How to Split a Large Pandas DataFrame into Multiple Groups with Uneven Divisions Using np.array_split?

How to Split a Large Pandas DataFrame into Multiple Groups with np.array_split

When dealing with massive dataframes, it can be necessary to split them into smaller, more manageable chunks. This allows for more efficient processing and analysis. One method for splitting dataframes is to use the np.split() function. However, this function can encounter issues when the dataframe is not evenly divisible by the desired number of splits.

A more suitable alternative for this situation is to employ the np.array_split() function. This function allows the indices_or_sections parameter to be an integer that does not equally divide the axis.

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

# Create a large dataframe
df = pd.DataFrame(...)

# Define the number of groups to split the dataframe into
n_groups = 4

# Split the dataframe using np.array_split()
dataframe_chunks = np.array_split(df, n_groups)

# Iterate over the dataframe chunks and print their contents
for item in dataframe_chunks:
    print(item)</code>

Additional Notes:

• The np.array_split() function takes two arguments: the dataframe to be split and the number of desired groups.
• Unlike np.split(), np.array_split() allows for uneven divisions of the dataframe.
• The returned value of np.array_split() is a list of dataframes, each representing a split part of the original dataframe.

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