Deleting Rows in a Pandas DataFrame Based on Condition in Another Column

When working with pandas DataFrames, it’s common to encounter situations where you need to delete rows based on conditions specified in another column. This problem is particularly useful when dealing with large datasets and requires efficient processing.

In this article, we will explore a solution using Python and the pandas library, which provides an efficient way to delete rows from a DataFrame based on conditions in another column.

Problem Statement

The question presents a scenario where you want to delete all rows until you encounter a non-empty row in a particular column. This means that if you find a row with a non-empty value in Column2, you should delete all the rows before it up to the last row where there is a value in Column1.

Solution Overview

The proposed solution involves iterating through the DataFrame from the end (last row) and marking rows for removal based on the condition specified. Once all rows are marked for removal, we use these markings to create a new DataFrame that excludes the marked rows.

This approach has several advantages:

• It only requires one pass through the DataFrame.
• It’s more efficient than adding rows to a new DataFrame.
• It provides clear and concise code.

Step-by-Step Explanation

To implement this solution, we’ll follow these steps:

1. Import necessary libraries
2. Create a sample DataFrame with the desired structure
3. Initialize variables for tracking deletion status
4. Iterate through the DataFrame from last row to first (end) and mark rows for removal
5. Use the markings to create a new DataFrame excluding marked rows

Step-by-Step Code Implementation

import pandas as pd

# Define sample data
data = [
    ['A',   'x' ],
    [None,  'x' ],
    [None,  'Barrier' ],
    ['A',   'x' ],
    [None,  'x' ],
    [None,  'x' ],
    ['A',   'x' ],
    [None,  'x' ],
    [None,  'x'],
    [None,  'x'],
    ['B',   'x'],
    [None,  'x'],
    [None,  'x'],
    ['B',   'x'],
    [None,  'x'],
    [None,  'Barrier'],
    [None,  'x'],
    ['C',   'x'],
    [None,  'x'],
    [None,  'x']
]

# Create DataFrame
df = pd.DataFrame(data, columns=['column1', 'column2'])

# Initialize variables for tracking deletion status
deleting = False
idx = len(df) -1

# Iterate through the DataFrame from last row to first (end)
while idx >=0:
    # Check if column2 is 'Barrier'
    if df.loc[idx, 'column2'] == 'Barrier':
        # Mark rows for removal and update deletion status
        df.loc[idx, 'column2'] = 0
        deleting = True
    elif deleting and df.loc[idx, 'column1'] == None:
        # Update deletion status since this row should be removed
        df.loc[idx, 'column2'] = 0
    elif deleting and df.loc[idx, 'column1'] != None:
        # Update deletion status since this row is not to be removed
        df.loc[idx, 'column2'] = 0
        deleting = False
    else:
        # Reset deletion status for next iteration
        deleting = False
    idx -= 1

# Create new DataFrame excluding marked rows
df2 = df[df['column2'] != 0].reset_index(drop = True)

print(df2)

Explanation of Key Components

The provided code is well-structured and follows standard practices. Here’s a breakdown of the key components:

• Initialization: We start by initializing necessary variables such as deleting (boolean flag for tracking deletion status) and idx (index variable starting from last row).
• Looping through DataFrame rows: We use a while loop that iterates from the end of the DataFrame towards the beginning. In each iteration, we check if the current row meets our condition based on Column2.
• Marking rows for removal: If the condition is met and deleting is True, we update the value in Column2 to 0 (indicating it’s marked for removal). We also update the deletion status accordingly.
• Creating new DataFrame with marked rows removed: After looping through all rows, we create a new DataFrame that excludes rows where column2 equals 0. This effectively removes the marked rows from the original DataFrame.

Conclusion

This approach provides an efficient way to delete rows in a pandas DataFrame based on conditions specified in another column. By iterating from last row to first and marking rows for removal, we can create a new DataFrame with the desired structure without having to add rows to it.

Last modified on 2024-10-06