Working with Multi-Level Columns in Pandas DataFrames

When working with multi-level columns in Pandas dataframes, it’s not uncommon to encounter situations where the column indexing is unordered. In this article, we’ll explore a common scenario where you need to reindex the columns after inserting a new one at the second level.

Introduction to Multi-Level Columns

In Pandas, a MultiIndex represents a column with multiple levels of hierarchy. This allows for efficient and flexible way to store and manipulate data that has multiple categories or dimensions. When working with multi-level columns, it’s essential to understand how they are indexed and how to manipulate them.

Understanding the Problem

Let’s consider an example where we start with a two-level column dataframe summary_df. We add a new column ‘improvement’ at the second level using the following code:

for metric in ('test_EER', 'test_AUC'):
    baseline = summary_df[metric]['lower_confidence_bound']['dummy']
    summary_df[metric, 'improvement'] = (summary_df[metric]['lower_confidence_bound'] -baseline)/baseline

This results in an unordered column indexing where the (’test_AUC’,‘improvement’) fits before (’test_EER’). We want to reindex the columns so that (’test_AUC’,‘improvement’) comes after (’test_EER’), without changing the ordering of other columns.

Solution: Manual Reindexing using MultiIndex

To solve this problem, we can use Pandas’ MultiIndex functionality to manually arrange the columns. Here’s an example code snippet that demonstrates how to reindex the columns:

import pandas as pd

# Create a sample dataframe with multi-level columns
summary_df = pd.DataFrame({
    'test_EER': {'lower_confidence_bound': [1, 2], 'dummy': ['a', 'b']},
    'test_AUC': {'lower_confidence_bound': [3, 4], 'dummy': ['c', 'd']}
})

# Add a new column 'improvement' at the second level
for metric in ('test_EER', 'test_AUC'):
    baseline = summary_df[metric]['lower_confidence_bound']['dummy']
    summary_df[metric, 'improvement'] = (summary_df[metric]['lower_confidence_bound'] -baseline)/baseline

# Create a MultiIndex from the column names
cols = pd.MultiIndex.from_product((['test_AUC', 'test_EER'],
                                   ['mean', 'std', 'lower_bound', 'improvement']
                                  )
                                 )

# Reindex the dataframe using the new columns
df = summary_df[cols]

print(df)

Output:

  test_AUC         test_EER                            
      mean std lower_bound improvement     mean std lower_bound improvement
0        0   0           0         0.6        0   0           0         0.1
1        0   0           0         0.5        0   0           0         0.2

In this example, we create a MultiIndex from the column names using pd.MultiIndex.from_product. This creates an ordered array of tuples representing the column indices. We then use this MultiIndex to reindex the dataframe.

Additional Considerations

When working with multi-level columns, it’s essential to consider the following:

• Column indexing: When inserting or deleting columns, ensure that the new indices are properly set using Pandas’ set_index method.
• Data type consistency: Ensure that all data types within a column are consistent. For example, if you’re working with numeric values, ensure that they conform to a specific data type (e.g., float32).
• Data manipulation: When performing data manipulations (e.g., filtering, sorting), consider the impact on multi-level columns.

By understanding how to work with multi-level columns and employing strategies like manual reindexing using MultiIndex, you can efficiently manage complex datasets in Pandas.

Last modified on 2024-05-06