pandas.DataFrame.clip_lower

DataFrame.clip_lower(threshold, axis=None, inplace=False)

Return copy of the input with values below a threshold truncated.

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:

clipped : same type as input

See also

Series.clip

Return copy of input with values below and above thresholds truncated.

Series.clip_upper

Return copy of input with values above threshold truncated.

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(np.array([3, 3, 5]), axis='index')
   A  B
0  3  3
1  3  4
2  5  6

>>> df.clip_lower(np.array([4, 5]), axis='columns')
   A  B
0  4  5
1  4  5
2  5  6