Importing and Cleaning Data

In this week’s workshop, we will focus on importing data from various file formats and cleaning data to prepare it for analysis. Clean data is critical for any data science workflow. We will use the Pandas library for most of the tasks.

Introduction

Live coding

Review the material that we explored in Week 4’s live-coding session.

Lab

Exercise: Clean Gapminder Dataset

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Your task is to clean and prepare the Gapminder dataset for analysis:

1. Load the gapminder.csv into a DataFrame.
2. Inspect the data using:
   • head()
   • info()
   • describe()
3. Rename columns:
   • Rename gdpPercap to GDP_per_capita.
4. Handle missing values:
   • Drop rows where lifeExp is missing.
   • Fill missing pop values with median population.
5. Filter the dataset to include only data for:
   • Nigeria, Ghana, Kenya, and South Africa.
   • Years 2000 and above.
6. Export the cleaned dataset to gapminder_cleaned.csv.
7. Commit and push your work to GitHub.

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Further reading

• O’Reilly: McKinney, W. (2022). Python for Data Analysis. O’Reilly Media. https://www.oreilly.com/library/view/python-for-data/9781098104023/
• Pandas Official Documentation: Working with Missing Data. https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html