1.3. Read and clean immigration data

1. Read, clean, and prepare data to build maps

2. Problem & Solution

Reading Canada Immigration data

First, let's read data on Canadian immigration from an internet data file.

import pandas as pd

import numpy as np

df = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',

sheet_name='Canada by Citizenship',

skiprows=range(20),

skipfooter=2)

This will produce the following data frame:

For this purpose will employ the command drop.

df.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage', 'AreaName', 'RegName', 'DevName'], axis=1, inplace=True)

The following result will appear:

df.rename(columns={'OdName':'Country'}, inplace=True)

This will rename the column 'OdName' as 'Country':

The next code creates a new column with the total of immigration for all years per country.

df['Total'] = df.sum(axis=1)

This will result in:

The Python code with all the steps is summarized in this Google Colab (click on the link):

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