DataFrame.query() - ( Pandas DataFrame Basics )

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Syntax

df.query('column == value')

Example

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eric'],
        'age': [25, 20, 18, 32, 41],
        'city': ['New York', 'Paris', 'Tokyo', 'London', 'Dubai']}
df = pd.DataFrame(data)

filtered_df = df.query('age > 20')
print(filtered_df)

Output

      name  age     city
0    Alice   25  New York
3    David   32   London
4     Eric   41    Dubai

Explanation

Pandas DataFrame provides the query() function to filter the rows based on a condition. The function takes a string-based condition and returns a new DataFrame containing only those rows that satisfy the condition.

In the above example, we have a DataFrame df containing information about people's name, age, and city. We use the query function to filter only the rows where the age column is greater than 20.

Use

The query function is very useful for filtering large DataFrames based on a condition. It is faster and more readable than using the traditional loc function to filter the rows.

Important Points

• Pandas DataFrame provides the query() function to filter the rows based on a condition
• The function takes a string-based condition and returns a new DataFrame containing only those rows that satisfy the condition
• It is faster and more readable than using the traditional loc function to filter the rows.

Summary

In conclusion, the query() function in Pandas DataFrame is very useful for filtering large DataFrames based on a condition. It is faster and more readable than using the traditional loc function to filter the rows and can be very helpful when dealing with large datasets where query times are critical.