How to Drop a Column From Pandas DataFrame?

When working with pandas DataFrames, you’ll often encounter columns that aren’t needed for your analysis. Removing these unnecessary columns keeps your data focused and easier to manage.

Learn how to delete one or more columns from a Pandas DataFrame in Python.

Table of Contents

What is a Column in Pandas?

In pandas, a column is a labeled set of data within a DataFrame. Each column represents a specific type of information, such as a person’s name, their age, or a location. Think of it like a single category in a well-organized spreadsheet.

Why Drop a Column?

Dropping irrelevant columns helps streamline your dataset. For example, during data pre-processing, you prepare the data frame by combining multiple CSV files, but then you don’t need all the columns, so you delete the ones that don’t play a vital role in your analysis. It makes your data frame cleaner and easier to work with.

Remove Columns From Pandas DataFrame

To remove a column from a pandas DataFrame, use the drop() method with axis=1 for columns. The syntax is df.drop("Column_Name", axis=1, inplace=True).

Delete a Single Column Using the drop() Method

The most common method is drop(), which deletes specified columns (or rows) from your DataFrame. By default, drop() returns a new DataFrame, leaving the original unchanged, unless you set inplace=True.

Syntax

DataFrame.drop(labels=None, axis=0, inplace=False)

• labels: Name or list of columns to delete.
• axis=1: Indicates you’re dropping columns (axis=0 would drop rows).
• inplace: If True, modifies the original DataFrame.

Example Code

Below is a simple example of removing a column named “Age” from a DataFrame.

1. First a data frame is made using columns called : “Title”, “Age”, and “Location”.
2. The drop() method removes the “Age” column by specifying its name and setting axis=1.
3. The original DataFrame is not modified since inplace=False.

import pandas as pd

# Creating a sample DataFrame
data = {"Title": ["Hassan", "Novwera", "Mahnoor"], 
        "Age": [23, 36, 56], 
        "Location": ["Lucerne", "Valencia", "Brno"]}
df = pd.DataFrame(data)

# Dropping the "Age" column
new_df = df.drop("Age", axis=1)

print(new_df)

Output

      Title      Location
0    Hassan       Lucerne
1   Novwera       Valencia
2   Mahnoor       Brno

Drop Multiple Columns

To drop multiple columns at once, pass a list of column names:

Syntax

DataFrame.drop(labels=None, axis=1, inplace=False)

Example Code

The “Age” and “City” columns are dropped from the DataFrame in this case.

1. Creating a data frame and adding the columns Age, City, and Name.
2. Both “Age” and “City” are deleted using the drop() method, which passes a list of column names to the labels parameter.

import pandas as pd

# Creating a sample DataFrame
data = {"Name": ["Alice", "Bob", "Charlie"], 
      "Age": [23, 36, 56], 
        "City": ["Lucerne", "Valencia", "Brno"]}
df = pd.DataFrame(data)

# Dropping the "Age" and "City" columns
new_df = df.drop(["Age", "City"], axis=1)

print(new_df)

Output

   Name
0    Alice
1      Bob
2  Charlie

Remove Unnamed Columns

Unnamed columns in Pandas often appear when a file, such as a CSV, has extra separators (like commas) or empty columns. Similar columns do not carry any important data normally and make a mess in the DataFrame. Removing them ensures your data is clean and easy to work with.

Think of this like you are organizing an event and there are 20 chairs in the event and 18 people were invited so you would remove the chairs that are empty. The same way you would remove unnamed column that are not occupied by any data.

Syntax

DataFrame.drop(labels=None, axis=1, inplace=False)

Example Code

Here, we remove an unnamed column from a DataFrame:

1. A DataFrame is created with an “Unnamed: 0” column, which often happens during file imports.
2. To remove the “Unnamed: 0” column, use the drop() function.

import pandas as pd

# Making a DataFrame example with an unnamed column
data = {"Unnamed: 0": [1, 2, 3], "Title": ["Novwera", "Mahad", "Mahnoor"], "Lifetime": [21, 56, 38]}
df = pd.DataFrame(data)

# Dropping the unnamed column
cleaned_df = df.drop(["Unnamed: 0"], axis=1)

print(cleaned_df)

Output

    Title   Lifetime
0  Novwera    21
1    Mahad    56
2  Mahnoor    38

Using inplace=True

The inplace=True parameter modifies the original DataFrame directly. This is handy when you don’t want to store a separate updated DataFrame. Just remember that changes can’t be undone easily once applied in-place.

Syntax

DataFrame.drop(labels=None, axis=1, inplace=True)

Example Code

In this example, we remove a column named “Age” directly from the DataFrame:

1. A DataFrame is created with columns “Title”, “Age”, and “Location”.
2. The drop() method is used with inplace=True to remove the “Age” column directly from the original DataFrame.

import pandas as pd

# Creating a sample DataFrame
data = {"Title": ["Novwera", "Mahad", "Mahnoor"], "Age": [59, 21, 56], "Location": ["Lucerne", "Valencia", "Brno"]}
df = pd.DataFrame(data)

# Dropping the "Age" column in place
df.drop(["Age"], axis=1, inplace=True)

print(df)

Output

      Title    Location
0  Novwera     Lucerne
1    Mahad     Valencia
2  Mahnoor     Brno

Working with Indices and Axes

In Pandas, indices and axes are ways to locate and manipulate data in a DataFrame.

• Indices refer to row labels, which uniquely identify rows.
• Axes represent directions in the DataFrame:
  • Axis 0: Refers to rows (vertical direction).
  • Axis 1: Refers to columns (horizontal direction).

When you perform operations like removing columns or rows, specifying the correct axis ensures the operation targets the intended part of the DataFrame.

Syntax

DataFrame.drop(labels=None, axis=1)

Example Code

This example demonstrates how to remove a column by explicitly specifying axis=1:

1. A DataFrame is created with columns “Title”, “Lifetime”, and “Venue”.
2. The drop() method is used to remove the “Venue” column by setting axis=1.

import pandas as pd

# Creating a sample DataFrame
data = {"Title": ["Novwera", "Mahad", "Mahnoor"], "Lifetime": [72, 64, 56], "Venue": ["Lucerne", "Valencia", "Brno"]}
df = pd.DataFrame(data)

# Dropping the "City" column using axis=1
new_df = df.drop(["City"], axis=1)

print(new_df)

Output

      Title  Lifetime
0   Novwera    72
1     Mahad    64
2   Mahnoor    56

When working with Pandas DataFrame, you also encounter NaN values in a column, you can check if a value is Nan before proceeding to perform any other operation.

Conclusion

Removing unnecessary columns—whether they’re unnamed or simply not needed—makes your DataFrame cleaner and easier to analyze. Using drop(), along with parameters like inplace=True and axis=1, you can customize how you modify your data. Understanding indices and axes ensure you’re targeting the right parts of your DataFrame every time.

For more interesting articles, follow our Python tutorial series by SyntaxScenarios.