Melt for wide-to-long reshaping in Data Analysis Python - Step-by-Step Execution

Concept Flow - Melt for wide-to-long reshaping

Start with wide DataFrame

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Select id_vars and value_vars

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Apply pd.melt()

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Create long DataFrame with columns: id_vars + variable + value

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Use long DataFrame for analysis or visualization

This flow shows how we start with a wide table, choose which columns to keep fixed (id_vars) and which to reshape (value_vars), then apply melt to get a long table.

Execution Sample

Data Analysis Python

import pandas as pd

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

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

This code reshapes a wide DataFrame of student scores into a long format with one score per row.

Execution Table

Step	Action	DataFrame Shape	DataFrame Preview
1	Create wide DataFrame	(2, 3)	[{'Name': 'Anna', 'Math': 90, 'Science': 85}, {'Name': 'Bob', 'Math': 80, 'Science': 88}]
2	Call pd.melt with id_vars=['Name']	(4, 3)	[{'Name': 'Anna', 'Subject': 'Math', 'Score': 90}, {'Name': 'Bob', 'Subject': 'Math', 'Score': 80}, {'Name': 'Anna', 'Subject': 'Science', 'Score': 85}, {'Name': 'Bob', 'Subject': 'Science', 'Score': 88}]
3	Result is long DataFrame	(4, 3)	[{'Name': 'Anna', 'Subject': 'Math', 'Score': 90}, {'Name': 'Bob', 'Subject': 'Math', 'Score': 80}, {'Name': 'Anna', 'Subject': 'Science', 'Score': 85}, {'Name': 'Bob', 'Subject': 'Science', 'Score': 88}]

💡 All wide columns except 'Name' melted into 'Subject' and 'Score' columns, reshaping complete.

Variable Tracker

Variable	Start	After pd.melt	Final
df	[{'Name': 'Anna', 'Math': 90, 'Science': 85}, {'Name': 'Bob', 'Math': 80, 'Science': 88}]	N/A	Same as start
long_df	N/A	[{'Name': 'Anna', 'Subject': 'Math', 'Score': 90}, {'Name': 'Bob', 'Subject': 'Math', 'Score': 80}, {'Name': 'Anna', 'Subject': 'Science', 'Score': 85}, {'Name': 'Bob', 'Subject': 'Science', 'Score': 88}]	Same as after pd.melt

Key Moments - 3 Insights

Why do we specify 'id_vars' in pd.melt?

The 'id_vars' columns stay fixed and identify each row in the long format. Without them, we lose the link to original rows. See execution_table step 2 where 'Name' stays as is.

What happens to columns not in 'id_vars' during melt?

They become part of two new columns: one for the original column names ('variable') and one for their values ('value'). In our example, 'Math' and 'Science' become 'Subject' and 'Score' columns (execution_table step 2).

Why does the number of rows increase after melting?

Because each original row with multiple value columns turns into multiple rows, one per value column. Here, 2 rows with 2 score columns become 4 rows (execution_table step 2).

Visual Quiz - 3 Questions

Look at the execution_table at step 2. What is the value of 'Subject' for the third row in long_df?

A Math

B Science

C Name

D Score

💡 Hint

Check the 'DataFrame Preview' column in execution_table step 2; the third row has 'Subject' as 'Science'.

At which step does the DataFrame shape change from (2, 3) to (4, 3)?

A Step 2

B Step 3

C Step 1

D Never

💡 Hint

Look at the 'DataFrame Shape' column in execution_table; shape changes at step 2.

If we did not specify 'id_vars' in pd.melt, what would happen to the 'Name' column?

A It would stay as a fixed column

B It would be dropped

C It would be melted into variable and value columns

D It would cause an error

💡 Hint

Recall key_moments about 'id_vars' role; without it, all columns except index are melted.

Concept Snapshot

pd.melt reshapes wide DataFrames to long format.
Use id_vars to keep columns fixed.
value_vars are melted into variable and value columns.
Result has more rows, fewer columns.
Useful for tidy data and plotting.

Full Transcript

We start with a wide DataFrame containing student names and their scores in Math and Science. Using pd.melt, we specify 'Name' as the id_vars to keep it fixed. The other columns, 'Math' and 'Science', are melted into two new columns: 'Subject' for the original column names and 'Score' for their values. This reshapes the data from wide to long format, increasing the number of rows. The execution table shows the DataFrame shape and preview at each step. Key moments clarify why id_vars are needed and how columns transform. The visual quiz tests understanding of the reshaping process and the role of parameters.