cummin(self, axis=None, skipna=True, *args, **kwargs)

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters

axis : {0 or 'index', 1 or 'columns'}, default 0

The index or the name of the axis. 0 is equivalent to None or 'index'.

skipna : bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

*args, **kwargs :

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

scalar or Series

Return cumulative minimum of scalar or Series.

Return cumulative minimum over a DataFrame or Series axis.

See Also

Series.cummax

Return cumulative maximum over Series axis.

Series.cummin

Return cumulative minimum over Series axis.

Series.cumprod

Return cumulative product over Series axis.

Series.cumsum

Return cumulative sum over Series axis.

Series.min

Return the minimum over Series axis.

core.window.Expanding.min

Similar functionality but ignores NaN values.

Examples

Series

This example is valid syntax, but we were not able to check execution

>>> s = pd.Series([2, np.nan, 5, -1, 0])
... s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

This example is valid syntax, but we were not able to check execution

>>> s.cummin()
0    2.0
1    NaN
2    2.0
3   -1.0
4   -1.0
dtype: float64

To include NA values in the operation, use skipna=False

This example is valid syntax, but we were not able to check execution

>>> s.cummin(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
... df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index' .

This example is valid syntax, but we were not able to check execution

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row, use axis=1

This example is valid syntax, but we were not able to check execution

>>> df.cummin(axis=1)
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

See :

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File: /pandas/core/generic.py#10991
type: <class 'function'>
Commit: