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# Randomly splitting DataFrame into multiple DataFrames of equal size in Pandas

schedule Aug 10, 2023
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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 C0 1 5 91 2 6 102 3 7 113 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 C2 3 7 111 2 6 10 A B C0 1 5 93 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 C2 3 7 111 2 6 10 A B C3 4 8 12 A B C0 1 5 9 ```
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