03_911_Calls_Data_Capstone_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "03-911 Calls Data Capstone Project",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/annLiu310/data-analysis-basic/blob/master/03_911_Calls_Data_Capstone_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "D4eJDORoK-RK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 911 Calls Capstone Project \n"
      ]
    },
    {
      "metadata": {
        "id": "XoCh6FNVK-RM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "For this capstone project we will be analyzing some 911 call data from [Kaggle](https://www.kaggle.com/mchirico/montcoalert). The data contains the following fields:\n",
        "\n",
        "* lat : String variable, **Latitude**\n",
        "* lng: String variable, **Longitude**\n",
        "* desc: String variable, **Description** of the Emergency Call\n",
        "* zip: String variable, **Zipcode**\n",
        "* title: String variable, **Title**\n",
        "* timeStamp: String variable, YYYY-MM-DD HH:MM:SS\n",
        "* twp: String variable, **Township**\n",
        "* addr: String variable, **Address**\n",
        "* e: String variable, Dummy variable (always 1)\n",
        "\n",
        "Just go along with this notebook and try to complete the instructions or answer the questions in bold using your Python and Data Science skills!"
      ]
    },
    {
      "metadata": {
        "id": "J-rpQjw4K-RN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data and Setup"
      ]
    },
    {
      "metadata": {
        "id": "upLxdFgeK-RP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "____\n",
        "** Import numpy and pandas **"
      ]
    },
    {
      "metadata": {
        "id": "OSYTpiucK-RR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pDHkdw-hK-RW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Import visualization libraries and set %matplotlib inline. **"
      ]
    },
    {
      "metadata": {
        "id": "5XVzrWU0K-RX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style('whitegrid')\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YPEMKZ7YK-Ra",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Read in the csv file as a dataframe called df **"
      ]
    },
    {
      "metadata": {
        "id": "cSvc9b93K-Rc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/annLiu310/data-analysis-basic/master/911.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oSihDh0DK-Rf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Check the info() of the df **"
      ]
    },
    {
      "metadata": {
        "id": "PQZc6T5-K-Rh",
        "colab_type": "code",
        "outputId": "0059c13e-498b-4304-92c7-18f9ea5a72c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 99492 entries, 0 to 99491\n",
            "Data columns (total 9 columns):\n",
            "lat          99492 non-null float64\n",
            "lng          99492 non-null float64\n",
            "desc         99492 non-null object\n",
            "zip          86637 non-null float64\n",
            "title        99492 non-null object\n",
            "timeStamp    99492 non-null object\n",
            "twp          99449 non-null object\n",
            "addr         98973 non-null object\n",
            "e            99492 non-null int64\n",
            "dtypes: float64(3), int64(1), object(5)\n",
            "memory usage: 6.8+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "id": "LJB5JbuQK-Rn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Check the head of df **"
      ]
    },
    {
      "metadata": {
        "id": "T4kU9wZqK-Rp",
        "colab_type": "code",
        "outputId": "410632fb-261b-4163-a90a-7869d9f60478",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "execution_count": 5,
      "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>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>desc</th>\n",
              "      <th>zip</th>\n",
              "      <th>title</th>\n",
              "      <th>timeStamp</th>\n",
              "      <th>twp</th>\n",
              "      <th>addr</th>\n",
              "      <th>e</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40.297876</td>\n",
              "      <td>-75.581294</td>\n",
              "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
              "      <td>19525.0</td>\n",
              "      <td>EMS: BACK PAINS/INJURY</td>\n",
              "      <td>2015-12-10 17:40:00</td>\n",
              "      <td>NEW HANOVER</td>\n",
              "      <td>REINDEER CT &amp; DEAD END</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>40.258061</td>\n",
              "      <td>-75.264680</td>\n",
              "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
              "      <td>19446.0</td>\n",
              "      <td>EMS: DIABETIC EMERGENCY</td>\n",
              "      <td>2015-12-10 17:40:00</td>\n",
              "      <td>HATFIELD TOWNSHIP</td>\n",
              "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40.121182</td>\n",
              "      <td>-75.351975</td>\n",
              "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
              "      <td>19401.0</td>\n",
              "      <td>Fire: GAS-ODOR/LEAK</td>\n",
              "      <td>2015-12-10 17:40:00</td>\n",
              "      <td>NORRISTOWN</td>\n",
              "      <td>HAWS AVE</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         lat        lng                                               desc  \\\n",
              "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
              "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
              "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
              "\n",
              "       zip                    title            timeStamp                twp  \\\n",
              "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
              "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
              "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
              "\n",
              "                         addr  e  \n",
              "0      REINDEER CT & DEAD END  1  \n",
              "1  BRIAR PATH & WHITEMARSH LN  1  \n",
              "2                    HAWS AVE  1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "MDCbdvc-K-Ru",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Basic Questions"
      ]
    },
    {
      "metadata": {
        "id": "iV1od15XK-Rv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** What are the top 5 zipcodes for 911 calls? **"
      ]
    },
    {
      "metadata": {
        "id": "BTPatyjbMEbK",
        "colab_type": "code",
        "outputId": "2e1259c6-2693-4f16-ae2b-4b6ab521c49d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "cell_type": "code",
      "source": [
        "df['zip'].value_counts().head(10)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19401.0    6979\n",
              "19464.0    6643\n",
              "19403.0    4854\n",
              "19446.0    4748\n",
              "19406.0    3174\n",
              "19002.0    3050\n",
              "19468.0    2990\n",
              "19454.0    2781\n",
              "19090.0    2635\n",
              "19038.0    2578\n",
              "Name: zip, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "6hic3rCFK-Rw",
        "colab_type": "code",
        "outputId": "612414eb-40a1-4eb6-f2d9-be29902b8ebe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "df['zip'].value_counts().head(5)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19401.0    6979\n",
              "19464.0    6643\n",
              "19403.0    4854\n",
              "19446.0    4748\n",
              "19406.0    3174\n",
              "Name: zip, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "axO3HbdvK-R1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** What are the top 5 townships (twp) for 911 calls? **"
      ]
    },
    {
      "metadata": {
        "id": "2s7sEl_oK-R2",
        "colab_type": "code",
        "outputId": "d0536008-d25d-461f-e593-997219bb3b5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "df['twp'].value_counts().head(5)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LOWER MERION    8443\n",
              "ABINGTON        5977\n",
              "NORRISTOWN      5890\n",
              "UPPER MERION    5227\n",
              "CHELTENHAM      4575\n",
              "Name: twp, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "chhY52tMK-R9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Take a look at the 'title' column, how many unique title codes are there? **"
      ]
    },
    {
      "metadata": {
        "id": "x9_cMBL9MsHO",
        "colab_type": "code",
        "outputId": "408eb2e8-7236-4666-a45d-a65cdd583fc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "len(df['title'].unique())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "110"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "kP5NOhWqK-R-",
        "colab_type": "code",
        "outputId": "f1185a2d-b706-4ede-83d5-178969bf26f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "df['title'].nunique()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "110"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "BZOnonTrK-SF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Creating new features"
      ]
    },
    {
      "metadata": {
        "id": "RHzB0bKvK-SG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** In the titles column there are \"Reasons/Departments\" specified before the title code. These are EMS, Fire, and Traffic. Use .apply() with a custom lambda expression to create a new column called \"Reason\" that contains this string value.** \n",
        "\n",
        "**For example, if the title column value is EMS: BACK PAINS/INJURY , the Reason column value would be EMS. **"
      ]
    },
    {
      "metadata": {
        "id": "xLBIjLK4N2mS",
        "colab_type": "code",
        "outputId": "933be868-2f20-412e-97b6-eb83fbfc75de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "df['title'].head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     EMS: BACK PAINS/INJURY\n",
              "1    EMS: DIABETIC EMERGENCY\n",
              "2        Fire: GAS-ODOR/LEAK\n",
              "3     EMS: CARDIAC EMERGENCY\n",
              "4             EMS: DIZZINESS\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "vAqXNZKCOCAU",
        "colab_type": "code",
        "outputId": "e79bd896-f345-424b-9824-0ddafe8afb91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "df['title'].apply(lambda title: title.split(':')[0]).head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     EMS\n",
              "1     EMS\n",
              "2    Fire\n",
              "3     EMS\n",
              "4     EMS\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "NBvC1xvfOWZJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "27d38b46-42f0-453c-a3e3-31865c211410"
      },
      "cell_type": "code",
      "source": [
        "df['title'].apply(lambda title: title.split(':')).head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     [EMS,  BACK PAINS/INJURY]\n",
              "1    [EMS,  DIABETIC EMERGENCY]\n",
              "2        [Fire,  GAS-ODOR/LEAK]\n",
              "3     [EMS,  CARDIAC EMERGENCY]\n",
              "4             [EMS,  DIZZINESS]\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "sujK7hDSK-SH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JaH2UoN_K-SL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** What is the most common Reason for a 911 call based off of this new column? **"
      ]
    },
    {
      "metadata": {
        "id": "IVylfq2jK-SM",
        "colab_type": "code",
        "outputId": "3eb73233-f543-495b-dcae-42f6525e7923",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "df['Reason'].value_counts()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EMS        48877\n",
              "Traffic    35695\n",
              "Fire       14920\n",
              "Name: Reason, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "RCi9yzg0K-SR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now use seaborn to create a countplot of 911 calls by Reason. **"
      ]
    },
    {
      "metadata": {
        "id": "6h0flApnOi3l",
        "colab_type": "code",
        "outputId": "a03b3145-3896-4369-9701-b76cb1194ff4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Reason', data = df)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6713d4128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEGCAYAAABy53LJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzQTsrkAaAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GV2OVuScK-ST",
        "colab_type": "code",
        "outputId": "8374d6d8-f6c9-4f5f-82e9-a2fb0ca8b572",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Reason',data=df,palette='viridis')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa671872f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEGCAYAAABy53LJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8v/eJ82lbiDfkQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5kfVTFRJK-Sd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "___\n",
        "** Now let us begin to focus on time information. What is the data type of the objects in the timeStamp column? **"
      ]
    },
    {
      "metadata": {
        "id": "ViNlmMruO21o",
        "colab_type": "code",
        "outputId": "06657f68-18a4-4b3d-e1fb-f5c20b4f66e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "type(df['timeStamp'])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "9mLLMZOOK-Se",
        "colab_type": "code",
        "outputId": "b5001b7c-7756-4aaa-d9cd-1ec82f2d213a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "type(df['timeStamp'].iloc[0])"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "lUjcD_owK-Sj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** You should have seen that these timestamps are still strings. Use [pd.to_datetime](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) to convert the column from strings to DateTime objects. **"
      ]
    },
    {
      "metadata": {
        "id": "XuBbUzb7K-Sl",
        "colab_type": "code",
        "outputId": "95b7a2bd-1d28-4b07-cb83-19a7c44c9f7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "df['timeStamp'] =pd.to_datetime(df['timeStamp'])\n",
        "type(df['timeStamp'].iloc[0])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas._libs.tslibs.timestamps.Timestamp"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "lrAG84qRK-So",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** You can now grab specific attributes from a Datetime object by calling them. For example:**\n",
        "\n",
        "    time = df['timeStamp'].iloc[0]\n",
        "    time.hour\n",
        "\n",
        "**You can explore the various attributes you can call. Now that the timestamp column are actually DateTime objects, use .apply() to create 3 new columns called Hour, Month, and Day of Week. You will create these columns based off of the timeStamp column, reference the solutions if you get stuck on this step.**"
      ]
    },
    {
      "metadata": {
        "id": "lRnNqZZPP45a",
        "colab_type": "code",
        "outputId": "f8686379-4fb8-47c1-c9a1-28a0ae19150b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "    time = df['timeStamp'].iloc[0]\n",
        "    time.dayofweek"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "-YXhXbK2F4_C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4cf598a9-801e-47e0-cfc1-4c54af19842e"
      },
      "cell_type": "code",
      "source": [
        "time.hour"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "ryOEf9D5Pmqo",
        "colab_type": "code",
        "outputId": "94ee4f1b-d0e7-41f0-b76f-8deb37d344e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)\n",
        "\n",
        "\n",
        "df['Hour'].head()\n",
        "\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    17\n",
              "1    17\n",
              "2    17\n",
              "3    17\n",
              "4    17\n",
              "Name: Hour, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "4FZpOZuAQL3l",
        "colab_type": "code",
        "outputId": "5c96753f-627f-4783-97b3-d3c379694974",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "df['Month'] = df['timeStamp'].apply(lambda time: time.month)\n",
        "\n",
        "\n",
        "df['Month'].head()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    12\n",
              "1    12\n",
              "2    12\n",
              "3    12\n",
              "4    12\n",
              "Name: Month, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "rYAMBvWwK-Sp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)\n",
        "df['Month'] = df['timeStamp'].apply(lambda time: time.month)\n",
        "df['Day of Week(num)'] = df['timeStamp'].apply(lambda time: time.dayofweek)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "id": "FvOuoB4GK-Ss",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Notice how the Day of Week is an integer 0-6. Use the .map() with this dictionary to map the actual string names to the day of the week: **\n",
        "\n",
        "    dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
      ]
    },
    {
      "metadata": {
        "id": "u-E5c41vK-St",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vzRs-ml6K-Sx",
        "colab_type": "code",
        "outputId": "f44f221d-b7a5-47ff-c4d2-375526ae1864",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "df['Day of Week'] = df['Day of Week(num)'].map(dmap)\n",
        "df['Day of Week'].head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Thu\n",
              "1    Thu\n",
              "2    Thu\n",
              "3    Thu\n",
              "4    Thu\n",
              "Name: Day of Week, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "id": "UtbXOGGJK-S1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column. **"
      ]
    },
    {
      "metadata": {
        "id": "u72tDNUfRVbG",
        "colab_type": "code",
        "outputId": "9541cb62-b659-4678-83d3-12aba2c9d72d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Day of Week',data=df,palette='viridis')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa67153e940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEGCAYAAABy53LJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJKkSi0OSVMl/Azp8um0ZFf+DAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KdYJ4SyPK-S2",
        "colab_type": "code",
        "outputId": "68447662-1d9f-442e-c60f-4199b9df97c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Day of Week',data=df,hue='Reason',palette='viridis')\n",
        "\n",
        "# To relocate the legend\n",
        "plt.legend(bbox_to_anchor=(1.1,1), loc=2, borderaxespad=0.)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa66ccc8f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAEGCAYAAACq69bDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY/1lE/HP7AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "S3tr0z30K-S6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now do the same for Month:**"
      ]
    },
    {
      "metadata": {
        "id": "z5-R_WUoK-S7",
        "colab_type": "code",
        "outputId": "16f9cb73-f0fa-4fcc-ff0b-c767009892c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Month',data=df,hue='Reason',palette='viridis')\n",
        "\n",
        "# To relocate the legend\n",
        "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa671511240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdkAAAEGCAYAAAApL+/4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGLKSJBViyEqSVIghK0lSIf8fxz1aSSoyXH0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "1_vcccWXK-TA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "outputId": "7cffa5c2-489d-4961-f40a-90b10e4527bf"
      },
      "cell_type": "code",
      "source": [
        "Missing some months! 9,10, and 11 are not there."
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-43-587e194d9bf4>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    Missing some months! 9,10, and 11 are not there.\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "vRUF4wavK-TE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** You should have noticed it was missing some Months, let's see if we can maybe fill in this information by plotting the information in another way, possibly a simple line plot that fills in the missing months, in order to do this, we'll need to do some work with pandas...**"
      ]
    },
    {
      "metadata": {
        "id": "sgrF6uhCK-TF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now create a gropuby object called byMonth, where you group the DataFrame by the month column and use the count() method for aggregation. Use the head() method on this returned DataFrame. **"
      ]
    },
    {
      "metadata": {
        "id": "66lQ8FuEK-TG",
        "colab_type": "code",
        "outputId": "c9374885-1596-4fa6-a3c6-71091c7cf55b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "cell_type": "code",
      "source": [
        "byMonth = df.groupby('Month').count()\n",
        "byMonth"
      ],
      "execution_count": 44,
      "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>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>desc</th>\n",
              "      <th>zip</th>\n",
              "      <th>title</th>\n",
              "      <th>timeStamp</th>\n",
              "      <th>twp</th>\n",
              "      <th>addr</th>\n",
              "      <th>e</th>\n",
              "      <th>Reason</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Day of Week(num)</th>\n",
              "      <th>Day of Week</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13205</td>\n",
              "      <td>13205</td>\n",
              "      <td>13205</td>\n",
              "      <td>11527</td>\n",
              "      <td>13205</td>\n",
              "      <td>13205</td>\n",
              "      <td>13203</td>\n",
              "      <td>13096</td>\n",
              "      <td>13205</td>\n",
              "      <td>13205</td>\n",
              "      <td>13205</td>\n",
              "      <td>13205</td>\n",
              "      <td>13205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11467</td>\n",
              "      <td>11467</td>\n",
              "      <td>11467</td>\n",
              "      <td>9930</td>\n",
              "      <td>11467</td>\n",
              "      <td>11467</td>\n",
              "      <td>11465</td>\n",
              "      <td>11396</td>\n",
              "      <td>11467</td>\n",
              "      <td>11467</td>\n",
              "      <td>11467</td>\n",
              "      <td>11467</td>\n",
              "      <td>11467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11101</td>\n",
              "      <td>11101</td>\n",
              "      <td>11101</td>\n",
              "      <td>9755</td>\n",
              "      <td>11101</td>\n",
              "      <td>11101</td>\n",
              "      <td>11092</td>\n",
              "      <td>11059</td>\n",
              "      <td>11101</td>\n",
              "      <td>11101</td>\n",
              "      <td>11101</td>\n",
              "      <td>11101</td>\n",
              "      <td>11101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11326</td>\n",
              "      <td>11326</td>\n",
              "      <td>11326</td>\n",
              "      <td>9895</td>\n",
              "      <td>11326</td>\n",
              "      <td>11326</td>\n",
              "      <td>11323</td>\n",
              "      <td>11283</td>\n",
              "      <td>11326</td>\n",
              "      <td>11326</td>\n",
              "      <td>11326</td>\n",
              "      <td>11326</td>\n",
              "      <td>11326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>11423</td>\n",
              "      <td>11423</td>\n",
              "      <td>11423</td>\n",
              "      <td>9946</td>\n",
              "      <td>11423</td>\n",
              "      <td>11423</td>\n",
              "      <td>11420</td>\n",
              "      <td>11378</td>\n",
              "      <td>11423</td>\n",
              "      <td>11423</td>\n",
              "      <td>11423</td>\n",
              "      <td>11423</td>\n",
              "      <td>11423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>11786</td>\n",
              "      <td>11786</td>\n",
              "      <td>11786</td>\n",
              "      <td>10212</td>\n",
              "      <td>11786</td>\n",
              "      <td>11786</td>\n",
              "      <td>11777</td>\n",
              "      <td>11732</td>\n",
              "      <td>11786</td>\n",
              "      <td>11786</td>\n",
              "      <td>11786</td>\n",
              "      <td>11786</td>\n",
              "      <td>11786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>12137</td>\n",
              "      <td>12137</td>\n",
              "      <td>12137</td>\n",
              "      <td>10633</td>\n",
              "      <td>12137</td>\n",
              "      <td>12137</td>\n",
              "      <td>12133</td>\n",
              "      <td>12088</td>\n",
              "      <td>12137</td>\n",
              "      <td>12137</td>\n",
              "      <td>12137</td>\n",
              "      <td>12137</td>\n",
              "      <td>12137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9078</td>\n",
              "      <td>9078</td>\n",
              "      <td>9078</td>\n",
              "      <td>7832</td>\n",
              "      <td>9078</td>\n",
              "      <td>9078</td>\n",
              "      <td>9073</td>\n",
              "      <td>9025</td>\n",
              "      <td>9078</td>\n",
              "      <td>9078</td>\n",
              "      <td>9078</td>\n",
              "      <td>9078</td>\n",
              "      <td>9078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>7969</td>\n",
              "      <td>7969</td>\n",
              "      <td>7969</td>\n",
              "      <td>6907</td>\n",
              "      <td>7969</td>\n",
              "      <td>7969</td>\n",
              "      <td>7963</td>\n",
              "      <td>7916</td>\n",
              "      <td>7969</td>\n",
              "      <td>7969</td>\n",
              "      <td>7969</td>\n",
              "      <td>7969</td>\n",
              "      <td>7969</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
              "Month                                                                      \n",
              "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
              "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
              "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
              "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
              "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
              "6      11786  11786  11786  10212  11786      11786  11777  11732  11786   \n",
              "7      12137  12137  12137  10633  12137      12137  12133  12088  12137   \n",
              "8       9078   9078   9078   7832   9078       9078   9073   9025   9078   \n",
              "12      7969   7969   7969   6907   7969       7969   7963   7916   7969   \n",
              "\n",
              "       Reason   Hour  Day of Week(num)  Day of Week  \n",
              "Month                                                \n",
              "1       13205  13205             13205        13205  \n",
              "2       11467  11467             11467        11467  \n",
              "3       11101  11101             11101        11101  \n",
              "4       11326  11326             11326        11326  \n",
              "5       11423  11423             11423        11423  \n",
              "6       11786  11786             11786        11786  \n",
              "7       12137  12137             12137        12137  \n",
              "8        9078   9078              9078         9078  \n",
              "12       7969   7969              7969         7969  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "_8tRKrylK-TM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now create a simple plot off of the dataframe indicating the count of calls per month. **"
      ]
    },
    {
      "metadata": {
        "id": "NPhBsL9wK-TO",
        "colab_type": "code",
        "outputId": "87dc9c51-0156-4a42-c7ba-956831b8f3c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "# Could be any column\n",
        "byMonth['lat'].plot()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa671469198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEGCAYAAABsLkJ6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRCkFgIhIlFIAiIhEKQWAiEiU+v/aX7cB7UlBkwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "WSn6hIplHoYg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "de8483cf-5c1b-452b-ccef-9e85c0fb639b"
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Month',data=df, palette='viridis')\n",
        "\n",
        "# To relocate the legend\n",
        "plt.legend(bbox_to_anchor=(1.1,1), loc=2, borderaxespad=0.)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n",
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa66d792a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbcAAAEGCAYAAADmAds7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/3ZNuCcXLDF6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CmkhAnejK-TT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now see if you can use seaborn's lmplot() to create a linear fit on the number of calls per month. Keep in mind you may need to reset the index to a column. **"
      ]
    },
    {
      "metadata": {
        "id": "Kv5u499UK-TU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "outputId": "b21af077-5347-4e67-cac9-47a31a5604fd"
      },
      "cell_type": "code",
      "source": [
        "sns.lmplot(x='Month',y='lat',data=byMonth.reset_index()) # not really have month column, use reset_index"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fa66d792048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZpaJA9jMLBMHsJlZJg5gM7NMvgEKFRrDf/Cl7AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8JEoTuAbK-TY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Create a new column called 'Date' that contains the date from the timeStamp column. You'll need to use apply along with the .date() method. ** "
      ]
    },
    {
      "metadata": {
        "id": "b9YIzvtrIhzr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e60c95f1-7fe8-4427-ccc4-20862b85ec4d"
      },
      "cell_type": "code",
      "source": [
        "df['timeStamp'].iloc[0]"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2015-12-10 17:40:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "id": "y9r0HQF8IuTq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "988a3efd-8a12-4fdd-8a0b-d9d0b5fd2d2d"
      },
      "cell_type": "code",
      "source": [
        "df['timeStamp'].iloc[0].date()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.date(2015, 12, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "id": "h7vWj1lUK-TZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "612829b7-0398-4349-aa4d-59ac07d34433"
      },
      "cell_type": "code",
      "source": [
        "df['Date']=df['timeStamp'].apply(lambda t: t.date())\n",
        "df.head()"
      ],
      "execution_count": 55,
      "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>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>desc</th>\n",
              "      <th>zip</th>\n",
              "      <th>title</th>\n",
              "      <th>timeStamp</th>\n",
              "      <th>twp</th>\n",
              "      <th>addr</th>\n",
              "      <th>e</th>\n",
              "      <th>Reason</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day of Week(num)</th>\n",
              "      <th>Day of Week</th>\n",
              "      <th>Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40.297876</td>\n",
              "      <td>-75.581294</td>\n",
              "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
              "      <td>19525.0</td>\n",
              "      <td>EMS: BACK PAINS/INJURY</td>\n",
              "      <td>2015-12-10 17:40:00</td>\n",
              "      <td>NEW HANOVER</td>\n",
              "      <td>REINDEER CT &amp; DEAD END</td>\n",
              "      <td>1</td>\n",
              "      <td>EMS</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>Thu</td>\n",
              "      <td>2015-12-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>40.258061</td>\n",
              "      <td>-75.264680</td>\n",
              "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
              "      <td>19446.0</td>\n",
              "      <td>EMS: DIABETIC EMERGENCY</td>\n",
              "      <td>2015-12-10 17:40:00</td>\n",
              "      <td>HATFIELD TOWNSHIP</td>\n",
              "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
              "      <td>1</td>\n",
              "      <td>EMS</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>Thu</td>\n",
              "      <td>2015-12-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40.121182</td>\n",
              "      <td>-75.351975</td>\n",
              "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
              "      <td>19401.0</td>\n",
              "      <td>Fire: GAS-ODOR/LEAK</td>\n",
              "      <td>2015-12-10 17:40:00</td>\n",
              "      <td>NORRISTOWN</td>\n",
              "      <td>HAWS AVE</td>\n",
              "      <td>1</td>\n",
              "      <td>Fire</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>Thu</td>\n",
              "      <td>2015-12-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40.116153</td>\n",
              "      <td>-75.343513</td>\n",
              "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
              "      <td>19401.0</td>\n",
              "      <td>EMS: CARDIAC EMERGENCY</td>\n",
              "      <td>2015-12-10 17:40:01</td>\n",
              "      <td>NORRISTOWN</td>\n",
              "      <td>AIRY ST &amp; SWEDE ST</td>\n",
              "      <td>1</td>\n",
              "      <td>EMS</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>Thu</td>\n",
              "      <td>2015-12-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40.251492</td>\n",
              "      <td>-75.603350</td>\n",
              "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>EMS: DIZZINESS</td>\n",
              "      <td>2015-12-10 17:40:01</td>\n",
              "      <td>LOWER POTTSGROVE</td>\n",
              "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
              "      <td>1</td>\n",
              "      <td>EMS</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>Thu</td>\n",
              "      <td>2015-12-10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         lat        lng                                               desc  \\\n",
              "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
              "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
              "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
              "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
              "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
              "\n",
              "       zip                    title           timeStamp                twp  \\\n",
              "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
              "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
              "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
              "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
              "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
              "\n",
              "                         addr  e Reason  Hour  Month  Day of Week(num)  \\\n",
              "0      REINDEER CT & DEAD END  1    EMS    17     12                 3   \n",
              "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12                 3   \n",
              "2                    HAWS AVE  1   Fire    17     12                 3   \n",
              "3          AIRY ST & SWEDE ST  1    EMS    17     12                 3   \n",
              "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12                 3   \n",
              "\n",
              "  Day of Week        Date  \n",
              "0         Thu  2015-12-10  \n",
              "1         Thu  2015-12-10  \n",
              "2         Thu  2015-12-10  \n",
              "3         Thu  2015-12-10  \n",
              "4         Thu  2015-12-10  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "metadata": {
        "id": "Elel8JUjK-Tc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now groupby this Date column with the count() aggregate and create a plot of counts of 911 calls.**"
      ]
    },
    {
      "metadata": {
        "id": "fEloRQiEJNC6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "cea850c4-a0d5-465d-d1a4-a6d04d00b69e"
      },
      "cell_type": "code",
      "source": [
        "df.groupby('Date').count().head()"
      ],
      "execution_count": 59,
      "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>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>desc</th>\n",
              "      <th>zip</th>\n",
              "      <th>title</th>\n",
              "      <th>timeStamp</th>\n",
              "      <th>twp</th>\n",
              "      <th>addr</th>\n",
              "      <th>e</th>\n",
              "      <th>Reason</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day of Week(num)</th>\n",
              "      <th>Day of Week</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-12-10</th>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>100</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>113</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-11</th>\n",
              "      <td>396</td>\n",
              "      <td>396</td>\n",
              "      <td>396</td>\n",
              "      <td>333</td>\n",
              "      <td>396</td>\n",
              "      <td>396</td>\n",
              "      <td>395</td>\n",
              "      <td>391</td>\n",
              "      <td>396</td>\n",
              "      <td>396</td>\n",
              "      <td>396</td>\n",
              "      <td>396</td>\n",
              "      <td>396</td>\n",
              "      <td>396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-12</th>\n",
              "      <td>403</td>\n",
              "      <td>403</td>\n",
              "      <td>403</td>\n",
              "      <td>333</td>\n",
              "      <td>403</td>\n",
              "      <td>403</td>\n",
              "      <td>403</td>\n",
              "      <td>401</td>\n",
              "      <td>403</td>\n",
              "      <td>403</td>\n",
              "      <td>403</td>\n",
              "      <td>403</td>\n",
              "      <td>403</td>\n",
              "      <td>403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-13</th>\n",
              "      <td>319</td>\n",
              "      <td>319</td>\n",
              "      <td>319</td>\n",
              "      <td>280</td>\n",
              "      <td>319</td>\n",
              "      <td>319</td>\n",
              "      <td>319</td>\n",
              "      <td>317</td>\n",
              "      <td>319</td>\n",
              "      <td>319</td>\n",
              "      <td>319</td>\n",
              "      <td>319</td>\n",
              "      <td>319</td>\n",
              "      <td>319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-14</th>\n",
              "      <td>447</td>\n",
              "      <td>447</td>\n",
              "      <td>447</td>\n",
              "      <td>387</td>\n",
              "      <td>447</td>\n",
              "      <td>447</td>\n",
              "      <td>446</td>\n",
              "      <td>445</td>\n",
              "      <td>447</td>\n",
              "      <td>447</td>\n",
              "      <td>447</td>\n",
              "      <td>447</td>\n",
              "      <td>447</td>\n",
              "      <td>447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            lat  lng  desc  zip  title  timeStamp  twp  addr    e  Reason  \\\n",
              "Date                                                                        \n",
              "2015-12-10  115  115   115  100    115        115  115   113  115     115   \n",
              "2015-12-11  396  396   396  333    396        396  395   391  396     396   \n",
              "2015-12-12  403  403   403  333    403        403  403   401  403     403   \n",
              "2015-12-13  319  319   319  280    319        319  319   317  319     319   \n",
              "2015-12-14  447  447   447  387    447        447  446   445  447     447   \n",
              "\n",
              "            Hour  Month  Day of Week(num)  Day of Week  \n",
              "Date                                                    \n",
              "2015-12-10   115    115               115          115  \n",
              "2015-12-11   396    396               396          396  \n",
              "2015-12-12   403    403               403          403  \n",
              "2015-12-13   319    319               319          319  \n",
              "2015-12-14   447    447               447          447  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "metadata": {
        "id": "9pal-KbdK-Td",
        "colab_type": "code",
        "outputId": "e533e93a-e5e7-4b35-cafd-fff578b601f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "byDate = df.groupby('Date').count()\n",
        "byDate['lat'].plot()\n"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa66d647ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEGCAYAAACD7ClEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "boGl1kQVJmX8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "807ef0ba-33f3-4d05-e292-6b3ef2aef557"
      },
      "cell_type": "code",
      "source": [
        "df.groupby('Date').count()['lat'].plot()\n",
        "plt.tight_layout()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGF5iBsowDMPwEjNQhmEYhpf8P8iRSwsLwWEAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8V5HqIEjJmOr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "2ev3Q8wqSjrt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "outputId": "9e20464c-4288-4c03-8114-a5084ff416ff"
      },
      "cell_type": "code",
      "source": [
        "sns.lmplot(x='Date',y='twp',data=byDate.reset_index()  # error: float() argument must be a string or a number, not 'datetime.date'"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-63-8ee31632e069>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    sns.lmplot(x='Date',y='twp',data=byDate.reset_index()  # error: float() argument must be a string or a number, not 'datetime.date'\u001b[0m\n\u001b[0m                                                                                                                                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "T4biYH3cK-Th",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now recreate this plot but create 3 separate plots with each plot representing a Reason for the 911 call**"
      ]
    },
    {
      "metadata": {
        "id": "Gna-COMOTqf7",
        "colab_type": "code",
        "outputId": "ef362401-bc18-470d-ef98-e3967226f8a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "df[df['Reason']=='Traffic'].groupby('Date').count().head()"
      ],
      "execution_count": 65,
      "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>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>desc</th>\n",
              "      <th>zip</th>\n",
              "      <th>title</th>\n",
              "      <th>timeStamp</th>\n",
              "      <th>twp</th>\n",
              "      <th>addr</th>\n",
              "      <th>e</th>\n",
              "      <th>Reason</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day of Week(num)</th>\n",
              "      <th>Day of Week</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-12-10</th>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>35</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>41</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-11</th>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "      <td>108</td>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "      <td>137</td>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-12</th>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>109</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>144</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-13</th>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>54</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>76</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-14</th>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>150</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>184</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            lat  lng  desc  zip  title  timeStamp  twp  addr    e  Reason  \\\n",
              "Date                                                                        \n",
              "2015-12-10   43   43    43   35     43         43   43    41   43      43   \n",
              "2015-12-11  141  141   141  108    141        141  141   137  141     141   \n",
              "2015-12-12  146  146   146  109    146        146  146   144  146     146   \n",
              "2015-12-13   78   78    78   54     78         78   78    76   78      78   \n",
              "2015-12-14  186  186   186  150    186        186  186   184  186     186   \n",
              "\n",
              "            Hour  Month  Day of Week(num)  Day of Week  \n",
              "Date                                                    \n",
              "2015-12-10    43     43                43           43  \n",
              "2015-12-11   141    141               141          141  \n",
              "2015-12-12   146    146               146          146  \n",
              "2015-12-13    78     78                78           78  \n",
              "2015-12-14   186    186               186          186  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "metadata": {
        "id": "D9NwHcvtK-Ti",
        "colab_type": "code",
        "outputId": "16e828a1-27ac-48b5-d517-941fe299b93c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "df[df['Reason']=='Traffic'].groupby('Date').count()['twp'].plot()\n",
        "plt.title('Traffic')\n",
        "plt.tight_layout()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJBcoMzMrkguUmZkV6b8WfzsqCzse5QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "nd_YtJSuK-Tr",
        "colab_type": "code",
        "outputId": "29d6947c-52f4-4e4c-e13d-8e0d8f6041f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "df[df['Reason']=='Fire'].groupby('Date').count()['twp'].plot()\n",
        "plt.title('Fire')\n",
        "plt.tight_layout()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-SZVmf0vK-T0",
        "colab_type": "code",
        "outputId": "6e15b7b2-4bae-4f18-813e-1d2bd99bb035",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "df[df['Reason']=='EMS'].groupby('Date').count()['twp'].plot()\n",
        "plt.title('EMS')\n",
        "plt.tight_layout()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "id": "GArZiQycK-T4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "____\n",
        "** Now let's move on to creating  heatmaps with seaborn and our data. We'll first need to restructure the dataframe so that the columns become the Hours and the Index becomes the Day of the Week. There are lots of ways to do this, but I would recommend trying to combine groupby with an [unstack](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html) method. Reference the solutions if you get stuck on this!**"
      ]
    },
    {
      "metadata": {
        "id": "LzAncPkNK60T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1151
        },
        "outputId": "1d2a2d83-f129-4f70-a1c6-6768f7aed742"
      },
      "cell_type": "code",
      "source": [
        "df.groupby(by=['Day of Week', 'Hour']).count()['Reason']"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Day of Week  Hour\n",
              "Fri          0        275\n",
              "             1        235\n",
              "             2        191\n",
              "             3        175\n",
              "             4        201\n",
              "             5        194\n",
              "             6        372\n",
              "             7        598\n",
              "             8        742\n",
              "             9        752\n",
              "             10       803\n",
              "             11       859\n",
              "             12       885\n",
              "             13       890\n",
              "             14       932\n",
              "             15       980\n",
              "             16      1039\n",
              "             17       980\n",
              "             18       820\n",
              "             19       696\n",
              "             20       667\n",
              "             21       559\n",
              "             22       514\n",
              "             23       474\n",
              "Mon          0        282\n",
              "             1        221\n",
              "             2        201\n",
              "             3        194\n",
              "             4        204\n",
              "             5        267\n",
              "                     ... \n",
              "Tue          18       905\n",
              "             19       731\n",
              "             20       647\n",
              "             21       571\n",
              "             22       462\n",
              "             23       274\n",
              "Wed          0        250\n",
              "             1        216\n",
              "             2        189\n",
              "             3        209\n",
              "             4        156\n",
              "             5        255\n",
              "             6        410\n",
              "             7        701\n",
              "             8        875\n",
              "             9        808\n",
              "             10       800\n",
              "             11       789\n",
              "             12       903\n",
              "             13       872\n",
              "             14       904\n",
              "             15       867\n",
              "             16       990\n",
              "             17      1037\n",
              "             18       894\n",
              "             19       686\n",
              "             20       668\n",
              "             21       575\n",
              "             22       490\n",
              "             23       335\n",
              "Name: Reason, Length: 168, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "d9_-rzVWK-T6",
        "colab_type": "code",
        "outputId": "549272f4-f3d1-4548-a63a-35f9ef7c4e19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "cell_type": "code",
      "source": [
        "# unstack\n",
        "dayHour = df.groupby(by=['Day of Week','Hour']).count()['Reason'].unstack()\n",
        "dayHour.head(20)"
      ],
      "execution_count": 71,
      "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>Hour</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day of Week</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fri</th>\n",
              "      <td>275</td>\n",
              "      <td>235</td>\n",
              "      <td>191</td>\n",
              "      <td>175</td>\n",
              "      <td>201</td>\n",
              "      <td>194</td>\n",
              "      <td>372</td>\n",
              "      <td>598</td>\n",
              "      <td>742</td>\n",
              "      <td>752</td>\n",
              "      <td>...</td>\n",
              "      <td>932</td>\n",
              "      <td>980</td>\n",
              "      <td>1039</td>\n",
              "      <td>980</td>\n",
              "      <td>820</td>\n",
              "      <td>696</td>\n",
              "      <td>667</td>\n",
              "      <td>559</td>\n",
              "      <td>514</td>\n",
              "      <td>474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mon</th>\n",
              "      <td>282</td>\n",
              "      <td>221</td>\n",
              "      <td>201</td>\n",
              "      <td>194</td>\n",
              "      <td>204</td>\n",
              "      <td>267</td>\n",
              "      <td>397</td>\n",
              "      <td>653</td>\n",
              "      <td>819</td>\n",
              "      <td>786</td>\n",
              "      <td>...</td>\n",
              "      <td>869</td>\n",
              "      <td>913</td>\n",
              "      <td>989</td>\n",
              "      <td>997</td>\n",
              "      <td>885</td>\n",
              "      <td>746</td>\n",
              "      <td>613</td>\n",
              "      <td>497</td>\n",
              "      <td>472</td>\n",
              "      <td>325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <td>375</td>\n",
              "      <td>301</td>\n",
              "      <td>263</td>\n",
              "      <td>260</td>\n",
              "      <td>224</td>\n",
              "      <td>231</td>\n",
              "      <td>257</td>\n",
              "      <td>391</td>\n",
              "      <td>459</td>\n",
              "      <td>640</td>\n",
              "      <td>...</td>\n",
              "      <td>789</td>\n",
              "      <td>796</td>\n",
              "      <td>848</td>\n",
              "      <td>757</td>\n",
              "      <td>778</td>\n",
              "      <td>696</td>\n",
              "      <td>628</td>\n",
              "      <td>572</td>\n",
              "      <td>506</td>\n",
              "      <td>467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <td>383</td>\n",
              "      <td>306</td>\n",
              "      <td>286</td>\n",
              "      <td>268</td>\n",
              "      <td>242</td>\n",
              "      <td>240</td>\n",
              "      <td>300</td>\n",
              "      <td>402</td>\n",
              "      <td>483</td>\n",
              "      <td>620</td>\n",
              "      <td>...</td>\n",
              "      <td>684</td>\n",
              "      <td>691</td>\n",
              "      <td>663</td>\n",
              "      <td>714</td>\n",
              "      <td>670</td>\n",
              "      <td>655</td>\n",
              "      <td>537</td>\n",
              "      <td>461</td>\n",
              "      <td>415</td>\n",
              "      <td>330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thu</th>\n",
              "      <td>278</td>\n",
              "      <td>202</td>\n",
              "      <td>233</td>\n",
              "      <td>159</td>\n",
              "      <td>182</td>\n",
              "      <td>203</td>\n",
              "      <td>362</td>\n",
              "      <td>570</td>\n",
              "      <td>777</td>\n",
              "      <td>828</td>\n",
              "      <td>...</td>\n",
              "      <td>876</td>\n",
              "      <td>969</td>\n",
              "      <td>935</td>\n",
              "      <td>1013</td>\n",
              "      <td>810</td>\n",
              "      <td>698</td>\n",
              "      <td>617</td>\n",
              "      <td>553</td>\n",
              "      <td>424</td>\n",
              "      <td>354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue</th>\n",
              "      <td>269</td>\n",
              "      <td>240</td>\n",
              "      <td>186</td>\n",
              "      <td>170</td>\n",
              "      <td>209</td>\n",
              "      <td>239</td>\n",
              "      <td>415</td>\n",
              "      <td>655</td>\n",
              "      <td>889</td>\n",
              "      <td>880</td>\n",
              "      <td>...</td>\n",
              "      <td>943</td>\n",
              "      <td>938</td>\n",
              "      <td>1026</td>\n",
              "      <td>1019</td>\n",
              "      <td>905</td>\n",
              "      <td>731</td>\n",
              "      <td>647</td>\n",
              "      <td>571</td>\n",
              "      <td>462</td>\n",
              "      <td>274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed</th>\n",
              "      <td>250</td>\n",
              "      <td>216</td>\n",
              "      <td>189</td>\n",
              "      <td>209</td>\n",
              "      <td>156</td>\n",
              "      <td>255</td>\n",
              "      <td>410</td>\n",
              "      <td>701</td>\n",
              "      <td>875</td>\n",
              "      <td>808</td>\n",
              "      <td>...</td>\n",
              "      <td>904</td>\n",
              "      <td>867</td>\n",
              "      <td>990</td>\n",
              "      <td>1037</td>\n",
              "      <td>894</td>\n",
              "      <td>686</td>\n",
              "      <td>668</td>\n",
              "      <td>575</td>\n",
              "      <td>490</td>\n",
              "      <td>335</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Hour          0    1    2    3    4    5    6    7    8    9  ...    14   15  \\\n",
              "Day of Week                                                   ...              \n",
              "Fri          275  235  191  175  201  194  372  598  742  752 ...   932  980   \n",
              "Mon          282  221  201  194  204  267  397  653  819  786 ...   869  913   \n",
              "Sat          375  301  263  260  224  231  257  391  459  640 ...   789  796   \n",
              "Sun          383  306  286  268  242  240  300  402  483  620 ...   684  691   \n",
              "Thu          278  202  233  159  182  203  362  570  777  828 ...   876  969   \n",
              "Tue          269  240  186  170  209  239  415  655  889  880 ...   943  938   \n",
              "Wed          250  216  189  209  156  255  410  701  875  808 ...   904  867   \n",
              "\n",
              "Hour           16    17   18   19   20   21   22   23  \n",
              "Day of Week                                            \n",
              "Fri          1039   980  820  696  667  559  514  474  \n",
              "Mon           989   997  885  746  613  497  472  325  \n",
              "Sat           848   757  778  696  628  572  506  467  \n",
              "Sun           663   714  670  655  537  461  415  330  \n",
              "Thu           935  1013  810  698  617  553  424  354  \n",
              "Tue          1026  1019  905  731  647  571  462  274  \n",
              "Wed           990  1037  894  686  668  575  490  335  \n",
              "\n",
              "[7 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "id": "YPTFsOykK-UB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now create a HeatMap using this new DataFrame. **"
      ]
    },
    {
      "metadata": {
        "id": "n1LyLfV8K-UF",
        "colab_type": "code",
        "outputId": "4d69c899-e409-434b-f354-9fec3bebfd1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.heatmap(dayHour)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa66d0f7400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo4AAAFzCAYAAACw6oi6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njpIkSeqKhaMkSZK6YuEoSZKkrvx/VxQ4H5Mt4P8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "id": "vMG1leZ7K-UN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now create a clustermap using this DataFrame. **"
      ]
    },
    {
      "metadata": {
        "id": "EtBfsXtFK-UR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Now repeat these same plots and operations, for a DataFrame that shows the Month as the column. **"
      ]
    },
    {
      "metadata": {
        "id": "ECDUYtgPK-US",
        "colab_type": "code",
        "outputId": "1ee50e8c-9996-46f5-b313-839c6b371d3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "dayMonth = df.groupby(by=['Day of Week','Month']).count()['Reason'].unstack()\n",
        "dayMonth.head()"
      ],
      "execution_count": 77,
      "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>Month</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>12</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day of Week</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fri</th>\n",
              "      <td>1970</td>\n",
              "      <td>1581</td>\n",
              "      <td>1525</td>\n",
              "      <td>1958</td>\n",
              "      <td>1730</td>\n",
              "      <td>1649</td>\n",
              "      <td>2045</td>\n",
              "      <td>1310</td>\n",
              "      <td>1065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mon</th>\n",
              "      <td>1727</td>\n",
              "      <td>1964</td>\n",
              "      <td>1535</td>\n",
              "      <td>1598</td>\n",
              "      <td>1779</td>\n",
              "      <td>1617</td>\n",
              "      <td>1692</td>\n",
              "      <td>1511</td>\n",
              "      <td>1257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <td>2291</td>\n",
              "      <td>1441</td>\n",
              "      <td>1266</td>\n",
              "      <td>1734</td>\n",
              "      <td>1444</td>\n",
              "      <td>1388</td>\n",
              "      <td>1695</td>\n",
              "      <td>1099</td>\n",
              "      <td>978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <td>1960</td>\n",
              "      <td>1229</td>\n",
              "      <td>1102</td>\n",
              "      <td>1488</td>\n",
              "      <td>1424</td>\n",
              "      <td>1333</td>\n",
              "      <td>1672</td>\n",
              "      <td>1021</td>\n",
              "      <td>907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thu</th>\n",
              "      <td>1584</td>\n",
              "      <td>1596</td>\n",
              "      <td>1900</td>\n",
              "      <td>1601</td>\n",
              "      <td>1590</td>\n",
              "      <td>2065</td>\n",
              "      <td>1646</td>\n",
              "      <td>1230</td>\n",
              "      <td>1266</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Month          1     2     3     4     5     6     7     8     12\n",
              "Day of Week                                                      \n",
              "Fri          1970  1581  1525  1958  1730  1649  2045  1310  1065\n",
              "Mon          1727  1964  1535  1598  1779  1617  1692  1511  1257\n",
              "Sat          2291  1441  1266  1734  1444  1388  1695  1099   978\n",
              "Sun          1960  1229  1102  1488  1424  1333  1672  1021   907\n",
              "Thu          1584  1596  1900  1601  1590  2065  1646  1230  1266"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "metadata": {
        "id": "LuGvJ3MxK-UV",
        "colab_type": "code",
        "outputId": "c7c4c2e5-a797-4ae6-9099-431d5d94f7c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.heatmap(dayMonth,cmap='viridis')"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa66d565ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAApQAAAFzCAYAAACAZpmbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}