Merging & Joining

Lesson in the Python for Data Analysis Roadmap · Stage 7: Reading Local Files

Last updated 14 September 2026

What is Merging & Joining?

Pandas provides merge() and join() for combining DataFrames based on common columns or indices. Merge supports inner, left, right, and outer joins, mirroring SQL JOIN behavior. concat() stacks DataFrames vertically or horizontally. These operations are used to combine data from multiple sources into a single analysis-ready table.

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Free resources to learn Merging & Joining

Official docsMerge, join, concatenate and compare
ArticleCombining Data in pandas With merge(), .join(), and concat()
ArticlePandas: Combining Data

Merging & Joining: common questions

Can I learn Merging & Joining 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 Merging & Joining 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 Merging & Joining tutorial for beginners?

It is lesson 57 of 112 in the free Python for Data Analysis roadmap, in stage 7, Reading Local Files. 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.