Python Pandas DataFrame pop() Function

Dataframe pop – Python Pandas DataFrame pop() Function

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Pandas DataFrame pop() Function:

Dataframe pop: The pop() function of the Pandas DataFrame removes the given column from the DataFrame and returns the removed columns as a Pandas Series object. If the given item is not found, the function throws a KeyError exception.

Syntax:

DataFrame.pop(label)

Parameters:

label: This is required. It indicates the label of the column that has to be removed.

Return Value:

Pandas pop: The pop() function of the Pandas DataFrame returns the removed column, as a Pandas Series object.

Pandas DataFrame pop() Function in Python

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
  • Drop/remove some random column from the dataframe using the pop() function by passing the column name as an argument to it.
  • Here we removed the “emp_age” column from the dataframe
  • Print the altered dataframe after the removal of “emp_age” column.
  • 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({
  "emp_name": ["john", "nick" , "jessy", "mary"],
  "emp_age": [25, 35, 38, 22],
  "emp_salary": [25000, 40000, 22000, 80000]},
  index= [1, 2, 3, 4]
)
# Print the given dataframe
print("The given Dataframe:")
print(data_frme)
print()

# Drop/remove some random column from the dataframe using the pop() function
# by passing the column name as an argument to it.
# Here we removed the "emp_age" column from the dataframe
data_frme.pop("emp_age")
# Print the altered dataframe after the removal of "emp_age" column
print("The altered dataframe after the removal of 'emp_age' column:")
print(data_frme)

Output:

The given Dataframe:
  emp_name  emp_age  emp_salary
1     john       25       25000
2     nick       35       40000
3    jessy       38       22000
4     mary       22       80000

The altered dataframe after the removal of 'emp_age' column:
  emp_name  emp_salary
1     john       25000
2     nick       40000
3    jessy       22000
4     mary       80000