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# pandas.DataFrame.cummin

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

  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.

    skipnabool, 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