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# Pandas DataFrame | cummin method

schedule Aug 12, 2023
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Pandas `DataFrame.cummin(~)` method computes the cumulative minimum along the row or column of the source DataFrame.

# Parameters

1. `axis`link | `int` or `string` | `optional`

Whether to compute the cumulative minimum for each row or column:

Axis

Description

`0` or `"index"`

Compute the cumulative minimum of each column.

`1` or `"columns"`

Compute the cumulative minimum of each row.

By default, `axis=0`.

2. `skipna`link | `boolean` | `optional`

Whether or not to ignore `NaN`. By default, `skipna=True`.

# Return Value

A DataFrame holding the cumulative minimum of the row or column values.

# Examples

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,2,4],"B":[7,6,2],"C":[3,5,6]})df    A  B  C0  3  7  31  2  6  52  4  2  6 ```

## Cumulative minimum of each column

To compute the cumulative minimum for each column:

``` df.cummin()    A  B  C0  3  7  31  2  6  32  2  2  3 ```

## Cumulative minimum of each row

To compute the cumulative minimum for each row:

``` df.cummin(axis=1)    A  B  C0  3  3  31  2  2  22  4  2  2 ```

## Dealing with missing values

Consider the following DataFrame with a missing value:

``` df = pd.DataFrame({"A":[3,pd.np.nan,5]})df A0 3.01 NaN2 5.0 ```

By default, `skipna=True`, which means that missing values are ignored:

``` df.cummin()   # skipna=True A0 3.01 NaN2 3.0 ```

To take into account missing values:

``` df.cummin(skipna=False) A0 3.01 NaN2 NaN ```

Here, notice how we end up with a `NaN` after the first `NaN`.

Published by Isshin Inada
Edited by 0 others
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