pandas.DataFrame.clip

DataFrame.clip(lower=None, upper=None, axis=None, inplace=False, *args, **kwargs)

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.

upper : float or array_like, default None

Maximum threshold value. All values above this threshold will be set to it.

axis : int or string axis name, optional

Align object with lower and upper along the given axis.

inplace : boolean, default False

Whether to perform the operation in place on the data.

New in version 0.21.0.

*args, **kwargs

Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns:

Series or DataFrame

Same type as calling object with the values outside the clip boundaries replaced

See also

clip_lower

Clip values below specified threshold(s).

clip_upper

Clip values above specified threshold(s).

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