Pandas: Filter DataFrame with query method

Using query method, we can query the columns of a DataFrame using some boolean expression. query() method return a new DataFrame with all the matched data, if you want to chnages to be reflected in same data set, then you can set the inplace argument to True.

 

Example 1: Get all the persons whose name is Krishna

users_with_name_krishna = df.query('Name == "Krishna"')

 

Example 2: Get all the persons whose name is not Krishna

users_with_name_not_krishna = df.query('Name != "Krishna"')

 

Example 3: Get all the persons, whose Age > 34

users_age_greater_than_34 = df.query('Age > 34')

 

Example 4: Get all the persons whose Age > 34 and City is Bangalore

users_age_greater_34_and_city_is_bangalore = df.query('Age > 34 and City == "Bangalore"')

 

Example 5: Get all the persons whose Age > 34 or City is Bangalore

users_age_greater_34_or_city_is_bangalore = df.query('Age > 34 or City == "Bangalore"')

 

Example 6: Get all the persons whose Age is in [34, 55]

users_age_in_34_55 = df.query('Age in [34, 55]')

 

Example 7: Get all the persons whose age is not in [34, 55]

users_age_not_in_34_55 = df.query('Age not in [34, 55]')

 

query_method.py

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Krishna', 'Sailu', 'Joel', 'Chamu', 'Jitendra', "Krishna"],
        'Age': [34, 35, 29, 35, 52, 34],
        'City': ['Bangalore', 'Hyderabad', 'Hyderabad', 'Chennai', 'Bangalore', 'Chennai'],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male', 'Male'],
        'Rating': [39, 43, 67, 100, 41, 89]}
df = pd.DataFrame(data)
print('Original DataFrame')
print(df)

# Get all the persons whose name is Krishna
users_with_name_krishna = df.query('Name == "Krishna"')
print('\nusers_with_name_krishna: \n', users_with_name_krishna)

# Get all the persons whose name is not Krishna
users_with_name_not_krishna = df.query('Name != "Krishna"')
print('\nusers_with_name_not_krishna: \n', users_with_name_not_krishna)

# Get all the persons, whose Age > 34
users_age_greater_than_34 = df.query('Age > 34')
print('\nusers_age_greater_than_34: \n', users_age_greater_than_34)

# Get all the persons whose Age > 34 and City is Bangalore
users_age_greater_34_and_city_is_bangalore = df.query('Age > 34 and City == "Bangalore"')
print('\nusers_age_greater_34_and_city_is_bangalore: \n', users_age_greater_34_and_city_is_bangalore)

# Get all the persons whose Age > 34 or City is Bangalore
users_age_greater_34_or_city_is_bangalore = df.query('Age > 34 or City == "Bangalore"')
print('\nusers_age_greater_34_or_city_is_bangalore: \n', users_age_greater_34_or_city_is_bangalore)

# Get all the persons whose Age is in [34, 55]
users_age_in_34_55 = df.query('Age in [34, 55]')
print('\nusers_age_in_34_55: \n', users_age_in_34_55)

# Get all the persons whose age is not in [34, 55]
users_age_not_in_34_55 = df.query('Age not in [34, 55]')
print('\nusers_age_not_in_34_55: \n', users_age_not_in_34_55)

 

 

 

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