Trail_Usage_Analysis_shareable.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.1"
    },
    "colab": {
      "name": "Trail Usage Analysis_shareable.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "A7CfZZea-5ef",
        "5Dsk4ebah6p1"
      ],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kellytdunn/Seattle-parks/blob/master/Trail_Usage_Analysis_shareable.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfFATACT0GBZ"
      },
      "source": [
        "## How can I Avoid Crowds at Seattle Parks?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mi4dUT3hUaoV"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqYA5Sq10GBb"
      },
      "source": [
        "For my first Python project, I was curious how the pandemic was affecting transportation, as I am a transportation planner. I have gone out to exercise every day during the stay at home orders, and have observed many others doing the same, particularly at big city parks, like Myrtle Edwards. The City took notice and closed down some parks temporarily that were getting too crowded to allow for social distancing. I wondered if people were more likely to be in parks these days given that there are so few other places to be. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DkrrxBOpWASD"
      },
      "source": [
        "## Housekeeping"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWEEwFvDBujM"
      },
      "source": [
        "*Note: This notebook uses two API tokens that I want to keep private while I share the notebook publicly. I define them here in a cell that i can easily hide before sharing. If you want to run these notebooks you'll need to grab your own token.Tokens for bike/ped data can be found here: https://data.seattle.gov/login. Tokens for NOAA data can be found here: https://www.ncdc.noaa.gov/cdo-web/token *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47Ul8AjN0GBc"
      },
      "source": [
        "First, I import libraries:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdYDALvj0mxq"
      },
      "source": [
        "!pip install sodapy\n",
        "!pip install seaborn\n",
        "!pip install folium"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBAx7_lD0GBc"
      },
      "source": [
        "import sys\n",
        "!{sys.executable} -m pip install ipywidgets>=7.5\n",
        "import pandas as pd\n",
        "from plotly.offline import iplot\n",
        "import plotly.graph_objs as go\n",
        "import time\n",
        "import datetime\n",
        "from datetime import date\n",
        "import math\n",
        "import requests\n",
        "from sodapy import Socrata\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score\n",
        "%matplotlib inline \n",
        "import scipy\n",
        "from scipy import stats\n",
        "import folium"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mU1sgdkCVAiT"
      },
      "source": [
        "## Defining my Approach and Data Requirements"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UcuhLXmYVFTL"
      },
      "source": [
        "This being my first project, I started out not entirely knowing what I would find, but I was interested in building a predictive model to help predict how crowded a park would be on a given day. To start off, I just wanted to understand the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ui_mZNJ_VrlK"
      },
      "source": [
        "I found a dataset from the Seattle Department of Transportation on bicycle and pedestrian counts from 2014-2020 at a specific point at Myrtle Edwards Park: https://data.seattle.gov/Transportation/Elliott-Bay-Trail-in-Myrtle-Edwards-Park-Bicycle-a/4qej-qvrz. \n",
        "I decided to use this count data as a proxy for how many people were using this park on a given day. Perhaps I will expand to other parks later, but for now, I'll stick with this park because I spent a lot of time there early in the pandemic."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLUMdiKL0WZL"
      },
      "source": [
        "First, because I like maps, here's Myrtle Edwards Park in context. You can zoom in and out. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "id": "dd6x7r5Wyf-T",
        "outputId": "22b56944-6595-462b-ee28-71518f4194b9"
      },
      "source": [
        "folium.Map(location = [47.62, -122.36], zoom_start = 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=%3C%21DOCTYPE%20html%3E%0A%3Chead%3E%20%20%20%20%0A%20%20%20%20%3Cmeta%20http-equiv%3D%22content-type%22%20content%3D%22text/html%3B%20charset%3DUTF-8%22%20/%3E%0A%20%20%20%20%3Cscript%3EL_PREFER_CANVAS%3Dfalse%3B%20L_NO_TOUCH%3Dfalse%3B%20L_DISABLE_3D%3Dfalse%3B%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//cdn.jsdelivr.net/npm/leaflet%401.4.0/dist/leaflet.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//code.jquery.com/jquery-1.12.4.min.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js%22%3E%3C/script%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//cdn.jsdelivr.net/npm/leaflet%401.4.0/dist/leaflet.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css%22/%3E%0A%20%20%20%20%3Cstyle%3Ehtml%2C%20body%20%7Bwidth%3A%20100%25%3Bheight%3A%20100%25%3Bmargin%3A%200%3Bpadding%3A%200%3B%7D%3C/style%3E%0A%20%20%20%20%3Cstyle%3E%23map%20%7Bposition%3Aabsolute%3Btop%3A0%3Bbottom%3A0%3Bright%3A0%3Bleft%3A0%3B%7D%3C/style%3E%0A%20%20%20%20%0A%20%20%20%20%3Cmeta%20name%3D%22viewport%22%20content%3D%22width%3Ddevice-width%2C%0A%20%20%20%20%20%20%20%20initial-scale%3D1.0%2C%20maximum-scale%3D1.0%2C%20user-scalable%3Dno%22%20/%3E%0A%20%20%20%20%3Cstyle%3E%23map_297b3148e37240b2a611131a4afbcde9%20%7B%0A%20%20%20%20%20%20%20%20position%3A%20relative%3B%0A%20%20%20%20%20%20%20%20width%3A%20100.0%25%3B%0A%20%20%20%20%20%20%20%20height%3A%20100.0%25%3B%0A%20%20%20%20%20%20%20%20left%3A%200.0%25%3B%0A%20%20%20%20%20%20%20%20top%3A%200.0%25%3B%0A%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%3C/style%3E%0A%3C/head%3E%0A%3Cbody%3E%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%3Cdiv%20class%3D%22folium-map%22%20id%3D%22map_297b3148e37240b2a611131a4afbcde9%22%20%3E%3C/div%3E%0A%3C/body%3E%0A%3Cscript%3E%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20bounds%20%3D%20null%3B%0A%20%20%20%20%0A%0A%20%20%20%20var%20map_297b3148e37240b2a611131a4afbcde9%20%3D%20L.map%28%0A%20%20%20%20%20%20%20%20%27map_297b3148e37240b2a611131a4afbcde9%27%2C%20%7B%0A%20%20%20%20%20%20%20%20center%3A%20%5B47.62%2C%20-122.36%5D%2C%0A%20%20%20%20%20%20%20%20zoom%3A%2015%2C%0A%20%20%20%20%20%20%20%20maxBounds%3A%20bounds%2C%0A%20%20%20%20%20%20%20%20layers%3A%20%5B%5D%2C%0A%20%20%20%20%20%20%20%20worldCopyJump%3A%20false%2C%0A%20%20%20%20%20%20%20%20crs%3A%20L.CRS.EPSG3857%2C%0A%20%20%20%20%20%20%20%20zoomControl%3A%20true%2C%0A%20%20%20%20%20%20%20%20%7D%29%3B%0A%0A%0A%20%20%20%20%0A%20%20%20%20var%20tile_layer_3066d9f9d1cc4deaa3db681744b34fb1%20%3D%20L.tileLayer%28%0A%20%20%20%20%20%20%20%20%27https%3A//%7Bs%7D.tile.openstreetmap.org/%7Bz%7D/%7Bx%7D/%7By%7D.png%27%2C%0A%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%22attribution%22%3A%20null%2C%0A%20%20%20%20%20%20%20%20%22detectRetina%22%3A%20false%2C%0A%20%20%20%20%20%20%20%20%22maxNativeZoom%22%3A%2018%2C%0A%20%20%20%20%20%20%20%20%22maxZoom%22%3A%2018%2C%0A%20%20%20%20%20%20%20%20%22minZoom%22%3A%200%2C%0A%20%20%20%20%20%20%20%20%22noWrap%22%3A%20false%2C%0A%20%20%20%20%20%20%20%20%22opacity%22%3A%201%2C%0A%20%20%20%20%20%20%20%20%22subdomains%22%3A%20%22abc%22%2C%0A%20%20%20%20%20%20%20%20%22tms%22%3A%20false%0A%7D%29.addTo%28map_297b3148e37240b2a611131a4afbcde9%29%3B%0A%3C/script%3E onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ],
            "text/plain": [
              "<folium.folium.Map at 0x7f29ab8d5b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siccRhFlWLv7"
      },
      "source": [
        "I start the data analysis by accessing the API:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAQKC2R50GBe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "128b0aa7-2dbe-4e81-8410-bbff541fba91"
      },
      "source": [
        "#bike/ped count dataset\n",
        "client = Socrata(\"data.seattle.gov\",\n",
        "                  SEA_token)\n",
        "\n",
        "# returned as JSON from API / converted to Python list of dictionaries by sodapy.\n",
        "results = client.get(\"4qej-qvrz\", limit = 100000)\n",
        "\n",
        "# Convert to pandas DataFrame\n",
        "results_df = pd.DataFrame.from_records(results)\n",
        "\n",
        "results_df.head()\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>date</th>\n",
              "      <th>eilliott_bay_trail_in_myrtle_edwards_park_total</th>\n",
              "      <th>ped_north</th>\n",
              "      <th>ped_south</th>\n",
              "      <th>bike_north</th>\n",
              "      <th>bike_south</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-06-01T00:00:00.000</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-06-01T01:00:00.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-06-01T02:00:00.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-06-01T03:00:00.000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-06-01T04:00:00.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      date  ... bike_south\n",
              "0  2020-06-01T00:00:00.000  ...          1\n",
              "1  2020-06-01T01:00:00.000  ...          0\n",
              "2  2020-06-01T02:00:00.000  ...          0\n",
              "3  2020-06-01T03:00:00.000  ...          0\n",
              "4  2020-06-01T04:00:00.000  ...          0\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PCx005QWd1H"
      },
      "source": [
        "Already, I can see some ways that this dataset can be improved. Thus begins the process of preparing the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGyuZYK9Wkbk"
      },
      "source": [
        "## Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDD5xjZlvzsL"
      },
      "source": [
        "results_df['date']=pd.to_datetime(results_df['date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Qr5-2loEubL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d1ef901-6996-451d-b5ca-281badc45066"
      },
      "source": [
        "(results_df['date']).dtype"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('<M8[ns]')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YTh8dXUJ0GBf",
        "outputId": "20e2d5c3-6c73-410a-c9f8-62ae226ff10a"
      },
      "source": [
        "#convert the datetime field in the \"date\" column to something more readable and separate date and time.\n",
        "results_df['Date'] = pd.to_datetime(results_df['date']).dt.date \n",
        "results_df['Time'] = pd.to_datetime(results_df['date']).dt.time \n",
        "results_df.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>date</th>\n",
              "      <th>eilliott_bay_trail_in_myrtle_edwards_park_total</th>\n",
              "      <th>ped_north</th>\n",
              "      <th>ped_south</th>\n",
              "      <th>bike_north</th>\n",
              "      <th>bike_south</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-06-01 00:00:00</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-06-01 01:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>01:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-06-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>02:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-06-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>03:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-06-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>04:00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 date  ...      Time\n",
              "0 2020-06-01 00:00:00  ...  00:00:00\n",
              "1 2020-06-01 01:00:00  ...  01:00:00\n",
              "2 2020-06-01 02:00:00  ...  02:00:00\n",
              "3 2020-06-01 03:00:00  ...  03:00:00\n",
              "4 2020-06-01 04:00:00  ...  04:00:00\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "myevh7Rt0GBf",
        "outputId": "cbee0cab-2d0c-42bf-dfbb-6366e21f7683"
      },
      "source": [
        "#rename columns and add columns to help with data analysis.\n",
        "clean_df = results_df.rename(columns={\"date\": \"Timestamp_Date\", \"eilliott_bay_trail_in_myrtle_edwards_park_total\": \"Total\"})\n",
        "clean_df['Day of Week'] = pd.DatetimeIndex(clean_df['Date']).day_name() # week day name\n",
        "clean_df['Month'] = pd.DatetimeIndex(clean_df['Date']).month_name() # month name\n",
        "clean_df['Year'] = pd.DatetimeIndex(clean_df['Timestamp_Date']).year # year name\n",
        "clean_df.head()\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>Timestamp_Date</th>\n",
              "      <th>Total</th>\n",
              "      <th>ped_north</th>\n",
              "      <th>ped_south</th>\n",
              "      <th>bike_north</th>\n",
              "      <th>bike_south</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Day of Week</th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-06-01 00:00:00</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>Monday</td>\n",
              "      <td>June</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-06-01 01:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>01:00:00</td>\n",
              "      <td>Monday</td>\n",
              "      <td>June</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-06-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>02:00:00</td>\n",
              "      <td>Monday</td>\n",
              "      <td>June</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-06-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>03:00:00</td>\n",
              "      <td>Monday</td>\n",
              "      <td>June</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-06-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>04:00:00</td>\n",
              "      <td>Monday</td>\n",
              "      <td>June</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Timestamp_Date Total ped_north  ... Day of Week Month  Year\n",
              "0 2020-06-01 00:00:00     5         2  ...      Monday  June  2020\n",
              "1 2020-06-01 01:00:00     0         0  ...      Monday  June  2020\n",
              "2 2020-06-01 02:00:00     1         0  ...      Monday  June  2020\n",
              "3 2020-06-01 03:00:00     1         1  ...      Monday  June  2020\n",
              "4 2020-06-01 04:00:00     1         0  ...      Monday  June  2020\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWNj0kvl0GBg"
      },
      "source": [
        "APIrecords = clean_df.to_dict('records')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s5wm-Gt90GBg",
        "outputId": "c516b4e9-846e-4d42-d90b-1e1f60d2db50"
      },
      "source": [
        "len(results_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "64992"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLp_CX_T0GBg"
      },
      "source": [
        "So ```APIrecords``` is a list of dictionaries. Each dictionary represents one hour's worth of data and thus each dictionary has the same keys and different values. There are over 56,000 records in this dataset as of May 2020. As I play around with different functions, I need a quick way to select a subset of records so that every function doesn't iterate through the whole thing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGkHBZfQ0GBh"
      },
      "source": [
        "I create the below ```testlist``` to give myself a small section of the dataset to try out functions on. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4R7nFK200GBh",
        "outputId": "0fc2a8b2-4410-48a4-dedc-293e4847d78e"
      },
      "source": [
        "testlist = APIrecords[:5]\n",
        "testlist"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'Date': datetime.date(2020, 6, 1),\n",
              "  'Day of Week': 'Monday',\n",
              "  'Month': 'June',\n",
              "  'Time': datetime.time(0, 0),\n",
              "  'Timestamp_Date': Timestamp('2020-06-01 00:00:00'),\n",
              "  'Total': '5',\n",
              "  'Year': 2020,\n",
              "  'bike_north': '1',\n",
              "  'bike_south': '1',\n",
              "  'ped_north': '2',\n",
              "  'ped_south': '1'},\n",
              " {'Date': datetime.date(2020, 6, 1),\n",
              "  'Day of Week': 'Monday',\n",
              "  'Month': 'June',\n",
              "  'Time': datetime.time(1, 0),\n",
              "  'Timestamp_Date': Timestamp('2020-06-01 01:00:00'),\n",
              "  'Total': '0',\n",
              "  'Year': 2020,\n",
              "  'bike_north': '0',\n",
              "  'bike_south': '0',\n",
              "  'ped_north': '0',\n",
              "  'ped_south': '0'},\n",
              " {'Date': datetime.date(2020, 6, 1),\n",
              "  'Day of Week': 'Monday',\n",
              "  'Month': 'June',\n",
              "  'Time': datetime.time(2, 0),\n",
              "  'Timestamp_Date': Timestamp('2020-06-01 02:00:00'),\n",
              "  'Total': '1',\n",
              "  'Year': 2020,\n",
              "  'bike_north': '1',\n",
              "  'bike_south': '0',\n",
              "  'ped_north': '0',\n",
              "  'ped_south': '0'},\n",
              " {'Date': datetime.date(2020, 6, 1),\n",
              "  'Day of Week': 'Monday',\n",
              "  'Month': 'June',\n",
              "  'Time': datetime.time(3, 0),\n",
              "  'Timestamp_Date': Timestamp('2020-06-01 03:00:00'),\n",
              "  'Total': '1',\n",
              "  'Year': 2020,\n",
              "  'bike_north': '0',\n",
              "  'bike_south': '0',\n",
              "  'ped_north': '1',\n",
              "  'ped_south': '0'},\n",
              " {'Date': datetime.date(2020, 6, 1),\n",
              "  'Day of Week': 'Monday',\n",
              "  'Month': 'June',\n",
              "  'Time': datetime.time(4, 0),\n",
              "  'Timestamp_Date': Timestamp('2020-06-01 04:00:00'),\n",
              "  'Total': '1',\n",
              "  'Year': 2020,\n",
              "  'bike_north': '1',\n",
              "  'bike_south': '0',\n",
              "  'ped_north': '0',\n",
              "  'ped_south': '0'}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dOGyWdSh0GBh"
      },
      "source": [
        "### Creating Functions to Prepare the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxGPpmR20GBi"
      },
      "source": [
        "def timestamp(mmddyyyy):\n",
        "    return pd.to_datetime(mmddyyyy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3q1O9QV_7r5"
      },
      "source": [
        "def hourlycounts(date, data): #date must be entered as 'mm/dd/yy', including quotes\n",
        "    target_date = timestamp(date) #this caches the function so it doesn't have to recalculate with every iteration. \n",
        "    return [x['Total'] for x in data if x['Date'] == target_date] #this is a cleaner list comprehension. Thank you Stack Overflow.\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3GTjwJc0GBi"
      },
      "source": [
        "If I want to be able to create graphs with time or date as an independent variable, I need to be able to generate a list of all the values associated with a specific key in the list of dictionaries. Below, I create a function and test it out on ```testlist```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNlzUTIL0GBi",
        "outputId": "20c5129f-b97f-4ede-95c9-2432553bbc81"
      },
      "source": [
        "def list_from_dictionary(dictlist, key):\n",
        "    return [sub[key] for sub in dictlist]\n",
        "list_from_dictionary(testlist, 'Day of Week')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Monday', 'Monday', 'Monday', 'Monday', 'Monday']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKj-w8UQ0GBj"
      },
      "source": [
        "Trying this function out on ```testlist``` confirms that it would not filter out duplicates, and if I run this on the whole dataset it would return 54,000 items, which I don't want. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o75MpHP-0GBj"
      },
      "source": [
        "To create a list of all the values for a given key, without duplicates, I need a new function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0w6pU2kV0GBj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "571a3a69-848f-48f3-fdaa-98cd0a37d0a0"
      },
      "source": [
        "#generate list of all unique values in a given column in the dataset. Use to generate x values for graphs and in for loops.\n",
        "def unique(recordlist, key):\n",
        "    new_list=[]\n",
        "    list1 = list_from_dictionary(recordlist, key)\n",
        "    list_set = set(list1)\n",
        "    unique_list = list(list_set)\n",
        "    for x in unique_list:\n",
        "        new_list.append(x)\n",
        "    return new_list\n",
        "unique(testlist, 'Day of Week')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Monday']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdMW_vTi0GBk"
      },
      "source": [
        "I apply this to the 'Time' key, as I plan to use hour of day as an x-value for my initial graphs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbg_dLbo0GBk"
      },
      "source": [
        "alltimes = sorted(unique(APIrecords, 'Time'))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6bNKOqV50GBk"
      },
      "source": [
        "Now I want to know how April 2020 overall compares to other years to see if, overall, April 2020 might see different usage than prior years. I'll choose Fridays in April as a way to compare apples to apples across different years.\n",
        "To start with, the function below would give you, for example, the average count at noon for all the Fridays in April 2020. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3_ipOAO0GBk"
      },
      "source": [
        "def hourly_average_by_day(month, year, weekday, hour):\n",
        "    monthly_total = []\n",
        "    matching_records = list(record for record in APIrecords if record['Day of Week']==weekday and record['Month']==month and record['Time']==hour and record['Year']==year)\n",
        "    for record in matching_records:\n",
        "        monthly_total.append(int(record['Total']))\n",
        "    try: \n",
        "        return sum(monthly_total)/len(monthly_total)\n",
        "    except:\n",
        "        return 'error'\n",
        "\n",
        "#hourly_average_by_day('March', 2017, 'Friday', datetime.time(17, 0))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhAeTlvE0GBl"
      },
      "source": [
        "The function above gives me average counts for a specific hour of a specific weekday in the year and month I provide as arguments. Now I want to be able to apply that function to every year all at once, so I can graph it and see a change over time. Maybe people are using the park at different times of day than they used to, and I'm curious how that's changed. The following function applies ```hourly_average_by_day()``` to every year from 2014-2020."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZ0f6zag0GBl"
      },
      "source": [
        "First, though, I noticed when looking through the data that no counts were recorded for a large part of 2015; the equipment must have been broken. I don't want to get zeros in my plots, so I'm going to omit 2015 data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UFsr8L8R0GBm"
      },
      "source": [
        "def counts_across_years(month, weekday, hour): #note below how the hour must be formatted.\n",
        "    counts_list=[]\n",
        "    for year in [x for x in range(2014, 2021) if x != 2015]:\n",
        "        count = hourly_average_by_day(month, year, weekday, hour)\n",
        "        counts_list.append(count)\n",
        "    return counts_list\n",
        "\n",
        "#counts_across_years('April', 'Friday', datetime.time(12, 0))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gO9H0K1S0GBm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6371eba3-de2d-4824-85c0-f1e864b2b2b2"
      },
      "source": [
        "allyears=[]\n",
        "for i in [x for x in range(2014, 2021) if x != 2015]:\n",
        "    allyears.append(i)\n",
        "allyears"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2014, 2016, 2017, 2018, 2019, 2020]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-sBYOIWGW1DD"
      },
      "source": [
        "## Data Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzT7rD83W5VD"
      },
      "source": [
        "Now I want to start getting a sense of what's actually in this data, so I can determine how I want to analyze it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDxHerCT0GBm"
      },
      "source": [
        "### Graphing Trail Usage Over Time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQmewSVW0GBm"
      },
      "source": [
        "Now it's time to make my first graph. I will use my ```counts_across_years()``` function to graph how average hourly counts at noon for any given weekday have changed from 2014-2020. I am curious if 2020 will continue past trends or show a departure. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KGVhPEiR0GBm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "outputId": "42c8ac0c-c084-4fd8-e754-10bbf16a072d"
      },
      "source": [
        "width = 12\n",
        "height = 10\n",
        "plt.figure(figsize=(width, height))\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Monday', datetime.time(12, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Mondays, 12-1 pm'))\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Tuesday', datetime.time(12, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Tuesdays, 12-1 pm'))\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Wednesday', datetime.time(12, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Wednesdays, 12-1 pm'))\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Thursday', datetime.time(12, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Thursdays, 12-1 pm'))\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Friday', datetime.time(12, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Fridays, 12-1 pm'))\n",
        "fig.update_layout(title='Average Park Activity During Different Days of the Week in April, Lunchtime',\n",
        "                   xaxis_title='Year',\n",
        "                   yaxis_title='Average Bike + Ped Count')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"968c5d5a-6fde-4503-90cd-24db5cd89e2c\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"968c5d5a-6fde-4503-90cd-24db5cd89e2c\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '968c5d5a-6fde-4503-90cd-24db5cd89e2c',\n",
              "                        [{\"mode\": \"lines\", \"name\": \"Mondays, 12-1 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [318.0, 323.0, 274.0, 325.0, 326.8, 386.5]}, {\"mode\": \"lines\", \"name\": \"Tuesdays, 12-1 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [286.6, 316.25, 347.25, 325.25, 314.2, 296.5]}, {\"mode\": \"lines\", \"name\": \"Wednesdays, 12-1 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [229.2, 344.25, 178.0, 252.5, 255.25, 282.0]}, {\"mode\": \"lines\", \"name\": \"Thursdays, 12-1 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [166.5, 326.5, 282.75, 288.5, 255.75, 326.8]}, {\"mode\": \"lines\", \"name\": \"Fridays, 12-1 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [259.0, 277.0, 279.5, 244.25, 196.0, 334.5]}],\n",
              "                        {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Average Park Activity During Different Days of the Week in April, Lunchtime\"}, \"xaxis\": {\"title\": {\"text\": \"Year\"}}, \"yaxis\": {\"title\": {\"text\": \"Average Bike + Ped Count\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('968c5d5a-6fde-4503-90cd-24db5cd89e2c');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zU-JlIDP0GBn"
      },
      "source": [
        "This data suggests that park usage on any given weekday in April 2020 is higher compared to the same weekday in 2019, except on Tuesdays, but compared to prior years there's no recognizable pattern. This is just the lunch hour though. Will this pattern hold after work hours?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "un8Do8IN0GBn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "169e6058-67dc-451c-8922-41c3e09fff13"
      },
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Monday', datetime.time(17, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Mondays, 5-6 pm'))\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Tuesday', datetime.time(17, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Tuesdays, 5-6 pm'))\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Wednesday', datetime.time(17, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Wednesdays, 5-6 pm'))\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Thursday', datetime.time(17, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Thursdays, 5-6 pm'))\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Friday', datetime.time(17, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Fridays, 5-6 pm'))\n",
        "fig.add_trace(go.Scatter(x=allyears, y=counts_across_years('April', 'Saturday', datetime.time(17, 0)),\n",
        "                    mode='lines',\n",
        "                    name='Saturdays, 5-6 pm'))\n",
        "\n",
        "fig.update_layout(title='Average Park Activity During Different Days of the Week in March, Evenings',\n",
        "                   xaxis_title='Year',\n",
        "                   yaxis_title='Average Bike + Ped Count')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"50af977b-f03d-4990-ba95-0b6f5be76443\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"50af977b-f03d-4990-ba95-0b6f5be76443\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '50af977b-f03d-4990-ba95-0b6f5be76443',\n",
              "                        [{\"mode\": \"lines\", \"name\": \"Mondays, 5-6 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [557.25, 486.25, 378.25, 485.6, 770.4, 784.25]}, {\"mode\": \"lines\", \"name\": \"Tuesdays, 5-6 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [421.8, 421.75, 400.5, 512.0, 842.8, 622.0]}, {\"mode\": \"lines\", \"name\": \"Wednesdays, 5-6 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [411.8, 527.75, 260.75, 416.0, 806.25, 589.4]}, {\"mode\": \"lines\", \"name\": \"Thursdays, 5-6 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [266.0, 552.75, 410.25, 444.75, 430.25, 590.2]}, {\"mode\": \"lines\", \"name\": \"Fridays, 5-6 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [341.0, 458.6, 410.75, 353.75, 348.0, 501.0]}, {\"mode\": \"lines\", \"name\": \"Saturdays, 5-6 pm\", \"type\": \"scatter\", \"x\": [2014, 2016, 2017, 2018, 2019, 2020], \"y\": [151.25, 466.6, 233.0, 107.5, 632.0, 544.5]}],\n",
              "                        {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Average Park Activity During Different Days of the Week in March, Evenings\"}, \"xaxis\": {\"title\": {\"text\": \"Year\"}}, \"yaxis\": {\"title\": {\"text\": \"Average Bike + Ped Count\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('50af977b-f03d-4990-ba95-0b6f5be76443');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqbcDI1j0GBn"
      },
      "source": [
        "Interestingly, the same pattern doesn't hold from the 5 to 6 pm hour, at least not every day. While Tuesday and Wednesday afternoons see a steep drop from 2019, Thursday and Fridays see increases. But while in 2019 there was  noticeably higher usage earlier in the week, in 2020 usage is converging on all days except Monday. Perhaps the pandemic has wiped away most of the weekly rhythms that distinguish one weekday from another. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DwzGSEV-0GBo"
      },
      "source": [
        "Now I'd like to get a sense of how usage varies throughout the day. This could help people determine the least crowded time to visit the park, for example. The function below takes a month, year, and weekday (for example, Fridays in March 2020) and returns average counts for each hour: average counts at 10am, average counts at 11am, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kxJtHSD0GBo",
        "outputId": "db5c6ac8-fcf9-4831-bbc9-0eb7bc1c2d18"
      },
      "source": [
        "def monthly_average_by_hour(month, year, weekday):\n",
        "    hourly_average=[]\n",
        "    hourlist = sorted(unique(APIrecords, 'Time'))\n",
        "    for hour in hourlist:\n",
        "        hourly_average.append(hourly_average_by_day(month, year, weekday, hour))\n",
        "    return hourly_average\n",
        "monthly_average_by_hour('April', 2020, 'Friday')\n",
        "#it would be best if this were a dictionary so we could ensure keys and values are matched up."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[12.25,\n",
              " 4.0,\n",
              " 4.75,\n",
              " 2.25,\n",
              " 8.75,\n",
              " 20.0,\n",
              " 112.75,\n",
              " 204.0,\n",
              " 188.75,\n",
              " 210.5,\n",
              " 241.0,\n",
              " 282.75,\n",
              " 334.5,\n",
              " 371.0,\n",
              " 350.0,\n",
              " 403.75,\n",
              " 436.75,\n",
              " 501.0,\n",
              " 529.0,\n",
              " 342.75,\n",
              " 128.0,\n",
              " 40.5,\n",
              " 29.5,\n",
              " 10.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_tqOM1s0GBo"
      },
      "source": [
        "That data looks about right, based on what I've observed on various of my own walks! Usage peaks in late afternoon when people get off work and it is warmer. For now I'm going to apply this function to different years in the dataset to compare trends year over year. But instead of Friday, I'll graph Wednesday, because in transportation terms you get a better picture of people's habits if you choose data in the middle of the week. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLOfxhL50GBo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "outputId": "6ad3c33f-595b-4de5-98ca-4e65edac893b"
      },
      "source": [
        "plt.figure(figsize=(width, height))\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(x=alltimes, y=monthly_average_by_hour('April', 2020, 'Wednesday'),\n",
        "                    mode='lines',\n",
        "                    name='2020'))\n",
        "fig.add_trace(go.Scatter(x=alltimes, y=monthly_average_by_hour('April', 2019, 'Wednesday'),\n",
        "                    mode='lines',\n",
        "                    name='2019'))\n",
        "fig.add_trace(go.Scatter(x=alltimes, y=monthly_average_by_hour('April', 2018, 'Wednesday'),\n",
        "                    mode='lines',\n",
        "                    name='2018'))\n",
        "fig.add_trace(go.Scatter(x=alltimes, y=monthly_average_by_hour('April', 2017, 'Wednesday'),\n",
        "                    mode='lines',\n",
        "                    name='2017'))\n",
        "fig.add_trace(go.Scatter(x=alltimes, y=monthly_average_by_hour('April', 2016, 'Wednesday'),\n",
        "                    mode='lines',\n",
        "                    name='2016'))\n",
        "fig.update_layout(title='Average Hourly Park Activity for Wednesdays in April',\n",
        "                   xaxis_title='Hour of Day',\n",
        "                   yaxis_title='Average Bike + Ped Count')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"a67883c8-19bb-4705-962c-297586a70dd6\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"a67883c8-19bb-4705-962c-297586a70dd6\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        'a67883c8-19bb-4705-962c-297586a70dd6',\n",
              "                        [{\"mode\": \"lines\", \"name\": \"2020\", \"type\": \"scatter\", \"x\": [\"00:00:00\", \"01:00:00\", \"02:00:00\", \"03:00:00\", \"04:00:00\", \"05:00:00\", \"06:00:00\", \"07:00:00\", \"08:00:00\", \"09:00:00\", \"10:00:00\", \"11:00:00\", \"12:00:00\", \"13:00:00\", \"14:00:00\", \"15:00:00\", \"16:00:00\", \"17:00:00\", \"18:00:00\", \"19:00:00\", \"20:00:00\", \"21:00:00\", \"22:00:00\", \"23:00:00\"], \"y\": [3.2, 2.6, 1.0, 6.8, 3.0, 16.2, 95.8, 160.0, 157.8, 130.2, 150.8, 214.4, 282.0, 286.6, 334.0, 391.4, 505.4, 589.4, 642.8, 434.0, 133.6, 45.4, 41.8, 10.2]}, {\"mode\": \"lines\", \"name\": \"2019\", \"type\": \"scatter\", \"x\": [\"00:00:00\", \"01:00:00\", \"02:00:00\", \"03:00:00\", \"04:00:00\", \"05:00:00\", \"06:00:00\", \"07:00:00\", \"08:00:00\", \"09:00:00\", \"10:00:00\", \"11:00:00\", \"12:00:00\", \"13:00:00\", \"14:00:00\", \"15:00:00\", \"16:00:00\", \"17:00:00\", \"18:00:00\", \"19:00:00\", \"20:00:00\", \"21:00:00\", \"22:00:00\", \"23:00:00\"], \"y\": [4.5, 1.75, 3.75, 3.0, 5.0, 31.5, 162.0, 296.75, 293.75, 136.25, 97.75, 124.0, 255.25, 165.5, 215.0, 337.0, 563.5, 806.25, 593.0, 347.5, 95.5, 33.5, 20.5, 7.75]}, {\"mode\": \"lines\", \"name\": \"2018\", \"type\": \"scatter\", \"x\": [\"00:00:00\", \"01:00:00\", \"02:00:00\", \"03:00:00\", \"04:00:00\", \"05:00:00\", \"06:00:00\", \"07:00:00\", \"08:00:00\", \"09:00:00\", \"10:00:00\", \"11:00:00\", \"12:00:00\", \"13:00:00\", \"14:00:00\", \"15:00:00\", \"16:00:00\", \"17:00:00\", \"18:00:00\", \"19:00:00\", \"20:00:00\", \"21:00:00\", \"22:00:00\", \"23:00:00\"], \"y\": [8.75, 4.25, 3.25, 4.25, 4.25, 30.0, 134.75, 271.25, 297.25, 170.25, 110.5, 124.75, 252.5, 167.0, 140.5, 185.5, 268.0, 416.0, 346.0, 217.25, 125.75, 26.5, 15.25, 7.75]}, {\"mode\": \"lines\", \"name\": \"2017\", \"type\": \"scatter\", \"x\": [\"00:00:00\", \"01:00:00\", \"02:00:00\", \"03:00:00\", \"04:00:00\", \"05:00:00\", \"06:00:00\", \"07:00:00\", \"08:00:00\", \"09:00:00\", \"10:00:00\", \"11:00:00\", \"12:00:00\", \"13:00:00\", \"14:00:00\", \"15:00:00\", \"16:00:00\", \"17:00:00\", \"18:00:00\", \"19:00:00\", \"20:00:00\", \"21:00:00\", \"22:00:00\", \"23:00:00\"], \"y\": [5.0, 1.0, 1.0, 3.75, 7.75, 25.75, 116.75, 183.75, 188.5, 97.25, 70.0, 88.75, 178.0, 80.5, 101.75, 112.25, 179.75, 260.75, 253.0, 148.75, 60.25, 14.25, 7.5, 3.25]}, {\"mode\": \"lines\", \"name\": \"2016\", \"type\": \"scatter\", \"x\": [\"00:00:00\", \"01:00:00\", \"02:00:00\", \"03:00:00\", \"04:00:00\", \"05:00:00\", \"06:00:00\", \"07:00:00\", \"08:00:00\", \"09:00:00\", \"10:00:00\", \"11:00:00\", \"12:00:00\", \"13:00:00\", \"14:00:00\", \"15:00:00\", \"16:00:00\", \"17:00:00\", \"18:00:00\", \"19:00:00\", \"20:00:00\", \"21:00:00\", \"22:00:00\", \"23:00:00\"], \"y\": [3.25, 4.75, 1.5, 1.5, 4.5, 18.75, 116.5, 276.75, 363.0, 260.5, 176.0, 189.25, 344.25, 345.75, 227.75, 235.0, 317.5, 527.75, 590.75, 458.5, 243.75, 51.25, 25.0, 12.75]}],\n",
              "                        {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Average Hourly Park Activity for Wednesdays in April\"}, \"xaxis\": {\"title\": {\"text\": \"Hour of Day\"}}, \"yaxis\": {\"title\": {\"text\": \"Average Bike + Ped Count\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('a67883c8-19bb-4705-962c-297586a70dd6');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrMuOcso0GBp"
      },
      "source": [
        "So activity in April 2020 does not really stand out compared to other years."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pkjd9Dy30GBp"
      },
      "source": [
        "So far we've been looking at April 2020 only as a whole. Let's zoom in on April 2020 by graphing hourly counts for every Friday that month. I posit that counts will creep up week after week, as more and more people began working from home and looking for ways to get some exercise during and after work."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TL8adKtX0GBp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "9142a7f6-b6de-493a-856f-5bb91aaefc9d"
      },
      "source": [
        "from plotly.subplots import make_subplots\n",
        "\n",
        "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
        "\n",
        "fig.add_trace(go.Scatter(x=alltimes, y=hourlycounts('4/3/2020', APIrecords),\n",
        "                    mode='lines',\n",
        "                    name='April 3',\n",
        "                    line_color='rgb(40, 246, 212)'),\n",
        "                    secondary_y=False)\n",
        "fig.add_trace(go.Scatter(x=alltimes, y=hourlycounts('4/10/2020', APIrecords),\n",
        "                    mode='lines',\n",
        "                    name='April 10',\n",
        "                    line_color='rgb(27, 164, 161)'),\n",
        "                    secondary_y=False)\n",
        "fig.add_trace(go.Scatter(x=alltimes, y=hourlycounts('4/17/2020', APIrecords),\n",
        "                    mode='lines',\n",
        "                    name='April 17',\n",
        "                    line_color='rgb(15, 82, 110)'),\n",
        "                    secondary_y=False)\n",
        "fig.add_trace(go.Scatter(x=alltimes, y=hourlycounts('4/24/2020', APIrecords),\n",
        "                    mode='lines',\n",
        "                    name='April 24',\n",
        "                    line_color='rgb(3, 1, 60)'),\n",
        "                    secondary_y=False)\n",
        "\n",
        "fig.update_layout(title='Bike and Ped Activity in Myrtle Edwards Park - Fridays During Covid-19 (2020)',\n",
        "                   xaxis_title='Hour of Day',\n",
        "                   yaxis_title='Total Bike + Ped Count')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"13d34a1e-2019-4a1a-ad0c-97532ef6549c\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"13d34a1e-2019-4a1a-ad0c-97532ef6549c\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '13d34a1e-2019-4a1a-ad0c-97532ef6549c',\n",
              "                        [{\"line\": {\"color\": \"rgb(40, 246, 212)\"}, \"mode\": \"lines\", \"name\": \"April 3\", \"type\": \"scatter\", \"x\": [\"00:00:00\", \"01:00:00\", \"02:00:00\", \"03:00:00\", \"04:00:00\", \"05:00:00\", \"06:00:00\", \"07:00:00\", \"08:00:00\", \"09:00:00\", \"10:00:00\", \"11:00:00\", \"12:00:00\", \"13:00:00\", \"14:00:00\", \"15:00:00\", \"16:00:00\", \"17:00:00\", \"18:00:00\", \"19:00:00\", \"20:00:00\", \"21:00:00\", \"22:00:00\", \"23:00:00\"], \"xaxis\": \"x\", \"y\": [\"3\", \"2\", \"0\", \"7\", \"6\", \"7\", \"61\", \"201\", \"148\", \"154\", \"170\", \"187\", \"219\", \"183\", \"208\", \"204\", \"110\", \"243\", \"297\", \"200\", \"46\", \"4\", \"7\", \"10\"], \"yaxis\": \"y\"}, {\"line\": {\"color\": \"rgb(27, 164, 161)\"}, \"mode\": \"lines\", \"name\": \"April 10\", \"type\": \"scatter\", \"x\": [\"00:00:00\", \"01:00:00\", \"02:00:00\", \"03:00:00\", \"04:00:00\", \"05:00:00\", \"06:00:00\", \"07:00:00\", \"08:00:00\", \"09:00:00\", \"10:00:00\", \"11:00:00\", \"12:00:00\", \"13:00:00\", \"14:00:00\", \"15:00:00\", \"16:00:00\", \"17:00:00\", \"18:00:00\", \"19:00:00\", \"20:00:00\", \"21:00:00\", \"22:00:00\", \"23:00:00\"], \"xaxis\": \"x\", \"y\": [\"5\", \"3\", \"2\", \"1\", \"2\", \"33\", \"148\", \"212\", \"195\", \"204\", \"250\", \"338\", \"399\", \"400\", \"449\", \"524\", \"656\", \"698\", \"650\", \"399\", \"174\", \"89\", \"70\", \"22\"], \"yaxis\": \"y\"}, {\"line\": {\"color\": \"rgb(15, 82, 110)\"}, \"mode\": \"lines\", \"name\": \"April 17\", \"type\": \"scatter\", \"x\": [\"00:00:00\", \"01:00:00\", \"02:00:00\", \"03:00:00\", \"04:00:00\", \"05:00:00\", \"06:00:00\", \"07:00:00\", \"08:00:00\", \"09:00:00\", \"10:00:00\", \"11:00:00\", \"12:00:00\", \"13:00:00\", \"14:00:00\", \"15:00:00\", \"16:00:00\", \"17:00:00\", \"18:00:00\", \"19:00:00\", \"20:00:00\", \"21:00:00\", \"22:00:00\", \"23:00:00\"], \"xaxis\": \"x\", \"y\": [\"39\", \"7\", \"14\", \"1\", \"18\", \"23\", \"160\", \"218\", \"235\", \"270\", \"305\", \"364\", \"452\", \"585\", \"435\", \"477\", \"512\", \"518\", \"592\", \"378\", \"141\", \"40\", \"24\", \"6\"], \"yaxis\": \"y\"}, {\"line\": {\"color\": \"rgb(3, 1, 60)\"}, \"mode\": \"lines\", \"name\": \"April 24\", \"type\": \"scatter\", \"x\": [\"00:00:00\", \"01:00:00\", \"02:00:00\", \"03:00:00\", \"04:00:00\", \"05:00:00\", \"06:00:00\", \"07:00:00\", \"08:00:00\", \"09:00:00\", \"10:00:00\", \"11:00:00\", \"12:00:00\", \"13:00:00\", \"14:00:00\", \"15:00:00\", \"16:00:00\", \"17:00:00\", \"18:00:00\", \"19:00:00\", \"20:00:00\", \"21:00:00\", \"22:00:00\", \"23:00:00\"], \"xaxis\": \"x\", \"y\": [\"2\", \"4\", \"3\", \"0\", \"9\", \"17\", \"82\", \"185\", \"177\", \"214\", \"239\", \"242\", \"268\", \"316\", \"308\", \"410\", \"469\", \"545\", \"577\", \"394\", \"151\", \"29\", \"17\", \"2\"], \"yaxis\": \"y\"}],\n",
              "                        {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Bike and Ped Activity in Myrtle Edwards Park - Fridays During Covid-19 (2020)\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.94], \"title\": {\"text\": \"Hour of Day\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Total Bike + Ped Count\"}}, \"yaxis2\": {\"anchor\": \"x\", \"overlaying\": \"y\", \"side\": \"right\"}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('13d34a1e-2019-4a1a-ad0c-97532ef6549c');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aIiQxxbW0GBp"
      },
      "source": [
        "Well, I was wrong. Bicyle and pedestrian counts seem to vary quite a bit by day, and don't seem to be increasing over time. Could weather be a factor in day to day differences? Let's dig into some more data to find out."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPZpZU8i0GBp"
      },
      "source": [
        "### Investigating Weather Factors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SU__N1kZ0GBq"
      },
      "source": [
        "#x axis: weeks or days\n",
        "#y axis: total count of the week\n",
        "alldays = sorted(unique(APIrecords, 'Date')) #what is this for? not sure if sorting will matter once we have a df."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8zw4zp7W0GBq"
      },
      "source": [
        "### Accessing Weather Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-P3dFa0r0GBq"
      },
      "source": [
        "I will use the Climate Data Online API from NOAA: https://www.ncdc.noaa.gov/cdo-web/webservices/v2#gettingStarted. It includes a dataset on historical high temperatures by day. \n",
        "\n",
        "I will also use the cdo-api-py library for this, which is a huge help. https://pypi.org/project/cdo-api-py/. With this library I don't have to worry about creating separate queries to get around request limits, or converting Celsius to Fahrenheit. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WsZTdjjJNjHR",
        "outputId": "f7b2bf98-d456-46ac-cc50-5d9d57d9bffa"
      },
      "source": [
        "!pip install cdo_api_py\n",
        "!pip install config"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting cdo_api_py\n",
            "  Downloading https://files.pythonhosted.org/packages/ca/fc/0a86d9e9068216d971fa351cbd5396ff71bc9d763ba662233ad29119211d/cdo_api_py-1.0.0.dev13-py3-none-any.whl\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from cdo_api_py) (1.1.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from cdo_api_py) (2.23.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->cdo_api_py) (2018.9)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas->cdo_api_py) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->cdo_api_py) (2.8.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->cdo_api_py) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->cdo_api_py) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->cdo_api_py) (2021.5.30)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->cdo_api_py) (3.0.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->cdo_api_py) (1.15.0)\n",
            "Installing collected packages: cdo-api-py\n",
            "Successfully installed cdo-api-py-1.0.0.dev13\n",
            "Collecting config\n",
            "  Downloading https://files.pythonhosted.org/packages/67/af/a7c8be986afee4cf277045cfdb06605296ff3f1a1de415d62c18a7a33040/config-0.5.0.post0-py2.py3-none-any.whl\n",
            "Installing collected packages: config\n",
            "Successfully installed config-0.5.0.post0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbYKkjfINmeE"
      },
      "source": [
        "from cdo_api_py import Client\n",
        "\n",
        "# Utils\n",
        "import config\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "from pprint import pprint\n",
        "\n",
        "# Plotting\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vqjyttENvKI"
      },
      "source": [
        "# Define the client\n",
        "\n",
        "my_client = Client(NOAA_token, default_units='standard', default_limit=1000)\n",
        "\n",
        "######################################\n",
        "#           Configuration            #\n",
        "######################################\n",
        "\n",
        "# Select an area\n",
        "extent = {\n",
        "    \"north\": 47.9,\n",
        "    \"south\": 47.3,\n",
        "    \"east\": -122.26,\n",
        "    \"west\": -122.68,\n",
        "}\n",
        "\n",
        "# Selected Dates\n",
        "startdate = date(2014, 1, 1)\n",
        "enddate = date.today()\n",
        "\n",
        "# Datasets we will query\n",
        "datasetid='GHCND'\n",
        "\n",
        "\n",
        "# PRCP = Precipitation (mm or inches as per user preference, inches to hundredths on Daily Form pdf file)\n",
        "# SNOW = Snowfall (mm or inches as per user preference, inches to tenths on Daily Form pdf file)\n",
        "# TMAX = Maximum temperature (Fahrenheit or Celsius as per user preference, Fahrenheit to tenths on\n",
        "# Daily From pdf file\n",
        "\n",
        "# Data Types\n",
        "datatypeid=['TMAX', 'PRCP', 'SNOW']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eqvpdDNgN5R5",
        "outputId": "af7cc277-0a9d-4e71-fa9f-fe37396c94c9"
      },
      "source": [
        "# Find weather stations with data\n",
        "stations = my_client.find_stations(\n",
        "    datasetid=datasetid,\n",
        "    extent=extent,\n",
        "    startdate=startdate,\n",
        "    enddate=enddate,\n",
        "    datatypeid=datatypeid,\n",
        "    return_dataframe=True)\n",
        "\n",
        "\n",
        "# Create an empty dataframe to store climate data\n",
        "climate_data = pd.DataFrame()\n",
        "\n",
        "for rowid, station in stations.iterrows():  \n",
        "    station_data = my_client.get_data_by_station(\n",
        "        datasetid=datasetid,\n",
        "        stationid=station['id'],\n",
        "        startdate=startdate,\n",
        "        enddate=enddate,\n",
        "        return_dataframe=True,\n",
        "        include_station_meta=True   # flatten station metadata with ghcnd readings\n",
        "    )\n",
        "    climate_data = pd.concat([climate_data, station_data])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/stations?datasetid=GHCND&startdate=2014-01-01&enddate=2021-07-04&extent=47.3&extent=-122.68&extent=47.9&extent=-122.26&datatypeid=TMAX&datatypeid=PRCP&datatypeid=SNOW&limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/stations/GHCND:USW00024233?limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2014-01-01&enddate=2015-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2014-01-01&enddate=2015-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=1000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2014-01-01&enddate=2015-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=2000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2014-01-01&enddate=2015-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=3000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2014-01-01&enddate=2015-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=4000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2015-01-01&enddate=2016-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2015-01-01&enddate=2016-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=1000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2015-01-01&enddate=2016-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=2000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2015-01-01&enddate=2016-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=3000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2015-01-01&enddate=2016-01-01&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=4000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-01-01&enddate=2016-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-01-01&enddate=2016-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=1000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-01-01&enddate=2016-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=2000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-01-01&enddate=2016-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=3000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-01-01&enddate=2016-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=4000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-12-31&enddate=2017-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-12-31&enddate=2017-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=1000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-12-31&enddate=2017-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=2000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-12-31&enddate=2017-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=3000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2016-12-31&enddate=2017-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=4000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2017-12-31&enddate=2018-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2017-12-31&enddate=2018-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=1000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2017-12-31&enddate=2018-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=2000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2017-12-31&enddate=2018-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=3000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2017-12-31&enddate=2018-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=4000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2018-12-31&enddate=2019-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2018-12-31&enddate=2019-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=1000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2018-12-31&enddate=2019-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=2000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2018-12-31&enddate=2019-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=3000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2018-12-31&enddate=2019-12-31&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=4000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2019-12-31&enddate=2020-12-30&stationid=GHCND:USW00024233&limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2019-12-31&enddate=2020-12-30&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=1000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2019-12-31&enddate=2020-12-30&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=2000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2019-12-31&enddate=2020-12-30&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=3000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2019-12-31&enddate=2020-12-30&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=4000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2020-12-30&enddate=2021-07-04&stationid=GHCND:USW00024233&limit=1000&units=standard\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2020-12-30&enddate=2021-07-04&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=1000\n",
            "https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&startdate=2020-12-30&enddate=2021-07-04&stationid=GHCND:USW00024233&limit=1000&units=standard&offset=2000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "zKULvT-iN9dl",
        "outputId": "a7177114-ce5b-4b61-df78-b14c3efd6f0f"
      },
      "source": [
        "climate_data.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>station</th>\n",
              "      <th>date</th>\n",
              "      <th>AWND</th>\n",
              "      <th>PGTM</th>\n",
              "      <th>PRCP</th>\n",
              "      <th>SNOW</th>\n",
              "      <th>SNWD</th>\n",
              "      <th>TAVG</th>\n",
              "      <th>TMAX</th>\n",
              "      <th>TMIN</th>\n",
              "      <th>WDF2</th>\n",
              "      <th>WDF5</th>\n",
              "      <th>WSF2</th>\n",
              "      <th>WSF5</th>\n",
              "      <th>WT01</th>\n",
              "      <th>WT02</th>\n",
              "      <th>WT03</th>\n",
              "      <th>WT04</th>\n",
              "      <th>WT05</th>\n",
              "      <th>WT08</th>\n",
              "      <th>WT09</th>\n",
              "      <th>elevation</th>\n",
              "      <th>mindate</th>\n",
              "      <th>maxdate</th>\n",
              "      <th>latitude</th>\n",
              "      <th>name</th>\n",
              "      <th>datacoverage</th>\n",
              "      <th>elevationUnit</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GHCND:USW00024233</td>\n",
              "      <td>2014-01-01T00:00:00</td>\n",
              "      <td>2.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>8.1</td>\n",
              "      <td>8.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112.8</td>\n",
              "      <td>1948-01-01</td>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>47.4444</td>\n",
              "      <td>SEATTLE TACOMA AIRPORT, WA US</td>\n",
              "      <td>1</td>\n",
              "      <td>METERS</td>\n",
              "      <td>-122.3138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GHCND:USW00024233</td>\n",
              "      <td>2014-01-02T00:00:00</td>\n",
              "      <td>7.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112.8</td>\n",
              "      <td>1948-01-01</td>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>47.4444</td>\n",
              "      <td>SEATTLE TACOMA AIRPORT, WA US</td>\n",
              "      <td>1</td>\n",
              "      <td>METERS</td>\n",
              "      <td>-122.3138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GHCND:USW00024233</td>\n",
              "      <td>2014-01-03T00:00:00</td>\n",
              "      <td>5.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>14.1</td>\n",
              "      <td>16.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112.8</td>\n",
              "      <td>1948-01-01</td>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>47.4444</td>\n",
              "      <td>SEATTLE TACOMA AIRPORT, WA US</td>\n",
              "      <td>1</td>\n",
              "      <td>METERS</td>\n",
              "      <td>-122.3138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GHCND:USW00024233</td>\n",
              "      <td>2014-01-04T00:00:00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>10.1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112.8</td>\n",
              "      <td>1948-01-01</td>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>47.4444</td>\n",
              "      <td>SEATTLE TACOMA AIRPORT, WA US</td>\n",
              "      <td>1</td>\n",
              "      <td>METERS</td>\n",
              "      <td>-122.3138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GHCND:USW00024233</td>\n",
              "      <td>2014-01-05T00:00:00</td>\n",
              "      <td>8.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112.8</td>\n",
              "      <td>1948-01-01</td>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>47.4444</td>\n",
              "      <td>SEATTLE TACOMA AIRPORT, WA US</td>\n",
              "      <td>1</td>\n",
              "      <td>METERS</td>\n",
              "      <td>-122.3138</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             station                 date  ...  elevationUnit  longitude\n",
              "0  GHCND:USW00024233  2014-01-01T00:00:00  ...         METERS  -122.3138\n",
              "1  GHCND:USW00024233  2014-01-02T00:00:00  ...         METERS  -122.3138\n",
              "2  GHCND:USW00024233  2014-01-03T00:00:00  ...         METERS  -122.3138\n",
              "3  GHCND:USW00024233  2014-01-04T00:00:00  ...         METERS  -122.3138\n",
              "4  GHCND:USW00024233  2014-01-05T00:00:00  ...         METERS  -122.3138\n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XtfzFUSg0GBr"
      },
      "source": [
        "### Preparing Count Data for Further Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfBm1H170GBr"
      },
      "source": [
        "First I want take the list of dictionaries `APIrecords` and return a single dictionary with date: count as the key: value pair. However, currently each record represents an hour, so I will need to aggregate these into total daily counts first using a new ``dailysum`` function I create below. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7OibWqH0GBs"
      },
      "source": [
        "def dailysum(date):\n",
        "    strlist = hourlycounts(date, APIrecords) #produces a list of strings\n",
        "    try: \n",
        "        intlist = list(map(int, strlist)) #converts list of strings to integers\n",
        "        return sum(intlist) #sums the integers\n",
        "    except ValueError: #when data is NaN, give a 0 for that day, but it shouldn't affect other days\n",
        "        return 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2FbJWkp0GBs",
        "outputId": "8e1d1179-e0c2-402b-f320-27dc61c9902a"
      },
      "source": [
        "dailysum('03/20/2020')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6028"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wS99d7DS0GBs"
      },
      "source": [
        "Next I will utilize the `unique` function I created earlier to generate a unique list of all dates in the dataset which I will then plug into the next function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKXxc-SC0GBt"
      },
      "source": [
        "alldates = sorted(unique(APIrecords, 'Date')) \n",
        "testdates = alldates[2300:2305] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xaQTNvaBJnaQ",
        "outputId": "295fa06d-0d1f-43b1-a00f-578636f8eb1c"
      },
      "source": [
        "testdates"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[datetime.date(2020, 4, 19),\n",
              " datetime.date(2020, 4, 20),\n",
              " datetime.date(2020, 4, 21),\n",
              " datetime.date(2020, 4, 22),\n",
              " datetime.date(2020, 4, 23)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDDKopY70GBt"
      },
      "source": [
        "Finally I create `dict_of_counts` to get my dictionary with date:count as the key:value pair. I test it with the `testdates` list I created above, which is just a subset of the `alldates` list. `alldates` is over 2000 dates so it takes a long time to run."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwzZx1ix0GBt",
        "outputId": "9f501bc7-7b25-4b5f-8a9b-b77c35ae1dcd"
      },
      "source": [
        "def dict_of_counts(datelist):\n",
        "    master_dict = {}\n",
        "    for date in datelist:\n",
        "        master_dict.update({date: (dailysum(date))})\n",
        "        #master_dict[date] = dailysum(date)\n",
        "    return master_dict\n",
        "\n",
        "print(dict_of_counts(testdates))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{datetime.date(2020, 4, 19): 8319, datetime.date(2020, 4, 20): 6920, datetime.date(2020, 4, 21): 3752, datetime.date(2020, 4, 22): 1754, datetime.date(2020, 4, 23): 3299}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6N6yIRIP0GBt"
      },
      "source": [
        "### Preparing Weather Data for Further Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muv8v7kY0GBt"
      },
      "source": [
        "OK, that is the counts dictionary. Now I want to work on simplifying the weather dataframe."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EwDTCjeK0GBu"
      },
      "source": [
        "Now I need to create a new dataframe with temperature and count so I can associate each one with the same date and therefore ensure that the temperature and count are matched properly when I do a regression."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmIXTRry0GBv"
      },
      "source": [
        "date_list = alldates"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-e92_8uj0GBv"
      },
      "source": [
        "counts_series = pd.Series(dict_of_counts(date_list))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXjvDmSL0U2W"
      },
      "source": [
        "counts_series.index = dict_of_counts(date_list).keys()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FC5PR0Y0Ya0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f4f1af1-28cf-4e79-8972-d441e48fbd86"
      },
      "source": [
        "counts_series.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2014-01-01    2190\n",
              "2014-01-02    1580\n",
              "2014-01-03    2469\n",
              "2014-01-04    3337\n",
              "2014-01-05    2849\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpPHQWDU0GBv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6c3420e-d28e-4873-fcb5-9b5ee63bba66"
      },
      "source": [
        "temp_series = climate_data.iloc[:,8]\n",
        "temp_series.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    45.0\n",
              "1    51.0\n",
              "2    48.0\n",
              "3    46.0\n",
              "4    47.0\n",
              "Name: TMAX, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19J2r25FVWd9",
        "outputId": "0e31f41a-0dd1-4089-975f-35ec5dfe3f12"
      },
      "source": [
        "temp_series.index = pd.to_datetime(climate_data.iloc[:,1]).dt.date #takes column 1 from the climate_data df and uses it as the index here. At the same time, converts the date to the same format as temp_series. \n",
        "temp_series.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2014-01-01    45.0\n",
              "2014-01-02    51.0\n",
              "2014-01-03    48.0\n",
              "2014-01-04    46.0\n",
              "2014-01-05    47.0\n",
              "Name: TMAX, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BX3LotNIwDTO"
      },
      "source": [
        "Let's do the same for precipitation as we did for temperature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvSesZa1wGIN",
        "outputId": "9a202594-a89f-4c97-ce1b-ae9e392bd98c"
      },
      "source": [
        "precip_series = climate_data.iloc[:,4]\n",
        "precip_series.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.00\n",
              "1    0.16\n",
              "2    0.06\n",
              "3    0.00\n",
              "4    0.00\n",
              "Name: PRCP, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tu4zbjEgwdrn",
        "outputId": "d01f0189-2a3f-4a29-b8a4-0b69f4edf66e"
      },
      "source": [
        "precip_series.index = pd.to_datetime(climate_data.iloc[:,1]).dt.date #takes column 1 from the climate_data df and uses it as the index here. At the same time, converts the date to the same format as temp_series. \n",
        "precip_series.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2014-01-01    0.00\n",
              "2014-01-02    0.16\n",
              "2014-01-03    0.06\n",
              "2014-01-04    0.00\n",
              "2014-01-05    0.00\n",
              "Name: PRCP, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k89eYywQ8tWC"
      },
      "source": [
        "##Putting Back Into a Dataframe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KrMvVRFP3Yw"
      },
      "source": [
        "Now we put all the series together into one dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ccY2Ybn0GBw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cabcbb45-28fd-4759-a876-dea49fec5a2d"
      },
      "source": [
        "df_dict = {'count':counts_series, 'high temperature':temp_series, 'precipitation':precip_series}\n",
        "df = pd.DataFrame(df_dict)\n",
        "df.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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>2190.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>1580.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>2469.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-04</th>\n",
              "      <td>3337.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-05</th>\n",
              "      <td>2849.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count  high temperature  precipitation\n",
              "2014-01-01  2190.0              45.0           0.00\n",
              "2014-01-02  1580.0              51.0           0.16\n",
              "2014-01-03  2469.0              48.0           0.06\n",
              "2014-01-04  3337.0              46.0           0.00\n",
              "2014-01-05  2849.0              47.0           0.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pVCrr6kV7Yji"
      },
      "source": [
        "### Plotting the Distribution of Counts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JpuEIMqK7j1v"
      },
      "source": [
        "Before I do anything, it's a good idea to see how this data is distributed. Certain analyses require a normal distribution, so if it's is not nrmal, I may need to evaluate other methods."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "zKMTjH7f7vPP",
        "outputId": "8703c4f3-15d9-4a67-e9bf-a89c90a812fa"
      },
      "source": [
        "plt.figure(figsize=(width, height))\n",
        "\n",
        "ax1 = sns.histplot(data=df['count'], stat='count', bins=60)\n",
        "\n",
        "plt.title('Distribution of Counts')\n",
        "plt.xlabel('Daily Ped + Bike Count')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xM-cRe2NBsdO"
      },
      "source": [
        "This distribution is fairly normal-looking, except for the obvious outliers to the right."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UcP9tX4NCLX1"
      },
      "source": [
        "##Dealing with Outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ptwLNctK4T7"
      },
      "source": [
        "There are some extreme outliers here, and I'm not going to be able to make a good model as long as they are there. I suspect they are not mistakes, though, so it's not really right to just remove them. I think there was likely a special event that day that caused numbers to jump. But building a model that accounts for any special event is going to be tough. Let's see if we can understand these outliers a bit more."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAGeJ23nGGut",
        "outputId": "5ef585ab-ac1d-4386-9a53-81470735e15b"
      },
      "source": [
        "df['count'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     2708.000000\n",
              "mean      3669.436484\n",
              "std       2074.585832\n",
              "min          0.000000\n",
              "25%       2194.500000\n",
              "50%       3462.000000\n",
              "75%       4918.250000\n",
              "max      23740.000000\n",
              "Name: count, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlBG4-i8LRLa"
      },
      "source": [
        "A common way to define outliers is values more than 3 standard deviations above the mean. Based on the above, this would be any value over about 10,000, which sounds about right given the graph above. I build an array of the rows with z-scores over 3. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DnXpFVkCOG1",
        "outputId": "f62aad50-43f5-409c-ad7d-f059925e8146"
      },
      "source": [
        "z_array = scipy.stats.zscore(df['count'])\n",
        "z_array\n",
        "outliers = np.where(z_array>3)\n",
        "outliers = list(outliers)\n",
        "outliers\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([], dtype=int64)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muMwUgnzLlAa"
      },
      "source": [
        "This array is hard to work with; I want it in a list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzBTOSnkMBv1"
      },
      "source": [
        "output = [item for t in outliers for item in t]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wk-6owkFLnqU"
      },
      "source": [
        "Now these numbers refer to the sequence of the value in the long list of dates, so they don't mean much until I can associate the data with them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "zi1wbN7mJ0Ma",
        "outputId": "2ce8cddb-169b-428d-fc60-35efded7e6b1"
      },
      "source": [
        "df.iloc[output]"
      ],
      "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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [count, high temperature, precipitation]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNOQLsTMKK4v"
      },
      "source": [
        "I notice that many of these outliers are occurring in the same week of August for several years in a row. Was this a special event? I googled one of the date ranges: \"August 16-18, 2019, Myrtle Edwards Park.\" I got my answer clear as day - it was the week of Seattle's annual tribute to cannabis, [Hempfest](http://www.hempfest.org/about/seattle-hempfest-2/), which happens in the park. It figures!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rIoVzSbFPi3S"
      },
      "source": [
        "## Incorporating Other Variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9ZClW3eNJ0E"
      },
      "source": [
        "I am going to incorporate a variable \"Hempfest\" because the festival has such a big influence on bike and ped counts on the days it's held. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5ubdIYzlzG8"
      },
      "source": [
        "I look up the dates that the festival has been held since 2014. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pa5l62JLauHL",
        "outputId": "2e7de2f0-d4dc-4831-cee0-97c4c8435510"
      },
      "source": [
        "Hempfestlist=pd.DatetimeIndex(['2014-08-15', '2014-08-16', '2014-08-17', '2015-08-14', '2015-08-15', '2015-08-16', '2016-08-19', '2016-08-20', '2016-08-21', '2017-08-18', '2017-08-19', '2017-08-20', '2018-08-17', '2018-08-18', '2018-08-19', '2019-08-16', '2019-08-15', '2019-08-14']) \n",
        "Hempfestlist"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2014-08-15', '2014-08-16', '2014-08-17', '2015-08-14',\n",
              "               '2015-08-15', '2015-08-16', '2016-08-19', '2016-08-20',\n",
              "               '2016-08-21', '2017-08-18', '2017-08-19', '2017-08-20',\n",
              "               '2018-08-17', '2018-08-18', '2018-08-19', '2019-08-16',\n",
              "               '2019-08-15', '2019-08-14'],\n",
              "              dtype='datetime64[ns]', freq=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_tm-cLCqiEKV",
        "outputId": "491fb682-15eb-4b8e-8d3b-1932849c5b76"
      },
      "source": [
        "start = df.index.min()\n",
        "end = df.index.max()\n",
        "dr = pd.date_range(start, end)\n",
        "df['Hempfest'] = dr.isin(Hempfestlist).astype(int)\n",
        "df.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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>2190.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>1580.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>2469.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-04</th>\n",
              "      <td>3337.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-05</th>\n",
              "      <td>2849.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count  high temperature  precipitation  Hempfest\n",
              "2014-01-01  2190.0              45.0           0.00         0\n",
              "2014-01-02  1580.0              51.0           0.16         0\n",
              "2014-01-03  2469.0              48.0           0.06         0\n",
              "2014-01-04  3337.0              46.0           0.00         0\n",
              "2014-01-05  2849.0              47.0           0.00         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "calEJAuuPprk"
      },
      "source": [
        "That should help a lot, so I'm going to leave outliers alone for now. There are a couple other variables that I need to account for here which I expect to have an influence on some of the regular variation. First, was it a weekend or not? I would expect parks to be much more crowded on weekends."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqfFM2hVP4_s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "406efd05-141a-4197-a9ac-2dee5e3f5b8f"
      },
      "source": [
        "df['Is Weekend?'] = ((pd.DatetimeIndex(df.index).dayofweek) // 5 == 1).astype(int)\n",
        "df.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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Is Weekend?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>2190.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>1580.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>2469.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-04</th>\n",
              "      <td>3337.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-05</th>\n",
              "      <td>2849.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count  high temperature  precipitation  Hempfest  Is Weekend?\n",
              "2014-01-01  2190.0              45.0           0.00         0            0\n",
              "2014-01-02  1580.0              51.0           0.16         0            0\n",
              "2014-01-03  2469.0              48.0           0.06         0            0\n",
              "2014-01-04  3337.0              46.0           0.00         0            1\n",
              "2014-01-05  2849.0              47.0           0.00         0            1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fPxfoMKhUS7V"
      },
      "source": [
        "And on holidays:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLchTRESU5wZ"
      },
      "source": [
        "from pandas.tseries.holiday import USFederalHolidayCalendar as calendar"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XONi8wbJUW0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "4966097e-3871-4cfc-e816-d5e66b3dd81b"
      },
      "source": [
        "start = df.index.min()\n",
        "end = df.index.max()\n",
        "dr = pd.date_range(start, end)\n",
        "cal = calendar()\n",
        "holidays = cal.holidays(start=dr.min(), end=dr.max())\n",
        "\n",
        "df['Is Holiday?'] = dr.isin(holidays).astype(int)\n",
        "df"
      ],
      "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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Is Weekend?</th>\n",
              "      <th>Is Holiday?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>2190.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>1580.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>2469.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-04</th>\n",
              "      <td>3337.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-05</th>\n",
              "      <td>2849.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>102.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>104.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>108.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2738 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             count  high temperature  ...  Is Weekend?  Is Holiday?\n",
              "2014-01-01  2190.0              45.0  ...            0            1\n",
              "2014-01-02  1580.0              51.0  ...            0            0\n",
              "2014-01-03  2469.0              48.0  ...            0            0\n",
              "2014-01-04  3337.0              46.0  ...            1            0\n",
              "2014-01-05  2849.0              47.0  ...            1            0\n",
              "...            ...               ...  ...          ...          ...\n",
              "2021-06-26     NaN             102.0  ...            1            0\n",
              "2021-06-27     NaN             104.0  ...            1            0\n",
              "2021-06-28     NaN             108.0  ...            0            0\n",
              "2021-06-29     NaN              85.0  ...            0            0\n",
              "2021-06-30     NaN              74.0  ...            0            0\n",
              "\n",
              "[2738 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ui7E8cSUXUWk"
      },
      "source": [
        "Let's make sure it looks like we got all the holidays:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Us06ETOPVt2m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ae786d0-8769-4a25-9f8c-bb33dc968d9f"
      },
      "source": [
        "df['Is Holiday?'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2664\n",
              "1      74\n",
              "Name: Is Holiday?, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHr85xkEXZQi"
      },
      "source": [
        "Now let's combine these two columns into a single variable showing if it is a weekend or a holiday, or neither. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9sLotkgVXfGF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "d17d6c5c-7168-4407-c2dd-b98cb08968e5"
      },
      "source": [
        "df['Weekend or Holiday?'] = ((df['Is Weekend?'] + df['Is Holiday?']) >= 1).astype(int)\n",
        "df = df.drop(['Is Weekend?', 'Is Holiday?'], axis=1)\n",
        "df"
      ],
      "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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>2190.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>1580.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>2469.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-04</th>\n",
              "      <td>3337.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-05</th>\n",
              "      <td>2849.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>102.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>104.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>108.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2738 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             count  high temperature  ...  Hempfest  Weekend or Holiday?\n",
              "2014-01-01  2190.0              45.0  ...         0                    1\n",
              "2014-01-02  1580.0              51.0  ...         0                    0\n",
              "2014-01-03  2469.0              48.0  ...         0                    0\n",
              "2014-01-04  3337.0              46.0  ...         0                    1\n",
              "2014-01-05  2849.0              47.0  ...         0                    1\n",
              "...            ...               ...  ...       ...                  ...\n",
              "2021-06-26     NaN             102.0  ...         0                    1\n",
              "2021-06-27     NaN             104.0  ...         0                    1\n",
              "2021-06-28     NaN             108.0  ...         0                    0\n",
              "2021-06-29     NaN              85.0  ...         0                    0\n",
              "2021-06-30     NaN              74.0  ...         0                    0\n",
              "\n",
              "[2738 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JM4_Z4JGP7k7"
      },
      "source": [
        "Let's also look at data during the pandemic to see if stay at home orders caused more people to seek solace in parks. I add a variable for \"during pandemic?\" or not, using a (somewhat subjective) estimation for when the pandemic really took hold."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxGUP738QBbL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "b511c008-d023-4c42-efaa-efbc0de4be52"
      },
      "source": [
        "date = df.index\n",
        "pandemic_onset = pd.to_datetime('2020-03-08')\n",
        "df['During Pandemic?'] = (date >= pandemic_onset).astype(int)\n",
        "df"
      ],
      "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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "      <th>During Pandemic?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>2190.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>1580.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>2469.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-04</th>\n",
              "      <td>3337.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-05</th>\n",
              "      <td>2849.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>102.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>104.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>108.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2738 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             count  high temperature  ...  Weekend or Holiday?  During Pandemic?\n",
              "2014-01-01  2190.0              45.0  ...                    1                 0\n",
              "2014-01-02  1580.0              51.0  ...                    0                 0\n",
              "2014-01-03  2469.0              48.0  ...                    0                 0\n",
              "2014-01-04  3337.0              46.0  ...                    1                 0\n",
              "2014-01-05  2849.0              47.0  ...                    1                 0\n",
              "...            ...               ...  ...                  ...               ...\n",
              "2021-06-26     NaN             102.0  ...                    1                 1\n",
              "2021-06-27     NaN             104.0  ...                    1                 1\n",
              "2021-06-28     NaN             108.0  ...                    0                 1\n",
              "2021-06-29     NaN              85.0  ...                    0                 1\n",
              "2021-06-30     NaN              74.0  ...                    0                 1\n",
              "\n",
              "[2738 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LcPiLDPADZS_"
      },
      "source": [
        "##Putting it All Together"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXWcrOOnfWzz"
      },
      "source": [
        "Let's evaluate this dataframe to see if we have any missing data. We will be limited in how much analysis we can do if data is missing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wSEGc2feQjn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b8b73831-80d0-44a5-eeb8-ba92dc02631d"
      },
      "source": [
        "missing_data = df.isnull()\n",
        "missing_data.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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "      <th>During Pandemic?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-04</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-05</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            count  high temperature  ...  Weekend or Holiday?  During Pandemic?\n",
              "2014-01-01  False             False  ...                False             False\n",
              "2014-01-02  False             False  ...                False             False\n",
              "2014-01-03  False             False  ...                False             False\n",
              "2014-01-04  False             False  ...                False             False\n",
              "2014-01-05  False             False  ...                False             False\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmG77W3Hebrt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5940733-bd5b-4d2b-9ce2-f9be235144ed"
      },
      "source": [
        "for column in missing_data.columns.values.tolist():\n",
        "    print(column)\n",
        "    print (missing_data[column].value_counts())\n",
        "    print(\"\")  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count\n",
            "False    2708\n",
            "True       30\n",
            "Name: count, dtype: int64\n",
            "\n",
            "high temperature\n",
            "False    2737\n",
            "True        1\n",
            "Name: high temperature, dtype: int64\n",
            "\n",
            "precipitation\n",
            "False    2737\n",
            "True        1\n",
            "Name: precipitation, dtype: int64\n",
            "\n",
            "Hempfest\n",
            "False    2738\n",
            "Name: Hempfest, dtype: int64\n",
            "\n",
            "Weekend or Holiday?\n",
            "False    2738\n",
            "Name: Weekend or Holiday?, dtype: int64\n",
            "\n",
            "During Pandemic?\n",
            "False    2738\n",
            "Name: During Pandemic?, dtype: int64\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O0y-1Cb1fgPS"
      },
      "source": [
        "We can see here that (at least at the time of writing) there are a few dates missing a count value, 1 date missing temperature, and 1 date missing precipitation. Let's start with count since it has the most NaN values. There are many ways to deal with this missing data. In some instances I could replace these NaN values with the average count. But since count is my target - what I'm trying to predict - that doesn't seem like a good idea. The NaN values account for less 1% of my total dataset. Therefore, I could just remove these records.\n",
        "\n",
        "But wait - let's make sure that these missing count values don't just fall at the end of the dataset because the API hasn't updated yet. If I look at the last 30 rows, that should be enough to show if all 24 missing temperatures are at the end."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnIZmRyYkwG-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "outputId": "72de7d71-1b0e-4f3b-c644-d03854aaffc5"
      },
      "source": [
        "df.tail(30)"
      ],
      "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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "      <th>During Pandemic?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-06-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-02</th>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-03</th>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-04</th>\n",
              "      <td>NaN</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-05</th>\n",
              "      <td>NaN</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-06</th>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-07</th>\n",
              "      <td>NaN</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-08</th>\n",
              "      <td>NaN</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-09</th>\n",
              "      <td>NaN</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-10</th>\n",
              "      <td>NaN</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-12</th>\n",
              "      <td>NaN</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-14</th>\n",
              "      <td>NaN</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-16</th>\n",
              "      <td>NaN</td>\n",
              "      <td>72.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-18</th>\n",
              "      <td>NaN</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>76.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-20</th>\n",
              "      <td>NaN</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>89.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>76.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-23</th>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-24</th>\n",
              "      <td>NaN</td>\n",
              "      <td>83.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>102.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>104.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>108.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            count  high temperature  ...  Weekend or Holiday?  During Pandemic?\n",
              "2021-06-01    NaN              86.0  ...                    0                 1\n",
              "2021-06-02    NaN              85.0  ...                    0                 1\n",
              "2021-06-03    NaN              75.0  ...                    0                 1\n",
              "2021-06-04    NaN              68.0  ...                    0                 1\n",
              "2021-06-05    NaN              66.0  ...                    1                 1\n",
              "2021-06-06    NaN              55.0  ...                    1                 1\n",
              "2021-06-07    NaN              63.0  ...                    0                 1\n",
              "2021-06-08    NaN              69.0  ...                    0                 1\n",
              "2021-06-09    NaN              68.0  ...                    0                 1\n",
              "2021-06-10    NaN              65.0  ...                    0                 1\n",
              "2021-06-11    NaN              59.0  ...                    0                 1\n",
              "2021-06-12    NaN              73.0  ...                    1                 1\n",
              "2021-06-13    NaN              64.0  ...                    1                 1\n",
              "2021-06-14    NaN              65.0  ...                    0                 1\n",
              "2021-06-15    NaN              69.0  ...                    0                 1\n",
              "2021-06-16    NaN              72.0  ...                    0                 1\n",
              "2021-06-17    NaN               NaN  ...                    0                 1\n",
              "2021-06-18    NaN              74.0  ...                    0                 1\n",
              "2021-06-19    NaN              76.0  ...                    1                 1\n",
              "2021-06-20    NaN              81.0  ...                    1                 1\n",
              "2021-06-21    NaN              89.0  ...                    0                 1\n",
              "2021-06-22    NaN              76.0  ...                    0                 1\n",
              "2021-06-23    NaN              77.0  ...                    0                 1\n",
              "2021-06-24    NaN              83.0  ...                    0                 1\n",
              "2021-06-25    NaN              87.0  ...                    0                 1\n",
              "2021-06-26    NaN             102.0  ...                    1                 1\n",
              "2021-06-27    NaN             104.0  ...                    1                 1\n",
              "2021-06-28    NaN             108.0  ...                    0                 1\n",
              "2021-06-29    NaN              85.0  ...                    0                 1\n",
              "2021-06-30    NaN              74.0  ...                    0                 1\n",
              "\n",
              "[30 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31B4f5k4k74z"
      },
      "source": [
        "They are! In fact, this reminds me that the count data API only refreshes monthly and it looks like the NOAA data is a couple days behind. But that's ok, because I can exclude that data for now, but when I re-run this code in a future month, it will have updated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgtIkmBagTB_"
      },
      "source": [
        "df.dropna(subset=['count'], axis=0, inplace=True)\n",
        "df.dropna(subset=['high temperature'], axis=0, inplace=True)\n",
        "df.dropna(subset=['precipitation'], axis=0, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3DRCCyRnutqg"
      },
      "source": [
        "Now let's be sure that we took care of all our NaN values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJB0SZJwumb1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87d3bdc7-a073-4c4e-de9c-82c913d43169"
      },
      "source": [
        "missing_data = df.isnull()\n",
        "missing_data.tail()\n",
        "\n",
        "for column in missing_data.columns.values.tolist():\n",
        "    print(column)\n",
        "    print (missing_data[column].value_counts())\n",
        "    print(\"\")  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count\n",
            "False    2708\n",
            "Name: count, dtype: int64\n",
            "\n",
            "high temperature\n",
            "False    2708\n",
            "Name: high temperature, dtype: int64\n",
            "\n",
            "precipitation\n",
            "False    2708\n",
            "Name: precipitation, dtype: int64\n",
            "\n",
            "Hempfest\n",
            "False    2708\n",
            "Name: Hempfest, dtype: int64\n",
            "\n",
            "Weekend or Holiday?\n",
            "False    2708\n",
            "Name: Weekend or Holiday?, dtype: int64\n",
            "\n",
            "During Pandemic?\n",
            "False    2708\n",
            "Name: During Pandemic?, dtype: int64\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ecmno1gXuypp"
      },
      "source": [
        "no values are marked \"True,\" which would mean they were null values in this case, so we're good to go."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85ori6ZOdvZl"
      },
      "source": [
        "## Descriptive Statistics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsJOumRyd0We"
      },
      "source": [
        "Now let's run some basic stats to understand how counts are distributed based on these different variables. It will help me know what to expect when I run a regression. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0Qi8Mard94Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "717a9734-cea7-4788-e631-d702d87f6beb"
      },
      "source": [
        "df.describe()"
      ],
      "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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "      <th>During Pandemic?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2708.000000</td>\n",
              "      <td>2708.000000</td>\n",
              "      <td>2708.000000</td>\n",
              "      <td>2708.000000</td>\n",
              "      <td>2708.000000</td>\n",
              "      <td>2708.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3669.436484</td>\n",
              "      <td>61.761817</td>\n",
              "      <td>0.116507</td>\n",
              "      <td>0.006647</td>\n",
              "      <td>0.313146</td>\n",
              "      <td>0.166174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2074.585832</td>\n",
              "      <td>13.099480</td>\n",
              "      <td>0.260973</td>\n",
              "      <td>0.081273</td>\n",
              "      <td>0.463859</td>\n",
              "      <td>0.372306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2194.500000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3462.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4918.250000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>23740.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>3.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              count  high temperature  ...  Weekend or Holiday?  During Pandemic?\n",
              "count   2708.000000       2708.000000  ...          2708.000000       2708.000000\n",
              "mean    3669.436484         61.761817  ...             0.313146          0.166174\n",
              "std     2074.585832         13.099480  ...             0.463859          0.372306\n",
              "min        0.000000         29.000000  ...             0.000000          0.000000\n",
              "25%     2194.500000         51.000000  ...             0.000000          0.000000\n",
              "50%     3462.000000         60.000000  ...             0.000000          0.000000\n",
              "75%     4918.250000         72.000000  ...             1.000000          0.000000\n",
              "max    23740.000000         98.000000  ...             1.000000          1.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7AaKX24RT4v"
      },
      "source": [
        "At this point I might choose to standardize the variables to make them all on the same scale. I can see here that ``Temperature`` is on a very different scale than ``count`` which is different than the 1/0 binary scale that ``Weekend or Holiday?`` and ``During Pandemic?`` use. While standardizing might help make the data easier to work with, it should not affect the r-squared value or the relationships between the variables, as I understand. So I am not going to worry about that here, because standardizing might cause the variables to lose meaning when I go to interpret the results. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "efR79dUqDf9k"
      },
      "source": [
        "##Evaluating the Relationships between Variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzZkcDNqxLr9"
      },
      "source": [
        "Let's take an initial look at the correlation between some of these variables to see which relationships are worth exploring further."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcDwclldXSzN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "aa9985b8-a5b4-407a-ed2e-c00bb357f6f7"
      },
      "source": [
        "df.corr()"
      ],
      "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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "      <th>During Pandemic?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.660293</td>\n",
              "      <td>-0.390282</td>\n",
              "      <td>0.456279</td>\n",
              "      <td>0.005387</td>\n",
              "      <td>0.088211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>high temperature</th>\n",
              "      <td>0.660293</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.247088</td>\n",
              "      <td>0.100032</td>\n",
              "      <td>-0.038181</td>\n",
              "      <td>-0.009151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>precipitation</th>\n",
              "      <td>-0.390282</td>\n",
              "      <td>-0.247088</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.014928</td>\n",
              "      <td>-0.004021</td>\n",
              "      <td>-0.027139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hempfest</th>\n",
              "      <td>0.456279</td>\n",
              "      <td>0.100032</td>\n",
              "      <td>-0.014928</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.042757</td>\n",
              "      <td>-0.036518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "      <td>0.005387</td>\n",
              "      <td>-0.038181</td>\n",
              "      <td>-0.004021</td>\n",
              "      <td>0.042757</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.001959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>During Pandemic?</th>\n",
              "      <td>0.088211</td>\n",
              "      <td>-0.009151</td>\n",
              "      <td>-0.027139</td>\n",
              "      <td>-0.036518</td>\n",
              "      <td>-0.001959</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        count  ...  During Pandemic?\n",
              "count                1.000000  ...          0.088211\n",
              "high temperature     0.660293  ...         -0.009151\n",
              "precipitation       -0.390282  ...         -0.027139\n",
              "Hempfest             0.456279  ...         -0.036518\n",
              "Weekend or Holiday?  0.005387  ...         -0.001959\n",
              "During Pandemic?     0.088211  ...          1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyJuQTHRQR-m"
      },
      "source": [
        "First off, I'm surprised that there is no relationship between the count and the 'Weekend or Holiday?' variable. Let's plot this to understand it better:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uV7AgSLAeQyO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "ef583a16-a66e-426c-cd53-3eb49d35ab3c"
      },
      "source": [
        "sns.boxplot(x=\"Weekend or Holiday?\", y=\"count\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f29912230d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "QTbJEY5yf7gC"
      },
      "source": [
        "So while overall there is significant overlap of counts between weekends/holidays and regular weekdays, there are more outliers on the high end during weekends. Therefore, this alone isn't as good a predictor as I would have expected, but we can still explore it when it is combined with another variable like weather. After all, it makes sense to me that if the weather is bad it doesn't much matter what day it is, I'm not likely to go to the park. But if it's Saturday and it's sunny - I bet we'll see something there. We will get to that soon."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59vKr9wNWRS1"
      },
      "source": [
        "Let's do the same boxplot for whether we're in a pandemic or not."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HzO2j6dfmH5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f252f5f9-4558-4787-8fff-969e784ab804"
      },
      "source": [
        "sns.boxplot(x=\"During Pandemic?\", y=\"count\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2990e951d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "IPKzdE-9fp1-"
      },
      "source": [
        "Again, there's a lot of overlap there, but most of the outliers this time are in pre-pandemic days. Now that we know that those outliers are mostly the Hempfest festival, it makes sense. It wasn't held in person during the pandemic. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBYHur60w9ds"
      },
      "source": [
        "Just to see how satisfying a boxplot can look when there is a lot of variation, let's look at Hempfest days and non-Hempfest days."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "7txdHohUxJKz",
        "outputId": "ed387be3-386a-4285-ef94-23444fd8a101"
      },
      "source": [
        "sns.boxplot(x=\"Hempfest\", y=\"count\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2990ba2950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "3FBDrsVmxP6v"
      },
      "source": [
        "Though there are only a handful of datapoints where Hempfest=1 (meaning Hempfest was held that day), the variation is so stark that I can see why the correlation with ``counts`` is so strong."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A7CfZZea-5ef"
      },
      "source": [
        "#Looking at Precipitation a Different Way"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qzGKeQTDh1Sm"
      },
      "source": [
        "I also notice that the relationship between count and precipitation is negative, which makes sense - more rain generally makes people less apt to go outside, even in Seattle. But the relationship is weak, at -.38. I have a hypothesis that it doesn't so much matter *how much* it rains as *whether* it rains that day. In Seattle our *measured annual rainfall* is actually not as high as many other places (including Hawaii), but we have more *days of rain* per year.\n",
        "\n",
        "So there is another variable in particular that I think would affect bike and pedestrian counts on a given day (based on my own experience): Is it raining? In both cases, I'll want to set up a dummy variable for a yes/no. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66h3lRwEyVUO"
      },
      "source": [
        "To set a dummy variable for \"did it rain today?\" I'm going to use the bin feature. First, I'll run a quick histogram and descriptive stats to get a sense of how much rain is typical. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHhG9z0Yna5e",
        "outputId": "d6d5e7d5-0f13-4a13-e5a5-e67276327ceb"
      },
      "source": [
        "plt.figure(figsize=(width, height))\n",
        "\n",
        "ax1 = sns.histplot(data=df['precipitation'], stat='count', binwidth=.01)\n",
        "\n",
        "plt.title('Distribution of Precipitation')\n",
        "plt.xlabel('Daily Precipitation')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7pf8KWyy5Mj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50f868a7-ef77-4ed6-df90-24dc2d685549"
      },
      "source": [
        "df['precipitation'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2708.000000\n",
              "mean        0.116507\n",
              "std         0.260973\n",
              "min         0.000000\n",
              "25%         0.000000\n",
              "50%         0.000000\n",
              "75%         0.110000\n",
              "max         3.250000\n",
              "Name: precipitation, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48BFdI8gr5wK"
      },
      "source": [
        "Based on this, I'll set all values of 0-0.01 to go into one bin (I'll count that as basically no rain), and all values greater than 0.01 will go into another."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcOgsjVPviQb"
      },
      "source": [
        "bins = ([ 0, 0.01, df['precipitation'].max()])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sldFd6wq0FCT"
      },
      "source": [
        "group_names = ['0', '1']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KNZi9lyTz5JY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d78316e0-2238-4cb1-8c9b-2158e2667eff"
      },
      "source": [
        "rain = pd.cut(df['precipitation'], bins, labels=group_names, include_lowest=True )\n",
        "df['Did-it-Rain'] = rain\n",
        "df[['precipitation','Did-it-Rain']].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>precipitation</th>\n",
              "      <th>Did-it-Rain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>0.16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>0.06</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-04</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-05</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            precipitation Did-it-Rain\n",
              "2014-01-01           0.00           0\n",
              "2014-01-02           0.16           1\n",
              "2014-01-03           0.06           1\n",
              "2014-01-04           0.00           0\n",
              "2014-01-05           0.00           0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWmo-FvwI-Fx"
      },
      "source": [
        "df[[\"Did-it-Rain\"]] = df[[\"Did-it-Rain\"]].astype(\"float\")\n",
        "df[[\"Weekend or Holiday?\"]] = df[[\"Weekend or Holiday?\"]].astype(\"float\")\n",
        "df[[\"During Pandemic?\"]] = df[[\"During Pandemic?\"]].astype(\"float\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y96-AAeS02E1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a004f8c9-c171-44ec-9654-dfaa65891690"
      },
      "source": [
        "df.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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "      <th>During Pandemic?</th>\n",
              "      <th>Did-it-Rain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>2190.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>1580.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>2469.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-04</th>\n",
              "      <td>3337.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-05</th>\n",
              "      <td>2849.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count  high temperature  ...  During Pandemic?  Did-it-Rain\n",
              "2014-01-01  2190.0              45.0  ...               0.0          0.0\n",
              "2014-01-02  1580.0              51.0  ...               0.0          1.0\n",
              "2014-01-03  2469.0              48.0  ...               0.0          1.0\n",
              "2014-01-04  3337.0              46.0  ...               0.0          0.0\n",
              "2014-01-05  2849.0              47.0  ...               0.0          0.0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5WcJG6k0h-9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "788e9e85-b596-430e-9eb6-9f34c2f89cd4"
      },
      "source": [
        "df.corr()"
      ],
      "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>count</th>\n",
              "      <th>high temperature</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Hempfest</th>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "      <th>During Pandemic?</th>\n",
              "      <th>Did-it-Rain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.660293</td>\n",
              "      <td>-0.390282</td>\n",
              "      <td>0.456279</td>\n",
              "      <td>0.005387</td>\n",
              "      <td>0.088211</td>\n",
              "      <td>-0.501618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>high temperature</th>\n",
              "      <td>0.660293</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.247088</td>\n",
              "      <td>0.100032</td>\n",
              "      <td>-0.038181</td>\n",
              "      <td>-0.009151</td>\n",
              "      <td>-0.409762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>precipitation</th>\n",
              "      <td>-0.390282</td>\n",
              "      <td>-0.247088</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.014928</td>\n",
              "      <td>-0.004021</td>\n",
              "      <td>-0.027139</td>\n",
              "      <td>0.556966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hempfest</th>\n",
              "      <td>0.456279</td>\n",
              "      <td>0.100032</td>\n",
              "      <td>-0.014928</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.042757</td>\n",
              "      <td>-0.036518</td>\n",
              "      <td>-0.046551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weekend or Holiday?</th>\n",
              "      <td>0.005387</td>\n",
              "      <td>-0.038181</td>\n",
              "      <td>-0.004021</td>\n",
              "      <td>0.042757</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.001959</td>\n",
              "      <td>0.014001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>During Pandemic?</th>\n",
              "      <td>0.088211</td>\n",
              "      <td>-0.009151</td>\n",
              "      <td>-0.027139</td>\n",
              "      <td>-0.036518</td>\n",
              "      <td>-0.001959</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.013872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Did-it-Rain</th>\n",
              "      <td>-0.501618</td>\n",
              "      <td>-0.409762</td>\n",
              "      <td>0.556966</td>\n",
              "      <td>-0.046551</td>\n",
              "      <td>0.014001</td>\n",
              "      <td>-0.013872</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        count  high temperature  ...  During Pandemic?  Did-it-Rain\n",
              "count                1.000000          0.660293  ...          0.088211    -0.501618\n",
              "high temperature     0.660293          1.000000  ...         -0.009151    -0.409762\n",
              "precipitation       -0.390282         -0.247088  ...         -0.027139     0.556966\n",
              "Hempfest             0.456279          0.100032  ...         -0.036518    -0.046551\n",
              "Weekend or Holiday?  0.005387         -0.038181  ...         -0.001959     0.014001\n",
              "During Pandemic?     0.088211         -0.009151  ...          1.000000    -0.013872\n",
              "Did-it-Rain         -0.501618         -0.409762  ...         -0.013872     1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHoZZSrnsKrE"
      },
      "source": [
        "And here we go - as I epxected, ``Did-it-Rain`` has a stronger correlation with ``count`` than ``precipitation`` does. Let's look at the distribution of ``count`` on days when it did and did not rain."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCBXSFDhRxdV"
      },
      "source": [
        "df_grouptest = df[['Did-it-Rain','count']]\n",
        "grouped_test1 = df_grouptest.groupby(['Did-it-Rain'],as_index=False).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTKGl46gJpbK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2ab5432-6a3b-4323-b423-922e99d3b4db"
      },
      "source": [
        "sns.boxplot(x=\"Did-it-Rain\", y=\"count\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f29918f3ad0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "bK4Brt1-WyU9"
      },
      "source": [
        "Now we're getting somewhere, as we can see a real difference here. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8pLOWBnPGr1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e00703d0-4a24-4151-9ea2-1880fa40136a"
      },
      "source": [
        "df['Did-it-Rain'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    1656\n",
              "1.0    1052\n",
              "Name: Did-it-Rain, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5VjEgP3Dqcz"
      },
      "source": [
        "lm2 = LinearRegression()\n",
        "var = df[['normalized-losses','highway-mpg']]\n",
        "lm2.fit(var, df['price'])\n",
        "more in lesson 4 lab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mAqK4PQoV9at"
      },
      "source": [
        "#Strength of Relationship"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31F4z7VR8eIv"
      },
      "source": [
        "Turning back to our correlation table, we see the strongest relationship is between temperature and count. Is it statistically significant?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYHn0R1UcBcq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a3c58a6-57a6-4912-a0eb-ff19b3e3c40d"
      },
      "source": [
        "pearson_coef, p_value = stats.pearsonr(df['count'], df['high temperature'])\n",
        "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Pearson Correlation Coefficient is 0.6602926813720086  with a P-value of P = 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxXih9NuhKzc"
      },
      "source": [
        "The p-value is 0, showing that the relationship between temperature and count is statistically significant and about as certain as we can be. The strength of the relationship is only moderate, with the correlation coefficient of .66. Now let's do a simple linear regression and look at this on a graph."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XL-DsOrbJadD"
      },
      "source": [
        "## Fitting a Model: Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zz5-aq3vUUT"
      },
      "source": [
        "Now comes the fun part, where we try to find a model that will predict ``count`` on any given day."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5N4mVlo7tjF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "a7d9efc8-824d-48db-eab4-2cc2fbe329d0"
      },
      "source": [
        "width = 12\n",
        "height = 10\n",
        "plt.figure(figsize=(width, height))\n",
        "sns.regplot(x=\"high temperature\", y=\"count\", data=df)\n",
        "plt.ylim(0,)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 24927.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UU28VGC9JgrP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afd931cc-dc3f-4ff4-c350-b63c784a4830"
      },
      "source": [
        "lm = LinearRegression()\n",
        "x = df[['high temperature']]\n",
        "y = df[['count']]\n",
        "lm.fit(x, y)\n",
        "lm.score(x, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.43598642507343677"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPac1ysU9Aq5"
      },
      "source": [
        "This shows that there is indeed a positive relationship between temperature and count, though with an r-squared of .43, the relationship is not too strong. \n",
        "\n",
        "I also notice that counts seem to drop off a bit when the temperature exceeds about 85 degrees. This is not surprising; Seattleites do not tend to enjoy what we consider \"extreme\" temperatures (having spent five years in the New York City area, I know this is laughable). This suggests though that a linear regression model might not be the best fit for this data. To be sure, though, let's do a graph of the residuals. \n",
        "\n",
        "A residual graph has a dot for each datapoint in the analysis. Each dot shows how far the predicted value was from the actual value. A perfect model then would have a residual graph that is just a straight line at y=0 across the whole graph. The further points are from zero, the more variation there is in the data that is not explained by the model. And if points are mostly on one side of zero, that would tell us that the model consistently over or underpredicts values, so it may not be the best model for this dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CajzAD_C9gd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "outputId": "5cad4e22-0d93-438b-e9e9-ea279e830758"
      },
      "source": [
        "plt.figure(figsize=(width, height))\n",
        "sns.residplot(df['high temperature'], df['count'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FJ4FXdq94ol"
      },
      "source": [
        "This is not too bad, because residuals are distributed fairly evenly between positive and negative, showing that a linear model works fairly well. It starts to break down at those high temperatures at the right where the actual values are mostly all less than the predicted values, so it's possible that a polynomial model could increase accuracy. At the lowest temperatures, conversely, we see that the model underpredicted. (It could be because when it gets that cold in Seattle it usually means it's sunny, or snowing, both of which are more appealing than gray.) \n",
        "\n",
        "Also, the residuals are large, reinforcing the fact that there are other variables at play here that we should account for. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTprM1L1-iEu"
      },
      "source": [
        "## Multiple Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVvqonYOhFgy"
      },
      "source": [
        "So far we've just looked at the relationship of pairs of variables. We might get more insight if we do a multivariate regression. After playing around a little and removing a couple variables that surprisingly had no effect on the r-squared, I'm running a multiple linear regression using just four variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9E42lP6hOaS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fc43716-4f22-4aa3-9846-74560ed93259"
      },
      "source": [
        "lm2 = LinearRegression()\n",
        "targets = df[['high temperature', 'precipitation', 'Did-it-Rain', 'Hempfest']] #'Weekend or Holiday?', 'During Pandemic?',\n",
        "lm2.fit(targets, df['count'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9VM-26_LAPE2"
      },
      "source": [
        "This fits a multiple regression model with the following coefficients for each of the five variables named above: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euOthc7BAMLm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fa894d4-4c98-4bd6-cd2c-f3ecb8ea3913"
      },
      "source": [
        "lm2.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   79.92107163, -1218.34656531,  -813.25998859, 10073.03021618])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7Z059WQAgcm"
      },
      "source": [
        "And this y-intercept, which of course can't actually happen in the real world:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UsRYgJiAfY-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0443cbd3-2421-45d5-c98d-bada563a3bd0"
      },
      "source": [
        "lm2.intercept_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-875.7096918385419"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "byldw3SkAqb-"
      },
      "source": [
        "Now let's evaluate how well this model fits. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXNgAcgFiip8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d525cd3-e9fd-4015-a296-c385edec946b"
      },
      "source": [
        "#get r-squared of multivariate regression model.\n",
        "lm2.score(targets, df['count'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6689420362389418"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgy1v0_3rfHN"
      },
      "source": [
        "The r-squared is still not as high as I would have hoped with so many variables, but merely by adding ``Hempfest`` I increased it from .52 to .67. Let's dig in a bit more to understand when the model is working and when it's not. \n",
        "\n",
        "Interestingly, the r-squared did not change when I included ``Weekend or Holiday`` or ``During Pandemic`` , so I'm keeping those out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNjzzeFUAHcV"
      },
      "source": [
        "Y_hat = lm2.predict(targets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ukkE1aY-4zt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "042bf190-e273-41e8-fd96-2e099c208432"
      },
      "source": [
        "plt.figure(figsize=(width, height))\n",
        "\n",
        "ax1 = sns.kdeplot(data = df['count'], color=\"r\", label=\"Actual Values\")\n",
        "sns.kdeplot(data = Y_hat, color=\"b\", label=\"Fitted Values\" , ax=ax1)\n",
        "\n",
        "plt.title('Actual vs Fitted Values for Counts')\n",
        "plt.xlabel('Daily Ped + Bike Count')\n",
        "plt.ylabel('Proportion of Days')\n",
        "\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwGes0k9A_an"
      },
      "source": [
        "So there is still some work to be done here. The fitted values, in blue, tend to underestimate days with very high counts while overestimating days with medium counts. Some of those medium count days are ones with very high temperatures where the model expects ``count`` to increase accordingly, but where people are actually less likely to venture out. \n",
        "\n",
        "The challenge, then, is to figure out what else drives the days with high counts and determine why the model is more likely to predict medium counts even when that's not accurate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQ96kQjwrNOs"
      },
      "source": [
        "#Polynomial Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJ-2ZZT6rvaw"
      },
      "source": [
        "The plot of residuals above showed there might not be a simple linear relationship between all the variables, particularly ``temperature`` and ``count``. It seems that there is an ideal temperature when people like to go outside, but above that point people are less likely to go. Can we account for that with a polynomial?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OV12fC0YsT_s"
      },
      "source": [
        "#check accuracy of the below."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITI41cGNBp7O"
      },
      "source": [
        "def PlotPolly(model, independent_variable, dependent_variable, Name):\n",
        "    x_new = np.linspace(25, 95, 100)\n",
        "    y_new = model(x_new)\n",
        "\n",
        "    plt.plot(independent_variable, dependent_variable, '.', x_new, y_new, '-')\n",
        "    plt.title('Polynomial Fit with Matplotlib for Count ~ Temperature')\n",
        "    ax = plt.gca()\n",
        "    ax.set_facecolor((0.898, 0.898, 0.898))\n",
        "    fig = plt.gcf()\n",
        "    plt.xlabel(Name)\n",
        "    plt.ylabel('Count')\n",
        "\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dV3KzzDttZLY"
      },
      "source": [
        "Here I try a second-order polynomial (meaning an exponent of two is involved)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xd6bRUhWCoG0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f97cdb3-ffe9-42b8-e4f5-38db40024614"
      },
      "source": [
        "x = df['high temperature']\n",
        "y = df['count']\n",
        "f = np.polyfit(x, y, 2)\n",
        "p2 = np.poly1d(f)\n",
        "print(p2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        2\n",
            "-0.129 x + 121 x - 3291\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7cmOAkfC5Te",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "48b6946c-b070-4db6-abe0-dd1853b20c59"
      },
      "source": [
        "PlotPolly(p2, x, y, 'temperature')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEWCAYAAABBvWFzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydeXxdV33gv7+3aN8seY3kxLZsJ47tyI6zQUoSlgbM0EIoky6Zhm7QGWAamE5bmM4MdBnKMG0pKS2dtAQIZAgpkLCahAJJIBASEu924kWWF9mybMmyZO3vvTN/3Pue7jnv6S3Sk/Qk/76fjz56dzv33PvuO7/7W48YY1AURVGU6RKa6w4oiqIoCwMVKIqiKEpRUIGiKIqiFAUVKIqiKEpRUIGiKIqiFAUVKIqiKEpRUIEyBUSkQ0TeMNf9CCIiO0TknXnuW3D/C2m/wHb3i8gdWbY/JSK/V+zzFhMRWSUiRkQiUzw+dY0ico+IPBnYZkRkbZ7tiIh8VkQuiMjzU+mLokyHy1qg+APrsIhcEpGzIvI5EamZ635NBWPMdmPM56fbjn8Pxvx7kvz71WD7IvJbIvLj6fcajDEbjTFP+e1+RES+OI2+3+EPwI8569v89U/l2c60+jGdto0xDxtj7pxi878A/CLQYoy5aYptWIhImd/nwyIy6P9mHhSRVcVoP8t57xCRUzPQ7v7Acx0XkZHA8n8r9vlmg1J66bqsBYrPLxljaoDrgRuA/z7H/SkFPm6MqQn8fXmuO1QA54BXiUhTYN07gUNz1J/Z5CqgwxgzWOiBWbSrrwC/DPwGUA+0AS8Cr59qJ+cS/wWmxv/N/wh4X+A5/+hc989lqlrvnJ3DGHPZ/gEdwBsCy/8H+Jb/+ZeB/UAf8BSwwT0OWA4MAU2BbdfjDWpR4LeAHwN/DVwAjgHbA/teAXwD6AWOAO8KbPsI8K/AF4EBYC+wHvgQ0A2cBO4M7P8U8Hv+51bgB0APcB54GGiY7Lqde/I54C8zrH8K+D1gAzACxIFLQF+GfV8L7A0sfw94IbD8I+Btzr18EzAGjPvt7g6c9y+AZ/378CSweJK+3wGcAv4JeK+/Lgx0Av8TeCqw7yf9e9iPN0C+xl+frR9/BTzvH/N1oNHftgowQCTb95qj7eR391vAjwP9NMAfAO3+d/l/gFCGa/9d53v5M3/9u/w+9Pp9usJp+73AYeBYhjbfAAwDK7P8hrI9w58j8Cwlvx/nOfyvwB7gIvBloAKo9s+b8K/lUrDfRfz9p+67v/w7wEG83+oTwFXOvXqPf68G8J7JVuAn/vPwKFDmPIf/zf/OOoB7Am2V440JJ4CzeM9rpXPsnwBdwBeARcC38MaVC/7nFn///+V/5yP+ffoUzvM4yTP2LPAJvDHiL7P1qZA/1VB8RGQl8GZgp4isB74EvB9YAnwH+KaIlAWPMcZ04X1RdwdW/ybwiDFm3F++GXgFWAx8HPiMiIi/7RG8h+cK4B3AR0XkdYG2fomJB2on3kMeApqBPwf+72SXgzf4XYEnAFbiCahpY4w5CPxH4KfGe6tryLDbc8A6EVksIlHgOuAKEakVkUo8TfBHTrvfBT4KfNlvty2w+TeA3waWAmV4g1A2HgLu9T+/EdgHnHb2eQHYAjQC/w/4VxGpyNGPe/EGnRVADLh/kvNn/F5ztJ2Nu/Du2fXAW/0+WBhjPoP9vXzYf5b+Cu/5XAEc9/sW5G14z+i1Gc77BuB5Y8zJLH3L9Qzn4m48Qbsa7zn5LeNpWNuB02ZCe3C/v6wEfmP57v9WPAHwdrzf/I/wxoAgbwS2AbcAfww8APwHvN/XJuDXA/sux/vNN+NpyA+IyNX+to/hvRxuAdb6+/xP59hGPI3z3Xi/+c/6y1fiCdtPARhj/hRb03pfnpd8M95LyjI8oZSrT3mhAgUeF5E+PE3iabwf/K8C3zbGfM8XDH8NVAKvznD85/EeKkQkjPdQfSGw/bgx5p+NMXF/3xXAMl+A3Qr8iTFmxBizC/gXJgZCgB8ZY54wxsTwtJUlwMf8Pj0CrBKRtAHdGHPE7/uoMeYc8LfA7QXck/8qIn3+3/kCjkuefxhvwL4N7we4G++N6Fa8H+NhY0xPAU1+1hhzyG/3UbyHPtv5fwI0+j/ge/EEjLvPF40xPcaYmDHmb/De0K5293P4gjFmnz/g/Q/gbv87T5Hn91oo/9sY02uMOQH8HfbAlY17gAeNMS8ZY0bxtNtXOf6Pv/LbHs5wfBNwZrLGi3St9xtjThtjeoFvkuO7dc7fLCKPiMhpEdkpIu/3110NPF5AH8ATxn9ljDno/94+CmwRkasC+3zcGNNvjNmP95LypDGm3RhzEdgBbHXa/B/+b/Bp4Nt4z4vgCYkP+Pd9wD/XrwWOSwAf9o8d9p/Trxpjhvz9/xeF/Z4zcdoY8/f+tY7k0ae8UIHimV4ajDFXGWPe4/+wrsB7mwPAGJPAM480Zzj+68C1IrIazyF60RgTjLDpCrQz5H+s8c+R/PKSHHfOcTbweRg47wum5HKyLQsRWeb/0DpFpB/PbLZ4kuvPxF/796TBGFPIcUGexlPfb/M/P4X3I7jdXy6ErsDnITJccwa+ALwPz/z2mLtRRP6riBwUkYv+C0U9ue9R8E39OJ5Z0z0mn++1UNzzXpHnce5zfAnPxBHsSzbtowfvBShb+9O91ql8t0n+Pd6L1ko8c+x1eJr8I3hm3kK4Cvhk8kUKz4QnZP89usvBvl8wti8r+b0tAaqAFwPn+q6/Psk5Y8xIckFEqkTk/4rIcf/3/AzQ4L7MFEjwe8+nT3mhAiUzp/EeMCClPq/Es8Vb+F/8o3haym9iaye5ztEoIrWBdVdmOscU+CieDXWzMabO71tBJoAc5FOi2hUoT5NboBSz9PUX8Gze3wkIcgBE5DV4Jou7gUW+2e4iE/dosn6sDHy+Es8X4mpwub7XqVyje958zT/uc1yNp3UEn7Fs/fk34CYRacnSfrZrHcQbqJIsz7PfufqV5H7/zT1ujHnRGPM7xpilxpitxphHCzgXeAPs7wdepBqMMZW+tjsVFvn3O0nyezuPJ3w2Bs5Tb7wggSTutf8hnvZ8s/97vs1fP9nzmhRk2e598Jh8+pQXKlAy8yjw70Tk9b4P4A+BUTwHXCYewnN0/TJ5ChTfLv0T4K9EpEJErsNzrBYjXLUWz0F3UUSagT8qQptBzgItrk/J4Sd4P4Kb8Ozw+/EGt5vx3rAma3eViEz7uTTGHMMTXn+aYXMtng/kHBARkf8J1OXRj/8gIteKSBWeD+srAY0xed5c3+tUrvGPRGSRb2K6D895nQ9fAn5bRLaISDnei8bPjDEd+RxsjPk3vICKx0Rkm4hEfD/YfxSR38njWncBbxaRRhFZjueTzJezQJOI1GfpX6KA9nLxT8CHRGQjgIjUi8i/n2abf+aHXb8GeAvwr36f/xn4hIgs9c/VLCJvzNJOLd6A3ycijcCHne1ngTXJBd/M3Yn3vIZF5HfwAggyMsU+ZUQFSgaMMa/gvdX/PZ70/iW88OKxSfZ/Fs/u+ZIx5nimfSbh1/EiMk7jmWU+7P+Ip8uf4TlwL+LZbr9WhDaD/AAvAq5rMh+Lr+6/BOwP3Lef4vmUuidp91/9/z0i8tJ0O2mM+fEkztwn8FT6Q3imiBFsE8Bk/fgCXuRSF1400h9Mcups3+tUrvHreJFou/C+z8/kc5B/zv8BfBXPF9JK4Xbxd+AFpXwZ73nahxcgkLyebNf6BTz/WQdedF7e4efGmJfxBGK7b4bJ18w3JYwxjwH/G3jENyvtwwsMmCpdeBFZp/HMb//RvybwIriOAM/55/o3svvv/g7Ph3seL+Dlu872TwLvEC+hNRko8i68F8keYCOTvwwnKbRPGRFjdIKtYiAiPwD+nzHmX+a6L0rxES8p8ov6/Sq5EK/ywxeNMZOZChcsM540czkgIjcyEdKpKIpyWaImr2kiIp/HUw/f70S7KIqiXFaoyUtRFEUpCqqhKIqiKEXhsvOhNDY2mpUrV+beUVEURUmxZ8+e88aYrMmOl51AWblyJTt27JjrbiiKoswrmpubc6ZEqMlLURRFKQoqUBRFUZSioAJFURRFKQoqUBRFUZSioAJFURRFKQoqUBRFUZSioAJFUZSSZO+ZQR56oYu9ZwZz76yUBJddHoqiKKXP3jOD/MFjhxmPG6LhLu6/ax2bV1TnPlCZU1RDURSl5Nh5aoDxuCFhYDxu2HlK667OB1SgKIpScmxtqSUaFkIC0bCwtaU290HKnKMmL0VRSo7NK6q5/6517Dw1wNaWWjV3zRNUoCiKUpJsXlGtgmSeoSYvRVEUpSioQFEUZcGiocezi5q8FEVZkGjo8eyjGoqiKAsSDT2efVSgKIqyINHQ49lHTV6KoixINPR49lGBoijKgkVDj2cXNXkpiqIoRUEFiqIoilIUVKAoiqIoRUEFiqIoilIUVKAoiqIoRUEFiqIoilIUVKAoiqIoRUEFiqIoilIUVKAoiqIoRUEFiqIoilIUVKAoipITnVdEyQet5aUoSlZ0XhElX1RDURQlKzqviJIvKlAURcmKziui5IuavBRFyYrOK6LkiwoURVFyovOKKPmgJi9FURSlKMyYQBGRlSLyQxE5ICL7ReQ+f32jiHxPRA77/xf560VE7heRIyKyR0SuD7T1Tn//wyLyzsD6bSKy1z/mfhGRmboeRVEUJTszqaHEgD80xlwL3AK8V0SuBT4IfN8Ysw74vr8MsB1Y5/+9G/g0eAII+DBwM3AT8OGkEPL3eVfguDfN4PUoiqIoWZgxgWKMOWOMecn/PAAcBJqBtwKf93f7PPA2//NbgYeMx3NAg4isAN4IfM8Y02uMuQB8D3iTv63OGPOcMcYADwXaUhRFUWaZWfGhiMgqYCvwM2CZMeaMv6kLWOZ/bgZOBg475a/Ltv5UhvWZzv9uEfm5iPy8p6dnWteiKMrMoNn4858Zj/ISkRrgq8D7jTH9QTeHMcaIiJnpPhhjHgAeAGhra5vx8ymKUhiajb8wmFENRUSieMLkYWPM1/zVZ31zFf7/bn99J7AycHiLvy7b+pYM6xVFmWdoNv7CYCajvAT4DHDQGPO3gU3fAJKRWu8Evh5Yf68f7XULcNE3jT0B3Ckii3xn/J3AE/62fhG5xT/XvYG2FEWZR2g2/sJgJk1etwK/CewVkV3+uv8GfAx4VER+FzgO3O1v+w7wZuAIMAT8NoAxpldE/gJ4wd/vz40xvf7n9wCfAyqBHf6foijzDM3GXxiIFyB1+dDW1mZ27FC5oyiKUgjNzc0vGmNuyLaPZsoriqIoRUEFiqIoilIUVKAoiqIoRUEFiqIoilIUVKAoiqIoRUEFiqIoilIUVKAoiqIoRUEFiqIo02YqhR21GOTCQ6cAVhRlWkylsKMWg1yYqIaiKMq0mEphRy0GuTBRgaIoyrSYSmFHLQa5MFGTl6Io02IqhR21GOTCRAWKoijTZvOK6oKFwlSOUUobNXkpiqIoRUEFiqIoc4KGDS881OSlKMqso2HDCxPVUBRFmXUu57DhhayZqYaiKMqs44UNd/kaSnHChveeGSz5qLGFrpmpQFEUZdYpdtjwfBmoM2lmpdjPqaICRVGUOaGYYcPzZaCeCc2slFCBoijKvGe+DNQLPaFTBYqiKPOe+TRQL+SEThUoiqIsCBbyQD1f0LBhRVEUpSioQFEURVGKggoURVEUpSioQFEUpSRYyBnklwvqlFcUZc6ZL4mJSnZUQ1EUZU4IaiSXc22vhYRqKIqizDhunS1XI7nvthaiYSkoMXE+1O663FCBoijKjJLJnOVqJP3DsYISE9VEVpqoQFEUZUbJZM7KVCqlkMTE+VK763JjxnwoIvKgiHSLyL7Auo+ISKeI7PL/3hzY9iEROSIir4jIGwPr3+SvOyIiHwysXy0iP/PXf1lEymbqWhRFmTqe8BBCgiU87r9rHe++ZcWUtItMbSpzjxhjZqZhkduAS8BDxphN/rqPAJeMMX/t7Hst8CXgJuAK4N+A9f7mQ8AvAqeAF4BfN8YcEJFHga8ZYx4RkX8CdhtjPp2rX21tbWbHjh3FuERFUfJkJvwd6kOZXZqbm180xtyQbZ8ZM3kZY54RkVV57v5W4BFjzChwTESO4AkXgCPGmHYAEXkEeKuIHAReB/yGv8/ngY8AOQWKoiizz0zU2dLaXaXHXIQNv09E9vgmsUX+umbgZGCfU/66ydY3AX3GmJizPiMi8m4R+bmI/Lynp6dY16EoiqIEmG2B8mmgFdgCnAH+ZjZOaox5wBhzgzHmhqamptk4paIoymXHrEZ5GWPOJj+LyD8D3/IXO4GVgV1b/HVMsr4HaBCRiK+lBPdXFEVR5oBZ1VBEZEVg8S4gGQH2DeDXRKRcRFYD64Dn8Zzw6/yIrjLg14BvGC+S4IfAO/zj3wl8fTauQVEURcnMjGkoIvIl4A5gsYicAj4M3CEiWwADdAC/D2CM2e9HbR0AYsB7jTFxv533AU8AYeBBY8x+/xR/AjwiIn8J7AQ+M1PXoiiKouRmxsKGSxUNG1YURSmcfMKGtTikoiiKUhRUoCiKoihFQQWKoiiKUhRUoCiKoihFQQWKoiiKUhRUoCiKoihFQQWKoiiKUhRUoCiKoihFQQWKoiiKUhTyEigicms+6xRFUZTLl3w1lL/Pc52iKIpymZK1OKSIvAp4NbBERP5LYFMdXrFGRVEUZZYp1emPc1UbLgNq/P1qA+v7mSgdryjKZU6pDnALkb1nBvmDxw4zHjdEw13cf9e6krnnWQWKMeZp4GkR+Zwx5vgs9UlRlHlEKQ9wC5GdpwYYjxsSBsbjhp2nBkrmfuc7H0q5iDwArAoeY4x53Ux0SlGU+UMpD3ALka0ttUTDXb4AF7a21OY+aJbIV6D8K/BPwL8A8ZnrjqIo841SHuAWIptXVHP/XetK0sSYr0CJGWM+PaM9URRlXlLKA9xCZfOK6pK8z/kKlG+KyHuAx4DR5EpjTO+M9EpRlHlFqQ5wyuySr0B5p///jwLrDLCmuN1RFEVR5it5CRRjzOqZ7oiiKIoyv8lLoIjIvZnWG2MeKm53FEVRlPlKviavGwOfK4DXAy8BKlAURVEUIH+T138OLotIA/DIjPRIURRFmZdMtXz9IKB+FUVRFCVFvj6Ub+JFdYFXFHID8OhMdUpRFEWZf+TrQ/nrwOcYcNwYc2oG+qMoygJAi0VenuTrQ3laRJYx4Zw/PHNdUhRlPjNTxSJVSJU++c7YeDfwPPDvgbuBn4mIlq9XFCWNTMUip0tSSD3w3Bn+4LHD7D0zmHGfh17oyritWMzGOeYz+Zq8/hS40RjTDSAiS4B/A74yUx1TFGV+MhPFInNVNJ6NEvpapj83+QqUUFKY+PQw9QgxRVEWMDNRLDKXkJqNEvpapj83+QqU74rIE8CX/OVfBb4zM11SFGW+U+xikbmE1GyU0Ncy/bkRY8zkG0XWAsuMMc+KyNuBX/A39QEPG2OOzkIfi0pbW5vZsWPHXHdDUUqGUnF2P77vPE8d6eOOtQ28bdPigo+fjesolXs1FzQ3N79ojLkh2z65NJS/Az4EYIz5GvA1ABHZ7G/7pckOFJEHgbcA3caYTf66RuDLeDM/dgB3G2MuiIgAnwTeDAwBv2WMeck/5p3Af/eb/UtjzOf99duAzwGVeNrSfSabdFQUJY3J/AKzPXA+vu88H//BSQCeP+E58QsVKvloRdO9Li3Tn51cfpBlxpi97kp/3aocx34OeJOz7oPA940x64Dv+8sA24F1/t+7gU9DSgB9GLgZuAn4sIgs8o/5NPCuwHHuuRRFyUEmv0A+EVXF5qkjfVmXi8FcXNflRi6B0pBlW2W2A40xzwDuBFxvBT7vf/488LbA+oeMx3NAg4isAN4IfM8Y02uMuQB8D3iTv63OGPOcr5U8FGhLUZQ88fwCQkhI+QVmIuw3F3esbci6XAzm4rouN3KZvH4uIu8yxvxzcKWI/B7w4hTOt8wYc8b/3AUs8z83AycD+53y12VbfyrD+oyIyLvxNB+amyfdTVFmnFKzwU/m7J5t53PSvDUdH0ou1Kk+8+QSKO8HHhORe5gQIDcAZcBd0zmxMcaIyKz4PIwxDwAPgOeUn41zKopLqeYxuH6BuZoj/m2bFk9LkOQS1nN1XZcTWQWKMeYs8GoReS2wyV/9bWPMD6Z4vrMissIYc8Y3WyVzWzqBlYH9Wvx1ncAdzvqn/PUtGfZXlJJlpvIYZkLrma7z2e3TdCO48jlfPsJaneozS761vH4I/LAI5/sG3vz0H/P/fz2w/n0i8gieA/6iL3SeAD4acMTfCXzIGNMrIv0icgvwM+Be4O+L0D9FmTFmwuRSilqP26d3tC3h4Re9d8epRnDlQpMOS4N8ExsLRkS+hKddLBaRU3jRWh8DHhWR3wWO49UFAy/s983AEbyw4d8G8AXHXwAv+Pv9uTEm6eh/DxNhwzv8P0UpWWbC5DKVgXSm/Thun57OEMFVbIGytaWWcKgLEzeEQ+ofmStmTKAYY359kk2vz7CvAd47STsPAg9mWP9zJsxwijIvKLbJpVCtp1gaTTah5Pbp9rUNKQ0FZiaCy8M4/5XZZsYEiqIoM0+hWk8xTEO5hFKmPjXXl8+oD2XnqQHiCU+UxBNM2eRValF48w0VKIoyz3AHvVxaT3D/fDWabANrPkLJ7dN0I7hyUQz/VCn6o+YbKlAUZR6Rz6AXFAZA2v65NJpc5yjFfI5i+KfUsT99VKAoyjyi0HlBtl/TmLb/vTcuzzpQ5jpHPoP3TJiO8skzmc658hGUahLLjgoURZlHFDovCAjRsBSkTXgRU2cwcQiHvOVCzGwzYTqajWCCXIJSTWK5UYGiKPOIQucF2b6hke0bGqfwVi14Lm7haM8wn3zmVN4D6VRNR+5gH1yejWCCXH1Sk1huVKAoyjzD1Q7cgfi+21pSEVXJ/QoZ+LyIKeNHTBmeOtJX0EA6Fcc/2L6e+25rsYTYfbe1FKxpZbquQsyFM9GHhY4KFEWZQ6Zrk882CO4+fYnWpsqC23UFwh1rG9h9+lLeA2kmLSqT9vG+rx0iFodI+Az/bsNia7B3hVj/cGzaTvdCzYUz0YeFjgoURZkjimGTzzUITsUs42o5b9u0mNamyoIG0qAWlek6dxzsYTzu7Tseh96hMevtv1Ahlu91FWIuzNSHTL4jddRPoAJFUWaQmfYLTFebmKyPmbScqQ6Wma6zdyjm7CVZhRikhz9PxS+T7TryEaSZ7pU66idQgaIoM8RM2eSz+UwK1SYyDYj5CLpC3soz1dk6cNaeLbF/ZJxPPH2KWNywszNdiD30QlfGybEKzadxj3H9ONkE6VTv1eWEChRFmSFmwiafr88k30Et04CYy9cwlbfyhPGc/Anj1dlqrCqztvcNx/0wZ68fOw72pvqXzPAPCqW6ykhOYeFe246Dvex4uWdSAb/9mibGYl4/x2PpwmEq9+pyQwWKoswQU7XJZ6PYPpNMA+JkkWKT9SHXOXcc7CGe8D7HE97y+qVV1j4NlRG4MJpa7h0aSxOcweKPh7qHHWHRw46Xey0B414bmKz3rndoPHWGBFBXaQ+Pk92rqbwULFSfiwoURZkhMg02hTq3XTIJqZ2dA1YS4nT7OJkPZbI+5D6npC0f6h621qTXB5a0wT9Y/BE8TSWpsbj7JysCBK8NCAiddAHfWBUJZN9A/3AsbfDPJDxyhXEHWeg+FxUoijKDuIPNdMuDuIOaR3AYnH4fi1F6Jcj2DY1888B54glP6G3f0MiOgz3WPn3DrpOetKivoOBcv7SKb+zvSeXKrF9aSfTldH+Ue23ZBDzYAieTWS3X95dLYCx0n4sKFEWZZ7jO6mASYjEm2Mqn9MrRnmFe6rxEXWUkrwFRfKEnvtDzTF4TQqWhMsLxgMmrsSqaVXA++XIvCV+tSRh4rqM/LyGXS8AH25jK4J/rmIXuc1GBoih5Uoq275mbYGvy0iv5TOkbvFc7DvYS80f/WCLpcLeNXPUVYaJhIRY3RPySMUHc7P3T/WPW9vOD49PW/iBdwBQ6+Of6Pqbic5lPqEBRlDwoVdt3PgNUrtwXSI+OisX9wTuD8zrXlL7uvdqwtNLav3doLC3Kq7GqjE+93dZIXKd80AS2qrGC7kvjqeNbGsqndR8zkenePr7vfNaJwvL5PqYj+GRsgOj5g0R7Xma86RrGrrhpSu3MFCpQFCUPimX7ngktJ5tTGNIH5qAzu64ywvu+dtjXDLr41NvXUVcZsaKd1i2pZGfnpdQxuab03XlqwAq/HYvb/W2sKmP7hka+fbDH0khcU16yjbFYeoj1J585ZbV5qm+UfMiWaJpr8H9833k+/oOTgK2ZzYjmagyhoXNEew54AuT8QaI9B4kMTFz3pet+RwWKosxH5suMgOnzodi5FYe6h6yckOc6+tPyP5bXRq02B0fj1jG3rWnIOqWvK5BaGso5cHYotX39Ui9qLKiRuPch2IYhPYR3PBmHHFjONLDnEq6fePpkqp7Yp96+Puv38VQGzay1qTJr8mpe37FJEO4/4QuOA0R7PAESHulN7RKru5LxxRsZuuYdjDdtYHzxBhJVS7K3OweoQFHmJbneCov91lgM23exM9DzOUfv0Jg1uHf0jlg5IScujDgtGAYclWJf12BaHskfv+7KSaf07R+OWXFnfc5yvx/Rlc30E2wjBBzqHuJvnzpFLGGIhLpoqbdNZuNxkzHRMVsBym/ut+uJ7TjYk/We37G2IaWZJJcLzguKjxG5cNQXGr720fsyoXFP4JpQlNiitYxeeQfjiz3BMd54NaasZtJ+lRIqUJR5R643/ZnSBKbr9J1KBvrRnuGsNvtc52isKrMG87G47Qy/clEFnf2j/qAL2zc08ZmfnbH2uTji2KwyhCcHfQtbW2opi6TnyiTPkbzubMLTbeNY74jl2I+E7T5cuaic4xdGUyaynacG6BoYI1sBynKnjfT6YnYfW5sqCYdIhT+3Nnm+ocmSV+vCo7y2qp2q/d+fEB4XjiAJz/eTiFYx3ngNQ+vvSmkdsUVrIVyW1o9s/SoFX14SFSjKvCPXm/5sxfoX+qPOpeW4/X74xeEMuiwAACAASURBVLM8034RyN9mnylPZaLciPCWjU0cPj+c8l3cs20Zt6yqs7Li3TfxO69exJd3nvO1A8/fEezD0Z5hy7fwx69bmdaHhJ+UmEhM3LtcwjOYrf/N/XbeynjcEBIvZDgksHJRBYZ+YMJE1jUwbh3TWFVmtfnEy73W9osjtkDJZD70K8dgDFby5MGOTm6t7mR9/EXuumov0fMHaRo9hTzrXXC8YhHjTRu4tPleX3hcS7z+KpDQ5A/MJJRqgAioQFHmIbne9IsV6z8TGc/ZtBy33+cH7QExk80+13ndMiqZqvi6WfFv27SYzoujPH2kj9vXNnDbmga+9JLnhE8YkxZGvMjxbzy6s5sPveGq1PLDL54lqRjFjbd87bIqy+nuCs/Oi6N8Zfe5VL/e0baEgwE/TDBvJWHgB4cuWH14rqOfW1bVWeuqy0PWtdaU2YP52YExS9PqH45ZfQSDCCwxF7gu1MGvDD3DoieP8PrzB3jjpdOpdsoqlnGqopW+q97IolVbGF98LYnqZSBTSzx1KeXkSBUoyrwj15t+MfwdM5XxnCvKKNjvZ9r7LGf2uiWVVgTVWIYCht7EVRNRWx+4PXvxyMmq+D66q5tY3Pu//8yglUT46M5uy9Hvvtn3DMWse7ei1jbhnB8cZ2Asbjnd93fZ1YeffOUCozFvj7GYYf8Ze/ug4+cZGLWXPd+Qbd77cftF61oT9mbG4glL07pzfQPNco6NcoxNoQ7uOHWKP40eZol4go9XIFa/ivGlbQxd++uMLb6WPbGr+P1vdxPrg8h5+NTV69lcU1w/WSknR6pAUeYlufwZ0/V3TDXjOZdWM9lgHzT7JDPQa8vCqWMFqC0L54x+2nGw14ra+tb+nqx5J5my4t3Jr145N2Sdo3coZjn63ffueDxhCRy38OPWlhoOn7NreSWrECcpC/g3DHDI2f+0Eybs1gKLJQwnLtj7jMUSlg/lzdc2pcKfhQQbo+dZFDrExtAxNkoHm090UF/uCbKYCdE+0sLTiTb2J65if2IV8SUb+Ptf3co/PNvJ07s9bW5wNJ7V0T9VzTafmmKlgAoUZc4pFQdjsB+ZBEauH7UrMD71dnuwyDXYP/xiF8+0e36A508McM+2pZRH7PpUybLuSdwii+7Qurg6QjhESmB4eScTkU8fuH0l6bXAbBEhzmgdc0J23cE8EhaMr10kSGdwNM66JZWWn2bdkiprOe6oD8YRODFneXjMPtPgWNwvGjnBeMJw/13r2HWyj1+oP0/dxR+zNvJTNoY6uFaOUzM6AmUwaiK8bK7kqdCreX50JXsTq3nFrKSmqoqegDZ2W00d//BsZ0ooPfxiN2sXVzhXKwUnlrpMJoRKSZAkUYGizCml4mDM1A/XsRwciJM5C9kExo6Dvdb2jl578B+P22/M5wdt09Hhc8NpQuvTz9oJfR29Q9aAVV0etrZXRMMEBcZzHRetN+hv7u9JqwW23slsryoPMxSb6FvcGcyjIWE4IFaiTvTUSUeb6B2KpUVUHTlva0H9TmRZU3WUzotjKbFXWx6mZ8iNPgv0uSzMuUvjRImxXk6xKXSM68aO0fbD07z20hHK8Eq3tIbLOWCu4ivx2zgWbeX5kSs5bJqJEWF5eZSu+IQfa0NDOT2Bft+zbTl/8WSHdd6LwzGrhMz6pZVpAjz4nbsCPlMuTCn7TFxUoChzSr4/lpnWYjL1494bl6fO9fEfnMgjZ8F9V7eX3ZDdaDhkCYyjPcOWzyQZdRU8T9+wPYh2DYxbgjAZyprkQNegJTBcM1BZWCy9ZGtLLQ+/eNbaZ9DxT7hXWVUWpn90QkOIhkPAxDFDjr+jfyTGiT47/8W9ruFxW+M4d8mN2IpaAiUcgmhijGvkBJtCHdw0dpx1kXbWRk5QJt5+A6aSA/1X8a3E69mbWM0hWc0r8RUk8Jzz4TgEvyLXlHfqon3vPvOzM1xRX86pixN1xVY3VXLH2oaUY/9Q95D13BzqHrKCJNztmZ6rUvaZuKhAUeaUfHwRMLX5xHPhmrhcX0IQ9406U87C9g1NfjmRiZyOIG/Z2GQJjLdsbLIERvJ/MCLLFaRXLiqnIyAUyiOhlPN6NGYosxUUrl1ezZmBMTBekJF7/OBY3IrAeqa9j8OOzySWZrdKajweI87gv6y2zArZDYm9/9mBMS46AsR1kLtCazQw0hugq/ci18txNoWOsUk62Bw6xjo5RUS8vlw01RyLruXBkTezL7GKvWY15yLLGBqfEBNhsU1yIbEFSnV5GALX4ZbY3915icYqewg9en44lYey+/QlrnEmEjvWO5IqkZ9pe6bnyo3UK1XtBFSgKHPMZL4IN/6/UJU/n0z69BkBJ59XxB043OXktXzg9pWT/vAnS4wL8rZNi1MJjHvPDPKerx7y9z/DP/7Keu7ZtpxnO/pTbVy9tMoq+z7o+BK6B8asLPeViyoISz9x4w2oXU7V3m/v76EiaofThhwnyrijabmJj53Om7yrbVwciaUJkGxUMcIGOc7m0DE2h46xSY6xVjoJ+/06b+rYl1jN9xNb2ZtYzX6zmorGFjatqOHxfRP5K8srogyNTwiI8ogwND55R04711ERCXEpcH/XLK7kpFNpYGA0xnicQB2zdC01+CyfHbDvf//IeMZIwL956iTxBLx4aiBtwjMoHT+kChSl6Ewl4S9bSC4Yy+6cyUHunj+XRpOpZEa2eUXc+TvcKWyT58020+HOUwNpiXHZ7s/DL561hMHDL57lY29Zw69tXZrKEXGjpdzBvaPXHvB+cOiCpZHEnJE9YWD9kkpLw6guDzMaMC/Z+ka6NtHr+IJcBWc0ZqiMZh7Mqxn2IqxCx1LaR6ucTgm1btPA3sRqvpu4kX2J1exLrOYMjbgvAeV9o9y8qt5ad+WiCuu66isiloBxhZzbPzdQ4PY19XzrQIxLYxNCoSoapi8eT133yoZyK3/m+pYa2nuGU89yRcQW3n3D8bRn9+EXu5znoIuPvaU1dUyp+CFhjgSKiHQAA3iG1pgx5gYRaQS+DKwCOoC7jTEXRESATwJvBoaA3zLGvOS3807gv/vN/qUx5vOzeR1KOsV4uF0z2PYNTWzf0JS3CSyfSJpMU+m6870HcetT9WeYYTBTjkjwvFtbaglJFwljCElmwRhcdutsnbgwwuP7zltRRXdevcja5xevXsQjL3WnNBDXQd47ZPsiXBZVRbhlVX0q2gw8f0Wv469IZArf8slH+WiuL+PM+QtslONsCrV72occY7V0pYRHl1nE3sRqvhW/hb3GEx7dLMrRssdoHJ71EyWT7D19yVoeHs9udnNxtY2BsTh1FREI+FBcobPHOefgaCLNb5bMfQFP6CVLyIz7z5AbrOEul5LTfi41lNcaY84Hlj8IfN8Y8zER+aC//CfAdmCd/3cz8GngZl8AfRi4Ae8ZflFEvmGMsVNmlVmlGAUQJ4uzz5aMly1HZLKpXDOdYzJz1daWWkKhM8QTEJpk7nY3R2RgLG6FEd+9ZYlVj+qZ9j4e3XXOyksJFkDctLzKyt+4clFFWgmSk32j/PHrJsxsgKWBuOYm15+xrLbMMpldvbQqrSSJG6UVzyJMMiFANUMp4dEWOkbbpWOsLJ8QHqdNI/sSq3k8fiv7fOFxjobsDTvnCA7lIYFRx/mT9DUluTiSPfw5F4fPDbO1pcbyi7lCqX/EffGwd0iaN4PfX7JiQALvmcrkewtSSk77UjJ5vRW4w//8eeApPIHyVuAh4wWiPyciDSKywt/3e8aYXgAR+R7wJuBLs9ttJchUCiDmmoci0zlEzqQczZlmxQs6MfuHYxkFUPAcucxVR3uGLbPD0Z7htP4FtRYBdp66ZIURf9sRBt/281CS2x96ocsSOJ2Of6MiGiJT2fbWpkr6h2O0NlXyd848IW7C4Lgz4rnziJzsG+VQt+2UdwfiXPKkKmW2mtA8WkMTBSfPmEb2J1bzlfitvuaxhvPUZ2kxHVeAuMsNleE0p7q4OxV4jqqyEAOBaLZ1SyqpLQtbmqtbXaUiGmIkFk/VHFu/tCrNLxb0m338Byes4w91D2U0rwYppUTHuRIoBnhSRAzwf40xDwDLjDHJp64LWOZ/bgZOBo495a+bbH0aIvJu4N0Azc0Zd1GKRKEFEKeinuca3F3hcN9tLanBJJMASvYrGC3l9svVDL65vyfjPCBJDKRFXFVEQxDwcYScuoAXHHNUj1PL60DXIBEnWe/SaJz3fvVwSqtZ5sxl4lh10iK2HCsOZWFJW5eNKkbYKB1cF2r3cj2k3TJbnUlqHuMTwmMouojh8UTBGkGQcMi+lnBISASE5ablNZwbzG7ecwVGWRhrMrCm6ohlXtraXGOZAmvLwmma69rFlew6PVEmpilgLkwYeGzPuTS/2D3blgXMuW5AiGSch8V99nIlOs6W036uBMovGGM6RWQp8D0ReTm40RhjfGFTFHyB9QBAW1tb0dpVMlNIAcTJ1PNsP4BcPzBXaD3nR0WBLYCC52h3nNfuslvqvDwsadPBunN4rGqsYu+ZoZQ/494bl/N/fnAy9Ta7sqGC3qGJwcdRBFLVdJNcu7yakfGEFfY7Fk9YWo0bNeRqJLmQLMN8BaNcK8e5LtTO5lA718kxy2Hu+TzW8HgOzaMsYYiEYLwA01nU2b++wk5sbKqOcO7SeOpe37NtGV/dc85yiIdDQtxNNAksureqpizM79y8IvUde9FrEwJlYCye9nKzcUU1e/35Y8KhZE7OBK758PC5IUtjf0ebPWnW+qWVrF9amTYPi8tMFDKdCnMiUIwxnf7/bhF5DLgJOCsiK4wxZ3yTVnKO0U5gZeDwFn9dJxMmsuT6p2a468o0yUc9z1XCJNNER0HcnJJ8qvZWOtE2O08NWD/SVY32m6eBtOlgt7bUEgl3EYsbwmGhujxk+TN2dV6yfCzu4O2+mzrKCGsaK9jaUsuP2y+SADIVPi/Uv+HS3jNKRUQwsVE2yAlfcLSn8jzCgWirPYnVfDtxM3sSa9ibWM25PB3mYQEJhRjP5tl3WFpbRmfA+d3SUEFPQBi/fv0iHt3VTSI+ofm5DnE33NkV2FXRkJWg2VAZtsxR73/8iHX84XPDaVF2h88N84+/sn7SAp+Lq6NWImRZII9oLGY4fG44Lfjj3huXW9WfXe1kpgqZToVZFygiUg2EjDED/uc7gT8HvgG8E/iY///r/iHfAN4nIo/gOeUv+kLnCeCjIpJ8iu8EPjSLl6JMkVzqeaYSJjARLeU6MjNPPDXxs3Qdp5lm2nMqlgB2qZW7tyy1trmawLf293DfbS3WVLk7T9kD2nMd/dayO1+Hm+cQdoz+7b0j1FVGUj6MBBBzTFpTUb/LGOdqOcl1oXa2mHY2h4+xNnwylSR4ztSxN7GGJxI3sifhaR5n8xQemQiHhLECJV99RcQSKK4wPnnBm4nSC/v2nhU34VIEgi6lJdVRugfHU9rE6qZKdgdeGuoqbPPhZC8y7rrg8+3WXlvTVMkZPzfIzSMyeGX5yyLpIfLJUv5f2X2O29Y0FCQwZtNpPxcayjLgMS8amAjw/4wx3xWRF4BHReR3gePA3f7+38ELGT6CFzb82wDGmF4R+QvgBX+/P0866JX5jj1Y9A6Npb2BBd8cAa/iq/8GV1sWtnJKasvC3LNtqfWGt/fMoPUjqyoL0R8oMTI8nrBKYvzYCUF1B8SysFc2I2j+GI/bo/2iqoh1DjcCyI3Icp3hO08NpIehOmVNchHxa1tt9v0dm0PtXCMT5Un6qOVlaeXT41vZl1jN7sQaujLkeWQjLOm+mSDD4wnWNFVw+PyEWdH1Z5SHvdDfJFtbanjl3FBqIHYv+/xgLC1Xqb13hCdfmQj6vHpplWUC+80bl1tzw3z62U6rzf6RmKWlTvYik017cJ/lxqqIpcG4s2P2DcfSNPjJphjIVsg0yGw67WddoBhj2oG2DOt7gNdnWG+A907S1oPAg8XuozKz5HIQuiVMGqvKrHLo7g/qmfa+tNyMYH2qusoIn3neM0U9umviDS8YCfbYnnOWxuDa090QVHeANV6Mj7XOdYgPOQJjxE2cc85pnFG2vjKSlpuSbeAOkWCNnKbNFxxtoXauleOUi3ed/aaKPYnVPJh4M7sTa9hr1hCvXkH/aILB9HoreVNfGbbyVspCEEzgr60Ic9d1S6z8i9bFFRwJCJgVdRNT+obw8jdCIiTwcnhaGsotrXNrSw1v2dhkhX0nn5Mkt6+p55cC+yQH/+QzOOrczL7hWM4XmVzaQ66E2AZn+oGGykiaBp9PGHwugTFb1YlLKWxYWSAU6iAEO+lw84pqPvX2ibe4oz0TtWwTpOd3VDsz7/2ovc/yXXxtz7mMJrRgJJg7CZTrv4g79n63YOLZgTHKHT/M6X7bAetGceUyTzkRv1wcjlEWDk1iLjJcJWdTwuO6UDub5BjV4vVh0JSzz6zmofgvsjexhj1mNcfNMozjiWlMGAYL8ZZnxL55kXCIscD9a6iIpIVYb1pezbGekZRT/e6tS/nE06dS/igwltbp1tUaHE2khX1vbamlPGK/uW9eUT2JiRR+aWOTpcEEkwwzTWYGuc1NbqDGoe7h1HVFwl2sXWyX33GvC9I1jFyFTOcSFShKUSnUQbjjYK+vjdgO+OBEU25WupvfEXKC/8ccU9GxHvutvqN32AoTHsswCdSKujLLJOOW4XAdvGPxRNqsg65Zxn3pL9TfcX4wxo1X1vL8iX6uoIfNoXa2hNrZJJ4AqRdvMBwxUQ6Yq3g0fgf7zWp2JdbQbq5IVdXNhiBp5qdCGXFUs4qoEJSlVy4qZyBwcwye9hYKYTnVCYQwrF9aRfhgLyZuCIckbT6V3qGxjINsIaaebEmGhvTJzCC/6ajLAkKtd2jcenbLnOjBTBFckK5hlEoio4sKFKWo5Cp74v4A3R/YjoO9VjmK508MpJUX6R+x3/SrokLQeeYW/XPNQl0DY2kDmpuz4C67Gks0IpaPo648wrlY9ryHXCypjnBuMP0NtYmLnsM8dJSbzh9nbflhlojn4B83YV42K/l2/BZ2m1b2JNak5vOYCtGwsKw2apn/XH9GLh+J6/tZ2VDBhaHBVOTSPduWpyVg7jw1YDnVnzrSR8wvshj3y74HBczgaNyKhmqsKkvzoUDhpp6gSeuhF7os7SJTuZ1c/gl3+46Ddj7T6sYK3nhNY44Ak8LOOZeoQFGKihuym8ne684HYWPS8kwOdA1ag4dbUdedS8PFHQArIiFeciKw3Kq7bpuXRu1zjjuDpiG9jHuhXByJU8uQ5++QdtpCR9kcaqdZvEEogXA03szTiS3sTqxhT2INL5srGaUsR8uT42ojw+OJtGxvV3hEQt4gP1kb4bTtxgqXPtoznJbXU1/pBSwkBUJQA0nglXUPChwQKxpq+4ZGtm9oLOog62oXU9UEgkLtaM8wrk/F9csU2mYpoQJFmQEmhv9D3UNWwcQdB3us+SDSE7mqWL/Ung722uXVqdh9gzd734XAgF9bHrbCbV2HemXUDse9e+tSvrbnnLVPrvw/d7MrOk72jeZsI+QcV84YG6WDttBRrvPzPVqjE1E/HYllvJhYz4OJNexNrKGjrJXesbK0BMjp4DY1OBZnSXWUvsAEWa4vx73OSEisBMr6yqg1IZYbHv3N/T1cvaTKyuvZtLyGP3rtlZafwNZAIpYGkkmA7D1jmxwzUUjGeL45U4UkDbo+lUxaz3xGBYpSVHaeGiAW9wRILG7oHYpZb6e9QzFLwOSTyFXr1DCpcZY3X1HD2UMXUvWSVjdVWs7VGkfgdF4cpdvJI8k1Rucy8+QKqk2G63qC4yhbQu2sl4lcjy6ziD2JNXxt/DXsMZ72cZEaq43oePBdf2aIhITXrV+UipoDLyorqLFFw25Sot2nSyP2oOnemzJ/atwg65dWpr11B7UDt+K0WzA0n4F9KhnjuTSBQpMGi6X1lCoqUJSi4lbczURwe4Ozf11lJC0U8/ZW21HZ5QiDQ91DqbfmhIGzA3Z0Vbfzhvzky71pJiwX14zjajlVznweLQ3lqQQ1IcEqOct1cpS2QMRVhR+u22eq2ZNYw/cTv8yexBp2J1rzLMtuCqqzNRViCcNJZ5rgmHPSuoqwFQItbtVFEcoiBMxXVZwZmMjjWd1YwaFuO8PcXc5Vcdoln4G9GJWwXQpNGixl/0cxUIGiFBV3YPBsxhOc7h+13l7dOSqefLmXW66qs+cV6bT3cd94+0dts0G/M8lUxiqzOUKZ3M1Djt8mKEyWcoGbRju4O3KI6+SoFXE1bMrYa1bzxfgb2JNoZZdp5YRZmuEqcjOVsiquZhUJZZrS18adAtiNaAs7EQoVEbHmCmmpj3LXdUtTfrLWpkp+evyiNTWy65zO9GUU4ifIZ2AvViVst4+FCohS9X8UAxUoyrQJvtX1DtnaQ9CWDl4F3aAzdcDJ5+i4MMKd1zRaWotxjPg15WHuWb8oZRJ7bM95a7sbkeU6iYfHE1Q7pchd3C3J5ToGfaf5Ubb4vo/lcgESMB4O80og4mp3opXDppk4Geq6ZMD1sbiIQI2jKRVKJCTWLI2uwFlcHU0b2l0zm5uDU10WtmpgrairsHJC7r9rnZVXlBxMg8mr2zfYc3wUipuoOllyX7Z9plrzaiELiEJRgaJMC/etboOTCRxxymQsro6yuqmS5zr6uWVVHe09w1aG9OLqaJqW45pckqaw5H837yRX/kcsYbhmaSW7TrsRZjbljLFBTtAWOur9yVFrXo/2xHJ+mriW3YlWTlas58eDLdOKuMolJlbUlXHJGcxdTcqtyruoyi7Bft0VNVbAw+vXL7LKk9x743KeeLnXnirXcbIvqYnSF9AC1y2ptGpiNVZFcibeucmr0x2Qc81nk88+pTRR1XxFBYqSlVw2ZXfaW3ea1KU1ZVa5dSA1gD35ygXqK+y3dwH2d9kmLnfe82hYrHPmciu4UUleAUdbjRESrJEzbJGjKQGyQY6nalx1mwZ2JVr52vhr2GVaOWhW02smnOYbaqpgZAiyRzAXRLr2EKGpOkpfIDqqLCxWyZC6igg9QxMCpCJiX+eFobGUJhQCfuW6JSypiVq1qA51D1lFEhdX2wJl4/JqOi6MpLSLW1bV89PjA6myKOuXVhH1I/myDczFfLMvhg9lofs3ZgMVKJcZhTgd87Epu074rS01HDo37E/4JNy6pp6OQMSQN6fEBBcdf8eZ/rE0m71r4jk7MJbT8Z+NhIGL507xxtARtoSO0CZH2Rw6Rq14mtGAqWRvYjWfSbyZXYlW9iRaOZOjQGLP4Di1FRFGMyQmThXXAd83HPdrQU0M9svr7Cl8G6tsgXKm3zY5Hj0/alUr/vSznew/O0gsDo/u6ua2NQ1p9aeub6mhvWd40ogrLylxoixKf4YChzNNMXwooOar6aICpYQoNMJkKvsX4nSc7I0ueN5gXL3g2deT084mjGH/GVvbcLOoXUIiadPcuvTniNBySSYLBrWP5XIBwl6m+QFzFY/Ff4E9Zg07E2tpNyvSalzlor4ixPELY1n3cX0kEUmfVCsbDZWRtAAFt4y+u+wKJffOvXJuyKqq7DnLbcE5OJrIGXHlDtSzPTDno12oBjLzqEApEQod7KcSkTKVmHl3oHDP+462JZa2sK9r0ArhfcXxh+QKe22siqQ56gshQoxr5ARbQp7TvE2OWrMKHk2s4CeJjexJrGFXYi0Hp5hp7kZLrair4IRj2nNxB/NCtatLY/G02k9u1uGI4zBy++n6WBKOPbB3KEZjlTssmKwColQG6nyEmGogM4sKlBKh0ME+H+3BXS5GzPxDL3TZIb05Spi4/o9cGAoZaA1XSjdb5IgnPEJH2SQdqfLs500duxKtfD3+6lSdq2SyYK5ExVy4jv4z/aOEQqGCYnvd89eUZY/g6uwbpd4pUOjuXV9h1wNbt8SeA2RVYzmHzwdNZFHOBBzwjVVRtm9oLDgCSwfqCXJZDmZrfve5QAVKiZDPYJ9LOLjaw323taSiWiabN8GdFz0Xrs/EfWN25UehuROn+iZ/y29gIKV1bAkdoS10lEbxBFoy3+Pz8TvZnWhlV6KVThYzmd/DTVScLh29I2nhyoXiTrDlEjekmQMzBRxYbY7ZRRQ3Lq+lvXc0FZH1mzcuD5RT90qaFCMCayEPmtnIZTmYzfnd5wIVKCVCLrNBpgfRjal3Z3Z76khfmhYTFFSP7zufNi+6O3mQe043pPeSE6PrFkgsdMhOjodljHOtHKct5AmPLXKE1aGzXptGOGya+bf4NnaZtexKtPKKWZl3vgd49cGC4bPTZTzhaT3ZSCui6GhJuZS5mvIQteURegKTV4VFiAVaHc4gJCNhAtpGeg2s4KyFQf/IVAe6Uho0Z1uw5bI0zOb87nOBCpQSItuPONM8Ijte7kmbUCiotdyxtoHdpy9NOtObO6nUt/b3WINL5lL09qh30SluN7V3fm9yqC1yhOvDR2mTI2yQ45SL1/ZZ08DOxDoeHX8tu3zT1SCVOdrMzpHz2XNQwuL5H0bjXvn2sXh2U5wwSUZ+ADfEd0VdWaroJcA1y2zz1OJqO4dk0/IaVi4qt6LmqstDjAYFTNj29C+tLaOzf4wJHSWdYpurSmXQnAvBls/8KAs510UFSgmR7W3KfRDBOL6MzBMKuQIiOKmU6/zuH4mlmcySg6QIqYc/aF9fXFNm2ezzmZypnkue01yOpJmuhkw5e81qPht/E7sSnvbRxfSyqDORq+R93Exk14/G07UJ9zprykOMxYylLbjkui/Xt9Tw8tmh1NB/fUutlXS4clF5WpZ6U1XEmm63pixsVQA4fXHUKtYZrPY8U4PsZObY2TaBzYVgK3R+lIWknYAKlJIh19uU+yB60+J6uQLJoorJ/dzjkstfDZRsN2SaddBYP8DnOvpTPpB4wqvL9bZNi/nA7StTprYHfnLaasMdNKOpqKsjvulq/QovkAAAFVtJREFUIts8abr6XvwGdplWdiXWcrb8KgbG7UikYiPkNi+5uA509/CKSIjRWPYsdnfJLXL5nQN2fasDzgyQh88Nc0VdubUuGg5bPhK3EKNb+r93KFaUQTZb8If7rAJzYgKbK20gl8a3kAMYVKCUCPm8TQUfxIdfPGtte66jP6NTPfhDdwcodwBsqo7SOxQjYbyM50NOkcAv7+ymtamSv33qFLGE4aVTl6iMBvM1DC1ynjY5ytbQYbaEjlpVdpPZ5l8dfw07zTr2JlZzCbtUS2i08MG+UKJhScvony59wzGW1domLBd3XpEyp65WSMQKeAgKAyBVaPFbB3pSiaNv2djE0UDS4T3blrFyUblV+j9YjNOdVyTfQTb4HAE5gz+Cz6rr25stE9hC1wZKERUoJUKhUV7nnSlq3eXk/sEfftsVNdYA5Ya+tp8fSq2LJdLLqAieIEsOgmWJYa5PtHNt+EhKgCwVb7bFERNln1nNF+K/mDJdZYu6SjLTwgTSi00WAxFPIAfvr3sWN+LtF1obbJNWQ7mV5T4ynkiZ2sICrU2V/rmM77MxtDZVpmkDwTpn993W4sxsmHlekWy4z9H2axpzBn+USo2shawNlCIqUGaRbHbkQqO83tG2hAMBB+5bNjalncPVetzCgiHHL+DmE8YCI6CQ4PevHmT/S0/w0cgRtoaOsF5OEhYDYa9Q4o8Sm9idWMtLibW8bK4kRiQvn0qQQvefCtMN74V0n0p9RZg+J0AhJLaAdJ327T3D/PHrJsyHz3VctI4/fG4odY64gR0He1leG7WmwnULLwbzhMZjk5dBKWSQdZ8jEEvLcYM/XIGhmsLlgwqUWSKfiJNCorwGR+OpUuSRkNDaVJkxDyUcIjW/++LqqNXm0toyOgNv1MGs6kX0c3PiKNdGDrNVPMd53a5hCMFFqWJXYi1PJG5IaR991E60ERg0o+H06r/ZuKK+jO6BcWtK2WIzjUT8FK5wiCXwgyUmqC4LW4EPrmJ04sKINZ/4cx391vb0O2ByOryDeUIJPN/adN/S3XPmG3ocRDWFywMVKLPEdCNOtrbUEg51YeLGn+BIrJpZXkgv1jkOdQ8RnN/9llV1/PR4fyqJrbWpks6LY6lyJbeWtXN1/BBb5XAq5yNmQrxsruTr8Vs5VXkNXbUb+cbp2lStq4qIMBIY/JfVltE1MJZKnKursENfXdxSIPdsW8ZDL3SlzUNebKarOYUcidJUHSUatut/NVZFGByLp6YmdudgqSkPp+0fZP2SSnqGxq2M9VwO7+3XNKZNqTwVsjnZM2k5KjAUUIEyaxTHjjzhsl2/tJLoy+nOVTu0WNKqwN69ZQn7Dx/l7mWdNPTu431l+7lO2lOO87MhL+fjy+OvZWdiHXvMaoapAKAxHuavbm3l6/96KNWj5bV2efr6igj3bFuWMuP84487s16Rq4gc6h7m9c6c5sU2g0VCXr+zOdBdXPOgm5F+RV0ZFdGwlUdSVRa26pq1NFRY229YWWe1sX1DE9880JMSxvdsW84925ZnTTp0Hd4gls8kn+csU8RWJm1aBYaSCxUos8R07cheifAJ2/lktvFg9nxrUyXff/kM1yTa2RY+wr87epKmi/u4QnrhJIwTYS+r+GL8DexMrONCw0Z+0ltD0nFeERZGLMe8sONgr9Uv1zRVUx7mE0+fJBaHnZ0D1JaHITBuuxV3y8LCsFVyN31O8/qKsDWhUy4B4253CyRuWl6VVnbF3ac6KgwGpvmtcrSLsnCIWCLYhqSVjTnmTH984sKItez6XMDTfJLzikDmN/9sJXgymaOykUl4lEpiojL/UIEyi0znLS+ThuO2t/f0Jb76zEtsMoep6TrMLY0n2R09RMh4A9eZgWU8n7iGXYlWXkqsY6h+Pe39idQb8R9uWclzPzyZMtFUlYcYCSTNlYUlbYrfXie67EDXoFUOvSoapjcw69TVTjb4qqZKa3loPME5p80V9eWsqIdD3UOsX1rFyoZyKzqqud72BbkZ6e7gf+c1TXz2ZxMzLwJEnBIm21bWWfOgb22u5Zn2Cad5fUWYISdZxvVRVZWFGAmEZldFQwwGBFlDZSQtiCKoUWYayDMJgOk43TMJj4Weza3MHCpQSphcdmwZHyJ6bh9l3buJnt3NrZ0v8f2IN9AOmXLOx66hp/U/sDPeSuO6m/jsvpg1KG6pqiQ0MJR6I/Z8LhPUldtZ2OuXVNFYZQ+anj9ncn3BjSwbHot7c7z7QswNdz7QNchvbFtmCZnrW2p4dFc3CeOVTFniDNyLq6OWQCmPhBiNB53hdv8OdQ9x5zWNllntumZ7atxbVtVxy6o6S9sL+p/WLal0qvRG2L6hiWc7Lqau7fduuYK/eepkanmJU1XgZN9oWhBFrhyRTALAnV63ECZ7UdGoLGUqqEApUdLeRN+2li3VPdzUsJuyo7uI/mQX0d5DiPEGzlj9VfQufxV/c3wZL8TX0R66kvddd1XK/BQ51MMHbl9pDXirGivYc2Yw9UYMYkWF3b11KX/9g5Op6WLv2bYMSJZe8QbWV6+ut7SFV6+u54dH+lLbVzVWsOu0nVCZHN+Ngea6Mmt62dsDVY+Tg/mhbnsSKDfhss+Z5KuxKkJ/QJA1VEYYtpz8wntvbQawEgBfODGQauNQ97BVK+3+u9bxqbfbznBPwNgO8z+8Y6VVvTkY/XS0Z9gK9V5SHeWV7qGUcMhnpsNiaw/ZHO4qSJRCUYFSouw73s31if1sDR3mejnMq77bTnXMSxpMRKsYX3Idl7b8HmPLtjC+tI1ExSIEuOXMIOWnBvjPLbXsONhjD8TdQ/zjr6y3BsVvH+xJCZD1Syv59sGJobnzoj1dbLL0SnBgTUaXJVnTWMGvOAPve756KCXE7t66NJBVLfynW5t5pr0vNbAnB/pgOG2yInKSiogdTXXVogq6BsZSbbrTDr9u/SK+vPNcKsR6+4ZGAN57a3PqfI/vO29lqfcOjVn5HJk0AbfE+94zg6lrSxbsDA7Myf9BrednJ/oLmulwJrQHFR5KsVCBUgoYQ7j/BGVnd1HWvYuys7v5Lz2vIGXecN5urqBvxWsYv2obY8u2EFu0FkKZS7UHBwfXgQ5ibd97ZpBgWPGh7iHLhv/0kT7r6KeO9PG2TYvTBqDyyORvzJtXVFtCLFPOwuYV1amBPRMTEz55Ws/dW5dac3jcs20Z92xbZgm5YLmRwdGElV2eCXcqY5C0fI5s9xo8c5RbsNMdqIOCEpiScFABoJQq816giMibgE8CYeBfjDEfm+Mu5STl+zi7i7Kzu4h27yY84g3+iWg140uv49L1v8+hyNX8aHg1G1Y1s3lFNdkLrqfjDsTJN/MkrhPYzYC+fW2D5We4Y21D2jnyLQQ4nZyFzSuqLa1oskS6YJtlEbsys5tdnqmac/CYxqpIwfkc7uRjmYRQpmtT4aAsFOa1QBGRMPAPwC8Cp4AXROQbxpgDc9uzAMYQHuj0NI+unUS7dxHteSXl+xivX83olbcztmyLp300tKa0jxXA3dM4daaBOEi+IadJc1TyzdoNFpiJQoDuTJKFDLybV1TzjrYlqX7ftqaBbx7oSSvD757DDbn++r6J6r9bW2r5yBMdPNfRzy2r6vjIG1fxD892WqY6V+j0D8fS7pXbhou7f6YZNd3zTnfK2ct1dsVM6L2YHvNaoAA3AUeMMe0AIvII8FZg7gRKbISy8/uJ+tpH2dldhIfPA+m+j7GlbZiKRTPanWwDca4M6L1nBq1Cg7et8TSUbCVkiuE0nspMksE+PL7vfEqzevjFbs5dGk8rw3+0Z9g6R+fF0dS17j59ibYraixt48PfPZbK3n/ylQu80j3EcT9fJqjFBWnvHeGzL3Sl+tl2xUQkWTKQIShUMtVrS7adPK7z4mjatT19tG/KU86W0uyKc43ei+kz3wVKMxD02J4CbnZ3EpF3A+8GaG6e3FY/FUKXzlB2dqcvPHYT7TmIJLyBJ1Z3JaMtr/a1j63EFq2b1PcxVxRSPyxTeRdXAymG0/ipSXw32foVPI97vFsjy90OnhYWbHN35yVru1sK5oSTfPn0kT6uqLfnKvFyciZv0+2Xe12ZfFinL9rnfa6jP+u9uNynpC0EvRfTZ74LlLwwxjwAPADQ1tY29Soe8TGiPS8HBMguwoNdACTCFYwv2cilze9kbNlWxpdtIVHZmKPB0mYybSOXBjJdv8AdaxusnBDXd5NLC3KPv2VVnRXanGwvuM/taxtSGko0LJY2AbC8NmoJlSsXlac0lOTxzfXlBbV5yyq79Ip7XZl8WEENJdnGhIZS+JSzmsQ4gd6L6TPfBUonsDKw3OKvKzqN3/1PlHf+FIl7g0isZgWjy69n3Nc+xpuuhlA0Ryvzi8m0jZlOenPzUNyJw3JpQZmO39Jck7G94Lrb1jRk9Xfk8qFMpc1c19VcX56x3/n6UHLdK01inEDvxfSRmZhsaLYQkQhwCHg9niB5AfgNY8z+yY5pa2szO3bsKPhcdT/1gsfGlm1lbNkWEtXLptRnRVGU+Uhzc/OLxpgbsu0zrzUUY0xMRN4HPIEXNvxgNmEyHfpf9cGZaFZRFGXBMK8FCoAx5jvAd+a6H4qiKJc7ody7KIqiKEpuVKAoiqIoRUEFiqIoilIUVKAoiqIoRUEFiqIoilIUVKAoiqIoRUEFiqIoilIU5nWm/FQQkXPA8bnuR4DFwPm57kQO5kMfQftZbOZDP+dDH2Fh9PMqY8ySbAdfdgKl1BCRn+cqZzDXzIc+gvaz2MyHfs6HPsLl0081eSmKoihFQQWKoiiKUhRUoMw9D8x1B/JgPvQRtJ/FZj70cz70ES6TfqoPRVEURSkKqqEoiqIoRUEFiqIoilIUVKDMEiJSISLPi8huEdkvIn/mr18tIj8TkSMi8mURKZvrvgKISFhEdorIt/zlkuuniHSIyF4R2SUiP/fXNYrI90TksP9/0Rz3sUFEviIiL4vIQRF5VQn28Wr/Hib/+kXk/aXWT7+vH/B/P/tE5Ev+76qknk0Ruc/v334Reb+/bs7vpYg8KCLdIrIvsC5jv8Tjfv+e7hGR6/M5hwqU2WMUeJ0xpg3YArxJRG4B/jfwCWPMWuAC8Ltz2Mcg9wEHA8ul2s/XGmO2BGLnPwh83xizDvi+vzyXfBL4rjHmGqAN756WVB+NMa/493ALsA0YAh6jxPopIs3AHwA3GGM24c3S+muU0LMpIpuAdwE34X3fbxGRtZTGvfwc8CZn3WT92g6s8//eDf+/vbsNkaqO4jj+/dWGtWqaWmKuoVmUIWZGamZSaUYRWqGRGQlGEQURUVFZYfSiKF9UUJuRJEkPYETKRmqZL0TMfH6q1ELz2TVLS/GF6enF+U/Ojjvubk57b3Q+cNl771z+c+bunTlz//fO+VPbrGcws5haeQKqgRXAIPxXqVVp/dXA3BzEV5MOrhuAOkA5jXML0KVk3QagW5rvBmzIML4OwGbSzS95jLGRmEcCi/IYJ9Ad2AZ0wkebrQNuytOxCYwFphUtPwc8mZd9CfQE1hUtNxoXMBUY19h2J5viDKUVpW6kVUA98CXwE7DfzP5Mm2zH3zRZew1/ExxLy53JZ5wGzJO0XNIDaV1XM9uV5ncDXbMJDYBewF7gvdR9+K6ktuQrxlJ3AR+l+VzFaWY7gCnAVmAXcABYTr6OzXXAtZI6S6oGbgF6kLN9WaRcXIXkXdCs/RoJpRWZ2VHzboUa/JT40oxDOoGkW4F6M1uedSzNMNTMBuCn5w9LGlb8oPlXqyzvi68CBgC1ZnYFcIiSro4cxPi3dO1hFDCz9LE8xJn690fjifp8oC0nduFkysy+x7vg5gFzgFXA0ZJtMt+XjalEXJFQMmBm+4EF+Ol5R0lV6aEaYEdmgblrgFGStgAf491er5O/OAvfWDGzerzPfyCwR1I3gPS3PrsI2Q5sN7MlafkTPMHkKcZiNwMrzGxPWs5bnCOAzWa218yOAJ/ix2uujk0zm2ZmV5rZMPyazkbyty8LysW1Az+zKmjWfo2E0koknSupY5o/C7gRv0C7ABiTNpsAzMomQmdmT5tZjZn1xLs/vjaz8eQsTkltJbUvzON9/+uA2Xh8kHGcZrYb2CbpkrRqOPAdOYqxxDiOd3dB/uLcCgyWVC1JHN+feTs2z0t/LwDuAD4kf/uyoFxcs4F7091eg4EDRV1j5WV18er/NgH9gJXAGvyD7/m0/kLgW+BHvKuhTdaxFsV8HVCXxzhTPKvTtB6YlNZ3xm8o2AR8BXTKOM7+wLL0f/8MOCdvMaY42wL7gA5F6/IY5wvAD+k9NANok8NjcyGe6FYDw/OyL/EvC7uAI/jZ833l4sJvxHkTv867Fr+zrsnniNIrIYQQKiK6vEIIIVREJJQQQggVEQklhBBCRURCCSGEUBGRUEIIIVREJJQQiqTqwA9lHUdTUjXg6qzjCKFYJJQQGuoIZJ5Q0g/KTvb+fBQvMtqSNqua3iqEfy4SSggNvQz0TuOCvCrpCUlL05gQhTFseqbxTaZL2ijpA0kjJC1K40oMTNtNljRD0uK0/v7Ck5yk3Q2S3sd/uNdDUq2kZWo4hs4jeC2rBZIWpHUHi9oeI2l6mp8u6W1JS4BXJPWWNCcV1FwoKXf15MJ/V3xjCaGhp4C+ZtZf0ki8pMdA/JfDs1MByq3ARXip8onAUuBuYCheXPEZ4LbUXj9gMP5L9JWSPgf64uNMNNbuxcAEM/sGQNIkM/tV0unAfEn9zOwNSY/hY8H80ozXVAMMMbOjkuYDD5rZJkmDgLfwem0hnLJIKCGUNzJNK9NyO/wDfytepHAtgKT1+CBFJmktPuZEwSwzOwwcTmcTA/HEU67dnwvJJLkzleavwseruAwv49ISM1MyaQcMAWZ6KSzAS5eEUBGRUEIoT8BLZja1wUqpJz4CZ8GxouVjNHxfldY2sibaPVS03At4HLjKzH5L3Vhnlom1+HlKtym0eRo+dkj/Mm2EcEriGkoIDf0BtE/zc4GJ6Zs9kroXKsm2wGj5uOed8WKbS1vQ7tl4MjggqSteXr6xOMHLkPdJF/JvbywQM/sd2CxpbHpeSbq8ha8nhLLiDCWEIma2L11cXwd8gZceX5y6iA4C91AyYFIT1uDl1bsAL5rZTmCnpD5NtWtmqyWtxKvrbgMWFT38DjBH0k4zux6/9lOHjxC5DO9Ga8x4oFbSs8AZ+Jg3q1vwekIoK6oNh/AvkTQZOGhmU7KOJYTWEF1eIYQQKiLOUEIIIVREnKGEEEKoiEgoIYQQKiISSgghhIqIhBJCCKEiIqGEEEKoiL8AeFBaK1iNAAMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXvGMZTrQJmn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "506b8b0b-9255-41d3-b0c9-7c162a8c3e5d"
      },
      "source": [
        "r2_score(y, p2(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4361280393857049"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKvsxOJYtiPp"
      },
      "source": [
        "This has not increased my r-squared value compared to the linear model. Let's try a third-order polynomial too:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSr7rRrqRJsa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "3aeb683b-b83c-4ed2-a931-b6f398b5dc45"
      },
      "source": [
        "f3 = np.polyfit(x, y, 3)\n",
        "p3 = np.poly1d(f3)\n",
        "print(p3)\n",
        "PlotPolly(p3, x, y, 'temperature')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         3        2\n",
            "-0.1007 x + 19.2 x - 1076 x + 2.061e+04\n"
          ],
          "name": "stdout"
        },
        {
          "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": "u9AxVxczRTUw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9ef2f1f-1e40-4104-c7b6-1e70f6db986c"
      },
      "source": [
        "r2_score(y, p3(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4605450391588989"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBC5yMCrtot9"
      },
      "source": [
        "This increased the r-squared only slightly. It looks like it did a better job of accounting for the slight dip at higher temperatures, but does nothing for the outliers and might be worse at lower temperatures. I tried several higher-order polynomials as well, but it did nothing for the r-squared, so I don't need to list them all here. The point seems to be that temperature only goes so far in explaining the variation in daily counts.\n",
        "\n",
        "Ideally, a multivariate polynomial regression might be the best model here, but it is beyond what I am comfortable doing at this point. For now, the best model that I have found is the multiple linear regression model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Dsk4ebah6p1"
      },
      "source": [
        "#Wrap Up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4psYtUG-58S0"
      },
      "source": [
        "#%%timeit cell magic to time functions, for my own learning. \n",
        "#test for skewness and kurtosis - p 258\n",
        "#weekend/holiday has low correlation with count. Should i exclude it?\n",
        "# color 2020 differently in scatterplot; \n",
        "#groupby() can analyze stats by category and subcategory. could use for temp categories, and Did It Rain. or compare mean counts for various temp categories.\n",
        "\n",
        "\n",
        "#ctrl+shift+L : allows you to edit all highlighted items at the same time"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}