911 Calls - Exploratory Analysis.ipynb

{
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   "cell_type": "markdown",
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    "nbpresent": {
     "id": "885a9c89-500a-4b55-a3cb-91e61176f47c"
    }
   },
   "source": [
    "# 911 Exploratory Analysis Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5aa43777-2a5d-4dfa-839a-56abb1ea5b64"
    }
   },
   "source": [
    "For this project we'll analyze the 911 call dataset 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",
    "Let's start with some data analysis and visualisation imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbpresent": {
     "id": "b45f2169-8133-4c6d-b07c-0c1e4c592457"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbpresent": {
     "id": "1307840b-2c6e-4a45-ac5b-097e9c25d7b8"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (6, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "nbpresent": {
     "id": "37ed96c5-6df7-4df4-8daf-231c146432ad"
    }
   },
   "outputs": [],
   "source": [
    "#Reading the data\n",
    "df = pd.read_csv('data/911.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "nbpresent": {
     "id": "ac572753-c139-4ebb-8b01-b279b904046f"
    }
   },
   "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": "code",
   "execution_count": 5,
   "metadata": {
    "nbpresent": {
     "id": "ede351b9-9fd5-42ef-8f54-69232d70a3d6"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": [
    "#Checking the head of the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "fadfe82a-9173-4481-94cb-976a67d003d2"
    }
   },
   "source": [
    "## Basic Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "1dfea5b7-e897-4ec3-88db-cedbeda7f44b"
    }
   },
   "source": [
    "Let's check out the top 5 zipcodes for calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "nbpresent": {
     "id": "7f53de11-6a3e-45ff-b833-05f46dc5af57"
    }
   },
   "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(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ff7dd8f5-2a02-4048-b51d-3a3320609554"
    }
   },
   "source": [
    "The top townships for the calls were as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "nbpresent": {
     "id": "09ddca36-38a2-43f3-a2d2-e5d8dd37b1f0"
    }
   },
   "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(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0618af18-9f26-462b-891d-257717304445"
    }
   },
   "source": [
    "For 90k + entries, how many unique call titles did we have? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbpresent": {
     "id": "987e03f9-a125-4fdf-89ff-4a096b6d9432"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b86e55db-7f97-49bf-8b7d-a8ecd30b9672"
    }
   },
   "source": [
    "## Data Wrangling for Feature Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can extract some generalised features from the columns in our dataset for further analysis. \n",
    "\n",
    "In the _title_ column, there's a kind of 'subcategory' or 'reason for call' alloted to each entry (denoted by the text before the colon). \n",
    "\n",
    "The timestamp column can be further segregated into Year, Month and Day of Week too. \n",
    "\n",
    "Let's start with creating a 'Reason' feature for each call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "nbpresent": {
     "id": "9fe57044-4a66-4db5-909f-d2dfdee1ce12"
    }
   },
   "outputs": [],
   "source": [
    "df['Reason'] = df['title'].apply(lambda x: x.split(':')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbpresent": {
     "id": "0d2300c3-7f2b-4fa0-a429-7e6c9559b04f"
    }
   },
   "outputs": [
    {
     "data": {
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       "<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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99487</th>\n",
       "      <td>40.132869</td>\n",
       "      <td>-75.333515</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST; NORRISTOWN; 2016-08-2...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:06:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99488</th>\n",
       "      <td>40.006974</td>\n",
       "      <td>-75.289080</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL; LOWER MERION; ...</td>\n",
       "      <td>19003.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:07:02</td>\n",
       "      <td>LOWER MERION</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99489</th>\n",
       "      <td>40.115429</td>\n",
       "      <td>-75.334679</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST;  NORRISTOWN; Station ...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: FALL VICTIM</td>\n",
       "      <td>2016-08-24 11:12:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99490</th>\n",
       "      <td>40.186431</td>\n",
       "      <td>-75.192555</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN;  HORSHAM; Station 352; ...</td>\n",
       "      <td>19002.0</td>\n",
       "      <td>EMS: NAUSEA/VOMITING</td>\n",
       "      <td>2016-08-24 11:17:01</td>\n",
       "      <td>HORSHAM</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99491</th>\n",
       "      <td>40.207055</td>\n",
       "      <td>-75.317952</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST; UPPER GWYNEDD; 2016-08...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:17:02</td>\n",
       "      <td>UPPER GWYNEDD</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             lat        lng  \\\n",
       "99487  40.132869 -75.333515   \n",
       "99488  40.006974 -75.289080   \n",
       "99489  40.115429 -75.334679   \n",
       "99490  40.186431 -75.192555   \n",
       "99491  40.207055 -75.317952   \n",
       "\n",
       "                                                    desc      zip  \\\n",
       "99487  MARKLEY ST & W LOGAN ST; NORRISTOWN; 2016-08-2...  19401.0   \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL; LOWER MERION; ...  19003.0   \n",
       "99489  CHESTNUT ST & WALNUT ST;  NORRISTOWN; Station ...  19401.0   \n",
       "99490  WELSH RD & WEBSTER LN;  HORSHAM; Station 352; ...  19002.0   \n",
       "99491  MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...  19446.0   \n",
       "\n",
       "                             title            timeStamp            twp  \\\n",
       "99487  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:06:00     NORRISTOWN   \n",
       "99488  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:07:02   LOWER MERION   \n",
       "99489             EMS: FALL VICTIM  2016-08-24 11:12:00     NORRISTOWN   \n",
       "99490         EMS: NAUSEA/VOMITING  2016-08-24 11:17:01        HORSHAM   \n",
       "99491  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:17:02  UPPER GWYNEDD   \n",
       "\n",
       "                                 addr  e   Reason  \n",
       "99487         MARKLEY ST & W LOGAN ST  1  Traffic  \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL  1  Traffic  \n",
       "99489         CHESTNUT ST & WALNUT ST  1      EMS  \n",
       "99490           WELSH RD & WEBSTER LN  1      EMS  \n",
       "99491          MORRIS RD & S BROAD ST  1  Traffic  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c2ceb94d-ce05-4c80-940b-296cc8d68ce1"
    }
   },
   "source": [
    "Now, let's find out the most common reason for 911 calls, according to our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "nbpresent": {
     "id": "cb024d41-5f10-44da-bfcb-64df5bfa88e9"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "nbpresent": {
     "id": "ef9a3a0b-cbe8-45d3-9e42-1541e2d61600"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sajal/.pyenv/versions/3.10.0/envs/ds_portfolio/lib/python3.10/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Reason', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Reason'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "41905b29-7a89-488d-b82f-13907c6a1b7c"
    }
   },
   "source": [
    "Let's deal with the time information we have. Checking the datatype of the timestamp column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "nbpresent": {
     "id": "c8fb7058-6a25-4f8f-b5e1-f3b7889b7d1c"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "469cbbdb-5389-4c26-b671-88ab3c9ef792"
    }
   },
   "source": [
    "As the timestamps are still string types, it'll make our life easier if we convert it to a python DateTime object, so we can extract the year, month, and day information more intuitively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "nbpresent": {
     "id": "51ec289d-4eec-4307-9ee2-1ae5df2b832c"
    }
   },
   "outputs": [],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a single DateTime object, we can extract information as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "nbpresent": {
     "id": "6d77d2f1-b36b-4d1a-b171-d187ef9a85ee"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hour: 17\n",
      "Month: 12\n",
      "Day of Week: 3\n"
     ]
    }
   ],
   "source": [
    "time = df['timeStamp'].iloc[0]\n",
    "\n",
    "print('Hour:',time.hour)\n",
    "print('Month:',time.month)\n",
    "print('Day of Week:',time.dayofweek)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create new features for the above pieces of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "nbpresent": {
     "id": "53ff2b35-83ff-405e-8020-8b03cbda7248"
    }
   },
   "outputs": [],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda x: x.hour)\n",
    "df['Month'] = df['timeStamp'].apply(lambda x: x.month)\n",
    "df['Day of Week'] = df['timeStamp'].apply(lambda x: x.dayofweek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "nbpresent": {
     "id": "ba3a4a52-b88f-426a-a66f-55c4515cfd4a"
    }
   },
   "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 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "\n",
       "                         addr  e 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    17     12            3  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "a9c58df5-7037-4345-b11f-0478d954acde"
    }
   },
   "source": [
    "The Day of the Week is an integer and it might not be instantly clear which number refers to which Day. We can map that information to a Mon-Sun string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "nbpresent": {
     "id": "2c3adcf6-a04e-409b-a094-542f9deecfc1"
    }
   },
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "nbpresent": {
     "id": "255f7628-442c-4e5a-998d-6b4b5d084879"
    }
   },
   "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>99489</th>\n",
       "      <td>40.115429</td>\n",
       "      <td>-75.334679</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST;  NORRISTOWN; Station ...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: FALL VICTIM</td>\n",
       "      <td>2016-08-24 11:12:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99490</th>\n",
       "      <td>40.186431</td>\n",
       "      <td>-75.192555</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN;  HORSHAM; Station 352; ...</td>\n",
       "      <td>19002.0</td>\n",
       "      <td>EMS: NAUSEA/VOMITING</td>\n",
       "      <td>2016-08-24 11:17:01</td>\n",
       "      <td>HORSHAM</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99491</th>\n",
       "      <td>40.207055</td>\n",
       "      <td>-75.317952</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST; UPPER GWYNEDD; 2016-08...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:17:02</td>\n",
       "      <td>UPPER GWYNEDD</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             lat        lng  \\\n",
       "99489  40.115429 -75.334679   \n",
       "99490  40.186431 -75.192555   \n",
       "99491  40.207055 -75.317952   \n",
       "\n",
       "                                                    desc      zip  \\\n",
       "99489  CHESTNUT ST & WALNUT ST;  NORRISTOWN; Station ...  19401.0   \n",
       "99490  WELSH RD & WEBSTER LN;  HORSHAM; Station 352; ...  19002.0   \n",
       "99491  MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...  19446.0   \n",
       "\n",
       "                             title           timeStamp            twp  \\\n",
       "99489             EMS: FALL VICTIM 2016-08-24 11:12:00     NORRISTOWN   \n",
       "99490         EMS: NAUSEA/VOMITING 2016-08-24 11:17:01        HORSHAM   \n",
       "99491  Traffic: VEHICLE ACCIDENT - 2016-08-24 11:17:02  UPPER GWYNEDD   \n",
       "\n",
       "                          addr  e   Reason  Hour  Month Day of Week  \n",
       "99489  CHESTNUT ST & WALNUT ST  1      EMS    11      8         Wed  \n",
       "99490    WELSH RD & WEBSTER LN  1      EMS    11      8         Wed  \n",
       "99491   MORRIS RD & S BROAD ST  1  Traffic    11      8         Wed  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day of Week'] = df['Day of Week'].map(dmap)\n",
    "\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's combine the newly created features, to check out the most common call reasons based on the day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "nbpresent": {
     "id": "6a417f2b-36cc-4b43-a752-58cb9c6fee18"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sajal/.pyenv/versions/3.10.0/envs/ds_portfolio/lib/python3.10/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28fa71540>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Day of Week'],hue=df['Reason'])\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.25,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It makes sense for the number of traffic related 911 calls to be the lowest during the weekends, what's also iteresting is that Emergency Service related calls are also low during the weekend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "nbpresent": {
     "id": "acacaba1-a7c1-4698-b868-ae369c27d920"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28c7c4c70>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['Month'],hue=df['Reason'])\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.25,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's check out the relationship between the number of calls and the month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "nbpresent": {
     "id": "39204883-e77a-4e9e-a737-de548b8fb932"
    }
   },
   "outputs": [],
   "source": [
    "byMonth = df.groupby(by='Month').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "nbpresent": {
     "id": "5386bc5c-9929-4d1d-b0a2-c0a28402f45f"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Calls')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth['e'].plot.line(y='e')\n",
    "plt.title('Calls per Month')\n",
    "plt.ylabel('Number of Calls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using seaborn, let's fit the number of calls to a month and see if there's any concrete correlation between the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "nbpresent": {
     "id": "2f349d5a-654f-4119-88b9-11224796cc40"
    }
   },
   "outputs": [],
   "source": [
    "byMonth.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "nbpresent": {
     "id": "89afa051-23ed-4b46-afb7-0792e16fbae2"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(5.924999999999997, 0.5, 'Number of Calls')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Month',y='e',data=byMonth)\n",
    "plt.ylabel('Number of Calls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, it does seem that there are fewer emergency calls during the holiday seasons.\n",
    "\n",
    "Let's extract the date from the timestamp, and see behavior in a little more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "nbpresent": {
     "id": "7d147fb7-5641-4cb7-b796-e5938168565f"
    }
   },
   "outputs": [],
   "source": [
    "df['Date']=df['timeStamp'].apply(lambda x: x.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "\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",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week        Date  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12         Thu  2015-12-10  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12         Thu  2015-12-10  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4607c7ae-f705-4723-98ed-36f205ff9262"
    }
   },
   "source": [
    "Grouping and plotting the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "nbpresent": {
     "id": "7deec6c3-81e3-476d-9679-125c441bfcef"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'pandas' has no attribute 'groupby'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [32]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m(df,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDate\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mcount()[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124me\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mplot\u001b[38;5;241m.\u001b[39mline(y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124me\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend()\u001b[38;5;241m.\u001b[39mremove()\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.0/envs/ds_portfolio/lib/python3.10/site-packages/pandas/__init__.py:244\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marrays\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msparse\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SparseArray \u001b[38;5;28;01mas\u001b[39;00m _SparseArray\n\u001b[1;32m    242\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _SparseArray\n\u001b[0;32m--> 244\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpandas\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'groupby'"
     ]
    }
   ],
   "source": [
    "pd.groupby(df,'Date').count()['e'].plot.line(y='e')\n",
    "\n",
    "plt.legend().remove()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "792c7f5f-53d2-433b-b3c8-52c922964135"
    }
   },
   "source": [
    "We can also check out the same plot for each reason separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "nbpresent": {
     "id": "ee5b5233-0913-41cc-869e-a05d5176c141"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame.groupby(df[df['Reason']=='Traffic'],'Date').count().plot.line(y='e')\n",
    "plt.title('Traffic')\n",
    "plt.legend().remove()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "nbpresent": {
     "id": "8a1cea6a-6f5e-493d-b70a-1d17880c00b0"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame.groupby(df[df['Reason']=='Fire'],'Date').count().plot.line(y='e')\n",
    "plt.title('Fire')\n",
    "plt.legend().remove()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "nbpresent": {
     "id": "af1f3c54-34ed-424f-b80a-ce6f8b8bdabf"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame.groupby(df[df['Reason']=='EMS'],'Date').count().plot.line(y='e')\n",
    "plt.title('EMS')\n",
    "plt.legend().remove()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "82306c0e-0b83-4cf9-a33a-8e11c3d05bc6"
    }
   },
   "source": [
    "Let's create a heatmap for the counts of calls on each hour, during a given day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "nbpresent": {
     "id": "eab620e5-286a-41ed-89be-930e650b02bc"
    }
   },
   "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",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>269</td>\n",
       "      <td>240</td>\n",
       "      <td>186</td>\n",
       "      <td>170</td>\n",
       "      <td>209</td>\n",
       "      <td>239</td>\n",
       "      <td>415</td>\n",
       "      <td>655</td>\n",
       "      <td>889</td>\n",
       "      <td>880</td>\n",
       "      <td>...</td>\n",
       "      <td>943</td>\n",
       "      <td>938</td>\n",
       "      <td>1026</td>\n",
       "      <td>1019</td>\n",
       "      <td>905</td>\n",
       "      <td>731</td>\n",
       "      <td>647</td>\n",
       "      <td>571</td>\n",
       "      <td>462</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>250</td>\n",
       "      <td>216</td>\n",
       "      <td>189</td>\n",
       "      <td>209</td>\n",
       "      <td>156</td>\n",
       "      <td>255</td>\n",
       "      <td>410</td>\n",
       "      <td>701</td>\n",
       "      <td>875</td>\n",
       "      <td>808</td>\n",
       "      <td>...</td>\n",
       "      <td>904</td>\n",
       "      <td>867</td>\n",
       "      <td>990</td>\n",
       "      <td>1037</td>\n",
       "      <td>894</td>\n",
       "      <td>686</td>\n",
       "      <td>668</td>\n",
       "      <td>575</td>\n",
       "      <td>490</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Hour          0    1    2    3    4    5    6    7    8    9   ...   14   15  \\\n",
       "Day of Week                                                    ...             \n",
       "Fri          275  235  191  175  201  194  372  598  742  752  ...  932  980   \n",
       "Mon          282  221  201  194  204  267  397  653  819  786  ...  869  913   \n",
       "Sat          375  301  263  260  224  231  257  391  459  640  ...  789  796   \n",
       "Sun          383  306  286  268  242  240  300  402  483  620  ...  684  691   \n",
       "Thu          278  202  233  159  182  203  362  570  777  828  ...  876  969   \n",
       "Tue          269  240  186  170  209  239  415  655  889  880  ...  943  938   \n",
       "Wed          250  216  189  209  156  255  410  701  875  808  ...  904  867   \n",
       "\n",
       "Hour           16    17   18   19   20   21   22   23  \n",
       "Day of Week                                            \n",
       "Fri          1039   980  820  696  667  559  514  474  \n",
       "Mon           989   997  885  746  613  497  472  325  \n",
       "Sat           848   757  778  696  628  572  506  467  \n",
       "Sun           663   714  670  655  537  461  415  330  \n",
       "Thu           935  1013  810  698  617  553  424  354  \n",
       "Tue          1026  1019  905  731  647  571  462  274  \n",
       "Wed           990  1037  894  686  668  575  490  335  \n",
       "\n",
       "[7 rows x 24 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_hour = df.pivot_table(values='lat',index='Day of Week',columns='Hour',aggfunc='count')\n",
    "\n",
    "day_hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bd195977-87f2-400b-870f-1968004038c4"
    }
   },
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "nbpresent": {
     "id": "53dc5fd4-ae5d-4d5a-a425-52979273e129"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(day_hour)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that most calls take place around the end of office hours on weekdays. We can create a clustermap to pair up similar Hours and Days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "nbpresent": {
     "id": "2a6b55a5-dd45-4514-a4cc-4be26384fcf7"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x29221a5c0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xbr755rz//e/fGFsGWCcCGYD18qlPfSrf+973Wm9ffPHFee211zJ06NBUKpV069Ytn/jEJ/Lkk0++5bjiG7i+//3vF8dAzXGJBQDtpmfPnnnwwQeTJE1NTXnooYey3XbbpXv37nnhhReSJI899ljr53fp4n9DQO1xBhmAdtOrV69svfXWqa+vT1NTUw488MDstttuOeKIIzJ16tT84he/yPbbb9/R2wR4R5Vq8d+6AADgPc6/bQEAQIFABgCAAoEMAAAFAhkAAAoEMgAAFAhkAAAoEMgAAFDw/wFfX3+tUdGPfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(day_hour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this concludes the exploratory analysis project."
   ]
  }
 ],
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