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Pandas DataFrame | cummin method

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

Parameters

1. axislink | 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. skipnalink | 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  C
0  3  7  3
1  2  6  5
2  4  2  6

Cumulative minimum of each column

To compute the cumulative minimum for each column:

df.cummin()
   A  B  C
0  3  7  3
1  2  6  3
2  2  2  3

Cumulative minimum of each row

To compute the cumulative minimum for each row:

df.cummin(axis=1)
   A  B  C
0  3  3  3
1  2  2  2
2  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
A
0 3.0
1 NaN
2 5.0

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

df.cummin()   # skipna=True
A
0 3.0
1 NaN
2 3.0

To take into account missing values:

df.cummin(skipna=False)
A
0 3.0
1 NaN
2 NaN

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

robocat
Published by Isshin Inada
Edited by 0 others
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