Python

Merging, Joining & Reshaping

Combine multiple datasets like a SQL Pro. Learn Merge, Join, Concat, Pivot, and Melt.

By TechCoder TeamLast updated: 2026-06-02
In a Nutshell

Combine multiple datasets like a SQL Pro. Learn Merge, Join, Concat, Pivot, and Melt. This hands-on tutorial focuses on practical implementation of merging, joining & reshaping concepts.

Module 7: Merging, Joining & Reshaping

Data often lives in multiple tables. Pandas gives you SQL-like powers to join them together and reshape the structure for analysis.

Lesson 15: Combining DataFrames

1. pd.concat()

Stacks DataFrames on top of each other (like appending rows).

df_combined = pd.concat([df1, df2])

2. pd.merge()

The most powerful join function. It links rows based on a common key (column).

  • how='inner': Only matches (Default).
  • how='left': Keep all rows from left table.
  • how='outer': Keep all rows from both tables.
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Lesson 16: Reshaping Data

Sometimes you need to rotate your data from clear to wide format (Pivot) or wide to long (Melt).

1. pivot_table()

Excel users know this well. It aggregates data.

df.pivot_table(index='Date', columns='City', values='Temp', aggfunc='mean')

2. melt()

un-pivots data. Turns "wide" tables into "long" tables (better for analysis).

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Practice: Data Architect

Challenge:

  1. Create a customers DataFrame with ID and Name.
  2. Create a purchases DataFrame with CustomerID and Product.
  3. Perform a Left Join to show ALL customers and their purchases (if any). Customers with no purchases should show NaN.

Quiz

Question 1 of 5

Which parameter controls the type of join (inner, left, outer)?

join_type
mode
how
method

Key Takeaways

✅ pd.merge is your SQL JOIN equivalent.
✅ pivot_table is essential for summarizing data.
✅ concat is for sticking tables together vertically.