Smart_Data_Science_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d48d0a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a5557af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c99f1c8a",
   "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 17:10:52</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 17:29:21</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 14:39:21</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 16:47:36</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 16:56:52</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 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 16:56:52   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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52a5db0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "663522"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d2c899f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21999814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19525., 19446., 19401.,    nan, 19044., 19426., 19438., 19462.,\n",
       "       19428., 19040., 19027., 18936., 18974., 19031., 19403., 19422.,\n",
       "       19085., 18964., 19038., 19406., 19468., 19010., 19095., 19464.,\n",
       "       19444., 19041., 19440., 19405., 19002., 19096., 19454., 19465.,\n",
       "       19004., 19066., 19072., 18041., 19046., 19090., 19012., 19025.,\n",
       "       19473., 18073., 18969., 18074., 19460., 19001., 18054., 19009.,\n",
       "       19006., 19035., 19150., 19075., 19034., 19151., 19453., 19003.,\n",
       "       18914., 19512., 18976., 19120., 18915., 18076., 19477., 19087.,\n",
       "       18966., 19131., 19128., 19083., 19053., 19475., 18960., 19504.,\n",
       "       18070., 19492., 18932., 19118., 18092., 19490., 19518., 18056.,\n",
       "       19119., 19107., 17752., 19111., 18927., 19435., 18951., 19472.,\n",
       "       19503., 19126., 19505., 19423., 19138., 36107., 18036., 19116.,\n",
       "       19139., 19129., 19115., 19355., 77316., 19457., 19082., 19127.,\n",
       "       19443., 17555., 19520., 19063., 19020., 19404., 19382., 19474.,\n",
       "       19057., 19073., 19121., 18958., 19026., 19018., 19047., 19064.,\n",
       "       19602., 19486., 19348., 18051., 18049., 19333., 19144., 18101.,\n",
       "       19607., 19450., 19380., 17506.,  8361., 18940., 18104.,  7203.,\n",
       "       19030.,  8033., 19104., 17545.,  8832., 19021., 19106.,  8065.,\n",
       "       15301., 18911., 18902., 18944.,  3366., 19545., 19390., 19140.,\n",
       "       18901., 19601., 19341., 19301., 19425., 23005., 19054., 18040.,\n",
       "       18102., 17603., 18080., 17901., 19153., 21701., 18103., 19134.,\n",
       "       19135.,  8502., 19122., 19320.,  3103., 19610., 19102., 17331.,\n",
       "       19050., 19023., 17810.,  8077.,  8628., 19605., 19437., 19312.,\n",
       "       19147., 19456., 19604., 17507.,  1104., 18042., 18011., 15090.,\n",
       "       19543., 19124., 19609., 19445., 19310., 19070.,  7081.,  7726.,\n",
       "       17566., 19008., 19365., 19103., 18938.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "592ecdd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0fdabfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lat           25949\n",
       "lng           25980\n",
       "desc         663282\n",
       "zip             204\n",
       "title           148\n",
       "timeStamp    640754\n",
       "twp              68\n",
       "addr          41292\n",
       "e                 1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "632698e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 663522 entries, 0 to 663521\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   lat        663522 non-null  float64\n",
      " 1   lng        663522 non-null  float64\n",
      " 2   desc       663522 non-null  object \n",
      " 3   zip        583323 non-null  float64\n",
      " 4   title      663522 non-null  object \n",
      " 5   timeStamp  663522 non-null  object \n",
      " 6   twp        663229 non-null  object \n",
      " 7   addr       663522 non-null  object \n",
      " 8   e          663522 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 32.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f542c323",
   "metadata": {},
   "source": [
    "# What are the top 5 townships (twp) for 911 calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f371659c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LOWER MERION    55490\n",
       "ABINGTON        39947\n",
       "NORRISTOWN      37633\n",
       "UPPER MERION    36010\n",
       "CHELTENHAM      30574\n",
       "Name: twp, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effa7c47",
   "metadata": {},
   "source": [
    "# What are the top 5 zip codes for 911 calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "08cfa750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19401.0    45606\n",
       "19464.0    43910\n",
       "19403.0    34888\n",
       "19446.0    32270\n",
       "19406.0    22464\n",
       "Name: zip, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b5c8ab",
   "metadata": {},
   "source": [
    "# Take a look a \"title\" column, how many unique title codes are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bad77a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca56d0c",
   "metadata": {},
   "source": [
    "# Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a94d8ee",
   "metadata": {},
   "source": [
    "# In the titles column there are \"Reasons/Departments\" specified before the title code.These are Ems,fire,and Traffic.Create a new column called \"Reason\" that contain this string value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9f67f27c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =df['title'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "79311b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' BACK PAINS/INJURY'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.split(':')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "90adf2bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a75d1c8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             EMS\n",
       "1             EMS\n",
       "2            Fire\n",
       "3             EMS\n",
       "4             EMS\n",
       "           ...   \n",
       "663517    Traffic\n",
       "663518        EMS\n",
       "663519        EMS\n",
       "663520       Fire\n",
       "663521    Traffic\n",
       "Name: Reason, Length: 663522, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b473df23",
   "metadata": {},
   "source": [
    "# What is the most common Reason for a 911 call based off of this new column? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e56bd9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        332692\n",
       "Traffic    230208\n",
       "Fire       100622\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f517e6",
   "metadata": {},
   "source": [
    "# Now use seaborn to create a countplot of 911 calls by Reason."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9665513c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Reason', ylabel='count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEGCAYAAABYV4NmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAYH0lEQVR4nO3df7CeZX3n8fdHgkirUH4EignTMBJrgdW4pIHR3a4rTkg704IW1jhbyW5x4jLo6q47Dji7CwubrbQqI1qYwSXyY62Qoha2I2IatP5C4KDITxkyxUoEIRpEbIVu4nf/eK4jTw5PjoeY6znk5P2aeebcz/e+r+tc9zwDn1z3dZ/7SVUhSdKu9oLZHoAkaW4yYCRJXRgwkqQuDBhJUhcGjCSpi3mzPYDni4MPPrgWLVo028OQpN3K7bff/oOqmj9qnwHTLFq0iImJidkehiTtVpL8/Y72eYlMktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFf8m/k5a/+bzZHsKc9/lr/vtsD0HSL8EZjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhfdAibJi5LcmuRbSe5J8j9a/cAk65M80H4eMNTm7CQbk9yf5MSh+rFJ7mr7LkqSVt8nyTWtfkuSRUNtVrXf8UCSVb3OU5I0Ws8ZzNPA66vqVcASYEWS44GzgA1VtRjY0N6T5ChgJXA0sAK4OMlera9LgNXA4vZa0eqnA49X1ZHAhcAFra8DgXOA44BlwDnDQSZJ6q9bwNTAT9rbvdurgJOAK1r9CuDktn0ScHVVPV1VDwIbgWVJDgP2q6qbq6qAK6e0mezrWuCENrs5EVhfVVuq6nFgPc+EkiRpDLquwSTZK8kdwGMM/od/C3BoVT0C0H4e0g5fADw01HxTqy1o21Pr27Wpqq3AE8BB0/Q1dXyrk0wkmdi8efMvcaaSpKm6BkxVbauqJcBCBrORY6Y5PKO6mKa+s22Gx3dpVS2tqqXz58+fZmiSpOdqLHeRVdWPgC8yuEz1aLvsRfv5WDtsE3D4ULOFwMOtvnBEfbs2SeYB+wNbpulLkjQmPe8im5/k19r2vsAbgG8D1wOTd3WtAq5r29cDK9udYUcwWMy/tV1GezLJ8W195bQpbSb7OgW4qa3T3AgsT3JAW9xf3mqSpDGZ17Hvw4Ar2p1gLwDWVdVfJ7kZWJfkdOC7wKkAVXVPknXAvcBW4Myq2tb6OgO4HNgXuKG9AC4DrkqykcHMZWXra0uS84Hb2nHnVdWWjucqSZqiW8BU1Z3Aq0fUfwicsIM2a4A1I+oTwLPWb6rqKVpAjdi3Flj73EYtSdpV/Et+SVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK66BYwSQ5P8oUk9yW5J8m7Wv3cJN9Lckd7/d5Qm7OTbExyf5ITh+rHJrmr7bsoSVp9nyTXtPotSRYNtVmV5IH2WtXrPCVJo83r2PdW4D1V9Y0kLwFuT7K+7buwqj4wfHCSo4CVwNHAS4G/SfLyqtoGXAKsBr4OfBZYAdwAnA48XlVHJlkJXAC8OcmBwDnAUqDa776+qh7veL6SpCHdZjBV9UhVfaNtPwncByyYpslJwNVV9XRVPQhsBJYlOQzYr6purqoCrgROHmpzRdu+FjihzW5OBNZX1ZYWKusZhJIkaUzGsgbTLl29Grilld6R5M4ka5Mc0GoLgIeGmm1qtQVte2p9uzZVtRV4Ajhomr6mjmt1kokkE5s3b975E5QkPUv3gEnyYuBTwLur6scMLne9DFgCPAJ8cPLQEc1rmvrOtnmmUHVpVS2tqqXz58+f7jQkSc9R14BJsjeDcPlEVX0aoKoeraptVfUz4GPAsnb4JuDwoeYLgYdbfeGI+nZtkswD9ge2TNOXJGlMet5FFuAy4L6q+tBQ/bChw94I3N22rwdWtjvDjgAWA7dW1SPAk0mOb32eBlw31GbyDrFTgJvaOs2NwPIkB7RLcMtbTZI0Jj3vInst8FbgriR3tNr7gLckWcLgktV3gLcDVNU9SdYB9zK4A+3MdgcZwBnA5cC+DO4eu6HVLwOuSrKRwcxlZetrS5LzgdvacedV1ZYuZylJGqlbwFTVVxi9FvLZadqsAdaMqE8Ax4yoPwWcuoO+1gJrZzpeSdKu5V/yS5K6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSeqi5x9aStIu984N75rtIcx5Hznhw7ukH2cwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR10S1gkhye5AtJ7ktyT5J3tfqBSdYneaD9PGCozdlJNia5P8mJQ/Vjk9zV9l2UJK2+T5JrWv2WJIuG2qxqv+OBJKt6nackabSeM5itwHuq6reA44EzkxwFnAVsqKrFwIb2nrZvJXA0sAK4OMlera9LgNXA4vZa0eqnA49X1ZHAhcAFra8DgXOA44BlwDnDQSZJ6q9bwFTVI1X1jbb9JHAfsAA4CbiiHXYFcHLbPgm4uqqerqoHgY3AsiSHAftV1c1VVcCVU9pM9nUtcEKb3ZwIrK+qLVX1OLCeZ0JJkjQGY1mDaZeuXg3cAhxaVY/AIISAQ9phC4CHhpptarUFbXtqfbs2VbUVeAI4aJq+po5rdZKJJBObN2/+Jc5QkjRV94BJ8mLgU8C7q+rH0x06olbT1He2zTOFqkuramlVLZ0/f/40Q5MkPVddAybJ3gzC5RNV9elWfrRd9qL9fKzVNwGHDzVfCDzc6gtH1Ldrk2QesD+wZZq+JElj0vMusgCXAfdV1YeGdl0PTN7VtQq4bqi+st0ZdgSDxfxb22W0J5Mc3/o8bUqbyb5OAW5q6zQ3AsuTHNAW95e3miRpTOZ17Pu1wFuBu5Lc0WrvA94PrEtyOvBd4FSAqronyTrgXgZ3oJ1ZVdtauzOAy4F9gRvaCwYBdlWSjQxmLitbX1uSnA/c1o47r6q2dDpPSdIIMwqYJBuq6oRfVBtWVV9h9FoIwMh2VbUGWDOiPgEcM6L+FC2gRuxbC6zd0fgkSX1NGzBJXgT8CnBwu9Q0GRj7AS/tPDZJ0m7sF81g3g68m0GY3M4zAfNj4M/7DUuStLubNmCq6sPAh5O8s6o+MqYxSZLmgBmtwVTVR5K8Blg03Kaqruw0LknSbm6mi/xXAS8D7gAm7+yafGyLJEnPMtPblJcCR7W/MZEk6Rea6R9a3g38es+BSJLmlpnOYA4G7k1yK/D0ZLGq/qDLqCRJu72ZBsy5PQchSZp7ZnoX2d/2HogkaW6Z6V1kT/LM4+5fCOwN/ENV7ddrYJKk3dtMZzAvGX6f5GQGX0UsSdJIO/W4/qr6K+D1u3YokqS5ZKaXyN409PYFDP4uxr+JkSTt0EzvIvv9oe2twHeAk3b5aCRJc8ZM12D+fe+BSJLmlhmtwSRZmOQzSR5L8miSTyVZ2HtwkqTd10wX+T8OXM/ge2EWAP+31SRJGmmmATO/qj5eVVvb63JgfsdxSZJ2czMNmB8k+aMke7XXHwE/7DkwSdLubaYB88fAvwG+DzwCnAK48C9J2qGZ3qZ8PrCqqh4HSHIg8AEGwSNJ0rPMdAbzyslwAaiqLcCrp2uQZG276+zuodq5Sb6X5I72+r2hfWcn2Zjk/iQnDtWPTXJX23dRkrT6PkmuafVbkiwaarMqyQPttWqG5yhJ2oVmGjAvSHLA5Js2g/lFs5/LgRUj6hdW1ZL2+mzr7yhgJXB0a3Nxkr3a8ZcAq4HF7TXZ5+nA41V1JHAhcMHQ2M4BjmPwvLRzhscuSRqPmQbMB4GvJTk/yXnA14A/na5BVX0J2DLD/k8Crq6qp6vqQWAjsCzJYcB+VXVz+7rmK4GTh9pc0bavBU5os5sTgfVVtaXNutYzOugkSR3NKGCq6krgD4FHgc3Am6rqqp38ne9Icme7hDY5s1gAPDR0zKZWW9C2p9a3a1NVW4EngIOm6etZkqxOMpFkYvPmzTt5OpKkUWb8NOWqureqPlpVH6mqe3fy910CvAxYwuButA+2ekb9ymnqO9tm+2LVpVW1tKqWzp/vn/VI0q60U4/r31lV9WhVbauqnwEf45nvlNkEHD506ELg4VZfOKK+XZsk84D9GVyS21FfkqQxGmvAtDWVSW8EJu8wux5Y2e4MO4LBYv6tVfUI8GSS49v6ymnAdUNtJu8QOwW4qa3T3AgsT3JAuwS3vNUkSWM007+Dec6SfBJ4HXBwkk0M7ux6XZIlDC5ZfQd4O0BV3ZNkHXAvg68DOLOqtrWuzmBwR9q+wA3tBXAZcFWSjQxmLitbX1uSnA/c1o47r91WLUkao24BU1VvGVG+bJrj1wBrRtQngGNG1J8CTt1BX2uBtTMerCRplxvrJTJJ0p7DgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIX3QImydokjyW5e6h2YJL1SR5oPw8Y2nd2ko1J7k9y4lD92CR3tX0XJUmr75Pkmla/JcmioTar2u94IMmqXucoSdqxnjOYy4EVU2pnARuqajGwob0nyVHASuDo1ubiJHu1NpcAq4HF7TXZ5+nA41V1JHAhcEHr60DgHOA4YBlwznCQSZLGo1vAVNWXgC1TyicBV7TtK4CTh+pXV9XTVfUgsBFYluQwYL+qurmqCrhySpvJvq4FTmizmxOB9VW1paoeB9bz7KCTJHU2b8y/79CqegSgqh5JckirLwC+PnTcplb7f217an2yzUOtr61JngAOGq6PaCOx5H+eO9tDmPPu+K/nzvYQ9DzwfFnkz4haTVPf2Tbb/9JkdZKJJBObN2+e0UAlSTMz7oB5tF32ov18rNU3AYcPHbcQeLjVF46ob9cmyTxgfwaX5HbU17NU1aVVtbSqls6fP/+XOC1J0lTjDpjrgcm7ulYB1w3VV7Y7w45gsJh/a7uc9mSS49v6ymlT2kz2dQpwU1unuRFYnuSAtri/vNUkSWPUbQ0mySeB1wEHJ9nE4M6u9wPrkpwOfBc4FaCq7kmyDrgX2AqcWVXbWldnMLgjbV/ghvYCuAy4KslGBjOXla2vLUnOB25rx51XVVNvNpAkddYtYKrqLTvYdcIOjl8DrBlRnwCOGVF/ihZQI/atBdbOeLCSpF3u+bLIL0maYwwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6mJWASfKdJHcluSPJRKsdmGR9kgfazwOGjj87ycYk9yc5cah+bOtnY5KLkqTV90lyTavfkmTR2E9SkvZwszmD+ddVtaSqlrb3ZwEbqmoxsKG9J8lRwErgaGAFcHGSvVqbS4DVwOL2WtHqpwOPV9WRwIXABWM4H0nSkOfTJbKTgCva9hXAyUP1q6vq6ap6ENgILEtyGLBfVd1cVQVcOaXNZF/XAidMzm4kSeMxWwFTwOeT3J5kdasdWlWPALSfh7T6AuChobabWm1B255a365NVW0FngAOmjqIJKuTTCSZ2Lx58y45MUnSwLxZ+r2vraqHkxwCrE/y7WmOHTXzqGnq07XZvlB1KXApwNKlS5+1X5K082ZlBlNVD7efjwGfAZYBj7bLXrSfj7XDNwGHDzVfCDzc6gtH1Ldrk2QesD+wpce5SJJGG3vAJPnVJC+Z3AaWA3cD1wOr2mGrgOva9vXAynZn2BEMFvNvbZfRnkxyfFtfOW1Km8m+TgFuaus0kqQxmY1LZIcCn2lr7vOAv6iqzyW5DViX5HTgu8CpAFV1T5J1wL3AVuDMqtrW+joDuBzYF7ihvQAuA65KspHBzGXlOE5MkvSMsQdMVf0d8KoR9R8CJ+ygzRpgzYj6BHDMiPpTtICSJM2O59NtypKkOcSAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhdzOmCSrEhyf5KNSc6a7fFI0p5kzgZMkr2APwd+FzgKeEuSo2Z3VJK055izAQMsAzZW1d9V1T8BVwMnzfKYJGmPkaqa7TF0keQUYEVVva29fytwXFW9Y+iY1cDq9vY3gfvHPtDxORj4wWwPQjvNz2/3Ndc/u9+oqvmjdswb90jGKCNq26VpVV0KXDqe4cyuJBNVtXS2x6Gd4+e3+9qTP7u5fIlsE3D40PuFwMOzNBZJ2uPM5YC5DVic5IgkLwRWAtfP8pgkaY8xZy+RVdXWJO8AbgT2AtZW1T2zPKzZtEdcCpzD/Px2X3vsZzdnF/klSbNrLl8ikyTNIgNGktSFATMHJNmW5I6h11mt/sUk302SoWP/KslP2vYLklyU5O4kdyW5LckRs3Uee7IRn+GiJF+b7XFpIMlBQ5/N95N8b+j9C2fQ/pNJ7kzyn5K8orX7ZpKXzeXPec4u8u9hflpVS3aw70fAa4GvJPk14LChfW8GXgq8sqp+lmQh8A8dx6kdG/UZvmbqQUn2qqpt4xmSJlXVD4ElAEnOBX5SVR+Y3J9kXlVtHdU2ya8Dr6mq32jvzwKuq6pz2iHP+pznCmcwc9/VDG7RBngT8OmhfYcBj1TVzwCqalNVPT7m8WkHhmaar0vyhSR/AdyVZK8kf9ZmnHcmefssD3WPlOTyJB9K8gXggiTLknytzUy+luQ326GfBw5ps5ZzgHcDb2vtfv45t+33tqsJ30ry/nGf067mDGZu2DfJHUPv/6SqrmnbG4CPtYd/rmTwaJz/1vatYzCz+ZftuP9TVd8c05i1veHP8MGqeuOU/cuAY6rqwfaIoyeq6reT7AN8Ncnnq+rBcQ5YALwceENVbUuyH/A77U8k3gD8L+APgT8A/npyhtouWW83A2r13wVOZvBIq39McuAYz6MLA2ZumO4S2TbgKwwuh+1bVd+ZXJKpqk3tX1mvb68NSU6tqg1jGLO2N91nCHDrUIAsB17ZnrcHsD+wGDBgxu8vhy5Z7g9ckWQxg8dS7f0c+3oD8PGq+keAqtqy64Y5OwyYPcPVwGeAc6fuqKqngRuAG5I8yuBfUAbM88/w2liAd1bVjbM1GP3c8OdyPvCFqnpjkkXAF59jX2HK8xJ3d67B7Bm+DPwJ8MnhYpJ/nuSlbfsFwCuBvx//8PQc3QickWRvgCQvT/KrszwmDWYw32vb/24n2n8e+OMkvwLgJTI9X0xdg/lcVf38Gzxr8LiGDzyrFRzCYH1mn/b+VuCj3UapXeV/A4uAb7Tr+ZsZzDw1u/6UwSWy/wzc9FwbV9XnkiwBJpL8E/BZ4H27dojj5aNiJEldeIlMktSFASNJ6sKAkSR1YcBIkrowYCRJXXibstRZkm3AXQz+e3sQeGtV/WhWByWNgTMYqb+fVtWSqjoG2AKcOdsDksbBgJHG62ZgAUD7LpDPJbk9yZeTvKLVfz/JLe2pvH+T5NBW/1dD30HyzSQvycCfDX2nz5vbsa/L4PuArk3y7SSfSJ75XiBpHLxEJo1Je6L1CcBlrXQp8B+q6oEkxwEXM3jo6FeA46uqkrwNeC/wHuC/AGdW1VeTvBh4isFXMCwBXgUcDNyW5Eut/1cDRwMPA1+lfS9Q9xOVGgNG6m/yUT6LgNuB9S0gXgP85dDEYvKRPQuBa5IcBryQZ56S/FXgQ0k+AXy6PQ37XwCfbE/0fTTJ3wK/DfyYwROYNwEM/X4DRmPjJTKpv8lH8f8Gg8A4k8F/ez9qazOTr99qx38E+GhV/TPg7cCLAKrq/cDbgH2Br7dLatNd9np6aHsb/oNSY2bASGNSVU8A/5HBpa6fAg8mORUGX0KV5FXt0OGn8q6abJ/kZVV1V1VdAEwArwC+BLy5fcvlfOB3GDy0VJp1Bow0Ru0bQ7/F4NtF/y1wepJvAfcAJ7XDzmVw6ezLwA+Gmr+7LeZ/i0FA3cDge37ubH3eBLy3qr4/jnORfhGfpixJ6sIZjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQu/j8q01+WA8oukwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['Reason'],data=df,palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7074b0fc",
   "metadata": {},
   "source": [
    "# What is the data type of the objects in the timeStamp column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "23ed680a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 663522 entries, 0 to 663521\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   lat        663522 non-null  float64\n",
      " 1   lng        663522 non-null  float64\n",
      " 2   desc       663522 non-null  object \n",
      " 3   zip        583323 non-null  float64\n",
      " 4   title      663522 non-null  object \n",
      " 5   timeStamp  663522 non-null  object \n",
      " 6   twp        663229 non-null  object \n",
      " 7   addr       663522 non-null  object \n",
      " 8   e          663522 non-null  int64  \n",
      " 9   Reason     663522 non-null  object \n",
      "dtypes: float64(3), int64(1), object(6)\n",
      "memory usage: 35.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f1bc5448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9c12fe",
   "metadata": {},
   "source": [
    "# Convert the above column from strings to DateTime objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "036db288",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timeStamp']= pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bd1cddf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fcc0ce8",
   "metadata": {},
   "source": [
    "# Grab specific attributes from a Datetime object by calling them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e526f419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time=df['timeStamp'].iloc[0]\n",
    "time.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "24ef7230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "292e3718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "66ab9642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.day_of_week"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8e2cd70",
   "metadata": {},
   "source": [
    "# Create 3 new columns called Hour,Month, and Day of Week. Create these columns based off of the timeStamp column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2a189eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hour']= df['timeStamp'].apply (lambda time: time.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c0acb9f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         17\n",
       "1         17\n",
       "2         14\n",
       "3         16\n",
       "4         16\n",
       "          ..\n",
       "663517    15\n",
       "663518    15\n",
       "663519    15\n",
       "663520    15\n",
       "663521    15\n",
       "Name: Hour, Length: 663522, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df['Hour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0814bc93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month']= df['timeStamp'].apply (lambda time: time.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "62864a45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         12\n",
       "1         12\n",
       "2         12\n",
       "3         12\n",
       "4         12\n",
       "          ..\n",
       "663517     7\n",
       "663518     7\n",
       "663519     7\n",
       "663520     7\n",
       "663521     7\n",
       "Name: Month, Length: 663522, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b9d3d89a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day_of_week']= df['timeStamp'].apply (lambda time: time.day_of_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "43585281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3\n",
       "1         3\n",
       "2         3\n",
       "3         3\n",
       "4         3\n",
       "         ..\n",
       "663517    2\n",
       "663518    2\n",
       "663519    2\n",
       "663520    2\n",
       "663521    2\n",
       "Name: Day_of_week, Length: 663522, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day_of_week']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3abc7b92",
   "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",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day_of_week</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 17:10:52</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",
       "    </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 17:29:21</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",
       "    </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 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>3</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 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>3</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 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>3</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 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month  Day_of_week  \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    14     12            3  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    16     12            3  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    16     12            3  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1def2b99",
   "metadata": {},
   "source": [
    "# Use the .map() with a dictionary to map the actual string names to the day of the week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c9d3ee43",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thur',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "eb279e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day_of_week'] = df['Day_of_week'].map(dmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6d1af1c4",
   "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",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day_of_week</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 17:10:52</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>Thur</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 17:29:21</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>Thur</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 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>Thur</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 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>Thur</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 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>Thur</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 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month Day_of_week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12        Thur  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12        Thur  \n",
       "2                    HAWS AVE  1   Fire    14     12        Thur  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    16     12        Thur  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    16     12        Thur  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90542cdd",
   "metadata": {},
   "source": [
    "# Use seaborn to craete a countplot of the Day of week column with hue based of the Reason column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fc334816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f9a11c0>"
      ]
     },
     "execution_count": 92,
     "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'],data=df,hue=df['Reason'],palette='viridis')\n",
    "# To relocate the legend\n",
    "plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c299f0c9",
   "metadata": {},
   "source": [
    "# Do the above for the month column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "bb4aae73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f9d5508>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeUAAAEGCAYAAABb4I1OAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAfh0lEQVR4nO3df5RWZb338fdnZlAgQEEHJIYOphwRf2EQj+f4VB4hIUMxRcX8wSoUHw+VZsv88bTKYwdXrpP6qKUr0wLNUiJNTC0R67RChQNC8jshOYr8GsKEJIEZvs8f9zXHG7wZhmH2PXuYz2ute933/d372te1geE717WvvS9FBGZmZtb6Klq7AWZmZlbgpGxmZpYTTspmZmY54aRsZmaWE07KZmZmOVHV2g0ot8MPPzz69evX2s0wM2tT5s2btzEiqvfzGD2rqqoeAI6nfXYKdwKL6urqLh88ePCGUju0u6Tcr18/5s6d29rNMDNrUyT99/4eo6qq6oEjjjji2Orq6rcrKira3f24O3fuVG1t7cB169Y9AJxdap/2+JuKmZm1juOrq6s3t8eEDFBRURHV1dXvUBgpKL1PGdtjZmbtW0V7TcgN0vnvMfc6KZuZmeVEu7umbGZm+XDGhbcMbsnjPffYN+ftbZ/KysrB/fv3/3vD93PPPXfTrbfeum7o0KHHvPnmmwe99dZbCysqCv3V4cOHH/Xiiy9227p16/z6+nrGjx/fd9asWd0kxUEHHRTTpk1bOWDAgO0teQ5OymZm1m4cfPDBO5ctW7ak1LauXbvWz5gxo8uIESP+tnHjxsoNGzZ0aNj2wAMP9Fi3bl2HZcuWLa6srGTlypUdunXrtrOl2+fhazMzMwq95kceeaQHwE9+8pNDzzrrrL82bFu7dm2HXr167aisrATgqKOO2lFdXV3f0m1wUjYzs3Zj27ZtFQMGDBjY8PrhD3/YvWHbGWecseXll1/uUldXx89//vMel1122aaGbZdeeumm559//tABAwYMvOKKK2pmzZrVKYv2efjazMzajcaGr6uqqmLo0KF/e+CBB3q89957Fcccc8z/XC8+6qijdqxYsWLRU0891XXmzJndzjzzzGMeeuihlaNHj97Sku1zUjYzM0suvvjiTRdddNHR11133Zrdt3Xq1CkuuOCCzRdccMHmXr167Xj88ccPdVJuQWdceMsetz332DfL2BIzM8uDESNG/O0rX/nK2i9+8YubiuN/+MMfOtfU1Ozo16/fjvr6ehYuXNjphBNO+PuejtNc7Topm5lZ62nKLUwtreGacsP3008//Z177733rYbvFRUV3HLLLet3L7du3bqqK6+88h+2b99eATBo0KB3b7jhhpLPr94fTspmZtZu1NfXl/xFYM6cOctLxbdu3TofYMyYMZvHjBmzOcu2QYazryV1lDRH0h8lLZb0byneQ9IMSa+l9+5FZW6UtELSckkjiuKDJS1M2+6WpBQ/WNJjKT5bUr+szsfMzCxrWd4StQ04PSJOAgYBIyWdAtwAzIyI/sDM9B1JA4GxwHHASOBeSZXpWPcBE4D+6TUyxccDb0fE0cCdwG0Zno+ZmVmmMkvKUfC39LVDegUwGpiS4lOAc9Ln0cCjEbEtIl4HVgBDJfUGukXESxERwEO7lWk41jRgWEMv2szMrK3J9Jpy6unOA44Gvh8RsyX1ioi1ABGxVlLPtHsf4OWi4qtTbEf6vHu8ocyb6Vh1kt4BDgM27taOCRR62nzkIx9puRPMOc8ub7sa+7sD//2ZHagyfaJXRNRHxCCghkKvd49rSAKlerjRSLyxMru34/6IGBIRQ6qrq/fSajMzs9ZRlsdsRsRfgd9RuBa8Pg1Jk94bppSvBvoWFasB1qR4TYn4LmUkVQGHALvcW2ZmZtZWZDZ8Laka2BERf5XUCRhOYSLWdGAc8J30/mQqMh34qaQ7gA9TmNA1JyLqJW1Jk8RmA5cB9xSVGQe8BIwBXkjXnc1alIeTzVreoH+/uUWXblzwjZv3eenGJ598csXYsWM/On/+/GUt2ZbmyvKacm9gSrquXAFMjYhfSXoJmCppPPAGcD5ARCyWNBVYAtQBEyOiYQWOq4DJQCfg2fQCeBB4WNIKCj3ksRmej+WIk6SZNUepZ1+XSsh1dXVUVZX/UR6Z1RgRrwInl4j/BRi2hzKTgEkl4nOBD1yPjoj3SEndzMysOTp37nzy1q1b5//qV7/q+u1vf7t3z549dyxZsqTz8uXLF0+cOLFm1qxZXbdv364rrrhiw3XXXbdx70dsPj/Ry8zM2o3ix2z27dt324wZM1YWb3/11Vc/NH/+/MUDBgzY/t3vfvfwQw45pH7RokVL//73v+vjH//4gLPOOmvzgAEDtpc++v5zUjYzs3ajsaUbAU488cR3G5Lu888/323ZsmWdp0+f3h1gy5YtlUuWLOnopGxmZnvkZxK0nM6dO+9s+BwRuv32298477zzMn/mdYOy3BJlZmbW1nz6059+57777qvetm2bAF599dWDN2/enGnedE/ZWox/WzezfdGUW5ha01e/+tWNq1atOviEE044NiLUo0ePHc8888zKvZdsPiflMnLSMjNrXQ1LMZaKjRo1asuoUaO2NMQrKyv53ve+9xbw1u5lsuLhazMzs5xwUjYzM8sJJ2UzM7OccFI2MzPLCSdlMzOznHBSNjMzywnfEmVmZq3iyzOvbtGlG+8Zdlej9z2vW7eu8rTTTjsGYOPGjR0qKiqiR48edQALFixY2rFjx0aX/j3rrLOOXL58eaeLL75449lnn735oosu+qgkpk2btvKSSy45siWWf3RSNjOzduGII46ob3ju9bXXXvvhLl261N9yyy3rG7bv2LGDDh06lCz7xhtvVM2bN6/LmjVrFgLcdNNNR3zmM5/565133rkGSi//2BxOymZm1m6dd955/bp37163cOHCzieeeOLWz3/+85uuvfbaj7z33nsVHTt23Dl58uTXTzrppG3Dhw//x02bNnUYMGDAwM9+9rNvT5kypWdFRUW8+OKLXWbPnv2nhuUfAb7xjW/0mjp16mGSGDZs2Dv33ntvkx8+4qRsZmbt2sqVKzvOmjXrT1VVVWzatKlizpw5yzp06MAvf/nLrl//+tdrfvOb36x86qmnVowaNap/Q087IrR7Txtg6tSp3Z5++unu8+bNW9a1a9ed69evr9yXtjgpm5lZu3buuee+XVVVSIebNm2qvPDCC49ctWpVR0mxY8cO7cuxZsyY0e2SSy7Z2LVr150AvXr1qt+X8p59bWZm7VqXLl3+Z7nG66+/vs+nPvWpLa+99trip556asX27dv3KU9GBNI+5fFdOCmbmZklmzdvrqypqdkO8IMf/ODwfS0/cuTIzQ8//PDhW7ZsqQDw8LWZmbUJe7uFqTVcf/316y6//PIj77777iM+8YlPbN7X8mPGjNn8yiuvdB40aNCxHTp0iOHDh7+TVppqEidlMzNrd+644441peLDhw9/d9WqVYsavt91111rAI455pjtr7322uI9lS9eEvLWW29dd+utt65rTrs8fG1mZpYTTspmZmY54aRsZmblsnPnzp3Nn5p8AEjnv3NP2zNLypL6SvqtpKWSFku6OsVvlvSWpAXpdWZRmRslrZC0XNKIovhgSQvTtruV5ptLOljSYyk+W1K/rM7HzMz226La2tpD2mti3rlzp2praw8BFu1pnywnetUBX4uIVyR1BeZJmpG23RkR3y3eWdJAYCxwHPBh4HlJ/xgR9cB9wATgZeAZYCTwLDAeeDsijpY0FrgNuDDDczIzs2aqq6u7fN26dQ+sW7fueNrnSO1OYFFdXd3le9ohs6QcEWuBtenzFklLgT6NFBkNPBoR24DXJa0AhkpaBXSLiJcAJD0EnEMhKY8Gbk7lpwHfk6SIaHSlDzMzK7/BgwdvAM5u7XbkWVluiUrDyicDs4FTgS9JugyYS6E3/TaFhP1yUbHVKbYjfd49Tnp/EyAi6iS9AxwGbMzsZMyszTnjwlv2uO25x75ZxpaYNS7z4QNJXYBfANdExGYKQ9FHAYMo9KRvb9i1RPFoJN5Ymd3bMEHSXElza2tr9+0EzMzMyiTTpCypA4WE/EhEPA4QEesjoj4idgI/BIam3VcDfYuK1wBrUrymRHyXMpKqgEOATbu3IyLuj4ghETGkurq6pU7PzMysRWU5+1rAg8DSiLijKN67aLfP8f4stOnA2DSj+kigPzAnXZveIumUdMzLgCeLyoxLn8cAL/h6spmZtVVZXlM+FbgUWChpQYrdBFwkaRCFYeZVwJUAEbFY0lRgCYWZ2xPTzGuAq4DJQCcKE7yeTfEHgYfTpLBNFGZvm5mZtUlZzr7+A6Wv+T7TSJlJwKQS8bnA8SXi7wHn70czzYzGJ0KBJ0OZlUt7vE/MzMwsl7xKlJmVnXvmZqW5p2xmZpYT7imbmbUwP6zEmss9ZTMzs5xwUjYzM8sJJ2UzM7OccFI2MzPLCSdlMzOznHBSNjMzywknZTMzs5xwUjYzM8sJJ2UzM7Oc8BO9munLM6/e47Z7ht1VxpaYmdmBwj1lMzOznHBSNjMzywknZTMzs5xwUjYzM8sJJ2UzM7OccFI2MzPLCSdlMzOznHBSNjMzywk/PMRK8sNRzNoH/6zni3vKZmZmOeGeslnGGuuJgHsjZva+zHrKkvpK+q2kpZIWS7o6xXtImiHptfTevajMjZJWSFouaURRfLCkhWnb3ZKU4gdLeizFZ0vql9X5mJmZZS3L4es64GsRcSxwCjBR0kDgBmBmRPQHZqbvpG1jgeOAkcC9kirTse4DJgD902tkio8H3o6Io4E7gdsyPB8zM7NMZTZ8HRFrgbXp8xZJS4E+wGjgtLTbFOB3wPUp/mhEbANel7QCGCppFdAtIl4CkPQQcA7wbCpzczrWNOB7khQRkdV5meWdh8vN2q6yTPRKw8onA7OBXilhNyTunmm3PsCbRcVWp1if9Hn3+C5lIqIOeAc4rET9EyTNlTS3tra2hc7KzMysZWWelCV1AX4BXBMRmxvbtUQsGok3VmbXQMT9ETEkIoZUV1fvrclmZmatItOkLKkDhYT8SEQ8nsLrJfVO23sDG1J8NdC3qHgNsCbFa0rEdykjqQo4BNjU8mdiZmaWvSxnXwt4EFgaEXcUbZoOjEufxwFPFsXHphnVR1KY0DUnDXFvkXRKOuZlu5VpONYY4AVfTzYzs7Yqy/uUTwUuBRZKWpBiNwHfAaZKGg+8AZwPEBGLJU0FllCYuT0xIupTuauAyUAnChO8nk3xB4GH06SwTRRmb5s1yhOhzCyvspx9/QdKX/MFGLaHMpOASSXic4HjS8TfIyV1MzOzts5P9DIzyxE/i7p9c1JuI/yDamZ24POCFGZmZjnhpGxmZpYTHr42M2uELx1ZObmnbGZmlhNOymZmZjnhpGxmZpYTTUrKkmY2JWZmZmbN1+hEL0kdgc7A4ZK68/4TuroBH864bWZmZu3K3mZfXwlcQyEBz+P9pLwZ+H52zTIzM2t/Gk3KEXEXcJekL0fEPWVqk5mZWbvUpPuUI+IeSf8M9CsuExEPZdQuMzOzdqdJSVnSw8BRwAKgYTnFAJyUzczMWkhTn+g1BBgYEZFlY6x98vrGZmYFTb1PeRFwRJYNMTMza++a2lM+HFgiaQ6wrSEYEWdn0iozM7N2qKlJ+eYsG2FmZmZNn339n1k3xMzaJq+iZNZymjr7eguF2dYABwEdgHcjoltWDTMzM2tvmtpT7lr8XdI5wNAsGmRmZtZeNfWa8i4i4peSbmjpxpiZ7Y2Hy+1A1tTh63OLvlZQuG/Z9yybmZm1oKb2lM8q+lwHrAJGt3hrzMzM2rEmPTwkIr5Q9LoiIiZFxIbGykj6kaQNkhYVxW6W9JakBel1ZtG2GyWtkLRc0oii+GBJC9O2uyUpxQ+W9FiKz5bUb5/P3szMLEealJQl1Uh6IiXZ9ZJ+IalmL8UmAyNLxO+MiEHp9Uw6/kBgLHBcKnOvpMq0/33ABKB/ejUcczzwdkQcDdwJ3NaUczEzM8urpj5m88fAdArrKvcBnkqxPYqI3wObmnj80cCjEbEtIl4HVgBDJfUGukXES+m52w8B5xSVmZI+TwOGNfSizczM2qKmJuXqiPhxRNSl12Sgupl1fknSq2l4u3uK9QHeLNpndYr1SZ93j+9SJiLqgHeAw5rZJjMzs1bX1KS8UdIlkirT6xLgL82o7z4KS0AOAtYCt6d4qR5uNBJvrMwHSJogaa6kubW1tfvUYDMzs3JpalL+InABsI5CMh0DfGFfK4uI9RFRHxE7gR/y/gNIVgN9i3atAdakeE2J+C5lJFUBh7CH4fKIuD8ihkTEkOrq5nbwzczMstXUpPxtYFxEVEdETwpJ+uZ9rSxdI27wOQpLQkLhevXYNKP6SAoTuuZExFpgi6RT0vXiy4Ani8qMS5/HAC94vWczM2vLmnqf8okR8XbDl4jYJOnkxgpI+hlwGnC4pNXAt4DTJA2iMMy8CrgyHW+xpKnAEgr3QU+MiPp0qKsozOTuBDybXgAPAg9LWkGhhzy2iediZmaWS01NyhWSujckZkk99lY2Ii4qEX6wkf0nAZNKxOcCx5eIvwecv5d2m5mZtRlNTcq3Ay9Kmkahl3sBJRKomZmZNV9TV4l6SNJc4HQKs57PjYglmbbMzMysnWnyKlEpCTsRm5mZZaSps6/NzMwsY07KZmZmOeGkbGZmlhNOymZmZjnhpGxmZpYTTspmZmY54aRsZmaWE07KZmZmOeGkbGZmlhNOymZmZjnhpGxmZpYTTspmZmY54aRsZmaWE07KZmZmOeGkbGZmlhNOymZmZjnhpGxmZpYTTspmZmY54aRsZmaWE07KZmZmOeGkbGZmlhNOymZmZjmRWVKW9CNJGyQtKor1kDRD0mvpvXvRthslrZC0XNKIovhgSQvTtrslKcUPlvRYis+W1C+rczEzMyuHLHvKk4GRu8VuAGZGRH9gZvqOpIHAWOC4VOZeSZWpzH3ABKB/ejUcczzwdkQcDdwJ3JbZmZiZmZVBZkk5In4PbNotPBqYkj5PAc4pij8aEdsi4nVgBTBUUm+gW0S8FBEBPLRbmYZjTQOGNfSizczM2qJyX1PuFRFrAdJ7zxTvA7xZtN/qFOuTPu8e36VMRNQB7wCHlapU0gRJcyXNra2tbaFTMTMza1lVrd2ApFQPNxqJN1bmg8GI+4H7AYYMGVJyH7P9Mejfb97jtk/8U/naYWZtW7mT8npJvSNibRqa3pDiq4G+RfvVAGtSvKZEvLjMaklVwCF8cLjc7IDkXwLMDkzlHr6eDoxLn8cBTxbFx6YZ1UdSmNA1Jw1xb5F0SrpefNluZRqONQZ4IV13NjMza5My6ylL+hlwGnC4pNXAt4DvAFMljQfeAM4HiIjFkqYCS4A6YGJE1KdDXUVhJncn4Nn0AngQeFjSCgo95LFZnYuZmVk5ZJaUI+KiPWwatof9JwGTSsTnAseXiL9HSupmZmYHgrxM9Mqdxq7ZQdu/bnegn5+ZWVvkx2yamZnlhJOymZlZTnj42g5IvmXIzNoi95TNzMxywknZzMwsJzx8bWXh4WQzs71zUs4J36JkZlnw/y1ti4evzczMcsI9ZTNr19yTtDxxUjazvfKcALPy8PC1mZlZTrinbGa5cyD3zD1cbo1xT9nMzCwnnJTNzMxywknZzMwsJ5yUzczMcsJJ2czMLCeclM3MzHLCSdnMzCwnnJTNzMxywknZzMwsJ5yUzczMcsJJ2czMLCdaJSlLWiVpoaQFkuamWA9JMyS9lt67F+1/o6QVkpZLGlEUH5yOs0LS3ZLUGudjZmbWElqzp/wvETEoIoak7zcAMyOiPzAzfUfSQGAscBwwErhXUmUqcx8wAeifXiPL2H4zM7MWlafh69HAlPR5CnBOUfzRiNgWEa8DK4ChknoD3SLipYgI4KGiMmZmZm1OayXlAJ6TNE/ShBTrFRFrAdJ7zxTvA7xZVHZ1ivVJn3ePf4CkCZLmSppbW1vbgqdhZmbWclprPeVTI2KNpJ7ADEnLGtm31HXiaCT+wWDE/cD9AEOGDCm5j5mZWWtrlZ5yRKxJ7xuAJ4ChwPo0JE1635B2Xw30LSpeA6xJ8ZoScTMzszap7ElZ0ockdW34DJwBLAKmA+PSbuOAJ9Pn6cBYSQdLOpLChK45aYh7i6RT0qzry4rKmJmZtTmtMXzdC3gi3b1UBfw0In4t6b+AqZLGA28A5wNExGJJU4ElQB0wMSLq07GuAiYDnYBn08vMzKxNKntSjog/AyeViP8FGLaHMpOASSXic4HjW7qNZmZmrSFPt0SZmZm1a07KZmZmOeGkbGZmlhNOymZmZjnhpGxmZpYTTspmZmY54aRsZmaWE07KZmZmOeGkbGZmlhNOymZmZjnhpGxmZpYTTspmZmY54aRsZmaWE07KZmZmOeGkbGZmlhNOymZmZjnhpGxmZpYTTspmZmY54aRsZmaWE07KZmZmOeGkbGZmlhNOymZmZjnhpGxmZpYTTspmZmY54aRsZmaWE20+KUsaKWm5pBWSbmjt9piZmTVXm07KkiqB7wOfAQYCF0ka2LqtMjMza542nZSBocCKiPhzRGwHHgVGt3KbzMzMmkUR0dptaDZJY4CREXF5+n4p8L8i4ku77TcBmJC+HgMsb0Z1hwMb96O5rs/1HQh1ub72W98/RER1SzfGdlXV2g3YTyoR+8BvGRFxP3D/flUkzY2IIftzDNfn+tp6Xa7P9Vm22vrw9Wqgb9H3GmBNK7XFzMxsv7T1pPxfQH9JR0o6CBgLTG/lNpmZmTVLmx6+jog6SV8CfgNUAj+KiMUZVbdfw9+uz/UdIHW5PtdnGWrTE73MzMwOJG19+NrMzOyA4aRsZmaWE07KeyHpR5I2SFpUhrr6SvqtpKWSFku6OuP6OkqaI+mPqb5/y7K+onorJc2X9Ksy1LVK0kJJCyTNLUN9h0qaJmlZ+nv8pwzrOiadV8Nrs6Rrsqov1fnV9G9lkaSfSeqYcX1Xp7oWZ3FupX6+JfWQNEPSa+m9e8b1nZ/Ob6ekFr1VaQ/1/Uf69/mqpCckHdqSddr+cVLeu8nAyDLVVQd8LSKOBU4BJmb82NBtwOkRcRIwCBgp6ZQM62twNbC0DPU0+JeIGFSmezPvAn4dEQOAk8jwPCNieTqvQcBgYCvwRFb1SeoDfAUYEhHHU5hcOTbD+o4HrqDw5L6TgFGS+rdwNZP54M/3DcDMiOgPzEzfs6xvEXAu8PsWrKex+mYAx0fEicCfgBszqNeayUl5LyLi98CmMtW1NiJeSZ+3UPgPvU+G9UVE/C197ZBemc78k1QDfBZ4IMt6WoOkbsAngQcBImJ7RPy1TNUPA1ZGxH9nXE8V0ElSFdCZbJ8LcCzwckRsjYg64D+Bz7VkBXv4+R4NTEmfpwDnZFlfRCyNiOY8ZbC59T2X/jwBXqbwfAfLCSflnJLUDzgZmJ1xPZWSFgAbgBkRkWl9wP8Dvg7szLieBgE8J2leetxqlj4K1AI/TsPzD0j6UMZ1NhgL/CzLCiLiLeC7wBvAWuCdiHguwyoXAZ+UdJikzsCZ7PqwoKz0ioi1UPhFGehZhjpbyxeBZ1u7EfY+J+UcktQF+AVwTURszrKuiKhPw581wNA0ZJgJSaOADRExL6s6Sjg1Ij5GYSWxiZI+mWFdVcDHgPsi4mTgXVp26LOk9OCcs4GfZ1xPdwq9yCOBDwMfknRJVvVFxFLgNgrDrb8G/kjhEo+1AEn/l8Kf5yOt3RZ7n5NyzkjqQCEhPxIRj5er3jTM+juyvX5+KnC2pFUUVvQ6XdJPMqyPiFiT3jdQuN46NMPqVgOri0YbplFI0ln7DPBKRKzPuJ7hwOsRURsRO4DHgX/OssKIeDAiPhYRn6QwDPtalvUl6yX1BkjvG8pQZ1lJGgeMAi4OP6wiV5yUc0SSKFyPXBoRd5ShvuqGmZeSOlH4T3dZVvVFxI0RURMR/SgMt74QEZn1tCR9SFLXhs/AGRSGRDMREeuANyUdk0LDgCVZ1VfkIjIeuk7eAE6R1Dn9Wx1GxhP2JPVM7x+hMBmqHOc5HRiXPo8DnixDnWUjaSRwPXB2RGxt7fbYrtr0YzbLQdLPgNOAwyWtBr4VEQ9mVN2pwKXAwnSdF+CmiHgmo/p6A1MkVVL4BW1qRGR+m1IZ9QKeKOQPqoCfRsSvM67zy8AjaUj5z8AXsqwsXWv9NHBllvUARMRsSdOAVygMe84n+0c2/kLSYcAOYGJEvN2SBy/18w18B5gqaTyFX0TOz7i+TcA9QDXwtKQFETEiw/puBA4GZqSfjZcj4v+0RH22//yYTTMzs5zw8LWZmVlOOCmbmZnlhJOymZlZTjgpm5mZ5YSTspmZWU44KZu1MEkh6eGi71WSapu7KlZaeepfi76fVo4Vtsys/JyUzVreu8Dx6YEsULiP+K39ON6hwL/ubScza/uclM2y8SyF1bBgtydupfV6f5nWs31Z0okpfnNa//Z3kv4s6SupyHeAo9Kayf+RYl30/rrNj6QnbJlZG+ekbJaNR4GxkjoCJ7Lral//BsxP69neBDxUtG0AMILCM7q/lZ6FfgOFZRkHRcR1ab+TgWuAgRRWpzo1w3MxszJxUjbLQES8CvSj0Eve/TGp/xt4OO33AnCYpEPStqcjYltEbKSwEEKvPVQxJyJWR8ROYEGqy8zaOD/72iw70ymsP3wacFhRvNRQc8PzbrcVxerZ889oU/czszbEPWWz7PwIuCUiFu4W/z1wMRRmUgMb97Ju9hagaxYNNLN88W/XZhmJiNXAXSU23Qz8WNKrwFbeXyZwT8f5i6RZkhZRmED2dEu31czywatEmZmZ5YSHr83MzHLCSdnMzCwnnJTNzMxywknZzMwsJ5yUzczMcsJJ2czMLCeclM3MzHLi/wNDXywy1BuCaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['Month'],data=df,hue=df['Reason'],palette='viridis')\n",
    "# To relocate the legend\n",
    "plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b63db7f",
   "metadata": {},
   "source": [
    "# Create a heatmap with the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1fd11802",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tc = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7952e137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Tc,annot=True,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3abb8e06",
   "metadata": {},
   "source": [
    "# Creat a clustermap using the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b59e3c33",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tcc =df.pivot_table(index='Reason',columns='Month',values='Hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "debb3d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1a5b7ad8>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(Tcc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "866b4b32",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c57a76c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}