Efficiently Remove Rows in Pandas Dataframe Based on Column Values

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To remove rows in a Pandas DataFrame based on a condition, you can use the drop method. Here’s an example:

Suppose you have a DataFrame df with a column named ‘age’ and you want to remove all rows where the age is less than 18.

import pandas as pd

# Create a sample DataFrame
data = {'name': ['Alice', 'Bob', 'Charlie', 'Dave'],
        'age': [25, 17, 21, 16]}
df = pd.DataFrame(data)

# Remove all rows where age is less than 18
df = df.drop(df[df.age < 18].index)

# Print the modified DataFrame


      name  age
0    Alice   25
2  Charlie   21

In the above code, the drop method is used to drop rows where the age is less than 18. The condition df.age < 18 returns a Boolean Series that indicates which rows satisfy the condition. The drop method is then called on the DataFrame with the index of these rows to remove them. The index attribute of the Boolean Series returns the index of the rows where the condition is True.

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