How to delete only one row in csv with Python?

In this tutorial, we will learn to delete only one row in CSV with Python using the Pandas library. Pandas is an open-source library for data analysis that provides several functionalities to perform operations on data sets.

We will use the drop() method to delete a row from any CSV file. This tutorial illustrates three different approaches to delete a row from CSV files using the same method.

Syntax

Here's the basic syntax to delete a row from a CSV file ?

import pandas as pd

# Read CSV file
df = pd.read_csv("filename.csv")

# Delete row by index
df = df.drop(df.index[index_number])

# Save updated data
df.to_csv("filename.csv", index=False)

In this syntax, we first read the DataFrame, then use the drop() method to remove the specified row by index, and finally save the updated data back to the CSV file.

Method 1: Delete Last Row

Here's how to delete the last row from a CSV file using the drop() method ?

import pandas as pd

# Create sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, 30, 35, 40],
    'City': ['New York', 'London', 'Paris', 'Tokyo']
}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Delete the last row
df = df.drop(df.index[-1])
print("\nAfter deleting last row:")
print(df)

Original DataFrame:
      Name  Age      City
0    Alice   25  New York
1      Bob   30    London
2  Charlie   35     Paris
3    David   40     Tokyo

After deleting last row:
      Name  Age      City
0    Alice   25  New York
1      Bob   30    London
2  Charlie   35     Paris

Method 2: Delete Row by Index Label

When you have a specific column set as index, you can delete rows by their label ?

import pandas as pd

# Create sample data with custom index
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, 30, 35, 40],
    'City': ['New York', 'London', 'Paris', 'Tokyo']
}
df = pd.DataFrame(data, index=['row1', 'row2', 'row3', 'row4'])
print("Original DataFrame:")
print(df)

# Delete row by label
df = df.drop('row2')
print("\nAfter deleting 'row2':")
print(df)

Original DataFrame:
       Name  Age      City
row1  Alice   25  New York
row2    Bob   30    London
row3  Charlie   35     Paris
row4  David   40     Tokyo

After deleting 'row2':
       Name  Age      City
row1  Alice   25  New York
row3  Charlie   35     Paris
row4  David   40     Tokyo

Method 3: Delete Row with Condition

You can delete rows based on specific conditions using boolean indexing ?

import pandas as pd

# Create sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'David'],
    'Age': [25, 30, 35, 28, 40],
    'City': ['New York', 'London', 'Paris', 'Boston', 'Tokyo']
}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Delete rows where Name equals 'Alice' (only first occurrence)
condition = df['Name'] == 'Alice'
first_match_index = df[condition].index[0]
df = df.drop(first_match_index)
print("\nAfter deleting first occurrence of 'Alice':")
print(df)

Original DataFrame:
      Name  Age      City
0    Alice   25  New York
1      Bob   30    London
2  Charlie   35     Paris
3    Alice   28    Boston
4    David   40     Tokyo

After deleting first occurrence of 'Alice':
      Name  Age      City
1      Bob   30    London
2  Charlie   35     Paris
3    Alice   28    Boston
4    David   40     Tokyo

Comparison

Conclusion

The Pandas drop()