How to Split a Large Pandas Dataframe into Multiple Parts When the Number of Rows is Not Evenly Divisible?

Splitting Large Pandas Dataframes into Multiple Parts

When working with massive datasets, it often becomes necessary to split them into smaller, manageable chunks. This can improve performance, enhance memory usage, and facilitate parallel processing. In this article, we'll address an encountered issue while attempting to split a large pandas dataframe using np.split().

Understanding the Issue

The provided code snippet employed np.split() to partition a dataframe into four subgroups. However, it resulted in a ValueError due to an unequal division. This error arises when the number of elements in the dataframe is not evenly divisible by the desired number of splits.

Solution: Using np.array_split()

To overcome this challenge, we employ np.array_split(), a more versatile alternative to np.split(). As its documentation states, array_split() allows for non-equal division, making it suitable for situations like ours.

Implementation

Here's a Python code example using np.array_split() to split the dataframe into four parts:

<code class="python">import pandas as pd
import numpy as np

# Create a sample dataframe
df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
                    'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
                    'C': np.random.randn(8),
                    'D': np.random.randn(8)})

# Split the dataframe into four groups using array_split
groups = np.array_split(df, 3)

# Print the split groups
for group in groups:
    print(group)</code>

This will effectively partition the dataframe into three approximately equal-sized groups. Each group can be accessed and processed independently, addressing the initial challenge of unequal division.

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