Pandas: Series - clip() function

Last update on September 15 2022 12:54:16 (UTC/GMT +8 hours)

Trim values at input in Pandas

The clip() function is used to 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.

Syntax:

Series.clip(self, lower=None, upper=None, axis=None, inplace=False, *args, **kwargs)

Parameters:

Name	Description	Type/Default Value	Required / Optional
lower	Minimum threshold value. All values below this threshold will be set to it.	float or array_like Default Value: None	Required
upper	Maximum threshold value. All values above this threshold will be set to it.	float or array_like Default Value : None	Required
axis	Align object with lower and upper along the given axis.	int or str axis name	Optional
inplace	Whether to perform the operation in place on the data.	bool Default Value : False	Required
args, *kwargs	Additional keywords have no effect but might be accepted for compatibility with numpy.		Required

Returns: Series or DataFrame
Same type as calling object with the values outside the clip boundaries replaced.

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
data = {'c_0': [7, -3, 0, -4, 5], 'c_1': [-2, -5, 6, 9, -5]}
df = pd.DataFrame(data)
df

Output:

Example - Clips per column using lower and upper thresholds:

Python-Pandas Code:

import numpy as np
import pandas as pd
data = {'c_0': [7, -3, 0, -4, 5], 'c_1': [-2, -5, 6, 9, -5]}
df = pd.DataFrame(data)
df.clip(-2, 6)

Output:

Example - Clips using specific lower and upper thresholds per column element:

Python-Pandas Code:

import numpy as np
import pandas as pd
t = pd.Series([0, -4, -2, 5, 3])
t

Output:

0    0
1   -4
2   -2
3    5
4    3
dtype: int64

Python-Pandas Code:

import numpy as np
import pandas as pd
data = {'c_0': [7, -3, 0, -4, 5], 'c_1': [-2, -5, 6, 9, -5]}
df = pd.DataFrame(data)
t = pd.Series([0, -4, -2, 5, 3])
df.clip(t, t + 2, axis=0)

Output:

     c_0	c_1
0	   2	0
1	  -3   -4
2	   0	0
3	   5	7
4	   5	3

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