Pandas.DataFrame.query() by Examples

Pandas DataFrame.query() method is used to query the rows based on the expression (single or multiple column conditions) provided and returns a new DataFrame. In case you wanted to update the existing referring DataFrame use inplace=True argument.

In this article, I will explain the syntax of the Pandas DataFrame query() method and several working examples like query with multiple conditions and query with string contains to new few.


1. Quick Examples of pandas query()

If you are in hurry, below are quick examples of how to use pandas.DataFrame.query() method.

# Below are the quick examples.

# Query Rows using DataFrame.query()
df2=df.query("Courses == 'Spark'")

# Using variable
df2=df.query("Courses == @value")

# Inpace
df.query("Courses == 'Spark'",inplace=True)

# Not equals, in & multiple conditions
df.query("Courses != 'Spark'")
df.query("Courses in ('Spark','PySpark')")
df.query("`Courses Fee` >= 23000")
df.query("`Courses Fee` >= 23000 and `Courses Fee` <= 24000")

If you are a learner, Let’s see with sample data and run through these examples and explore the output to understand better. First, let’s create a pandas DataFrame from Dict.

# Create DataFrame
import pandas as pd
import numpy as np
technologies= {
    'Fee' :[22000,25000,23000,24000,26000],
    'Duration':['30days','50days','30days', None,np.nan],
df = pd.DataFrame(technologies)

Note that the above DataFrame also contains None and Nan values on Duration column that I would be using in my examples below to select rows that has None & Nan values or select ignoring these values.

3. Using DataFrame.query()

Following is the syntax of DataFrame.query() method.

# Query() method syntax
DataFrame.query(expr, inplace=False, **kwargs)
  • expr – expression takes conditions to query rows
  • inplace – Defaults to False. When set toTrue, it updates the referring DataFrame and query() method returns None.
  • **kwargs –  Keyword arguments that works with eval()

DataFrame.query() takes condition in expression to select rows from a DataFrame. This expression can have one or multiple conditions.

# Query all rows with Courses equals 'Spark'
df2=df.query("Courses == 'Spark'")

Yields below output.

# Output:
  Courses    Fee Duration  Discount
0   Spark  22000   30days      1000

In case you wanted to use a variable in the expression, use @ character.

# Query Rows by using Python variable
df2=df.query("Courses == @value")

If you notice the above examples return a new DataFrame after filtering the rows. if you wanted to update the existing DataFrame use inplace=True

# Replace current esisting DataFrame
df.query("Courses == 'Spark'",inplace=True)

If you wanted to select based on column value not equals then use != operator.

# Not equals condition
df2=df.query("Courses != 'Spark'")

Yields below output.

# Output:
   Courses  Courses Fee Duration  Discount
1  PySpark        25000   50days      2300
2   Hadoop        23000   30days      1000
3   Python        24000     None      1200
4   Pandas        26000      NaN      2500

4. Select Rows Based on List of Column Values

If you have values in a python list and wanted to select the rows based on the list of values, use in operator, it’s like checking a value contains in a list of string values.

# Query Rows by list of values
print(df.query("Courses in ('Spark','PySpark')"))

Yields below output.

# Output:
   Courses    Fee Duration  Discount
0    Spark  22000   30days      1000
1  PySpark  25000   50days      2300

You can also write with a list of values in a python variable.

# Query Rows by list of values
print(df.query("Courses in @values"))

To select rows that are not in a list of column values can be done using not in operator.

# Query Rows not in list of values
print(df.query("Courses not in @values"))

If you have column names with special characters using column name surrounded by tick ` character .

# Using columns with special characters
print(df.query("`Courses Fee` >= 23000"))

5. Query with Multiple Conditions

In Pandas or any table-like structures, most of the time we would need to select the rows based on multiple conditions by using multiple columns, you can do that in Pandas DataFrame as below.

# Query by multiple conditions
print(df.query("`Courses Fee` >= 23000 and `Courses Fee` <= 24000"))

Yields below output. Alternatively, you can also use pandas loc with multiple conditions.

# Output:
  Courses  Courses Fee Duration  Discount
2  Hadoop        23000   30days      1000
3  Python        24000     None      1200

6. Query Rows using apply()

pandas.DataFrame.apply() method is used to apply the expression row-by-row and return the rows that matched the values. The below example returns every match when Courses contains a list of specified string values.

# By using lambda function
print(df.apply(lambda row: row[df['Courses'].isin(['Spark','PySpark'])]))

Yields below output. A lambda expression is used with pandas to apply the function for each row.

# Output:
   Courses    Fee Duration  Discount
0    Spark  22000   30days      1000
1  PySpark  25000   50days      2300

8. Other Examples using df[] and loc[]

# Other examples you can try to query rows
df[df["Courses"] == 'Spark'] 
df.loc[df['Courses'] == value]
df.loc[df['Courses'] != 'Spark']
df.loc[(df['Discount'] >= 1000) & (df['Discount'] <= 2000)]
df.loc[(df['Discount'] >= 1200) & (df['Fee'] >= 23000 )]

# Select based on value contains

# Select after converting values

# Select startswith


In this article, I have explained multiple examples of how to query Pandas DataFrame Rows based on single and multiple conditions, from a list of values (checking column value exists in list of string values) e.t.c. Remember when you query DataFrame Rows, it always returns a new DataFrame with selected rows, in order to update existing df you have to use inplace=True. I hope this article helps you learn Pandas.

Happy Learning !!


Naveen (NNK)

Naveen (NNK) is a Data Engineer with 20+ years of experience in transforming data into actionable insights. Over the years, He has honed his expertise in designing, implementing, and maintaining data pipelines with frameworks like Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. Naveen journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. In this blog, he shares his experiences with the data as he come across. Follow Naveen @ @ LinkedIn

This Post Has One Comment

  1. Anonymous

    Hi there, I liked this article. One point…

    Perhaps the implementation has changed, but I don’t think the pandas query likes spaces in the column names. I had a column named “Return Date” and I had to change it to “Return_Date” to avoid getting a key error.

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