Intermediate level · Module 2: Data Wrangling with pandas and NumPy · Lesson 1 of 3
Tidy Data and Reshaping
Organise data so each variable is a column and each observation a row, and convert between wide and long formats.
In this lesson you will
- Explain tidy data principles
- Reshape with melt and pivot_table
Key concepts
tidy datawide vs long formatmeltpivot_table
The lesson
Tidy data has one variable per column, one observation per row and one value per cell. Most analysis and plotting tools expect it.
Reports often come wide: one column per month. melt turns them long — school, month, attendance — which makes grouping and charting by month easy.
pivot_table does the reverse, for example to present a table of schools by month, and can aggregate while it reshapes.
Step by step
- Identify untidy features in the attendance file.
- Melt monthly columns into long format.
- Summarise attendance by month.
- Pivot back into a presentation table.
Practice activity
Convert the attendance records into tidy format and produce a monthly summary table.
Expected result: Tidy data and a correct monthly table.
Check your understanding
-
What does melt do?
Show answer
Turns wide columns into long rows.
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Name one principle of tidy data.
Show answer
Each variable has its own column (or each observation its own row).
Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.