Pandas: Series - clip() function
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:
c_0 c_1 0 7 -2 1 -3 -5 2 0 6 3 -4 9 4 5 -5
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:
c_0 c_1 0 6 -2 1 -2 -2 2 0 6 3 -2 6 4 5 -2
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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