Let’s assume,  in the same working directory as our project, we have a csv file (Population.csv) with population info per each country for the years 2013 and 2015 (Data taken from WHO website):

Country,2015,2013
Afghanistan,32526.6,30682.5
Albania,2896.7,2883.3
Algeria,39666.5,38186.1
Andorra,70.5,75.9
Angola,25022,23448.2
. . .

import csv

We can use Python’s standard library CSV to do the import:

import csv
 
fileCSV = open('Population.csv', 'rt')
data = csv.reader(fileCSV)
 
for row in data:
    print(row)

CSV library is first imported, then ‘Population.csv’ is opened to the variable ‘fileCSV’. We read the file to another variable ‘data’ using csv.reader function. Now we can use the for loop to print every row in our ‘data’ variable, which outputs as below:

['Country', '2015', '2013']
['Afghanistan', '32526.6', '30682.5']
['Albania', '2896.7', '2883.3']
['Algeria', '39666.5', '38186.1']
['Andorra', '70.5', '75.9']
['Angola', '25022', '23448.2']
. . .

read_csv

Another way is to use Pandas to import the csv file directly to DataFrame for future editing purposes:

import pandas as pd
populData = pd.read_csv('Population.csv')
populData.head()

Pandas is generally imported under alias pd. The function read_csv is used to gather the data to the variable populData. We can check the data by using .head() which returns first 5 rows of the data.