Let's begin by looking at the steps to import an online CSV file as a pandas DataFrame. In this example, we are going to use the annual population summary published by the Department of Economic and Social Affairs, United Nations, in 2015. Projected population figures towards 2100 were also included in the dataset:

import numpy as np # Python scientific computing package
import pandas as pd # Python data analysis package

# URL for Annual Population by Age and Sex - Department of Economic
# and Social Affairs, United Nations
source = "https://github.com/PacktPublishing/Matplotlib-2.x-By-Example/blob/master/WPP2015_DB04_Population_Annual.zip"

# Pandas support both local or online files 
data = pd.read_csv(source, header=0, compression='zip', encoding='latin_1') 

# Show the first five rows of the DataFrame
data.head() 

The expected output of the code is shown here:

As we progress through this chapter, we are going to integrate this population dataset with other datasets in Quandl. However, Quandl uses three-letter country codes (ISO 3166 alpha-3) to denote geographical locations; therefore we need to reformat the location names accordingly.

The pycountry package is an excellent choice for conversion of country names according to ISO 3166 standards. Similarly, pycountry can be installed through PyPI:

pip install pycountry 

Continuing the previous code example, we are going to add a new country column to the dataframe:

from pycountry import countries


def get_alpha_3(location):
    """Convert full country name to three letter code (ISO 3166 alpha-3)
 
    Args:
        location: Full location name
    Returns:
        three letter code or None if not found"""

    try:
        return countries.get(name=location).alpha_3
    except:
        return None
 
# Add a new country column to the dataframe
population_df['country'] = population_df['Location'].apply(lambda x: get_alpha_3(x))
population_df.head()

The expected output of the code is shown here: