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Python

How to Delete a Pandas DataFrame Column

By Filip on 10/05/2024

Learn how to efficiently delete columns from your Pandas DataFrames in Python, covering various methods and scenarios.

How to Delete a Pandas DataFrame Column

Table of Contents

Introduction

This article provides a step-by-step guide on how to delete a column from a Pandas DataFrame in Python. We will use the drop() method to achieve this.

Step-by-Step Guide

To delete a column from a Pandas DataFrame, you can use the drop() method.

  1. Import the Pandas library:
import pandas as pd
  1. Create a sample DataFrame (or use your existing one):
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 28],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)
  1. Use the drop() method to delete a column:
df = df.drop('Age', axis=1)
  • Replace 'Age' with the name of the column you want to delete.
  • The axis=1 argument specifies that you want to delete a column. If you want to delete a row, use axis=0.
  1. (Optional) Use inplace=True to modify the DataFrame directly:
df.drop('City', axis=1, inplace=True)
  • This will delete the column without creating a new DataFrame.

Now, the DataFrame df will no longer contain the 'Age' and 'City' columns.

Code Example

This Python code demonstrates deleting columns from a Pandas DataFrame. It first creates a sample DataFrame with Name, Age, and City columns. Then, it shows how to delete the 'Age' column, creating a new DataFrame without modifying the original. Finally, it demonstrates deleting the 'City' column "in place," directly modifying the existing DataFrame. The output shows the DataFrame before and after each deletion.

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 28],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Delete the 'Age' column
df = df.drop('Age', axis=1)

print("\nDataFrame after deleting 'Age' column:")
print(df)

# Delete the 'City' column (inplace modification)
df.drop('City', axis=1, inplace=True)

print("\nDataFrame after deleting 'City' column:")
print(df)

Output:

Original DataFrame:
      Name  Age      City
0    Alice   25  New York
1      Bob   30    London
2  Charlie   28     Paris

DataFrame after deleting 'Age' column:
      Name      City
0    Alice  New York
1      Bob    London
2  Charlie     Paris

DataFrame after deleting 'City' column:
      Name
0    Alice
1      Bob
2  Charlie

This code demonstrates how to delete columns from a Pandas DataFrame using the drop() method. It covers both creating a new DataFrame without the deleted column and modifying the DataFrame in place.

Additional Notes

  • Deleting Multiple Columns: You can delete multiple columns at once by passing a list of column names to the drop() method:

    df = df.drop(['Age', 'City'], axis=1)

  • Errors: If you try to delete a column that doesn't exist, drop() will raise a KeyError. To avoid this, you can use the errors parameter:

    df.drop('NonexistentColumn', axis=1, errors='ignore')

    This will simply ignore the error if the column doesn't exist.

  • Alternatives to drop(): While drop() is the most common way to delete columns, you can also achieve this by selecting the columns you want to keep:

    df = df[['Name', 'Age']]  # Keep only 'Name' and 'Age' columns

  • Performance: Using inplace=True can be slightly faster, especially for large DataFrames, as it modifies the DataFrame directly instead of creating a copy. However, it also makes your code less readable and harder to debug.

  • Chaining: You can chain the drop() method with other DataFrame methods for more complex data manipulation tasks.

  • Remember: Deleting columns with drop(..., inplace=True) modifies the DataFrame directly. Always double-check your code before running it, especially when working with important data.

Summary

This article explains how to delete columns from a Pandas DataFrame using the drop() method.

Key Points:

  • Import Pandas: import pandas as pd
  • drop() Method: Use to delete columns.
    • Specify the column name(s) to delete.
    • Use axis=1 to indicate column deletion.
    • Use inplace=True to modify the DataFrame directly (optional).
  • Example: 
    import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 28], 'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)
df = df.drop('Age', axis=1)  # Delete 'Age' column
df.drop('City', axis=1, inplace=True)  # Delete 'City' column directly

Conclusion

In conclusion, the drop() method in Pandas provides a straightforward and efficient way to delete columns from DataFrames. By understanding its parameters like axis, inplace, and errors, users can confidently manipulate their data. Whether removing single or multiple columns, or choosing to modify the DataFrame directly, Pandas offers flexibility for data cleaning and transformation tasks. Remember to consult the Pandas documentation for more advanced usage and explore alternative methods for a comprehensive understanding of column deletion in DataFrames.

References

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