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Pandas DataFrame - clip() function



The Pandas DataFrame clip() function trims values at input threshold(s). It 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

DataFrame.clip(lower=None, upper=None, 
               axis=None, inplace=False)

Parameters

lower Optional. Specify minimum threshold value as float or array-like. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value. Default: None
upper Optional. Specify maximum threshold value as float or array-like. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value. Default: None
axis Optional. Specify int or str axis name to align object with lower and upper along the given axis. Default: None
inplace Optional. If True, the operation is performed in place on the data. Default: False

Return Value

Returns same type as calling object (Series or DataFrame or None) with the values outside the clip boundaries replaced or None if inplace=True.

Example: using threshold values as float

In the example below, a DataFrame df is created. The clip() function is used to clip the given dataframe according to the specified minimum and maximum threshold value.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "x": [-10, -5, 5, 5, 8],
  "y": [-5, 5, 8, 15, -5]
})

print("The DataFrame is:")
print(df)

#clipping the dataframe using
#minimum threshold value = 1
#maximum threshold value = 10
print("\ndf.clip(1,10) returns:")
print(df.clip(1,10))

The output of the above code will be:

The DataFrame is:
    x   y
0 -10  -5
1  -5   5
2   5   8
3   5  15
4   8  -5

df.clip(1,10) returns:
   x   y
0  1   1
1  1   5
2  5   8
3  5  10
4  8   1

Example: using threshold values as array

It is possible to specify clipping threshold as an array. In this case, the clipping is performed element-wise in the specified axis.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "x": [-10, -5, 5, 5, 8],
  "y": [-5, 5, 8, 15, -5]
})

#specifying minimum threshold value element-wise
lbound = pd.Series([2, 3, 4, 5, 6])
#specifying maximum threshold value element-wise
ubound = lbound + 5

print("The DataFrame is:")
print(df, "\n")

print("The minimum threshold value is:")
print(lbound, "\n")

print("The maximum threshold value is:")
print(ubound, "\n")

#clipping the dataframe column-wise
print("df.clip(lbound, ubound, axis=0) returns:")
print(df.clip(lbound, ubound, axis=0))

The output of the above code will be:

The DataFrame is:
    x   y
0 -10  -5
1  -5   5
2   5   8
3   5  15
4   8  -5 

The minimum threshold value is:
0    2
1    3
2    4
3    5
4    6
dtype: int64 

The maximum threshold value is:
0     7
1     8
2     9
3    10
4    11
dtype: int64 

df.clip(lbound, ubound, axis=0) returns:
   x   y
0  2   2
1  3   5
2  5   8
3  5  10
4  8   6

❮ Pandas DataFrame - Functions

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