How to Delete Column of Pandas DataFrame?

Pandas DataFrame – Delete Column

You can delete a column from a Pandas DataFrame with the del statement, the drop() method, or the pop() method. The best choice depends on whether you need a new DataFrame, want to modify the existing DataFrame, or need to keep the removed column.

• Use del to remove one column from the existing DataFrame.
• Use drop() to remove one or more columns, with the option to return a new DataFrame or modify the original.
• Use pop() to remove one column and return it as a Pandas Series.

Delete DataFrame Column using del keyword

To delete a column of DataFrame using del keyword, use the following syntax.

 del myDataFrame['column_name']

In the following example, we shall initialize a DataFrame with three columns and delete one of the column using del keyword.

Python Program

import pandas as pd

#initialize a dataframe
df = pd.DataFrame({
	'a':[14, 52, 46],
	'b':[32, 85, 64],
	'c':[88, 47, 36]})
	
#delete column 'b'
del df['b']

#print the dataframe
print(df)

Output

    a   c
0  14  88
1  52  47
2  46  36

The column with name b has been deleted from the dataframe. The del statement changes df directly and does not return the deleted values.

Delete DataFrame Column using drop() method

pandas.DataFrame.drop() method returns a new DataFrame with the specified columns dropped from the original DataFrame. The original DataFrame is not modified.

Python Program

import pandas as pd

#initialize a dataframe
df = pd.DataFrame({
	'a':[14, 52, 46],
	'b':[32, 85, 64],
	'c':[88, 47, 36]})
	
#delete column 'b'
df1 = df.drop(['b'], axis=1)

#print the dataframe
print(df1)

Output

    a   c
0  14  88
1  52  47
2  46  36

Here, axis=1 tells Pandas to remove a column rather than a row. The clearer equivalent is df.drop(columns=['b']).

Remove Multiple Pandas DataFrame Columns with drop()

Pass a list of column labels to the columns parameter when several columns must be removed at once.

import pandas as pd

df = pd.DataFrame({
    'name': ['Asha', 'Ravi', 'Mina'],
    'age': [24, 31, 28],
    'city': ['Pune', 'Delhi', 'Chennai'],
    'score': [82, 91, 88]
})

result = df.drop(columns=['age', 'score'])

print(result)

Output

   name     city
0  Asha     Pune
1  Ravi    Delhi
2  Mina  Chennai

The original df still contains all four columns because the returned DataFrame was assigned to result.

Delete a Pandas Column In Place with drop()

Set inplace=True when the existing DataFrame should be changed directly. In this form, drop() returns None.

df.drop(columns=['city'], inplace=True)

print(df)

Alternatively, assign the result back to the same variable:

df = df.drop(columns=['city'])

Reassignment is often easier to follow in a sequence of DataFrame transformations.

Delete DataFrame Column using pop() method

pandas.DataFrame.pop() method deletes specified column from the DataFrame and returns the deleted column.

Python Program

import pandas as pd

#initialize a dataframe
df = pd.DataFrame({
	'a':[14, 52, 46],
	'b':[32, 85, 64],
	'c':[88, 47, 36]})
	
#delete column 'b'
poppedColumn = df.pop('b')

#print the dataframe
print(df)

print('\nDeleted Column\n-------------')
#print deleted column
print(poppedColumn)

Output

    a   c
0  14  88
1  52  47
2  46  36

Deleted Column
-------------
0    32
1    85
2    64
Name: b, dtype: int64

Use pop() when the removed column is still needed for another calculation. It accepts one column label at a time.

Handle a Missing DataFrame Column During Deletion

del, pop(), and drop() normally raise a KeyError when the requested column does not exist. With drop(), use errors='ignore' when a missing label should be skipped.

df = df.drop(columns=['temporary_column'], errors='ignore')

Use this option only when ignoring an absent column is intentional. Otherwise, allowing the error can reveal a misspelled or unexpected column name.

Choose Between del, drop(), and pop() for Pandas Columns

Method	Changes original DataFrame	Removes multiple columns	Returns removed column
del df['column']	Yes	No	No
df.drop(columns=[...])	Only with inplace=True or reassignment	Yes	No
df.pop('column')	Yes	No	Yes, as a Series

For most column-removal tasks, drop(columns=[...]) is the most explicit and flexible form. Use del for a simple direct deletion and pop() when the deleted values must be retained.

Frequently Asked Questions About Deleting Pandas Columns

How do I delete a Pandas DataFrame column by name?

Use df.drop(columns=['column_name']) to return a DataFrame without that column. Use del df['column_name'] to modify the current DataFrame directly.

How do I delete several columns from a Pandas DataFrame?

Pass all required labels in a list: df.drop(columns=['column_a', 'column_b']).

Does DataFrame.drop() change the original DataFrame?

Not by default. Save the returned DataFrame, assign it back to the same variable, or specify inplace=True.

How do I delete a column only when it exists?

Use df.drop(columns=['column_name'], errors='ignore'), or check if 'column_name' in df.columns before deleting it.

Summary of Pandas DataFrame Column Deletion

In this Pandas Tutorial, we learned how to delete a column from DataFrame using del, drop(), and pop(). We also removed multiple columns, updated a DataFrame in place, and handled missing column labels.