Summary  
This chapter explains how to use the pandas melt() function to reshape tabular data from wide format to long format by unpivoting specified columns into a variable column and a value column.  

General domain of usage  
Data analysis

When working with data in pandas, you often encounter DataFrames in a wide format, where each variable is in its own column. However, for many analytical tasks — such as plotting, aggregation, or working with certain libraries — you need your data in a long format, where variables are stored in a single column and their values in another. The process of converting wide-format data to long-format is called **melting**. The `melt()` function in pandas is a powerful tool for this transformation, making it easier to tidy your data and prepare it for further analysis.

import pandas as pd

# Sample wide-format DataFrame
df = pd.DataFrame({
    "Name": ["Alice", "Bob"],
    "Math": [85, 90],
    "Science": [92, 88]
})

# Melt the DataFrame to long format
long_df = pd.melt(
    df,
    id_vars=["Name"],
    value_vars=["Math", "Science"],
    var_name="Subject",
    value_name="Score"
)

print(long_df)

The `melt()` function offers several parameters to control how your DataFrame is reshaped:

- **`id_vars`**: columns to keep fixed (not unpivoted); these often serve as identifiers, like names or IDs;
- **`value_vars`**: columns to unpivot, which become variable entries in the new long-format DataFrame;
- **`var_name`**: name for the new column that will hold the names of the unpivoted columns; if not specified, defaults to "variable";
- **`value_name`**: name for the new column that will hold the values from the unpivoted columns; if not specified, defaults to "value".

By carefully selecting these parameters, you can reshape your data exactly as needed for your analysis tasks.

Which parameter of the pandas `melt()` function specifies the columns to keep fixed and not unpivoted?