How to Remove Rows from a Pandas DataFrame Based on a Condition?

Conditional Deletion of Rows in Pandas DataFrames

The original question sought to remove rows from a DataFrame based on the string length of a specific column. While the proposed solution was incorrect, this article aims to provide a comprehensive understanding of conditional row deletion in Pandas.

Using the drop Method

To directly address the title's question, the drop method offers a straightforward approach for eliminating rows based on a conditional expression. The syntax is as follows:

df = df.drop(some labels)
df = df.drop(df[<some boolean condition>].index)

Example

To remove all rows where the score column value is less than 50:

df = df.drop(df[df.score < 50].index)

For an in-place modification, you can use:

df.drop(df[df.score < 50].index, inplace=True)

Multiple Conditions

Pandas supports the use of logical operators (| for OR, & for AND, ~ for NOT) for creating complex conditions. Remember to enclose them in parentheses.

To remove all rows where score is both less than 50 and greater than 20:

df = df.drop(df[(df.score < 50) & (df.score > 20)].index)

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