xorbits.pandas.DataFrame.clip#

DataFrame.clip(lower=None, upper=None, *, axis: Axis | None = None, inplace: bool_t = False, **kwargs) → Self | None#

Trim values at input threshold(s).

Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.

Parameters

  • lower (float or array-like, default None) – Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.

  • upper (float or array-like, default None) – Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.

  • axis ({{0 or 'index', 1 or 'columns', None}}, default None) – Align object with lower and upper along the given axis. For Series this parameter is unused and defaults to None.

  • inplace (bool, default False) – Whether to perform the operation in place on the data.

  • *args – Additional keywords have no effect but might be accepted for compatibility with numpy.

  • **kwargs – Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns

Same type as calling object with the values outside the clip boundaries replaced or None if inplace=True.

Return type

Series or DataFrame or None

See also

Series.clip

Trim values at input threshold in series.

DataFrame.clip

Trim values at input threshold in dataframe.

numpy.clip

Clip (limit) the values in an array.

Examples

>>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}  
>>> df = pd.DataFrame(data)  
>>> df  
   col_0  col_1
0      9     -2
1     -3     -7
2      0      6
3     -1      8
4      5     -5

Clips per column using lower and upper thresholds:

>>> df.clip(-4, 6)  
   col_0  col_1
0      6     -2
1     -3     -4
2      0      6
3     -1      6
4      5     -4

Clips using specific lower and upper thresholds per column element:

>>> t = pd.Series([2, -4, -1, 6, 3])  
>>> t  
0    2
1   -4
2   -1
3    6
4    3
dtype: int64

>>> df.clip(t, t + 4, axis=0)  
   col_0  col_1
0      6      2
1     -3     -4
2      0      3
3      6      8
4      5      3

Clips using specific lower threshold per column element, with missing values:

>>> t = pd.Series([2, -4, np.nan, 6, 3])  
>>> t  
0    2.0
1   -4.0
2    NaN
3    6.0
4    3.0
dtype: float64

>>> df.clip(t, axis=0)  
col_0  col_1
0      9      2
1     -3     -4
2      0      6
3      6      8
4      5      3

Warning

This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.frame.DataFrame.