pandas.Series.cummin¶

Series.cummin(axis=None, skipna=True, *args, **kwargs)[source]

Return cumulative minimum over a DataFrame or Series axis.

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 : boolean, 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. cummin : scalar or Series

pandas.core.window.Expanding.min
Similar functionality but ignores NaN values.
Series.min
Return the minimum over Series axis.
Series.cummax
Return cumulative maximum over Series axis.
Series.cummin
Return cumulative minimum over Series axis.
Series.cumsum
Return cumulative sum over Series axis.
Series.cumprod
Return cumulative product over Series axis.

Examples

Series

>>> 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.

>>> 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

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


DataFrame

>>> 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'.

>>> 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

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

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