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Classe « DataFrame »

Méthode pandas.DataFrame.cummin

Signature de la méthode cummin

def cummin(self, axis: 'Axis | None' = None, skipna: 'bool' = True, *args, **kwargs) 

Description

help(DataFrame.cummin)

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'.
    For `Series` this parameter is unused and defaults to 0.
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
-------
Series or DataFrame
    Return cumulative minimum of Series or DataFrame.

See Also
--------
core.window.expanding.Expanding.min : Similar functionality
    but ignores ``NaN`` values.
DataFrame.min : Return the minimum over
    DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame 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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