Unstacking a Series in pandas: Reshaping MultiIndex Data

Requirements

• The Series must have a MultiIndex. A MultiIndex is a special type of index that has multiple levels.

What it Does

• Values in the original Series are placed in the corresponding cells of the DataFrame.
• The remaining levels in the index become the new row index of the DataFrame.
• Takes one level (or multiple levels) from the MultiIndex and converts them into columns in the resulting DataFrame.

Benefits

• Provides a more tabular view of the data for better visualization and manipulation.
• Makes it easier to analyze data with categorical variables in multiple dimensions.

Using unstack

• For example, series.unstack(level=0) would unstack the first level of the MultiIndex.
• Optionally, you can specify the level(s) of the index to unstack by name or position.
• You can call unstack() on your Series directly.

Important Note

• If the Series doesn't have a MultiIndex, unstack() will simply return the original Series (similar to how stack() works on a DataFrame without a MultiIndex).

Here are some resources for further exploration:

• Example of using unstack with code: [Stack Overflow Pandas How to unstack a series python ON Stack Overflow stackoverflow.com]
• Pandas documentation on Series.unstack: [pandas Series unstack ON PyData | pandas.pydata.org]

---

import pandas as pd

# Create a sample Series with MultiIndex
data = {'City': ['New York', 'New York', 'Chicago', 'Chicago', 'Los Angeles'],
        'Product': ['Apple', 'Orange', 'Apple', 'Banana', 'Orange'],
        'Sales': [100, 150, 200, 120, 80]}

df = pd.DataFrame(data)
series = df.set_index(['City', 'Product'])['Sales']

# Unstack the Series (default: unstacks the last level)
unstacked_df = series.unstack()

print(unstacked_df)

This code will output a DataFrame like this:

       Apple  Banana  Orange
City                          
Chicago   200     120     NaN
Los Angeles   NaN      NaN      80
New York   100      NaN     150

As you can see, the Product level (which was originally in the index) has become columns in the DataFrame. The City level remains in the row index.

# Unstack the 'City' level (level 0)
unstacked_df_city = series.unstack(level=0)

print(unstacked_df_city)

This will output a DataFrame with "City" as columns and "Product" as the row index:

          Sales
Product         
Apple  Chicago   200
        Los Angeles   NaN
        New York   100
Banana  Chicago    120
        Los Angeles   NaN
        New York     NaN
Orange Chicago     NaN
        Los Angeles   80
        New York    150

---

1. pivot_table
   This function is more versatile and can be used on both DataFrames and Series with MultiIndex. It provides more control over the transformation, allowing you to specify values, index, and columns for the resulting DataFrame.

import pandas as pd

# ... (same data creation as previous example)

pivoted_df = pd.pivot_table(series, index='City', columns='Product', values='Sales')

print(pivoted_df)

This will produce the same DataFrame as the default unstack behavior.

1. groupby with aggregation
   This approach is useful if you want to perform additional aggregations (like sum, mean, count) besides just reshaping the data.

grouped_df = series.groupby('City').agg(Sales=('Sales', 'sum'))

print(grouped_df)

This will create a DataFrame with "City" as the index and a column named "Sales" containing the sum of sales for each city.

1. Manual reshaping
   For simpler cases, you can achieve reshaping with conditional assignment or list comprehension. However, this is generally less efficient and less readable compared to unstack or pivot_table for complex datasets.

• Readability and maintainability
  For clarity and efficiency with standard reshaping, unstack or pivot_table are preferred.
• Additional operations
  If you need to perform calculations after reshaping, groupby with aggregation might be a good choice.
• Complexity of reshaping
  If you need more control and aggregations, pivot_table might be better.