pandas.Panel.cummin

Panel.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.
Returns:	cummin : DataFrame or Panel

See also

pandas.core.window.Expanding.min

Similar functionality but ignores NaN values.

Panel.min

Return the minimum over Panel axis.

Panel.cummax

Return cumulative maximum over Panel axis.

Panel.cummin

Return cumulative minimum over Panel axis.

Panel.cumsum

Return cumulative sum over Panel axis.

Panel.cumprod

Return cumulative product over Panel 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