911 Calls project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Capstone Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this capstone project i 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Import numpy and pandas **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import visualization libraries and set %matplotlib inline. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.dates as mdates\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the csv file as a dataframe called df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"911.csv\")\n",
    "df.head()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() of the df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <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",
       "    </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",
       "    </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  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 zipcodes for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"zip\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 townships (twp) for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LOWER MERION    8443\n",
       "ABINGTON        5977\n",
       "NORRISTOWN      5890\n",
       "UPPER MERION    5227\n",
       "CHELTENHAM      4575\n",
       "Name: twp, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"twp\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Take a look at the 'title' column, how many unique title codes are there? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"title\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            EMS\n",
       "1            EMS\n",
       "2           Fire\n",
       "3            EMS\n",
       "4            EMS\n",
       "          ...   \n",
       "99487    Traffic\n",
       "99488    Traffic\n",
       "99489        EMS\n",
       "99490        EMS\n",
       "99491    Traffic\n",
       "Name: reason, Length: 99492, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"reason\"] = df[\"title\"].apply(lambda x:x.split(\":\")[0])\n",
    "df[\"reason\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the most common Reason for a 911 call based off of this new column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: reason, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"reason\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='reason', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df[\"reason\"],palette = \"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "** Now let us begin to focus on time information. What is the data type of the objects in the timeStamp column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"timeStamp\"][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"timeStamp\"]= pd.to_datetime(df[\"timeStamp\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3\n",
       "1        3\n",
       "2        3\n",
       "3        3\n",
       "4        3\n",
       "        ..\n",
       "99487    2\n",
       "99488    2\n",
       "99489    2\n",
       "99490    2\n",
       "99491    2\n",
       "Name: Day of Week, Length: 99492, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Month\"] = df[\"timeStamp\"].apply(lambda time: time.month)\n",
    "df[\"Hour\"] = df[\"timeStamp\"].apply(lambda time:time.hour)\n",
    "df[\"Day of Week\"] = df[\"timeStamp\"].apply(lambda time:time.dayofweek)\n",
    "\n",
    "df[\"Day of Week\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Thu\n",
       "1        Thu\n",
       "2        Thu\n",
       "3        Thu\n",
       "4        Thu\n",
       "        ... \n",
       "99487    Wed\n",
       "99488    Wed\n",
       "99489    Wed\n",
       "99490    Wed\n",
       "99491    Wed\n",
       "Name: Day of Week, Length: 99492, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Day of Week\"] = df[\"Day of Week\"].map(dmap)\n",
    "\n",
    "df[\"Day of Week\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c98b116c70>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = df[\"Day of Week\"], hue = df[\"reason\"], palette = \"viridis\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c98b4a7640>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = df[\"Day of Week\"], hue = df[\"reason\"], palette = \"viridis\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c98b565430>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = df[\"Month\"], hue = df[\"reason\"],palette = \"viridis\")\n",
    "plt.legend(bbox_to_anchor = (1.05,1), loc  = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "byMonth = df.groupby(\"Month\").count()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a simple plot off of the dataframe indicating the count of calls per month. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth[\"reason\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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. ** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2015-12-10\n",
       "1        2015-12-10\n",
       "2        2015-12-10\n",
       "3        2015-12-10\n",
       "4        2015-12-10\n",
       "            ...    \n",
       "99487    2016-08-24\n",
       "99488    2016-08-24\n",
       "99489    2016-08-24\n",
       "99490    2016-08-24\n",
       "99491    2016-08-24\n",
       "Name: Date, Length: 99492, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = df[\"timeStamp\"].iloc[0]\n",
    "df['Date']=df['timeStamp'].apply(lambda t: t.date())\n",
    "\n",
    "df[\"Date\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now groupby this Date column with the count() aggregate and create a plot of counts of 911 calls.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots()\n",
    "axis.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))\n",
    "\n",
    "df.groupby('Date').count()['twp'].plot()\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now recreate this plot but create 3 separate plots with each plot representing a Reason for the 911 call**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots()\n",
    "axis.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))\n",
    "\n",
    "df[df[\"reason\"] == \"Traffic\"].groupby(\"Date\").count()[\"twp\"].plot()\n",
    "plt.title(\"Traffic\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots()\n",
    "axis.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))\n",
    "\n",
    "\n",
    "df[df[\"reason\"] == \"Fire\"].groupby(\"Date\").count()[\"twp\"].plot()\n",
    "plt.title(\"Fire\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots()\n",
    "axis.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))\n",
    "\n",
    "\n",
    "df[df[\"reason\"] == \"EMS\"].groupby(\"Date\").count()[\"twp\"].plot()\n",
    "plt.title(\"EMS\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "\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",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_week = df.groupby([\"Day of Week\",\"Hour\"])[\"reason\"].count()\n",
    "by_week = by_week.unstack(level = 1)\n",
    "by_week.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "sns.heatmap(by_week,cmap = \"viridis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}