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Case Study: WDI

Updated Nov 11, 2019 ·

Overview​

In this case study, we'll use Python functions, iterators, and list comprehensions to analyze World Bank's World Development Indicators dataset. The dataset includes global economic and social indicators over several decades.

  • Covers 217 countries from 1960 to 2015
  • Includes indicators like population, electricity use, and CO2 emissions
  • Tracks literacy rates, unemployment, and mortality

Objectives​

  • Work with Dictionaries

    • Store and retrieve country-level data efficiently
    • Use keys for quick lookups of indicators

  • Use List Comprehensions

    • Extract specific indicators in a single line
    • Improve readability and performance

  • Apply Custom Functions

    • Automate data transformations
    • Handle missing values and format data consistently

Jupyter Notebook​

To access the Jupyter notebook, please see Case Study: World Development Indicators

To open the notebook in Google Colab, please see Google Colab: Case Study: World Development Indicators