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  1. 1Series and DataFrames
  2. 2Filtering and sorting
  3. 3GroupBy and aggregation
  4. 4Cleaning data
  5. 5Combining data
← Pandas

Lesson 5 of 5

Combining data

Watch, read the card, then practise. merge · concat

Video coming soon

The video for this lesson is being recorded.

Meanwhile, the notes below and the 60-second cards cover the same ideas.

Practise this topic
  • merge

    merge joins two tables on a shared column, like a SQL join.

    Remember: merge = join tables side by side on a key.

  • concat

    concat stacks tables on top of each other (or side by side).

    Remember: concat = stack. merge = join on a key.

Try the example in Code Lab

import pandas as pd
s = pd.DataFrame({"name": ["Asha", "Ravi", "Karan"], "dept_id": [10, 20, 99]})
d = pd.DataFrame({"dept_id": [10, 20], "dept": ["CSE", "ECE"]})
print(len(pd.merge(s, d, on="dept_id")), len(pd.merge(s, d, on="dept_id", how="left")))
← Cleaning data

In this lesson

  • merge
  • concat

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5 questions at your level. The best way to make it stick.

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