Load the notebook with commands developed in step 2.4. (click on the link):

https://colab.research.google.com/drive/1ZxQNqyr3KssnHHIXhmxu1Q_UU7PyVQtL?usp=sharing

Country's risk site

The Numbeo site gives partial information about the risks of a country. In particular, the site provides this information within the format of a map: https://www.numbeo.com/crime/rankings_by_country.jsp

Extract the country's risk from an Excel file

The next code helps to extract data about the risk of each country, including the metrics from Numbeo site.

import pandas as pd

#https://docs.google.com/spreadsheets/d/1Z0ynhW1qJRLHmRbCHqBDGGXm_4tc-YdZ/edit?usp=sharing&ouid=106640872116257813737&rtpof=true&sd=true

url = 'https://drive.google.com/uc?export=download&id=1Z0ynhW1qJRLHmRbCHqBDGGXm_4tc-YdZ'

xls = pd.ExcelFile(url)

xls

<pandas.io.excel._base.ExcelFile at 0x7ba5ca19f8b0> 

Extracting names of the sheets

The next code helps to show the name of all available sheets.

sheet_names = xls.sheet_names

sheet_names

['Data source_country', 'Country Crime Risk Score', 'Country Tax Risk Score', 'Country Risk Score', 'Number of countries for RiskCat'] 

Extracting data from the fourth sheet

The next code extracts the information from the fourth sheet and stores it in the variable df_risk .

df_risk = pd.read_excel(xls, sheet_names[3])

df_risk

Obtaining the column's names

The next code obtains the names of the columns from the variable df_risk .

df_risk.columns

Index(['Countryname', 'Country Crime Risk Score', 'Country Tax Risk Score', '(Country Crime Risk Score+Country Tax Risk Score)/2=Country Risk Score'], dtype='object') 

Renaming the columns' names

The next code renames the names of the columns from the variable df_risk .

df_risk.reset_index(inplace=True)

df_risk.rename(columns={'Countryname': 'country', 'Country Crime Risk Score': 'Crime_Score', 'Country Tax Risk Score': 'Tax_Score', '(Country Crime Risk Score+Country Tax Risk Score)/2=Country Risk Score': 'Combined_Score'},inplace=True)

df_risk