Deleting rows based on multiple conditions in a pandas dataframe

Deleting rows based on multiple conditions in a pandas dataframe

You can delete rows based on multiple conditions in a pandas DataFrame using boolean indexing. You can combine conditions using logical operators such as & (AND) and | (OR). Here's how to do it:

Let's say you have a DataFrame named df, and you want to delete rows that meet multiple conditions.

import pandas as pd # Sample DataFrame data = { 'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50] } df = pd.DataFrame(data) # Define conditions condition1 = df['A'] > 2 condition2 = df['B'] < 40 # Combine conditions using logical operators (& for AND, | for OR) combined_condition = condition1 & condition2 # Delete rows that satisfy the combined condition df = df[~combined_condition] print(df)

In this example, the rows that satisfy both condition1 (A > 2) and condition2 (B < 40) are deleted from the DataFrame using boolean indexing with the ~ (not) operator. The resulting DataFrame will contain only the rows that do not meet the combined condition.

Make sure to adjust the conditions and logical operators based on your specific requirements.

Examples

  1. How to delete rows in a pandas DataFrame based on multiple conditions in Python?

    Description: This query focuses on deleting rows from a pandas DataFrame based on multiple conditions using logical operators.

    import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Delete rows based on multiple conditions df = df[~((df['A'] > 2) & (df['B'] < 9))]

  2. Removing rows from a pandas DataFrame with multiple criteria in Python

    Description: This query explores removing rows from a pandas DataFrame based on multiple criteria specified using logical operators.

    import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Define multiple conditions condition1 = df['A'] > 2 condition2 = df['B'] < 9 # Delete rows based on multiple conditions df = df[~(condition1 & condition2)]

  3. Deleting rows in pandas DataFrame based on multiple conditions with OR logic

    Description: This query focuses on deleting rows from a pandas DataFrame based on multiple conditions using OR logic.

    import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Delete rows based on multiple conditions using OR logic df = df[~((df['A'] > 2) | (df['B'] < 9))]

  4. Removing rows from a pandas DataFrame with multiple conditions using query function

    Description: This query explores using the query function to remove rows from a pandas DataFrame based on multiple conditions.

    import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Delete rows based on multiple conditions using query function df = df.query('not (A > 2 and B < 9)')

  5. Deleting rows from pandas DataFrame based on multiple conditions with method chaining

    Description: This query demonstrates using method chaining to delete rows from a pandas DataFrame based on multiple conditions.

    import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Delete rows based on multiple conditions with method chaining df = df.loc[~((df['A'] > 2) & (df['B'] < 9))]

  6. Python pandas delete rows based on multiple conditions with AND & OR logic

    Description: This query seeks to delete rows from a pandas DataFrame based on multiple conditions using both AND and OR logic.

    import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Delete rows based on multiple conditions with AND & OR logic df = df[~((df['A'] > 2) & (df['B'] < 9) | (df['C'] == 13))]

  7. Deleting rows from pandas DataFrame based on multiple conditions with isin method

    Description: This query demonstrates using the isin method to delete rows from a pandas DataFrame based on multiple conditions.

    import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Delete rows based on multiple conditions using isin method conditions = (df['A'] > 2) & (df['B'] < 9) df = df[~df.isin(df[conditions]).all(1)]

  8. Python pandas delete rows based on multiple conditions with query method

    Description: This query explores using the query method to delete rows from a pandas DataFrame based on multiple conditions.

    import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Delete rows based on multiple conditions using query method df = df.query('~(A > 2 & B < 9)')

  9. Removing rows from pandas DataFrame based on multiple conditions with loc method

    Description: This query illustrates using the loc method to remove rows from a pandas DataFrame based on multiple conditions.

    import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Delete rows based on multiple conditions using loc method df = df.loc[~((df['A'] > 2) & (df['B'] < 9))]

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