DataFrame.
cummin
Return cumulative minimum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative minimum.
Note
the current implementation of cummin uses Spark’s Window without specifying partition specification. This leads to moveing all data into a single partition in a single machine and could cause serious performance degradation. Avoid this method with very large datasets.
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
See also
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.
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
>>> df = ps.DataFrame([[2.0, 1.0], [3.0, None], [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.
>>> df.cummin() A B 0 2.0 1.0 1 2.0 NaN 2 1.0 0.0
It works identically in Series.
>>> df.A.cummin() 0 2.0 1 2.0 2 1.0 Name: A, dtype: float64