pandas.Series.clip#
- Series.clip(lower=None, upper=None, *args, axis=None, inplace=False, **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.
- 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.
- 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