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# Splitting Pandas DataFrame based on column values

schedule Aug 12, 2023
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Consider the following DataFrame:

``` import pandas as pddf = pd.DataFrame({'A':[5,6,7,8,9]})df A0 51 62 73 84 9 ```

# Solution

To split a Pandas DataFrame based on column values:

``` mask = df['A'] > 7df1 = df[mask]df2 = df[~mask] ```

`df1` is:

``` df1 A3 84 9 ```

`df2` is:

``` df2 A0 51 62 7 ```

# Explanation

We first build a mask where `True` means the condition is satisfied, and `False` means condition is not satisfied:

``` mask = df['A'] > 7mask # Series 0 False1 False2 False3 True4 TrueName: A, dtype: bool ```

We then use the `[~]` syntax to extract rows that correspond to `True`:

``` df[mask] A3 84 9 ```

To get the other DataFrame with rows that do not satisfy the condition, use `~` to negate the mask:

``` ~mask A0 51 62 7 ```
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