Pandas Dataframe.pop()

The pop() method in Pandas is used to remove a column from a DataFrame and return it as a Series. This is similar in concept to the dictionary pop() method in Python, but specifically designed for use with Pandas DataFrames. It's key features include:

• Removes a specified column from a DataFrame.
• Returns the removed column as a Pandas Series.
• Modifies the original DataFrame in-place.

Example:

import pandas as pd
df = pd.DataFrame({
    'name': ['Alice', 'Bob'],
    'age': [25, 30],
    'city': ['New York', 'Paris']
})
a = df.pop('age')

print(a)
print(df)

Output

Explanation: pop() removes the 'age' column and returns it as a Series. After the operation, a holds the age values and df retains only the name and city columns.

Syntax of Pandas Dataframe.pop()

DataFrame.pop(label)

Parameters: label (str) is the name of the column to be removed.

Returns: A Series containing the removed column values.

Note: This method raises KeyError if the column does not exist in the DataFrame.

Examples of Pandas Dataframe.pop()

Example 1: This example shows that trying to pop a column which does not exist in the DataFrame will raise a KeyError.

import pandas as pd
df = pd.DataFrame({
    'product': ['Book', 'Pen'],
    'price': [100, 10]
})

df.pop('quantity')

Output

Traceback (most recent call last):
  File "...", line ...
    df.pop('quantity')
  File "...", line ...
    raise KeyError(key) from err
KeyError: 'quantity'

Explanation: Since the column 'quantity' does not exist in the DataFrame, calling pop('quantity') raises a KeyError.

Example 2: This example shows how you can use pop() in a loop to remove and process columns until the DataFrame is empty.

import pandas as pd
df = pd.DataFrame({
    'A': [1, 2],
    'B': [3, 4],
    'C': [5, 6]
})

while not df.empty:
    col = df.columns[0]
    val = df.pop(col)
    print(col, val)

Output

Explanation: Loop runs until the DataFrame is empty, removing the first column each time with pop(), which deletes it in-place and returns it as a Series. The column name and values are printed until all columns are processed.

Example 3: This example demonstrates how to pop a column from one DataFrame and assign it to another DataFrame.

import pandas as pd

df1 = pd.DataFrame({
    'x': [10, 20],
    'y': [30, 40]
})

df2 = pd.DataFrame()
df2['y'] = df1.pop('y')

print(df1)
print(df2)

Output

Explanation: df1 is created with columns 'x' and 'y' and an empty df2 is initialized. The pop() method removes 'y' from df1 and assigns it to df2['y']. As a result, df1 contains only 'x' and df2 contains the original 'y' values.