pandas.DataFrame.clip — pandas 2.2.3 documentation (original) (raw)
DataFrame.clip(lower=None, upper=None, *, 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
Trim values at input threshold in series.
Trim values at input threshold in dataframe.
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