Introduction to Pandas DataFrame.query() Function

Python is one of the most commonly used programming languages by data scientists. The reason for the proliferation of this language is that it is easy to use and enjoys strong community support, with dedicated libraries for data analysis and predictive modeling. Pandas is one such library that is widely accepted by the data science community for performing data cleaning and analysis.

Sometimes, during EDA (Exploratory Data Analysis), selecting or filtering data frames can be tedious. Hence, pandas provide some functions that make this task a cakewalk even for beginners. The query is a highly useful function. It enables users to analyze and filter the data just like the where clause in SQL. Also, it helps in compiling several conditions simultaneously in a memory-efficient manner as it does not use temporary variables.

This article will show an exciting pandas data frame function Query.

Overview:

• Understand how DataFrame.query() can replace traditional filtering methods, offering an intuitive, SQL-like approach to selecting data in Pandas.
• With query(), you can write cleaner, more readable code, especially when filtering data with multiple conditions.
• Dive into examples that show how query() optimizes memory usage by eliminating temporary variables, making it a great choice for working with large datasets.
• See how query() simplifies the handling of nested and combined conditions, improving both code simplicity and efficiency.
• Be aware of the few constraints of query(), such as limited support for complex string operations, and understand when to opt for alternative methods like .loc[].

What is the DataFrame.query() Function?

The DataFrame.query() function is a method that allows for efficient, SQL-like querying on columns of a DataFrame. Using a string-based expression, you can specify filtering criteria directly. This method shines when handling complex conditions and large datasets, offering an alternative to the DataFrame.loc[] method, which can sometimes be harder to read with long conditions.

Why Use DataFrame.query()?

query() offers several benefits that can enhance data manipulation and filtering in your Pandas workflow:

• Improved Readability: Expressions in query() look similar to SQL queries, making complex conditions easy to read.
• Code Efficiency: The query() method allows for efficient code execution, particularly for large datasets, as it optimizes behind the scenes.
• Cleaner Syntax for Complex Conditions: Nested conditions and combinations are simpler and more readable in query() than in the DataFrame.loc[] method.

Limitations of DataFrame.query()

While query() is highly effective, it has some limitations:

• String Operations: Limited support for complex string operations; use .loc[] for more elaborate string handling.
• Reserved Keywords: Certain words, like and, or, and in, can cause conflicts. Wrap column names in backticks if they overlap with reserved keywords.
• Syntax Differences: Unlike .loc[], you need to use SQL-style operators (and/or) in query() expressions.

Syntax

Let’s have a look at the syntax before jump into the query.

pandas.DataFrame.query(expr,inplace=false,**kwargs)
expr:The condition to filter the data.
inplace: weather the query modify the original data or return a modified copy.
**kwargs: Any other keyword argument you want to pass

Now we will look at some examples of how this amazing function eases your Analysis.

Let’s first create a Dataframe for the demo

import pandas as pd
import numpy as np
df= pd.DataFrame({'Name':['Vikram','John', 'Alex','Paul','Andrew','Rafel' ],
'Age':[28,39,21,50,35,43],
'Department':['HR', 'Finance','IT','HR','IT','IT'],
'Country':['USA','India','Germany','USA','India','India']
})
print(df.head())

Now, let’s Filter the dataset using the query function provided by pandas for its DataFrame objects.

Filter employees of IT department

In this example want to select only the employees who work for the IT department.

import pandas as pd
import numpy as np 
df= pd.DataFrame({'Name':['Vikram','John', 'Alex','Paul','Andrew','Rafel' ],
                  'Age':[28,39,21,50,35,43], 
                  'Department':['HR', 'Finance','IT','HR','IT','IT'],
                  'Country':['USA','India','Germany','USA','India','India'] })
print(df.query('Department == "IT"'))

Here is the required result. We have all the IT department employees present in the dataset.

Implementation of the OR Condition

Here, we present another example of how to implement the ‘OR’ clause. That means selecting the records satisfying either the first condition or the other.

import pandas as pd
import numpy as np 
df= pd.DataFrame({'Name':['Vikram','John', 'Alex','Paul','Andrew','Rafel' ],
                         'Age':[28,39,21,50,35,43], 
                         'Department':['HR', 'Finance','IT','HR','IT','IT'],
                        'Country':['USA','India','Germany','USA','India','India'] })
print(df.query('Age>= 40 or Country =="India"'))

Output

Using Environment Variable

Sometimes, we want to use the environment variables in the expression; we can do this using @ before the variable name, just like SQL, where you can use a subquery to reference when selecting rows where conditions are satisfied. The ‘@’ method saves the value you want to compare against. as shown in the example below.

In the example, Location is a local variable. To use it in a query expression, you just need to put @ before it, and it’s done.

import pandas as pd
import numpy as np

 df= pd.DataFrame({'Name':['Vikram','John', 'Alex','Paul','Andrew','Rafel' ], 
                   'Age':[28,39,21,50,35,43],
                   'Department':['HR', 'Finance','IT','HR','IT','IT'], 
                   'Country':['USA','India','Germany','USA','India','India'] }
Location= " India"                              
print(df.query( Country == @Location'))

Output

Note: Always remember if there is any space in your column name, but that between a pair of backtick (“) like strings while using an expression or replace the space with “_”

Conclusion

The DataFrame.query() function in Pandas is an invaluable tool for data scientists and analysts. It simplifies the process of filtering and querying data with an SQL-like syntax. Its ability to handle complex filtering conditions efficiently and its readability makes it a go-to function for many during Exploratory Data Analysis (EDA). Although it has some limitations, such as handling certain string operations, its benefits for large datasets and clean syntax make it an essential function in any data analyst’s toolkit.

Frequently Asked Questions