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chevron_left Miscellaneous Cookbook
Adjusting number of rows that are printedAppending DataFrame to an existing CSV fileChecking differences between two indexesChecking if a DataFrame is emptyChecking if a variable is a DataFrameChecking if index is sortedChecking if value exists in IndexChecking memory usage of DataFrameChecking whether a Pandas object is a view or a copyConcatenating a list of DataFramesConverting a DataFrame to a listConverting a DataFrame to a SeriesConverting DataFrame to a list of dictionariesConverting DataFrame to list of tuplesCounting the number of negative valuesCreating a DataFrame using cartesian product of two DataFramesDisplaying DataFrames side by sideDisplaying full non-truncated DataFrame valuesDrawing frequency histogram of DataFrame columnExporting Pandas DataFrame to PostgreSQL tableHighlighting a particular cell of a DataFrameHighlighting DataFrame cell based on valueHow to solve "ValueError: If using all scalar values, you must pass an index"Importing BigQuery table as Pandas DataFramePlotting two columns of DataFramePrinting DataFrame on a single linePrinting DataFrame without indexPrinting DataFrames in tabular formatRandomly splitting DataFrame into multiple DataFrames of equal sizeReducing DataFrame memory sizeSaving a DataFrame as a CSV fileSaving DataFrame as Excel fileSaving DataFrame as feather fileSetting all values to zeroShowing all dtypes without truncationSplitting DataFrame into multiple DataFrames based on valueSplitting DataFrame into smaller equal-sized DataFramesWriting DataFrame to SQLite
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chevron_left Miscellaneous Cookbook
Adjusting number of rows that are printedAppending DataFrame to an existing CSV fileChecking differences between two indexesChecking if a DataFrame is emptyChecking if a variable is a DataFrameChecking if index is sortedChecking if value exists in IndexChecking memory usage of DataFrameChecking whether a Pandas object is a view or a copyConcatenating a list of DataFramesConverting a DataFrame to a listConverting a DataFrame to a SeriesConverting DataFrame to a list of dictionariesConverting DataFrame to list of tuplesCounting the number of negative valuesCreating a DataFrame using cartesian product of two DataFramesDisplaying DataFrames side by sideDisplaying full non-truncated DataFrame valuesDrawing frequency histogram of DataFrame columnExporting Pandas DataFrame to PostgreSQL tableHighlighting a particular cell of a DataFrameHighlighting DataFrame cell based on valueHow to solve "ValueError: If using all scalar values, you must pass an index"Importing BigQuery table as Pandas DataFramePlotting two columns of DataFramePrinting DataFrame on a single linePrinting DataFrame without indexPrinting DataFrames in tabular formatRandomly splitting DataFrame into multiple DataFrames of equal sizeReducing DataFrame memory sizeSaving a DataFrame as a CSV fileSaving DataFrame as Excel fileSaving DataFrame as feather fileSetting all values to zeroShowing all dtypes without truncationSplitting DataFrame into multiple DataFrames based on valueSplitting DataFrame into smaller equal-sized DataFramesWriting DataFrame to SQLite
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Randomly splitting DataFrame into multiple DataFrames of equal size in Pandas

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Miscellaneous Cookbook
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Consider the following DataFrame:

df = pd.DataFrame({"A":[1,2,3,4],"B":[5,6,7,8],"C":[9,10,11,12]})
df
A B C
0 1 5 9
1 2 6 10
2 3 7 11
3 4 8 12

Solution

To randomly split df into two DataFrames of equal size:

df_shuffled = df.sample(frac=1)
df_splits = np.array_split(df_shuffled, 2)
for df in df_splits:
display(df)
A B C
2 3 7 11
1 2 6 10
A B C
0 1 5 9
3 4 8 12

Note the following:

  • we first use DataFrame's sample(~) method to randomly shuffle the rows. The frac=1 means we want all rows returned.

  • we then use NumPy's array_split(~,2) method to split the DataFrame into 2 equally sized sub-DataFrames. The return type is a list of DataFrames.

Case when equally-sized DataFrame is not possible

When the number of splits do not evenly divide the number of rows, then the resulting DataFrames will not all be of equal size:

df_shuffled = df.sample(frac=1)
df_splits = np.array_split(df_shuffled, 3)
for df in df_splits:
display(df)
A B C
2 3 7 11
1 2 6 10
A B C
3 4 8 12
A B C
0 1 5 9
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Published by Isshin Inada
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