pandas.Series.clip_lower — pandas 0.24.0rc1 documentation (original) (raw)
Series. clip_lower(threshold, axis=None, inplace=False)[source]¶
Trim values below a given threshold.
Deprecated since version 0.24.0: Use clip(lower=threshold) instead.
Elements below the threshold will be changed to match thethreshold value(s). Threshold can be a single value or an array, in the latter case it performs the truncation element-wise.
| Parameters: | threshold : numeric or array-like Minimum value allowed. All values below threshold will be set to this value. float : every value is compared to threshold. array-like : The shape of threshold should match the object it’s compared to. When self is a Series, threshold should be the length. When self is a DataFrame, threshold should 2-D and the same shape as self for axis=None, or 1-D and the same length as the axis being compared. axis : {0 or ‘index’, 1 or ‘columns’}, default 0 Align self with threshold along the given axis. inplace : boolean, default False Whether to perform the operation in place on the data. New in version 0.21.0. |
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| Returns: | Series or DataFrame Original data with values trimmed. |
See also
General purpose method to trim Series values to given threshold(s).
General purpose method to trim DataFrame values to given threshold(s).
Examples
Series single threshold clipping:
s = pd.Series([5, 6, 7, 8, 9]) s.clip(lower=8) 0 8 1 8 2 8 3 8 4 9 dtype: int64
Series clipping element-wise using an array of thresholds. thresholdshould be the same length as the Series.
elemwise_thresholds = [4, 8, 7, 2, 5] s.clip(lower=elemwise_thresholds) 0 5 1 8 2 7 3 8 4 9 dtype: int64
DataFrames can be compared to a scalar.
df = pd.DataFrame({"A": [1, 3, 5], "B": [2, 4, 6]}) df A B 0 1 2 1 3 4 2 5 6
df.clip(lower=3) A B 0 3 3 1 3 4 2 5 6
Or to an array of values. By default, threshold should be the same shape as the DataFrame.
df.clip(lower=np.array([[3, 4], [2, 2], [6, 2]])) A B 0 3 4 1 3 4 2 6 6
Control how threshold is broadcast with axis. In this casethreshold should be the same length as the axis specified byaxis.
df.clip(lower=[3, 3, 5], axis='index') A B 0 3 3 1 3 4 2 5 6
df.clip(lower=[4, 5], axis='columns') A B 0 4 5 1 4 5 2 5 6