Summary  
This chapter covers using pandas’ unstack() method to reshape a MultiIndex Series or DataFrame by pivoting a specified index level into columns, converting long-form data into a wide format and allowing you to choose which level to unstack.

General domain of usage  
Data analysis

The `unstack()` method in pandas is a powerful tool for reshaping data, especially when working with DataFrames or Series that use a **MultiIndex**. You can use `unstack()` to pivot one of the row index levels into column labels, effectively reversing the transformation performed by `stack()`. This operation is particularly useful when you want to convert a long-form table (with multiple index levels) into a wide-form table (with columns for each unique value of a specific index level).

import pandas as pd

# Create a Series with a MultiIndex
index = pd.MultiIndex.from_tuples(
    [('A', 2022), ('A', 2023), ('B', 2022), ('B', 2023)],
    names=['company', 'year']
)
sales = pd.Series([100, 120, 90, 110], index=index, name="sales")

# Unstack the 'year' level to columns
df_unstacked = sales.unstack()
print(df_unstacked)

The `level` parameter in `unstack()` determines which index level will be moved from the row index into the columns. By default, `unstack()` will use the innermost index level, but you can specify any level by its name or integer position. This flexibility allows you to reshape your data in the way that best fits your analysis. For example:

- Unstacking the **outer index level** creates columns for each unique value of that level, while keeping the remaining levels as the row index;
- Unstacking the **innermost index level** (the default) pivots only that level to columns.

Always choose the `level` that matches your desired table structure for analysis.

What is the primary purpose of the `unstack()` method in pandas?