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Intro to Data Science · Working with Data · Lesson 4 of 12

Cleaning Missing Data

Concept

Cleaning Missing Data

Real datasets often have missing values. dropna() removes them, and fillna() replaces them with a default.

By the end of this lesson
  • Explain the core idea behind Cleaning Missing Data.
  • Predict output before running a change.
  • Test one realistic and one unusual input.
  • Use the result to make the next decision.
How to study this page
  1. 1. Read one concept.
  2. 2. Change the example.
  3. 3. Run, compare, and explain.
  4. 4. Complete the challenge below.
ExampleRunnable
df_clean = df.dropna()
df_filled = df.fillna(0)
Try it Yourself »
Self-check before continuing

Without looking at the example, describe what changes when you modify one input in Cleaning Missing Data. Then reopen the editor and prove your explanation with a small test.

You are ready to continue when you can predict, test, and explain the result.

Your turn

Explain a situation where fillna(0) would be misleading for a dataset.

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Console

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