Understanding the Performance Warning: DataFrame is Highly Fragmented

When working with DataFrames in pandas, it’s not uncommon to encounter performance warnings related to fragmentation. In this post, we’ll delve into what causes this warning and provide solutions using the rank method and concat.

Introduction

DataFrames are a powerful data structure in pandas that allow us to easily manipulate and analyze tabular data. However, when dealing with large DataFrames, performance issues can arise due to fragmentation.

Fragmentation occurs when individual columns of a DataFrame are inserted or updated multiple times, causing the underlying storage to become fragmented. This can lead to slower performance and even crashes in extreme cases.

The Warning Message

The warning message provided by pandas is straightforward:

PerformanceWarning: DataFrame is highly fragmented.  This is usually
the result of calling `frame.insert` many times, which has poor
performance.  Consider using pd.concat instead.  To get a
de-fragmented frame, use `newframe = frame.copy()`

This warning indicates that the DataFrame has become fragmented due to repeated insertions or updates.

The Problem: Creating Multiple Rankings

The example code provided illustrates how fragmentation occurs when creating multiple rankings:

for x in range(1, num_sims + 1):
    ranking[x] = df[x].rank(ascending=False, method='min')

In this code snippet, we’re creating a new DataFrame ranking by ranking each column of the original DataFrame df. This process is repeated for multiple columns.

The Solution: Using rank and concat

To avoid fragmentation, pandas suggests using the rank method directly on the original DataFrame instead of inserting or updating individual columns. One way to achieve this is by concatenating all columns at once:

ranking = pd.concat([df[range(1, num_sims + 1)].rank(ascending=False, method='min')], axis=1)

This approach creates a new DataFrame with the ranked values for all specified columns.

Alternative Solution: Using rank Directly

Another solution is to use the rank method directly on the original DataFrame without creating multiple intermediate DataFrames:

ranking = df[range(1, num_sims + 1)].rank(ascending=False, method='min')

This approach avoids fragmentation altogether.

Sanity Check: Comparing Results

To ensure that both approaches produce the same results, we can compare them using NumPy’s array equality test:

import numpy as np
import pandas as pd

np.random.seed(5)
df = pd.DataFrame(np.random.randint(1, 100, (4, 5000)))
df.columns = df.columns + 1
ranking = pd.DataFrame()
num_sims = len(df.columns)

for x in range(1, num_sims + 1):
    ranking[x] = df[x].rank(ascending=False, method='min')

print(ranking.eq(pd.concat(
    [pd.DataFrame(),
     df[range(1, num_sims + 1)].rank(ascending=False, method='min')],
    axis=1
)).all(axis=None))  # True

This sanity check confirms that both approaches produce identical results.

Best Practices

To avoid fragmentation when working with DataFrames:

Use the rank method directly on the original DataFrame instead of inserting or updating individual columns.
Concatenate all columns at once using pd.concat.
Create a copy of the DataFrame before making changes to prevent unintended modifications.

Last modified on 2023-10-05