTRIES  ...        DATE previous_entries\n",
              "1  A002_R051_02-00-00_59 ST  7170943  ...  08/17/2019        7170294.0\n",
              "2  A002_R051_02-00-00_59 ST  7171363  ...  08/18/2019        7170943.0\n",
              "3  A002_R051_02-00-00_59 ST  7172478  ...  08/19/2019        7171363.0\n",
              "4  A002_R051_02-00-00_59 ST  7173517  ...  08/20/2019        7172478.0\n",
              "5  A002_R051_02-00-00_59 ST  7174625  ...  08/21/2019        7173517.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4jSTA_kI4FY",
        "outputId": "d5fb6c40-15c1-4521-a190-0b74b70f980c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#That's odd! Some of the turnstiles are counting backwards.\n",
        "df_events_per_day[ df_events_per_day[\"ENTRIES\"] < df_events_per_day[\"previous_entries\"] ].head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TURNSTILE_ID</th>\n",
              "      <th>ENTRIES</th>\n",
              "      <th>C/A</th>\n",
              "      <th>UNIT</th>\n",
              "      <th>SCP</th>\n",
              "      <th>STATION</th>\n",
              "      <th>TIME</th>\n",
              "      <th>date_time</th>\n",
              "      <th>DATE</th>\n",
              "      <th>previous_entries</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1339</th>\n",
              "      <td>A011_R080_01-03-00_57 ST-7 AV</td>\n",
              "      <td>885829355</td>\n",
              "      <td>A011</td>\n",
              "      <td>R080</td>\n",
              "      <td>01-03-00</td>\n",
              "      <td>57 ST-7 AV</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>08/20/2019</td>\n",
              "      <td>885829659.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1340</th>\n",
              "      <td>A011_R080_01-03-00_57 ST-7 AV</td>\n",
              "      <td>885828839</td>\n",
              "      <td>A011</td>\n",
              "      <td>R080</td>\n",
              "      <td>01-03-00</td>\n",
              "      <td>57 ST-7 AV</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-22</td>\n",
              "      <td>08/21/2019</td>\n",
              "      <td>885829355.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1341</th>\n",
              "      <td>A011_R080_01-03-00_57 ST-7 AV</td>\n",
              "      <td>885828245</td>\n",
              "      <td>A011</td>\n",
              "      <td>R080</td>\n",
              "      <td>01-03-00</td>\n",
              "      <td>57 ST-7 AV</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-23</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>885828839.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1342</th>\n",
              "      <td>A011_R080_01-03-00_57 ST-7 AV</td>\n",
              "      <td>885827671</td>\n",
              "      <td>A011</td>\n",
              "      <td>R080</td>\n",
              "      <td>01-03-00</td>\n",
              "      <td>57 ST-7 AV</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-24</td>\n",
              "      <td>08/23/2019</td>\n",
              "      <td>885828245.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1343</th>\n",
              "      <td>A011_R080_01-03-00_57 ST-7 AV</td>\n",
              "      <td>885827233</td>\n",
              "      <td>A011</td>\n",
              "      <td>R080</td>\n",
              "      <td>01-03-00</td>\n",
              "      <td>57 ST-7 AV</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-25</td>\n",
              "      <td>08/24/2019</td>\n",
              "      <td>885827671.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       TURNSTILE_ID    ENTRIES  ...        DATE previous_entries\n",
              "1339  A011_R080_01-03-00_57 ST-7 AV  885829355  ...  08/20/2019      885829659.0\n",
              "1340  A011_R080_01-03-00_57 ST-7 AV  885828839  ...  08/21/2019      885829355.0\n",
              "1341  A011_R080_01-03-00_57 ST-7 AV  885828245  ...  08/22/2019      885828839.0\n",
              "1342  A011_R080_01-03-00_57 ST-7 AV  885827671  ...  08/23/2019      885828245.0\n",
              "1343  A011_R080_01-03-00_57 ST-7 AV  885827233  ...  08/24/2019      885827671.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-evo__dMLEC9"
      },
      "source": [
        "#Here's where we identified cases where \n",
        "def get_daily_counts(row, max_counter):\n",
        "    counter = row[\"ENTRIES\"] - row[\"previous_entries\"]\n",
        "    if counter < 0:\n",
        "        # Maybe counter is reversed?\n",
        "        counter = -counter\n",
        "    if counter > max_counter:\n",
        "        # Maybe counter was reset to 0 due to turnstile behavior, or maintenance?\n",
        "        print(row[\"STATION\"], row[\"DATE\"], row[\"ENTRIES\"], row[\"previous_entries\"])\n",
        "        counter = min(row[\"ENTRIES\"], row[\"previous_entries\"])\n",
        "    if min(row[\"ENTRIES\"], row[\"previous_entries\"]) < 10000 and max(row[\"ENTRIES\"], row[\"previous_entries\"]) > 20000:\n",
        "      #This is another, similar check. \n",
        "        return min(row[\"ENTRIES\"], row[\"previous_entries\"])\n",
        "    if counter > max_counter:\n",
        "        # Check it again to make sure we're not still giving a counter that's too big\n",
        "        return 0\n",
        "    return counter"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZOQSvStLa_7",
        "outputId": "a1d49981-cc2b-4f1b-d67d-bd04949700ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df_events_per_day[\"DAILY_ENTRIES\"] = df_events_per_day.apply(get_daily_counts, axis=1, max_counter=86400)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "57 ST-7 AV 08/24/2019 458755 7.0\n",
            "49 ST 09/07/2019 12 6170094.0\n",
            "14 ST-UNION SQ 08/20/2019 451 303204.0\n",
            "BOTANIC GARDEN 09/11/2019 716 171608.0\n",
            "NECK RD 08/19/2019 459073 68448066.0\n",
            "SHEEPSHEAD BAY 09/15/2019 275 112691.0\n",
            "BOROUGH HALL 09/02/2019 143 1599329.0\n",
            "DEKALB AV 08/21/2019 1638739 805417.0\n",
            "DEKALB AV 09/06/2019 460787 6805255.0\n",
            "4AV-9 ST 08/21/2019 138 290539.0\n",
            "BAY RIDGE AV 09/09/2019 73 6697765.0\n",
            "BAY PKWY 09/19/2019 875479593 775219651.0\n",
            "CONEY IS-STILLW 08/20/2019 458754 3774.0\n",
            "BEDFORD AV 08/22/2019 721 915984.0\n",
            "JEFFERSON ST 09/18/2019 590840 9156730.0\n",
            "CHAUNCEY ST 09/17/2019 134 1478959.0\n",
            "JFK JAMAICA CT1 09/06/2019 597 479848.0\n",
            "METROPOLITAN AV 09/18/2019 85 771630.0\n",
            "135 ST 09/03/2019 67 202729.0\n",
            "125 ST 09/18/2019 222 6053291.0\n",
            "96 ST 08/27/2019 26 1652502.0\n",
            "14 ST 08/21/2019 22 2129136933.0\n",
            "14 ST 08/21/2019 21 13593836.0\n",
            "14 ST 08/21/2019 27 7808518.0\n",
            "14 ST 08/21/2019 21 2129136817.0\n",
            "14 ST 08/21/2019 28 13594033.0\n",
            "14 ST 08/21/2019 21 7809012.0\n",
            "CHAMBERS ST 09/15/2019 80 2781090.0\n",
            "WORLD TRADE CTR 08/22/2019 1462 989392.0\n",
            "FULTON ST 09/08/2019 458753 3979.0\n",
            "KINGSTON-THROOP 09/05/2019 342 100079.0\n",
            "FAR ROCKAWAY 08/29/2019 23 2130567.0\n",
            "161/YANKEE STAD 09/06/2019 721441481 2880437.0\n",
            "BEDFORD PK BLVD 09/10/2019 373 550304.0\n",
            "46 ST 09/01/2019 14 4584481.0\n",
            "JKSN HT-ROOSVLT 08/17/2019 524292 3878.0\n",
            "PARSONS BLVD 08/29/2019 134 3376441.0\n",
            "PARSONS BLVD 08/29/2019 351 3839369.0\n",
            "B'WAY-LAFAYETTE 09/09/2019 8 175496.0\n",
            "GRAND ST 09/05/2019 1565 349750.0\n",
            "14TH STREET 08/30/2019 2285 206623.0\n",
            "TWENTY THIRD ST 09/18/2019 1408 125316.0\n",
            "THIRTY ST 09/09/2019 10 155439.0\n",
            "THIRTY THIRD ST 09/07/2019 84 154658.0\n",
            "PATH NEW WTC 08/27/2019 2054 159430.0\n",
            "FULTON ST 09/16/2019 1507350 3900.0\n",
            "HOUSTON ST 09/09/2019 535 4777158.0\n",
            "18 ST 09/03/2019 102459236 210849.0\n",
            "72 ST 08/26/2019 292 372401.0\n",
            "72 ST 08/26/2019 70 5631392.0\n",
            "72 ST 08/26/2019 871 2543403.0\n",
            "72 ST 08/26/2019 468 620636.0\n",
            "72 ST 08/26/2019 892 1975856.0\n",
            "72 ST 08/23/2019 438 205932.0\n",
            "157 ST 09/12/2019 2473 22554699.0\n",
            "MARBLE HILL-225 09/08/2019 281 1449982.0\n",
            "BOWLING GREEN 09/17/2019 524288 14.0\n",
            "BROOKLYN BRIDGE 08/20/2019 6 545478.0\n",
            "BROOKLYN BRIDGE 08/24/2019 458755 0.0\n",
            "GRD CNTRL-42 ST 08/29/2019 2160 392982.0\n",
            "GRD CNTRL-42 ST 09/15/2019 568 216736.0\n",
            "GRD CNTRL-42 ST 08/26/2019 933 1285128.0\n",
            "GRD CNTRL-42 ST 08/21/2019 1543 10213611.0\n",
            "51 ST 09/12/2019 569320958 779190.0\n",
            "FORDHAM RD 09/18/2019 33601461 187873.0\n",
            "PARKCHESTER 09/17/2019 935 3674106.0\n",
            "5 AVE 09/11/2019 1865 991539888.0\n",
            "36 AV 08/30/2019 583 1447158.0\n",
            "JUNCTION BLVD 09/15/2019 0 341471.0\n",
            "BOROUGH HALL 08/31/2019 552 12409427.0\n",
            "BOROUGH HALL 08/31/2019 328 8327272.0\n",
            "BOROUGH HALL 08/31/2019 112 5369177.0\n",
            "RIT-MANHATTAN 09/19/2019 1395 492094.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AsN8ON9CLxAy",
        "outputId": "eebe678d-55c1-45e5-f5c6-1ca37ce96905",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "#Plot the number of entries per day as a histogram to examine the shape of the data. It looks pretty good- roughly what you'd expect, where there is little activity much of the time. \n",
        "plt.figure(figsize = [10,10])\n",
        "plt.hist(df_events_per_day[\"DAILY_ENTRIES\"],50,log=True);\n",
        "# we will drop outliers\n",
        "df_events_per_day.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(166028, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X90UW02BFjmz",
        "outputId": "30e35b29-1518-40db-ea79-1315618805a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#explore what the high-end outliers look like\n",
        "df_events_per_day[df_events_per_day[\"DAILY_ENTRIES\"] > 20000].groupby([\"STATION\"]).head()\n",
        "\n",
        "#if min(entries, previous_entries)<10000 and max(entries, previous entries)>20000: daily entries = min(entries, previous entries)\n",
        "\n",
        "#df_events_per_day[(df_events_per_day[\"STATION\"] == \"NECK RD\") & (df_events_per_day[\"DATE\"] == \"8/20/2019\")].head()\n",
        "#df_events_per_day[(df_events_per_day[\"STATION\"] == \"AVENUE H\") & (df_events_per_day[\"DATE\"] == \"09/06/2019\")]\n",
        "df_events_per_day.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(166028, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDRUZykaIHy8",
        "outputId": "71a090a1-c701-439a-ddeb-809652dfec22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "#Examining the US Daily Open. Is it special?\n",
        "df_events_per_day[(df_events_per_day[\"DATE\"] > '08/26/2019') & (df_events_per_day[\"DATE\"] < '09/08/2019')]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TURNSTILE_ID</th>\n",
              "      <th>ENTRIES</th>\n",
              "      <th>C/A</th>\n",
              "      <th>UNIT</th>\n",
              "      <th>SCP</th>\n",
              "      <th>STATION</th>\n",
              "      <th>TIME</th>\n",
              "      <th>date_time</th>\n",
              "      <th>DATE</th>\n",
              "      <th>previous_entries</th>\n",
              "      <th>DAILY_ENTRIES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7179982</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-28 00:00:00</td>\n",
              "      <td>08/27/2019</td>\n",
              "      <td>7178840.0</td>\n",
              "      <td>1142.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7181051</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-29 00:00:00</td>\n",
              "      <td>08/28/2019</td>\n",
              "      <td>7179982.0</td>\n",
              "      <td>1069.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7182148</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-30 00:00:00</td>\n",
              "      <td>08/29/2019</td>\n",
              "      <td>7181051.0</td>\n",
              "      <td>1097.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7183242</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-31 00:00:00</td>\n",
              "      <td>08/30/2019</td>\n",
              "      <td>7182148.0</td>\n",
              "      <td>1094.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7184008</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-09-01 00:00:00</td>\n",
              "      <td>08/31/2019</td>\n",
              "      <td>7183242.0</td>\n",
              "      <td>766.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170945</th>\n",
              "      <td>TRAM2_R469_00-05-01_RIT-ROOSEVELT</td>\n",
              "      <td>5554</td>\n",
              "      <td>TRAM2</td>\n",
              "      <td>R469</td>\n",
              "      <td>00-05-01</td>\n",
              "      <td>RIT-ROOSEVELT</td>\n",
              "      <td>01:00:00</td>\n",
              "      <td>2019-09-04 01:00:00</td>\n",
              "      <td>09/03/2019</td>\n",
              "      <td>5554.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170946</th>\n",
              "      <td>TRAM2_R469_00-05-01_RIT-ROOSEVELT</td>\n",
              "      <td>5554</td>\n",
              "      <td>TRAM2</td>\n",
              "      <td>R469</td>\n",
              "      <td>00-05-01</td>\n",
              "      <td>RIT-ROOSEVELT</td>\n",
              "      <td>01:00:00</td>\n",
              "      <td>2019-09-05 01:00:00</td>\n",
              "      <td>09/04/2019</td>\n",
              "      <td>5554.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170947</th>\n",
              "      <td>TRAM2_R469_00-05-01_RIT-ROOSEVELT</td>\n",
              "      <td>5554</td>\n",
              "      <td>TRAM2</td>\n",
              "      <td>R469</td>\n",
              "      <td>00-05-01</td>\n",
              "      <td>RIT-ROOSEVELT</td>\n",
              "      <td>01:00:00</td>\n",
              "      <td>2019-09-06 01:00:00</td>\n",
              "      <td>09/05/2019</td>\n",
              "      <td>5554.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170948</th>\n",
              "      <td>TRAM2_R469_00-05-01_RIT-ROOSEVELT</td>\n",
              "      <td>5554</td>\n",
              "      <td>TRAM2</td>\n",
              "      <td>R469</td>\n",
              "      <td>00-05-01</td>\n",
              "      <td>RIT-ROOSEVELT</td>\n",
              "      <td>01:00:00</td>\n",
              "      <td>2019-09-07 01:00:00</td>\n",
              "      <td>09/06/2019</td>\n",
              "      <td>5554.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170949</th>\n",
              "      <td>TRAM2_R469_00-05-01_RIT-ROOSEVELT</td>\n",
              "      <td>5554</td>\n",
              "      <td>TRAM2</td>\n",
              "      <td>R469</td>\n",
              "      <td>00-05-01</td>\n",
              "      <td>RIT-ROOSEVELT</td>\n",
              "      <td>01:00:00</td>\n",
              "      <td>2019-09-08 01:00:00</td>\n",
              "      <td>09/07/2019</td>\n",
              "      <td>5554.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>58626 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             TURNSTILE_ID  ...  DAILY_ENTRIES\n",
              "11               A002_R051_02-00-00_59 ST  ...         1142.0\n",
              "12               A002_R051_02-00-00_59 ST  ...         1069.0\n",
              "13               A002_R051_02-00-00_59 ST  ...         1097.0\n",
              "14               A002_R051_02-00-00_59 ST  ...         1094.0\n",
              "15               A002_R051_02-00-00_59 ST  ...          766.0\n",
              "...                                   ...  ...            ...\n",
              "170945  TRAM2_R469_00-05-01_RIT-ROOSEVELT  ...            0.0\n",
              "170946  TRAM2_R469_00-05-01_RIT-ROOSEVELT  ...            0.0\n",
              "170947  TRAM2_R469_00-05-01_RIT-ROOSEVELT  ...            0.0\n",
              "170948  TRAM2_R469_00-05-01_RIT-ROOSEVELT  ...            0.0\n",
              "170949  TRAM2_R469_00-05-01_RIT-ROOSEVELT  ...            0.0\n",
              "\n",
              "[58626 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0zQKWg7Pxz3",
        "outputId": "1e8f3004-900d-49a6-f1e2-8a7c9faff23c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        }
      },
      "source": [
        "plt.figure(figsize = [10,15])\n",
        "plt.hist(df_events_per_day[\"DAILY_ENTRIES\"],50,log=True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EMQXqUEQpeS"
      },
      "source": [
        "#remove the daily_entries entries where daily_enries == 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOYv4IjHBJ8S"
      },
      "source": [
        "yearmark = pd.Timestamp('20190101')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zxs9Q2_MFCe5"
      },
      "source": [
        "#Challenge 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27IOHgtFY3TT"
      },
      "source": [
        "We will plot the daily time series for a single turnstile.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSDgb2Yn7yKY",
        "outputId": "a07df998-0296-4fc7-9b52-58c5b4f5d439",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "chosen_id = 'A002_R051_02-00-00_59 ST'\n",
        "\n",
        "df_oneturnstile_daily = df_events_per_day[df_events_per_day['TURNSTILE_ID']==chosen_id]\n",
        "df_oneturnstile_daily.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TURNSTILE_ID</th>\n",
              "      <th>ENTRIES</th>\n",
              "      <th>C/A</th>\n",
              "      <th>UNIT</th>\n",
              "      <th>SCP</th>\n",
              "      <th>STATION</th>\n",
              "      <th>TIME</th>\n",
              "      <th>date_time</th>\n",
              "      <th>DATE</th>\n",
              "      <th>previous_entries</th>\n",
              "      <th>DAILY_ENTRIES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7170943</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-18</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>7170294.0</td>\n",
              "      <td>649.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7171363</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>08/18/2019</td>\n",
              "      <td>7170943.0</td>\n",
              "      <td>420.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7172478</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-20</td>\n",
              "      <td>08/19/2019</td>\n",
              "      <td>7171363.0</td>\n",
              "      <td>1115.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7173517</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>08/20/2019</td>\n",
              "      <td>7172478.0</td>\n",
              "      <td>1039.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7174625</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-22</td>\n",
              "      <td>08/21/2019</td>\n",
              "      <td>7173517.0</td>\n",
              "      <td>1108.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               TURNSTILE_ID  ENTRIES  ... previous_entries DAILY_ENTRIES\n",
              "1  A002_R051_02-00-00_59 ST  7170943  ...        7170294.0         649.0\n",
              "2  A002_R051_02-00-00_59 ST  7171363  ...        7170943.0         420.0\n",
              "3  A002_R051_02-00-00_59 ST  7172478  ...        7171363.0        1115.0\n",
              "4  A002_R051_02-00-00_59 ST  7173517  ...        7172478.0        1039.0\n",
              "5  A002_R051_02-00-00_59 ST  7174625  ...        7173517.0        1108.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vN2MpAXS-CE6",
        "outputId": "55f82c01-f13d-4927-cdf3-acda4763c5dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "plt.figure(figsize=(15,3))\n",
        "plt.plot(df_oneturnstile_daily['DATE'],df_oneturnstile_daily['ENTRIES'])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ciONOuXrDl6L"
      },
      "source": [
        "#df.groupby(['TURNSTILE_ID','DATE'])['TIME'].min().reset_index().sort_values(by='TIME',ascending=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sqDwK9JwGH9A"
      },
      "source": [
        "#Challenge 5\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7LN5XcVcdT_n"
      },
      "source": [
        "Group turnstiles in the same control area/unit/station."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dNnC0yWED0A",
        "outputId": "357c28cd-4a98-4ed6-c296-01325f0f0831",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "# same value for the C/A, UNIT and STATION columns, different values for the SCP column.\n",
        "df_events_per_day['turnstile_group'] = df_events_per_day['C/A']+'_'+ df_events_per_day['UNIT']+'_' + df_events_per_day['STATION']\n",
        "\n",
        "df_events_per_day.groupby(['turnstile_group','DATE'])['DAILY_ENTRIES'].sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "turnstile_group           DATE      \n",
              "A002_R051_59 ST           08/17/2019     7640.0\n",
              "                          08/18/2019     6135.0\n",
              "                          08/19/2019    11387.0\n",
              "                          08/20/2019    11906.0\n",
              "                          08/21/2019    11957.0\n",
              "                                         ...   \n",
              "TRAM2_R469_RIT-ROOSEVELT  09/15/2019     4084.0\n",
              "                          09/16/2019     3246.0\n",
              "                          09/17/2019     3488.0\n",
              "                          09/18/2019     3431.0\n",
              "                          09/19/2019     3629.0\n",
              "Name: DAILY_ENTRIES, Length: 25349, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5gPn79oIL1Z"
      },
      "source": [
        "#Challenge 6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAgx_FbOdeBq"
      },
      "source": [
        "Combine everything in each station, and create daily time series."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxjbODdUH6rF",
        "outputId": "e068026b-4a46-4813-c200-c865b77d6255",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "df_daily_station = df_events_per_day.groupby(['STATION','DATE'])['DAILY_ENTRIES', 'date_time', 'DATE'].sum().reset_index()\n",
        "df_daily_station[\"day_of_week\"] = pd.to_datetime(df_daily_station['DATE']).dt.day_name()\n",
        "df_daily_station.to_csv(\"Csv_cleaned.csv\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFU2QjbAIPWQ"
      },
      "source": [
        "#Challenge 7"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcS7S8BAIRG6",
        "outputId": "ca1cf882-6ccb-4382-898d-5f6c7c8f1d96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "df_onestation_daily = df_daily_station[df_daily_station['STATION']=='1 AV']\n",
        "\n",
        "\n",
        "\n",
        "plt.figure(figsize=(15,3))\n",
        "plt.plot(df_onestation_daily['DATE'],df_onestation_daily['DAILY_ENTRIES'])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy00pn1sGo7H",
        "outputId": "bf036882-5916-43c5-9331-18cb58dc68ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df_events_per_day.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TURNSTILE_ID</th>\n",
              "      <th>ENTRIES</th>\n",
              "      <th>C/A</th>\n",
              "      <th>UNIT</th>\n",
              "      <th>SCP</th>\n",
              "      <th>STATION</th>\n",
              "      <th>TIME</th>\n",
              "      <th>date_time</th>\n",
              "      <th>DATE</th>\n",
              "      <th>previous_entries</th>\n",
              "      <th>DAILY_ENTRIES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7170943</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-18</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>7170294.0</td>\n",
              "      <td>649.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7171363</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>08/18/2019</td>\n",
              "      <td>7170943.0</td>\n",
              "      <td>420.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7172478</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-20</td>\n",
              "      <td>08/19/2019</td>\n",
              "      <td>7171363.0</td>\n",
              "      <td>1115.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7173517</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>08/20/2019</td>\n",
              "      <td>7172478.0</td>\n",
              "      <td>1039.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>7174625</td>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>2019-08-22</td>\n",
              "      <td>08/21/2019</td>\n",
              "      <td>7173517.0</td>\n",
              "      <td>1108.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               TURNSTILE_ID  ENTRIES  ... previous_entries DAILY_ENTRIES\n",
              "1  A002_R051_02-00-00_59 ST  7170943  ...        7170294.0         649.0\n",
              "2  A002_R051_02-00-00_59 ST  7171363  ...        7170943.0         420.0\n",
              "3  A002_R051_02-00-00_59 ST  7172478  ...        7171363.0        1115.0\n",
              "4  A002_R051_02-00-00_59 ST  7173517  ...        7172478.0        1039.0\n",
              "5  A002_R051_02-00-00_59 ST  7174625  ...        7173517.0        1108.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wlz1AYtp2ZUi"
      },
      "source": [
        "# Challenge 8\n",
        "> Make one list of counts for one week for one station. Monday's count, Tuesday's count, etc. so it's a list of 7 counts. Make the same list for another week, and another week, and another week. (hint: pandas datetime day of week may be useful)\n",
        "plt.plot(week_count_list) for every week_count_list you created this way. You should get a rainbow plot of weekly commute numbers on top of each other.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKQkmUCJ2aZx",
        "outputId": "c7f85ea8-86a6-47ae-fb6a-e89531dd1833",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#df[\"date_time\"].dt.dayofweek()\n",
        "#df[\"date_time\"]\n",
        "\n",
        "#creating a new col where each entry is the day of the week corresponding to the date_time value for that entry\n",
        "df[\"day_of_week\"] = df[\"date_time\"].dt.day_name()\n",
        "df[\"day_of_week\"]\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C/A</th>\n",
              "      <th>UNIT</th>\n",
              "      <th>SCP</th>\n",
              "      <th>STATION</th>\n",
              "      <th>LINENAME</th>\n",
              "      <th>DIVISION</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>DESC</th>\n",
              "      <th>ENTRIES</th>\n",
              "      <th>EXITS</th>\n",
              "      <th>date_time</th>\n",
              "      <th>TURNSTILE_ID</th>\n",
              "      <th>HOURLY_ENTRY</th>\n",
              "      <th>STATION_ID</th>\n",
              "      <th>day_of_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>820571</th>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>NQR456W</td>\n",
              "      <td>BMT</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>7170294</td>\n",
              "      <td>2427642</td>\n",
              "      <td>2019-08-17 00:00:00</td>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A002_R051_59 ST</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820572</th>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>NQR456W</td>\n",
              "      <td>BMT</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>04:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>7170310</td>\n",
              "      <td>2427649</td>\n",
              "      <td>2019-08-17 04:00:00</td>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>16.0</td>\n",
              "      <td>A002_R051_59 ST</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820573</th>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>NQR456W</td>\n",
              "      <td>BMT</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>08:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>7170330</td>\n",
              "      <td>2427678</td>\n",
              "      <td>2019-08-17 08:00:00</td>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>20.0</td>\n",
              "      <td>A002_R051_59 ST</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820574</th>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>NQR456W</td>\n",
              "      <td>BMT</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>12:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>7170410</td>\n",
              "      <td>2427748</td>\n",
              "      <td>2019-08-17 12:00:00</td>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>80.0</td>\n",
              "      <td>A002_R051_59 ST</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820575</th>\n",
              "      <td>A002</td>\n",
              "      <td>R051</td>\n",
              "      <td>02-00-00</td>\n",
              "      <td>59 ST</td>\n",
              "      <td>NQR456W</td>\n",
              "      <td>BMT</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>16:00:00</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>7170594</td>\n",
              "      <td>2427804</td>\n",
              "      <td>2019-08-17 16:00:00</td>\n",
              "      <td>A002_R051_02-00-00_59 ST</td>\n",
              "      <td>184.0</td>\n",
              "      <td>A002_R051_59 ST</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         C/A  UNIT       SCP  ... HOURLY_ENTRY       STATION_ID day_of_week\n",
              "820571  A002  R051  02-00-00  ...          NaN  A002_R051_59 ST    Saturday\n",
              "820572  A002  R051  02-00-00  ...         16.0  A002_R051_59 ST    Saturday\n",
              "820573  A002  R051  02-00-00  ...         20.0  A002_R051_59 ST    Saturday\n",
              "820574  A002  R051  02-00-00  ...         80.0  A002_R051_59 ST    Saturday\n",
              "820575  A002  R051  02-00-00  ...        184.0  A002_R051_59 ST    Saturday\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0I6hCWUh4bQC",
        "outputId": "e86b8c64-6976-41e0-9dda-8a1dc4c54d1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "station_id = \"59 ST\"\n",
        "df_onestation_daily.head(7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>STATION</th>\n",
              "      <th>DATE</th>\n",
              "      <th>DAILY_ENTRIES</th>\n",
              "      <th>day_of_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>6208.0</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/18/2019</td>\n",
              "      <td>4367.0</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/19/2019</td>\n",
              "      <td>14726.0</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/20/2019</td>\n",
              "      <td>15737.0</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/21/2019</td>\n",
              "      <td>15953.0</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>16260.0</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/23/2019</td>\n",
              "      <td>16765.0</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  STATION        DATE  DAILY_ENTRIES day_of_week\n",
              "0    1 AV  08/17/2019         6208.0    Saturday\n",
              "1    1 AV  08/18/2019         4367.0      Sunday\n",
              "2    1 AV  08/19/2019        14726.0      Monday\n",
              "3    1 AV  08/20/2019        15737.0     Tuesday\n",
              "4    1 AV  08/21/2019        15953.0   Wednesday\n",
              "5    1 AV  08/22/2019        16260.0    Thursday\n",
              "6    1 AV  08/23/2019        16765.0      Friday"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FRS2qJ47ODP",
        "outputId": "c4a61bdd-478f-44b0-cf8f-98e9c9bf6633",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "df_onestation_daily.iloc[:7]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\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>STATION</th>\n",
              "      <th>DATE</th>\n",
              "      <th>DAILY_ENTRIES</th>\n",
              "      <th>day_of_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>6208.0</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/18/2019</td>\n",
              "      <td>4367.0</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/19/2019</td>\n",
              "      <td>14726.0</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/20/2019</td>\n",
              "      <td>15737.0</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/21/2019</td>\n",
              "      <td>15953.0</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>16260.0</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/23/2019</td>\n",
              "      <td>16765.0</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  STATION        DATE  DAILY_ENTRIES day_of_week\n",
              "0    1 AV  08/17/2019         6208.0    Saturday\n",
              "1    1 AV  08/18/2019         4367.0      Sunday\n",
              "2    1 AV  08/19/2019        14726.0      Monday\n",
              "3    1 AV  08/20/2019        15737.0     Tuesday\n",
              "4    1 AV  08/21/2019        15953.0   Wednesday\n",
              "5    1 AV  08/22/2019        16260.0    Thursday\n",
              "6    1 AV  08/23/2019        16765.0      Friday"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfjlJvU97juC",
        "outputId": "e8930675-9d28-47ab-ef1d-073900d56a29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df_onestation_daily[\"day_of_week\"] = pd.to_datetime(df_onestation_daily['DATE']).dt.day_name()\n",
        "df_onestation_daily.head(28)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>STATION</th>\n",
              "      <th>DATE</th>\n",
              "      <th>DAILY_ENTRIES</th>\n",
              "      <th>day_of_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>6208.0</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/18/2019</td>\n",
              "      <td>4367.0</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/19/2019</td>\n",
              "      <td>14726.0</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/20/2019</td>\n",
              "      <td>15737.0</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/21/2019</td>\n",
              "      <td>15953.0</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>16260.0</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/23/2019</td>\n",
              "      <td>16765.0</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/24/2019</td>\n",
              "      <td>6260.0</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/25/2019</td>\n",
              "      <td>4463.0</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/26/2019</td>\n",
              "      <td>14736.0</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/27/2019</td>\n",
              "      <td>15819.0</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/28/2019</td>\n",
              "      <td>15969.0</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/29/2019</td>\n",
              "      <td>16465.0</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/30/2019</td>\n",
              "      <td>13850.0</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/31/2019</td>\n",
              "      <td>4995.0</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/01/2019</td>\n",
              "      <td>4169.0</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/02/2019</td>\n",
              "      <td>3657.0</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/03/2019</td>\n",
              "      <td>15620.0</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/04/2019</td>\n",
              "      <td>16739.0</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/05/2019</td>\n",
              "      <td>18923.0</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/06/2019</td>\n",
              "      <td>19652.0</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/07/2019</td>\n",
              "      <td>6833.0</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/08/2019</td>\n",
              "      <td>5195.0</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/09/2019</td>\n",
              "      <td>17826.0</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/10/2019</td>\n",
              "      <td>18577.0</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/11/2019</td>\n",
              "      <td>18863.0</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/12/2019</td>\n",
              "      <td>19204.0</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>09/13/2019</td>\n",
              "      <td>19929.0</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   STATION        DATE  DAILY_ENTRIES day_of_week\n",
              "0     1 AV  08/17/2019         6208.0    Saturday\n",
              "1     1 AV  08/18/2019         4367.0      Sunday\n",
              "2     1 AV  08/19/2019        14726.0      Monday\n",
              "3     1 AV  08/20/2019        15737.0     Tuesday\n",
              "4     1 AV  08/21/2019        15953.0   Wednesday\n",
              "5     1 AV  08/22/2019        16260.0    Thursday\n",
              "6     1 AV  08/23/2019        16765.0      Friday\n",
              "7     1 AV  08/24/2019         6260.0    Saturday\n",
              "8     1 AV  08/25/2019         4463.0      Sunday\n",
              "9     1 AV  08/26/2019        14736.0      Monday\n",
              "10    1 AV  08/27/2019        15819.0     Tuesday\n",
              "11    1 AV  08/28/2019        15969.0   Wednesday\n",
              "12    1 AV  08/29/2019        16465.0    Thursday\n",
              "13    1 AV  08/30/2019        13850.0      Friday\n",
              "14    1 AV  08/31/2019         4995.0    Saturday\n",
              "15    1 AV  09/01/2019         4169.0      Sunday\n",
              "16    1 AV  09/02/2019         3657.0      Monday\n",
              "17    1 AV  09/03/2019        15620.0     Tuesday\n",
              "18    1 AV  09/04/2019        16739.0   Wednesday\n",
              "19    1 AV  09/05/2019        18923.0    Thursday\n",
              "20    1 AV  09/06/2019        19652.0      Friday\n",
              "21    1 AV  09/07/2019         6833.0    Saturday\n",
              "22    1 AV  09/08/2019         5195.0      Sunday\n",
              "23    1 AV  09/09/2019        17826.0      Monday\n",
              "24    1 AV  09/10/2019        18577.0     Tuesday\n",
              "25    1 AV  09/11/2019        18863.0   Wednesday\n",
              "26    1 AV  09/12/2019        19204.0    Thursday\n",
              "27    1 AV  09/13/2019        19929.0      Friday"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igI6G8mC8uBA",
        "outputId": "ac2f3a94-f7b8-42d6-e031-37e009c4347c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_onestation_daily.iloc[:7]\n",
        "print(\"len is {}\".format(len(df_onestation_daily)))\n",
        "\n",
        "list_of_dailyentries_for_a_week = []\n",
        "\n",
        "for i in range( len(df_onestation_daily)//7 ):\n",
        "  list_of_dailyentries_for_a_week.append( df_onestation_daily.iloc[ 7*i : 7*i+7 ] )\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "len is 34\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWFClfcK78wt",
        "outputId": "ec636147-3933-42fe-ea77-563b3f292e58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "list_of_dailyentries_for_a_week"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[  STATION        DATE  DAILY_ENTRIES day_of_week\n",
              " 0    1 AV  08/17/2019         6208.0    Saturday\n",
              " 1    1 AV  08/18/2019         4367.0      Sunday\n",
              " 2    1 AV  08/19/2019        14726.0      Monday\n",
              " 3    1 AV  08/20/2019        15737.0     Tuesday\n",
              " 4    1 AV  08/21/2019        15953.0   Wednesday\n",
              " 5    1 AV  08/22/2019        16260.0    Thursday\n",
              " 6    1 AV  08/23/2019        16765.0      Friday,\n",
              "    STATION        DATE  DAILY_ENTRIES day_of_week\n",
              " 7     1 AV  08/24/2019         6260.0    Saturday\n",
              " 8     1 AV  08/25/2019         4463.0      Sunday\n",
              " 9     1 AV  08/26/2019        14736.0      Monday\n",
              " 10    1 AV  08/27/2019        15819.0     Tuesday\n",
              " 11    1 AV  08/28/2019        15969.0   Wednesday\n",
              " 12    1 AV  08/29/2019        16465.0    Thursday\n",
              " 13    1 AV  08/30/2019        13850.0      Friday,\n",
              "    STATION        DATE  DAILY_ENTRIES day_of_week\n",
              " 14    1 AV  08/31/2019         4995.0    Saturday\n",
              " 15    1 AV  09/01/2019         4169.0      Sunday\n",
              " 16    1 AV  09/02/2019         3657.0      Monday\n",
              " 17    1 AV  09/03/2019        15620.0     Tuesday\n",
              " 18    1 AV  09/04/2019        16739.0   Wednesday\n",
              " 19    1 AV  09/05/2019        18923.0    Thursday\n",
              " 20    1 AV  09/06/2019        19652.0      Friday,\n",
              "    STATION        DATE  DAILY_ENTRIES day_of_week\n",
              " 21    1 AV  09/07/2019         6833.0    Saturday\n",
              " 22    1 AV  09/08/2019         5195.0      Sunday\n",
              " 23    1 AV  09/09/2019        17826.0      Monday\n",
              " 24    1 AV  09/10/2019        18577.0     Tuesday\n",
              " 25    1 AV  09/11/2019        18863.0   Wednesday\n",
              " 26    1 AV  09/12/2019        19204.0    Thursday\n",
              " 27    1 AV  09/13/2019        19929.0      Friday]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iMniD5fi9u1y"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwwfamVV-vPj",
        "outputId": "97ec95aa-69bd-4836-f66e-e512df27361c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "list_of_dailyentries_for_a_week[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>STATION</th>\n",
              "      <th>DATE</th>\n",
              "      <th>DAILY_ENTRIES</th>\n",
              "      <th>day_of_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/17/2019</td>\n",
              "      <td>6208.0</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/18/2019</td>\n",
              "      <td>4367.0</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/19/2019</td>\n",
              "      <td>14726.0</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/20/2019</td>\n",
              "      <td>15737.0</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/21/2019</td>\n",
              "      <td>15953.0</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/22/2019</td>\n",
              "      <td>16260.0</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1 AV</td>\n",
              "      <td>08/23/2019</td>\n",
              "      <td>16765.0</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  STATION        DATE  DAILY_ENTRIES day_of_week\n",
              "0    1 AV  08/17/2019         6208.0    Saturday\n",
              "1    1 AV  08/18/2019         4367.0      Sunday\n",
              "2    1 AV  08/19/2019        14726.0      Monday\n",
              "3    1 AV  08/20/2019        15737.0     Tuesday\n",
              "4    1 AV  08/21/2019        15953.0   Wednesday\n",
              "5    1 AV  08/22/2019        16260.0    Thursday\n",
              "6    1 AV  08/23/2019        16765.0      Friday"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HysR3Iwv-wGw",
        "outputId": "5f5a8f64-d7a6-431e-eda0-f23000334f10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        }
      },
      "source": [
        "plt.figure(figsize=(10,3))\n",
        "plt.xlabel(\"Day of Week\")\n",
        "plt.ylabel(\"Numb of Entries\")\n",
        "\n",
        "for l in list_of_dailyentries_for_a_week:\n",
        "  plt.plot(l[\"day_of_week\"],l[\"DAILY_ENTRIES\"])\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1rqVZBr_QPy"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNVPJ_rp_jOF"
      },
      "source": [
        "# Challenge 9\n",
        ">Over multiple weeks, sum total ridership for each station and sort them, so you can find out the stations with the highest traffic during the time you investigate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTzi3XfB_loy",
        "outputId": "475ab1ab-344f-4109-861e-fc58c46f19db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df_daily_station.groupby([\"STATION\"])[\"DAILY_ENTRIES\"].sum().reset_index().sort_values([\"DAILY_ENTRIES\"], ascending=False)\n",
        "\n",
        "#for challenge 10\n",
        "#reset index\n",
        "#plot historgram\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>STATION</th>\n",
              "      <th>DAILY_ENTRIES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>34 ST-PENN STA</td>\n",
              "      <td>4609118.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>GRD CNTRL-42 ST</td>\n",
              "      <td>3997497.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>34 ST-HERALD SQ</td>\n",
              "      <td>3597977.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>23 ST</td>\n",
              "      <td>3274683.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>42 ST-PORT AUTH</td>\n",
              "      <td>3055070.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>BEACH 98 ST</td>\n",
              "      <td>18748.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>BEACH 105 ST</td>\n",
              "      <td>10509.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>BROAD CHANNEL</td>\n",
              "      <td>9422.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>NEWARK HM HE</td>\n",
              "      <td>4183.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>ORCHARD BEACH</td>\n",
              "      <td>626.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>378 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             STATION  DAILY_ENTRIES\n",
              "61    34 ST-PENN STA      4609118.0\n",
              "232  GRD CNTRL-42 ST      3997497.0\n",
              "59   34 ST-HERALD SQ      3597977.0\n",
              "46             23 ST      3274683.0\n",
              "68   42 ST-PORT AUTH      3055070.0\n",
              "..               ...            ...\n",
              "147      BEACH 98 ST        18748.0\n",
              "140     BEACH 105 ST        10509.0\n",
              "161    BROAD CHANNEL         9422.0\n",
              "299     NEWARK HM HE         4183.0\n",
              "308    ORCHARD BEACH          626.0\n",
              "\n",
              "[378 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWvBaK_bBvy0"
      },
      "source": [
        "# Challenge Part 10\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BVx_Q_ghpFw"
      },
      "source": [
        "Histogram of total ridership."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlskynHB_8qb",
        "outputId": "7440a588-7edd-47b3-ff98-365575b1d157",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "plt.hist(df_daily_station.groupby([\"STATION\"])[\"DAILY_ENTRIES\"].sum(), 75)\n",
        "plt.xlabel(xlabel=\"Number of entries to that station\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Number of entries to that station')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCdn0mj5BqhU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}