dask_expr._collection.Index.clip
dask_expr._collection.Index.clip¶
- Index.clip(lower=None, upper=None, axis=None, **kwargs)¶
Trim values at input threshold(s).
This docstring was copied from pandas.core.series.Series.clip.
Some inconsistencies with the Dask version may exist.
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 (Not supported in Dask)
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:
>>> df.clip([-2, -1], [4, 5]) col_0 col_1 0 4 -1 1 -2 -1 2 0 5 3 -1 5 4 4 -1
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