· Project Description/Outline: Our goal is to analyze Traffic violations data for the major US cities. With our analysis, we want to understand what are the primary reasons for violations, demography,...

{
"cells": [
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"#imports\n",
"import re\n",
"import warnings\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"#Setting environment variables\n",
"pd.options.display.max_columns = 50\n",
"warnings.filterwarnings(\"ignore\")\n",
"sns.set_style(\"darkgrid\")\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Ingestion\n",
"1. Reading data using Pandas. Pandas is very powerful and popular library to deal with CSV data in a DataFrame format.\n",
"2. Loading 10,000 rows only to avoid memory issues. Pandas provides generator functions also to account for data which are memory bound."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"#############################\n",
"# READING DATA \n",
"# 1. IMPORTING DATA INTO PANDAS DATAFRAME\n",
"# 2. LOADING 10K ROWS DUE TO OUT OF MEMORY ERROR\n",
"#############################\n",
"\n",
"df = pd.read_csv(\"Traffic_Violations.csv\", nrows=10000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Overview\n",
"1. Dataframe properties depicting data type identified in each data field/column\n",
"2. Cleaning data of values which are NaNs.\n",
"3. Replacing NaNs with 0s to avoid data information loss"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data Dimensions\n",
"Rows 10000, Columns 35\n",
"Date Of Stop object\n",
"Time Of Stop object\n",
"Agency object\n",
"SubAgency object\n",
"Description object\n",
"Location object\n",
"Latitude float64\n",
"Longitude float64\n",
"Accident object\n",
"Belts object\n",
"Personal Injury object\n",
"Property Damage object\n",
"Fatal object\n",
"Commercial License object\n",
"HAZMAT object\n",
"Commercial Vehicle object\n",
"Alcohol object\n",
"Work Zone object\n",
"State object\n",
"VehicleType object\n",
"Year float64\n",
"Make object\n",
"Model object\n",
"Color object\n",
"Violation Type object\n",
"Charge object\n",
"Article object\n",
"Contributed To Accident object\n",
"Race object\n",
"Gender object\n",
"Driver City object\n",
"Driver State object\n",
"DL State object\n",
"Arrest Type object\n",
"Geolocation object\n",
"dtype: object\n",
"NULL values for each column\n",
" Column name\n",
"Date Of Stop 0\n",
"Time Of Stop 0\n",
"Agency 0\n",
"SubAgency 0\n",
"Description 0\n",
"Location 0\n",
"Latitude 770\n",
"Longitude 770\n",
"Accident 0\n",
"Belts 0\n",
"Personal Injury 0\n",
"Property Damage 0\n",
"Fatal 0\n",
"Commercial License 0\n",
"HAZMAT 0\n",
"Commercial Vehicle 0\n",
"Alcohol 0\n",
"Work Zone 0\n",
"State 0\n",
"VehicleType 0\n",
"Year 74\n",
"Make 0\n",
"Model 0\n",
"Color 133\n",
"Violation Type 0\n",
"Charge 0\n",
"Article 323\n",
"Contributed To Accident 0\n",
"Race 0\n",
"Gender 0\n",
"Driver City 11\n",
"Driver State 0\n",
"DL State 8\n",
"Arrest Type 0\n",
"Geolocation 770\n",
"NULL values % for each column\n",
" Column name\n",
"Date Of Stop 0.00\n",
"Time Of Stop 0.00\n",
"Agency 0.00\n",
"SubAgency 0.00\n",
"Description 0.00\n",
"Location 0.00\n",
"Latitude 7.70\n",
"Longitude 7.70\n",
"Accident 0.00\n",
"Belts 0.00\n",
"Personal Injury 0.00\n",
"Property Damage 0.00\n",
"Fatal 0.00\n",
"Commercial License 0.00\n",
"HAZMAT 0.00\n",
"Commercial Vehicle 0.00\n",
"Alcohol 0.00\n",
"Work Zone 0.00\n",
"State 0.00\n",
"VehicleType 0.00\n",
"Year 0.74\n",
"Make 0.00\n",
"Model 0.00\n",
"Color 1.33\n",
"Violation Type 0.00\n",
"Charge 0.00\n",
"Article 3.23\n",
"Contributed To Accident 0.00\n",
"Race 0.00\n",
"Gender 0.00\n",
"Driver City 0.11\n",
"Driver State 0.00\n",
"DL State 0.08\n",
"Arrest Type 0.00\n",
"Geolocation 7.70\n"
]
}
],
"source": [
"#############################\n",
"# 1. QUICK OVERVIEW OF DATA\n",
"# 2. DATAFRAME PROPERTIES\n",
"# 3. CLEAN DATA OF NAN/NULL VALUES\n",
"#############################\n",
"print (\"Data Dimensions\")\n",
"print (\"Rows %d, Columns %d\"%(df.shape))\n",
"print (df.dtypes)\n",
"\n",
"\n",
"print (\"NULL values for each column\")\n",
"column_nulls = pd.DataFrame(df.isnull().sum())\n",
"column_nulls.columns = ['Column name']\n",
"print (column_nulls)\n",
"\n",
"print (\"NULL values % for each column\")\n",
"column_nulls_percentage = pd.DataFrame((df.isnull().sum()/df.shape[0])*100)\n",
"column_nulls_percentage.columns = ['Column name']\n",
"print (column_nulls_percentage)\n",
"\n",
"#Filling NaNs with 0\n",
"df = df.fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"# 1. Gathering relevant data for Exploratory Data Analysis\n",
"# 2. Taking only columns which are specified in the assignment\n",
"\n",
"column_names = [\"Race\", \"Gender\", \"Driver City\", \"Driver State\", \\\n",
" \"Location\", \"Longitude\", \"Latitude\", \"Fatal\", \"Violation Type\"]\n",
"df = df[column_names]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Analysis\n",
"1. Some data analyis on understanding the **trend** of violations accross U.S\n",
"2. Plotting graphs to understand easily\n",
"3. An explanation to make understanding cleaner"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Aggregated count of Violations by RACE\n",
" Race Count\n",
"0 BLACK 3428\n",
"1 WHITE 2960\n",
"2 HISPANIC 2550\n",
"3 ASIAN 540\n",
"4 OTHER 511\n",
"5 NATIVE AMERICAN 11\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"---------------------------------------\n",
"Aggregated count of Violations by GENDER\n",
" Gender Count\n",
"0 M 7065\n",
"1 F 2932\n",
"2 U 3\n"
]
},
{
"data": {
"image/png":...