Drop Rows from Dataframe based on certain Condition applied on a Column - Pandas

Last Updated : 3 Oct, 2025

If a DataFrame has rows that don’t meet certain conditions, you can remove them while keeping all other rows unchanged. For example, suppose a DataFrame has player ages [22, 27, 19, 30] and you want to keep only rows where age ≥ 25, resulting DataFrame will have ages [27, 30].

Using Boolean Indexing

Boolean indexing allows dropping rows by creating a boolean mask that identifies rows to keep. Rows not satisfying the condition are automatically dropped.

In this article, we have used nba.csv dataset to download CSV used click here. 

Example: This code drops all NBA players whose Age is greater than and equal to 25.

Python 
import pandas as pd
df = pd.read_csv('nba.csv')

b = df[df['Age'] >= 25]
print(b.head(15))

Output

Explanation:

  • df['Age'] >= 25 generates a boolean Series for rows to keep.
  • df[df['Age'] >= 25] filters the DataFrame, automatically dropping rows where Age < 25.

Using DataFrame.query()

The query() method lets you drop rows using a string-based expression. It supports logical operators and is ideal for complex conditions.

Example: In this example, we drop players whose Age is not between 25 and 30.

Python 
df = df.query('25 <= Age <= 30')
print(df.head(10))

Output

querymethodEx

Explanation:

  • The expression '25 <= Age <= 30' evaluates for each row.
  • Only rows satisfying the condition are kept; all others are dropped.

Using DataFrame.loc[]

loc[] can filter rows based on a condition, dropping all rows not meeting the criteria. It works similarly to boolean indexing but allows selecting specific columns at the same time if needed.

Example: Here we, drop players whose Salary is below 5,000,000.

Python 
df = df.loc[df['Salary'] >= 5000000]
print(df.head(10))

Output

locmethodEx

Explanation:

  • df['Salary'] >= 5000000 creates a boolean Series.
  • df.loc[condition] retains only rows satisfying the condition and drops all others.

Using DataFrame.drop() with Conditional Index

drop() can remove rows by index. First, identify the rows to drop using a condition, then delete them permanently.

Example: This programs drop all players whose Weight is less than 185.

Python 
indices_to_drop = df[df['Weight'] < 185].index
df.drop(indices_to_drop, inplace=True)
print(df.head(10))

Output

dromethodEx

Explanation:

  • (df['Weight'] < 185) identifies rows to drop.
  • .index fetches their index labels.
  • df.drop(indices, inplace=True) permanently removes those rows.

For more, you can refer to: How to drop rows or columns based on their labels

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