From fabad5650997136b89dc898c6ae503bbf18d588b Mon Sep 17 00:00:00 2001 From: Nunsi Shiaki <89302881+Omi-1@users.noreply.github.com> Date: Fri, 30 Jun 2023 12:47:24 +0100 Subject: [PATCH] Data Analysis and Visualisation. --- Smart_Data_Science_Project.ipynb | 1496 ++++++++++++++++++++++++++++++ 1 file changed, 1496 insertions(+) create mode 100644 Smart_Data_Science_Project.ipynb diff --git a/Smart_Data_Science_Project.ipynb b/Smart_Data_Science_Project.ipynb new file mode 100644 index 0000000..73d7718 --- /dev/null +++ b/Smart_Data_Science_Project.ipynb @@ -0,0 +1,1496 @@ +{ + "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": [ + "
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latlngdescziptitletimeStamptwpaddre
040.297876-75.581294REINDEER CT & DEAD END; NEW HANOVER; Station ...19525.0EMS: BACK PAINS/INJURY2015-12-10 17:10:52NEW HANOVERREINDEER CT & DEAD END1
140.258061-75.264680BRIAR PATH & WHITEMARSH LN; HATFIELD TOWNSHIP...19446.0EMS: DIABETIC EMERGENCY2015-12-10 17:29:21HATFIELD TOWNSHIPBRIAR PATH & WHITEMARSH LN1
240.121182-75.351975HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...19401.0Fire: GAS-ODOR/LEAK2015-12-10 14:39:21NORRISTOWNHAWS AVE1
340.116153-75.343513AIRY ST & SWEDE ST; NORRISTOWN; Station 308A;...19401.0EMS: CARDIAC EMERGENCY2015-12-10 16:47:36NORRISTOWNAIRY ST & SWEDE ST1
440.251492-75.603350CHERRYWOOD CT & DEAD END; LOWER POTTSGROVE; S...NaNEMS: DIZZINESS2015-12-10 16:56:52LOWER POTTSGROVECHERRYWOOD CT & DEAD END1
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" + ], + "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": [ + "\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": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "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": [ + "\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": [ + "
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latlngdescziptitletimeStamptwpaddreReasonHourMonthDay_of_week
040.297876-75.581294REINDEER CT & DEAD END; NEW HANOVER; Station ...19525.0EMS: BACK PAINS/INJURY2015-12-10 17:10:52NEW HANOVERREINDEER CT & DEAD END1EMS17123
140.258061-75.264680BRIAR PATH & WHITEMARSH LN; HATFIELD TOWNSHIP...19446.0EMS: DIABETIC EMERGENCY2015-12-10 17:29:21HATFIELD TOWNSHIPBRIAR PATH & WHITEMARSH LN1EMS17123
240.121182-75.351975HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...19401.0Fire: GAS-ODOR/LEAK2015-12-10 14:39:21NORRISTOWNHAWS AVE1Fire14123
340.116153-75.343513AIRY ST & SWEDE ST; NORRISTOWN; Station 308A;...19401.0EMS: CARDIAC EMERGENCY2015-12-10 16:47:36NORRISTOWNAIRY ST & SWEDE ST1EMS16123
440.251492-75.603350CHERRYWOOD CT & DEAD END; LOWER POTTSGROVE; S...NaNEMS: DIZZINESS2015-12-10 16:56:52LOWER POTTSGROVECHERRYWOOD CT & DEAD END1EMS16123
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" + ], + "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": [ + "
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latlngdescziptitletimeStamptwpaddreReasonHourMonthDay_of_week
040.297876-75.581294REINDEER CT & DEAD END; NEW HANOVER; Station ...19525.0EMS: BACK PAINS/INJURY2015-12-10 17:10:52NEW HANOVERREINDEER CT & DEAD END1EMS1712Thur
140.258061-75.264680BRIAR PATH & WHITEMARSH LN; HATFIELD TOWNSHIP...19446.0EMS: DIABETIC EMERGENCY2015-12-10 17:29:21HATFIELD TOWNSHIPBRIAR PATH & WHITEMARSH LN1EMS1712Thur
240.121182-75.351975HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...19401.0Fire: GAS-ODOR/LEAK2015-12-10 14:39:21NORRISTOWNHAWS AVE1Fire1412Thur
340.116153-75.343513AIRY ST & SWEDE ST; NORRISTOWN; Station 308A;...19401.0EMS: CARDIAC EMERGENCY2015-12-10 16:47:36NORRISTOWNAIRY ST & SWEDE ST1EMS1612Thur
440.251492-75.603350CHERRYWOOD CT & DEAD END; LOWER POTTSGROVE; S...NaNEMS: DIZZINESS2015-12-10 16:56:52LOWER POTTSGROVECHERRYWOOD CT & DEAD END1EMS1612Thur
\n", + "
" + ], + "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": [ + "" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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