##Introduction

This data set is sourced from Wikipedia, and it shows the number of Road accidents resulting in deaths occurring in europe relative to other variables like vehicle ownership, population density, country etc.

Installation

Installation Requirements

• Python >= 3.9
• bs4(BeautifulSoup) >= 0.0.1 -pandas >= 1.3.1

For visualization only

• dash >= 1.21.0
• plotly >= 5.1.0

Getting Started

Here's a quick run of how to use this script to get the data on Road_safety_in_Europe. From Wikidpedia ###Run this app with python main.py and

1. Import the libraries

from bs4 import BeautifulSoup
import pandas as pd
import requests

2.Get the Data from Wikipedia using Html requests and bs4(BeautifulSoup)

response = requests.get('https://en.wikipedia.org/wiki/Road_safety_in_Europe')
url = response.text
soup = BeautifulSoup(url, 'html.parser')

table = soup.find(class_='wikitable sortable')

3. Convert the data into a dataFrame

df = pd.read_html(table.prettify())			

4.Transform and clean the data Eliminate columns not used and change column names and create a new dataframe

new_df = df[0].rename(columns={
    'Country': 'Country',
    'Area  (thousands of km  2  )  [24]': 'Area',
    'Population in 2018  [25]': 'Population',
    'GDP per capita in 2018  [26]': 'GDP per capita',
    'Population density  (inhabitants per km  2  ) in 2017  [27]': 'Population density',
    'Vehicle ownership  (per thousand inhabitants) in 2016  [28]': 'Vehicle ownership',
    'Total Road Deaths in 2018  [30]': 'Total road deaths',
    'Road deaths  per Million Inhabitants in 2018  [30]': 'Road deaths per Million Inhabitants',

})

new_df.drop(['Road Network Length  (in km) in 2013  [29]',
             'Number of People Killed  per Billion km  [30]',
             'Number of Seriously Injured in 2017/2018  [30]'],
            axis=1, inplace=True)

5. Create and insert the year column

year_column = [2018 for i in range(29)]
new_df.insert(1, 'Year', year_column)

6. Sort the data

sorted_df = new_df.sort_values(by='Road deaths per Million Inhabitants')

7. Convert the new dataframe to a csv file

# Converts the dataframe to csv
sorted_df.to_csv("myfile.csv", index=False)

##Lets Visualize the data with dash(use the viuslize script)

###Run this app with python visualize.py and ####visit http://127.0.0.1:8050/ in your web browser.

1. Import the libraries

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.express as px
import pandas as pd

2. Initialize the dash app

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

3. Get the data frame and plot it

df = pd.read_csv('myfile.csv')
df.drop(eu_index, inplace = True)

fig = px.pie(df2, values='Road deaths per Million Inhabitants', names='Country', title='Road deaths per Million Inhabitants')


app.layout = html.Div(children=[
    html.H1(children='Hello Dash'),

    html.Div(children='''
        Dash: A web application framework for Python.
    '''),
app.layout = html.Div(
    children=[
        html.H1(children='European Union Road Safety Visualizations'),
        html.Div(children='For the Challenge'),
        doc.Graph(
            id='chart',
            figure=fig
        )
    ]
)

4. Run the app

if __name__ == '__main__':
    app.run_server(debug=True)

Now you have the proportion of road mortatility for each european country in the data set. You can edit it to get more charts, view mine here Data visualizations for Road_safety_in_Europe.