Wide to long format conversion in Pandas - Step-by-Step Execution

Concept Flow - Wide to long format conversion

Start with wide DataFrame

↓

Select columns to keep as id_vars

↓

Select columns to melt into long format

↓

Apply pd.melt() function

↓

Create long format DataFrame

↓

Use or visualize long DataFrame

Convert a wide table with many columns into a longer table with fewer columns by melting selected columns.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Math': [90, 80],
    'English': [85, 88]
})

long_df = pd.melt(df, id_vars=['Name'], var_name='Subject', value_name='Score')

This code converts a wide DataFrame with subjects as columns into a long DataFrame with one subject column and one score column.

Execution Table

Step	Action	DataFrame State	Result
1	Create wide DataFrame	{'Name': ['Alice', 'Bob'], 'Math': [90, 80], 'English': [85, 88]}	DataFrame with 3 columns and 2 rows
2	Select id_vars=['Name']	Columns to keep: ['Name']	Name column stays as is
3	Select value_vars=['Math', 'English']	Columns to melt: ['Math', 'English']	These columns will be converted to rows
4	Apply pd.melt()	Melt columns into long format	DataFrame with columns: Name, Subject, Score
5	Resulting long DataFrame	Rows: 4 (2 names x 2 subjects)	DataFrame: Name Subject Score Alice Math 90 Bob Math 80 Alice English 85 Bob English 88
6	End	No further changes	Conversion complete

💡 All specified columns melted; long format DataFrame created

Variable Tracker

Variable	Start	After Step 1	After Step 4	Final
df	undefined	{'Name': ['Alice', 'Bob'], 'Math': [90, 80], 'English': [85, 88]}	Same wide DataFrame	Same wide DataFrame
long_df	undefined	undefined	Long format DataFrame with columns Name, Subject, Score	Same long format DataFrame

Key Moments - 2 Insights

Why do we need to specify id_vars in pd.melt()?

id_vars are columns to keep as identifiers and not melt. Without them, all columns would be melted, losing the original row identity. See execution_table step 2 and 3.

What happens if we don't specify var_name and value_name?

pd.melt() uses default column names 'variable' and 'value' for the melted columns. Specifying var_name and value_name gives meaningful names. See execution_table step 4.

Visual Quiz - 3 Questions

Look at the execution table at step 5, how many rows does the long DataFrame have?

A 2

B 3

C 4

D 6

💡 Hint

Check step 5 in execution_table: 2 names times 2 subjects equals 4 rows.

At which step do we specify which columns to keep as identifiers?

A Step 1

B Step 2

C Step 4

D Step 5

💡 Hint

See execution_table step 2 where id_vars=['Name'] is selected.

If we omit id_vars in pd.melt(), what happens to the 'Name' column?

A It becomes part of the melted variable column

B It is dropped from the DataFrame

C It stays as a column

D It becomes the index

💡 Hint

Without id_vars, all columns are melted, so 'Name' becomes part of the variable column (see key_moments question 1).

Concept Snapshot

pd.melt() converts wide to long format.
Use id_vars to keep columns as identifiers.
Use var_name and value_name to name melted columns.
Resulting DataFrame has fewer columns and more rows.
Useful for tidy data and plotting.

Full Transcript

We start with a wide DataFrame that has one row per person and multiple columns for subjects. We want to convert it to a long format where each row is one person-subject pair with a score. We keep the 'Name' column as an identifier using id_vars. We melt the subject columns into two columns: one for subject names and one for scores. The pd.melt() function does this conversion. The result is a longer DataFrame with columns Name, Subject, and Score. This format is easier for analysis and plotting. Key points are to specify id_vars to keep identifiers and optionally name the new columns with var_name and value_name.