Python Pandas: Multiple Ways to Remove Rows Based on Conditions

Boolean Indexing:

This is a powerful approach that uses a boolean expression to create a mask. This mask highlights which rows meet your condition for deletion.

• Example: Let's say you have a DataFrame df and want to delete rows where a column named "age" is greater than 30.

# Create a boolean mask to select rows where age <= 30
mask = df['age'] <= 30

# Use the mask to filter and get the new DataFrame
df_new = df[mask]

Here, mask is a boolean Series with True for rows where age is less than or equal to 30 and False otherwise. df_new will only contain the rows that satisfy the condition.

Drop Function:

The drop function offers more control over how you delete rows. You can specify the axis (0 for rows, 1 for columns) and whether to modify the original DataFrame (inplace=True).

• Example: Similar to the previous case, you can delete rows where "age" is greater than 30.

# Create a mask as before
mask = df['age'] > 30  # This time, condition for deletion

# Drop those rows (inplace modification by default)
df.drop(df[mask].index, inplace=True)

# Alternatively, create a new DataFrame without modification
df_new = df.drop(df[mask].index)

Other Methods:

• query allows for SQL-like expressions for filtering.
• loc for label-based selection with conditions.

Important Note:

By default, these methods filter the DataFrame to create a new one. If you want to modify the original DataFrame directly, use inplace=True with the drop function. Remember, this modifies the original data, so be cautious if you need to preserve it.

I hope this explanation clarifies deleting rows based on conditions in pandas!

import pandas as pd

# Sample data
data = {'name': ['Alice', 'Bob', 'Charlie', 'David'], 'age': [25, 32, 28, 40]}
df = pd.DataFrame(data)

# Delete rows where age is greater than 30
mask = df['age'] <= 30
df_filtered = df[mask]

print(df_filtered)

This code creates a DataFrame df with sample data. Then, it creates a boolean mask mask to select rows where age is less than or equal to 30. Finally, it uses this mask to filter the DataFrame and stores the result in df_filtered.

Drop Function (Modifying Original DataFrame):

import pandas as pd

# Sample data (same as above)
data = {'name': ['Alice', 'Bob', 'Charlie', 'David'], 'age': [25, 32, 28, 40]}
df = pd.DataFrame(data)

# Delete rows where age is greater than 30 (modifying original df)
df.drop(df[df['age'] > 30].index, inplace=True)

print(df)

Here, we use the drop function directly on the DataFrame. It takes the index of the rows to be deleted, which we obtain by filtering for rows where age is greater than 30. We set inplace=True to modify the original DataFrame df.

import pandas as pd

# Sample data (same as above)
data = {'name': ['Alice', 'Bob', 'Charlie', 'David'], 'age': [25, 32, 28, 40]}
df = pd.DataFrame(data)

# Delete rows where age is greater than 30 (creating a new df)
df_new = df.drop(df[df['age'] > 30].index)

print(df_new)

This code is similar to the previous one, but it creates a new DataFrame df_new that excludes the unwanted rows. The original DataFrame df remains unchanged.

These examples demonstrate different ways to achieve the same goal. Choose the method that best suits your needs based on whether you want to modify the original DataFrame or create a new one.

.query method:

This method allows you to write SQL-like expressions for filtering the DataFrame. It's concise and readable for complex conditions.

import pandas as pd

# Sample data (same as previous examples)
data = {'name': ['Alice', 'Bob', 'Charlie', 'David'], 'age': [25, 32, 28, 40]}
df = pd.DataFrame(data)

# Delete rows where age is greater than 30 using query
df_filtered = df.query("age <= 30")

print(df_filtered)

.loc with boolean indexing:

This method uses label-based selection with a boolean condition. It's helpful when you want more control over row selection based on index or labels.

import pandas as pd

# Sample data (same as previous examples)
data = {'name': ['Alice', 'Bob', 'Charlie', 'David'], 'age': [25, 32, 28, 40]}
df = pd.DataFrame(data)

# Delete rows where age is greater than 30 using loc
df_filtered = df.loc[df['age'] <= 30]  # Similar to boolean indexing

print(df_filtered)

Choosing the Right Method:

• Boolean Indexing: Simple and efficient for basic conditions.
• .drop function: Offers flexibility with modifying the original DataFrame or creating a new one.
• .query method: Concise and readable for complex conditions.

The best method depends on your specific needs and data manipulation style. Experiment with these methods to find the one that works best for you!