Pandas DataFrame.reorder_levels() method (5 examples)

Introduction

Working with multi-index or hierarchical indices in pandas DataFrames introduces a complex structure that can often require reordering of levels for better data manipulation and analysis. The DataFrame.reorder_levels() method in pandas is a powerful tool for rearranging the order of levels in such DataFrames. This tutorial will guide you through five practical examples, starting from basic to more advanced usage, to help you understand how to effectively use reorder_levels().

Understanding MultiIndex DataFrame

Before diving into the examples, let’s briefly understand what a MultiIndex DataFrame is. A MultiIndex DataFrame has an index that consists of multiple levels, enabling more complicated data arrangements. This is particularly useful for representing high-dimensional data compactly.

Preparation

Let’s create a sample DataFrame with MultiIndex to work with in the upcoming examples:

import pandas as pd
import numpy as np

data = {'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
        'score': [85, 88, 92, 85, 91],
        'subject': ['Math', 'Science', 'English', 'History', 'Physics']}
df = pd.DataFrame(data)

df = df.set_index(['subject', 'name'])
df = df.sort_index()

print(df)

Output:

                 score
subject name          
English Charlie     92
History David       85
Math    Alice       85
Physics Eve         91
Science Bob         88

Example 1: Basic Reordering

In this basic example, we’ll start with a simple DataFrame with a MultiIndex and reorder the levels.

# Initial order
print(df.index)

# Reordering index levels
reordered_df = df.reorder_levels(['name', 'subject'])
print(reordered_df.index)

Output:

MultiIndex([('English', 'Charlie'),
            ('History',   'David'),
            (   'Math',   'Alice'),
            ('Physics',     'Eve'),
            ('Science',     'Bob')],
           names=['subject', 'name'])

MultiIndex([('Charlie', 'English'),
            (  'David', 'History'),
            (  'Alice',    'Math'),
            (    'Eve', 'Physics'),
            (    'Bob', 'Science')],
           names=['name', 'subject'])

After reordering, the DataFrame index levels are now rearranged, which allows for different perspectives on the dataset.

Example 2: Reordering with sort

When reordering levels, it’s often useful to sort the data to maintain a logical order. This example demonstrates how to reorder and then sort the DataFrame.

reordered_df = df.reorder_levels(['name', 'subject']).sort_index()
print(reordered_df)

Output:

                 score
name    subject       
Alice   Math        85
Bob     Science     88
Charlie English     92
David   History     85
Eve     Physics     91

Example 3: Reordering in Multi-Dimensional Data

Note: This example doesn’t use the same DataFrame as the previous ones.

As data becomes more complex, the ability to reorder index levels efficiently becomes crucial. Here, we’ll work with a DataFrame that represents a more complicated structure.

import pandas as pd
import numpy as np

# Example dataset on sales
sales_data = {
    "Year": [2020, 2021, 2021, 2020, 2021],
    "Quarter": ["Q1", "Q2", "Q3", "Q4", "Q1"],
    "Product": ["A", "B", "C", "D", "A"],
    "Sales": [250, 300, 150, 200, 400],
}
sales_df = pd.DataFrame(sales_data)
sales_df = sales_df.set_index(["Year", "Quarter", "Product"]).sort_index()

# Reordering levels for a more intuitive analysis
reordered_sales_df = sales_df.reorder_levels(["Product", "Year", "Quarter"])
print(reordered_sales_df.index)

Output:

MultiIndex([('A', 2020, 'Q1'),
            ('D', 2020, 'Q4'),
            ('A', 2021, 'Q1'),
            ('B', 2021, 'Q2'),
            ('C', 2021, 'Q3')],
           names=['Product', 'Year', 'Quarter'])

Example 4: Applying reorder_levels() in GroupBy Operations

Note: This example uses the same DataFrame as Example #1 and Example #2.

GroupBy operations are integral for data analysis, and reordering levels post-grouping can provide additional insights. This example shows how to apply reorder_levels() after a GroupBy operation.

grouped_df = df.groupby(['subject', 'name']).mean()
reordered_grouped_df = grouped_df.reorder_levels(['name', 'subject'])
print(reordered_grouped_df)

Output:

                 score
name    subject       
Charlie English   92.0
David   History   85.0
Alice   Math      85.0
Eve     Physics   91.0
Bob     Science   88.0

Example 5: Advanced Scenario with Cross-Section

Note: This example is extended from Example #3.

In more advanced scenarios, you might want to perform cross-sections after reordering. This example explores how.

import pandas as pd

# Example dataset on sales
sales_data = {
    "Year": [2020, 2021, 2021, 2020, 2021],
    "Quarter": ["Q1", "Q2", "Q3", "Q4", "Q1"],
    "Product": ["A", "B", "C", "D", "A"],
    "Sales": [250, 300, 150, 200, 400],
}
sales_df = pd.DataFrame(sales_data)
sales_df = sales_df.set_index(["Year", "Quarter", "Product"]).sort_index()

# Reordering levels for a more intuitive analysis
reordered_sales_df = sales_df.reorder_levels(["Product", "Year", "Quarter"])

# Continuing with the reordered_sales_df from Example 3:
cross_section = reordered_sales_df.xs(key='A', level='Product', drop_level=False)
print(cross_section)

Outuput:

                      Sales
Product Year Quarter       
A       2020 Q1         250
        2021 Q1         400

Conclusion

In conclusion, the DataFrame.reorder_levels() method is an essential tool for manipulating the structure of MultiIndex DataFrames in pandas, offering flexibility in data analysis tasks. Through the examples provided, we see how it can be effectively utilized in a variety of scenarios, from basic data manipulations to more complex data structures and operations. Understanding how to use this method efficiently can significantly enhance your data analysis skills.