How to use `id_vars`?

The id_vars parameter in the melt function is used to specify which columns in the DataFrame should be treated as identifier variables. These columns will remain unchanged and will be repeated for each melted row. The other columns will be melted into a single column of values.

How to Use `id_vars`

Import pandas: Make sure to import the pandas library.
Create a DataFrame: Define your DataFrame with the data you want to melt.
Use the melt function: Call the melt function on the DataFrame, specifying the id_vars parameter with the names of the columns you want to keep as identifiers.

Example

Suppose you have the following DataFrame:

import pandas as pd

# Sample DataFrame
data = {
    'Name': ['Alice', 'Bob'],
    'Math': [85, 78],
    'Science': [90, 82],
    'English': [88, 80]
}
df = pd.DataFrame(data)

# Melt the DataFrame using id_vars
melted_df = df.melt(id_vars='Name', var_name='Subject', value_name='Score')

Resulting DataFrame

The resulting melted_df will look like this:

Name	Subject	Score
Alice	Math	85
Alice	Science	90
Alice	English	88
Bob	Math	78
Bob	Science	82
Bob	English	80

Explanation

id_vars='Name': The Name column is specified as the identifier variable. It remains unchanged and is repeated for each subject.
var_name='Subject': This parameter specifies the name of the new column that will hold the names of the melted columns (Math, Science, English).
value_name='Score': This parameter specifies the name of the new column that will hold the values from the melted columns.

Summary

Using id_vars allows you to keep certain columns intact while transforming the rest of the DataFrame into a long format, making it easier to analyze and visualize the data.