Python Pandas Series clip() Function

Pandas Series clip() Function:

The clip() function of the Pandas Series trims values at the input thresholds. It assigns non-boundary values to boundary values. Thresholds can be single values or arrays, and in the latter case, clipping is done element-by-element along the given axis.

Syntax:

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

Parameters

lower: This is optional. It indicates the minimum threshold value as a float or array-like. All values that are less than(below) this threshold will be set to it. The value will not be clipped if a threshold (for example- NA) is missing. None is the default.

upper: This is optional. It indicates the maximum threshold value as a float or array-like. All values that are greater than(above) this threshold will be set to it. The value will not be clipped if a threshold (for example- NA) is missing. None is the default.

axis: This is optional. It is the int or str axis name to align the object with lower and upper along the specified axis. None is the default.

inplace: This is optional. If set to True, the operation is done in place on the data. False by default.

Return Value:

The same type as calling object with the values outside the clip boundaries replaced or None if inplace=True is returned.

Python Pandas Series clip() Function

Example1

Approach:

Import pandas module using the import keyword.
Pass some random list as an argument to the Series() function of the pandas module to create a series.
Store it in a variable.
Print the above-given series.
Apply clip() function on the given series to clip the values based on the given lower and upper threshold values and print the result.
Here the lower threshold is 2 and the upper threshold = 12.
The Exit of the Program.

Below is the implementation:

# Import pandas module using the import keyword.
import pandas as pd
# Pass some random list as an argument to the Series() function
# of the pandas module to create a series.
# Store it in a variable.
gvn_series = pd.Series([-2, 4, -10, 5, 15, 20, 6, -3])
# Print the above given series
print("The given series is:")
print(gvn_series)
print()
# Apply clip() function on the given series to clip the values based on the
# given lower and upper threshold values and print the result.
# Here the lower threshold is 2 and upper threshold = 12
print("The given series after clipping:")
print(gvn_series.clip(2, 12))

Output:

The given series is:
0    -2
1     4
2   -10
3     5
4    15
5    20
6     6
7    -3
dtype: int64

The given series after clipping:
0     2
1     4
2     2
3     5
4    12
5    12
6     6
7     2
dtype: int64

Example2

Approach:

Import pandas module using the import keyword.
Pass some random key-value pair(dictionary), index list as arguments to the DataFrame() function of the pandas module to create a dataframe.
Store it in a variable.
Print the given dataframe.
Apply clip() function on the student_marks column of the dataframe to clip the values based on the given lower and upper threshold values and print the result.
Here the lower threshold = 30 and upper threshold = 85.
The Exit of the Program.

Below is the implementation:

# Import pandas module using the import keyword.
import pandas as pd
# Pass some random key-value pair(dictionary), index list as arguments to the 
# DataFrame() function of the pandas module to create a dataframe
# Store it in a variable.
data_frme = pd.DataFrame({
  "student_rollno": [1, 2, 3, 4],
  "student_marks": [80, 35, 25, 90]},
  index= ["virat", "nick" , "jessy", "sindhu"]
)
# Print the given dataframe
print("The given Dataframe:")
print(data_frme)
print()
# Apply clip() function on the student_marks column of the dataframe
# to clip the values based on the given lower and upper threshold values 
# and print the result.
# Here the lower threshold = 30 and upper threshold = 85
print("The dataframe after clipping the student_marks column:")
print(data_frme['student_marks'].clip(30, 85))

Output:

The given Dataframe:
        student_rollno  student_marks
virat                1             80
nick                 2             35
jessy                3             25
sindhu               4             90

The dataframe after clipping the student_marks column:
virat     80
nick      35
jessy     30
sindhu    85
Name: student_marks, dtype: int64

Python Pandas Series clip() Function

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