pandas.Series.clip

Series.clip(lower=None, upper=None, axis=None, inplace=False, *args, **kwargs)[source]

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
lowerfloat 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.

upperfloat 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.

axisint or str axis name, optional

Align object with lower and upper along the given axis.

inplacebool, default False

Whether to perform the operation in place on the data.

*args, **kwargs

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

Returns
Series or DataFrame or None

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

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

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