Dropping vs. Imputing

Lesson in the Python for Data Analysis Roadmap · Stage 9: Data Cleaning

Last updated 14 September 2026

What is Dropping vs. Imputing?

When handling missing values, dropping removes rows or columns with dropna(), while imputing fills them with a substitute value using fillna() or SimpleImputer from Scikit-learn. Dropping is appropriate when missing data is rare or random. Imputing is preferred when data is valuable or missing systematically, using the mean, median, mode, or a predicted value.

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Free resources to learn Dropping vs. Imputing

Official docspandas.DataFrame.fillna
ArticlePandas DataFrame fillna() Method
VideoPython Pandas Tutorial 5: Handle Missing Data: fillna, dropna, interpolate

Dropping vs. Imputing: common questions

Can I learn Dropping vs. Imputing in Hindi?

This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain Dropping vs. Imputing in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.

Is this Dropping vs. Imputing tutorial for beginners?

It is lesson 65 of 112 in the free Python for Data Analysis roadmap, in stage 9, Data Cleaning. If you are new to Python for Data Analysis, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.