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pandas.DataFrame.clip_lower

DataFrame.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 the threshold 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.

Returns:

Series or DataFrame

Original data with values trimmed.

See also

Series.clip
General purpose method to trim Series values to given threshold(s).
DataFrame.clip
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. threshold should 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 case threshold should be the same length as the axis specified by axis.

>>> 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
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