Summary  
This chapter explains how to reshape tabular data from a wide format to a long format by unpivoting specified columns into key–value pairs while keeping identifier columns fixed, using parameters to name the new variable and value fields.  

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?

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