How to delete a column from Pandas DataFrame

To delete a column from a Pandas DataFrame, you have several methods available: del statement, drop() method, and pop() method. Each approach has its own use cases and advantages.

Using del Statement

The del statement permanently removes a column from the DataFrame ?

import pandas as pd

# Create DataFrame
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90],
    "Price": [50000, 60000, 45000, 40000, 80000, 70000]
})

print("Original DataFrame:")
print(dataFrame)

# Delete a column using del
del dataFrame['Price']

print("\nDataFrame after deleting 'Price' column:")
print(dataFrame)

Original DataFrame:
       Car  Units  Price
0      BMW    100  50000
1    Lexus    150  60000
2     Audi    110  45000
3  Mustang     80  40000
4  Bentley    110  80000
5   Jaguar     90  70000

DataFrame after deleting 'Price' column:
       Car  Units
0      BMW    100
1    Lexus    150
2     Audi    110
3  Mustang     80
4  Bentley    110
5   Jaguar     90

Using drop() Method

The drop() method is more flexible and can delete multiple columns. By default, it returns a new DataFrame ?

import pandas as pd

# Create DataFrame
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang'],
    "Units": [100, 150, 110, 80],
    "Price": [50000, 60000, 45000, 40000],
    "Year": [2020, 2021, 2019, 2018]
})

print("Original DataFrame:")
print(dataFrame)

# Drop single column (returns new DataFrame)
new_df = dataFrame.drop('Price', axis=1)
print("\nAfter dropping 'Price' column:")
print(new_df)

# Drop multiple columns
new_df2 = dataFrame.drop(['Price', 'Year'], axis=1)
print("\nAfter dropping multiple columns:")
print(new_df2)

Original DataFrame:
       Car  Units  Price  Year
0      BMW    100  50000  2020
1    Lexus    150  60000  2021
2     Audi    110  45000  2019
3  Mustang     80  40000  2018

After dropping 'Price' column:
       Car  Units  Year
0      BMW    100  2020
1    Lexus    150  2021
2     Audi    110  2019
3  Mustang     80  2018

After dropping multiple columns:
       Car  Units
0      BMW    100
1    Lexus    150
2     Audi    110
3  Mustang     80

Using pop() Method

The pop() method removes the column and returns it as a Series ?

import pandas as pd

# Create DataFrame
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi'],
    "Units": [100, 150, 110],
    "Price": [50000, 60000, 45000]
})

print("Original DataFrame:")
print(dataFrame)

# Pop a column (removes and returns it)
popped_column = dataFrame.pop('Price')

print("\nDataFrame after popping 'Price' column:")
print(dataFrame)

print("\nPopped column:")
print(popped_column)

Original DataFrame:
     Car  Units  Price
0    BMW    100  50000
1  Lexus    150  60000
2   Audi    110  45000

DataFrame after popping 'Price' column:
     Car  Units
0    BMW    100
1  Lexus    150
2   Audi    110

Popped column:
0    50000
1    60000
2    45000
Name: Price, dtype: int64

Comparison

Conclusion

Use del for simple column deletion, drop() for flexible operations with multiple columns, and pop() when you need the removed column data. The drop() method is most commonly used as it's safer and more versatile.