Pandas dataframe query examples, Yields below output. eval() for details on referring to column names and variables in the query string. If the dataframe’s index is meaningful, for instance, a timestamp, querying based on it might be required. query("Department == 'IT' & Age > 25") print(result) Using Variables in Queries. Parameters: exprstr The query string to evaluate. 0 supports Python versions 3. query("Age > @age_threshold") print(result) Index-based Querying. DataFrame( { "Name": ["John", "Doe", "Elizabeth", "Mary"], "Age": [28, 34, 24, 29], "Department": ["HR", "Finance", "IT", "Marketing"], } ) age_threshold = 30 result = df. query for filtering rows with string expressions. query () is one of them. parser{‘pandas’, ‘python’}, default ‘pandas’ The parser to use to construct the syntax tree from the expression Jul 11, 2025 · Pandas Dataframe provide many methods to filter a Data frame and Dataframe. Note that the DataFrame may contain None and NaN values in the Duration column, which will be taken into account in the examples below for selecting rows with None & NaNvalues or selecting while disregarding these values Oct 26, 2022 · How to Use Pandas Query to Filter a DataFrame October 26, 2022 Pandas is the essential data analysis library in Python. Complete guide with examples. Pandas provides powerful tools for date-based filtering, but the first critical step is ensuring your date column is in the correct datetime64 format. 13. 5, Java versions 17 and 21, as well as Scala version 2. Pandas query () method Syntax Syntax: DataFrame. Moving onto combining conditions, let’s filter employees in the ‘IT’ department over the age of 25: import pandas as pd df = pd. query() for fast, readable data filtering. See the documentation for DataFrame. For an age threshold variable: import pandas as pd df = pd. First, let’s create a Pandas DataFrame. DataFrame({ 'Name': ['John', 'Doe', 'Elizabeth', 'Mary'], 'Age': [28, 34, 24, 29], 'Department': ['HR', 'Finance', 'IT', 'Marketing'] }) Combining Conditions. The query() method can also work on the index of a DataFrame. . 10, 3. Learn how to use pandas DataFrame. Related: How to run Pandas DataFrame on Apache Spark (PySpark)? What Version of Python PySpark Supports PySpark 4. query (expr, inplace=False, **kwargs) Parameters: expr: Expression in string form to filter data. contains (), where (), and loc []. Feb 16, 2026 · Updated: 2026-02-16 Pandas: Find and Filter Values in a DataFrame Column How to search, find, and filter values in Pandas DataFrame columns using boolean indexing, query (), isin (), str. Includes examples for single and multiple conditions, variables, and in-place modifications. Queries can utilize variables from the surrounding Python scope using ‘@’. This guide walks you through converting date strings, then demonstrates multiple methods to filter rows by date with clear examples and outputs. In this tutorial, you’ll learn how to use the Pandas query function to filter a DataFrame in plain English. See the documentation for eval() for details of supported operations and functions in the query string. DataFrame({ 'Name': ['John', 'Doe', 'Elizabeth', 'Mary'], 'Age': [28, 34, 24, 29], 'Department': ['HR', 'Finance', 'IT', 'Marketing'] }) result = df. Consider a DataFrame df containing employee data with columns ‘Name’, ‘Age’, and ‘Department’: import pandas as pd df = pd. Following are quick examples of the Pandas DataFrame query() method. 12, along with R version 3. Jul 6, 2024 · Learn to query multi-index Pandas DataFrames using index names, multiple conditions, regex, and variables, with code examples. Basic Query. Covering syntax, comparison with loc, boolean logic, variables, and use cases. Being able to use the library to filter data in meaningful ways will make you a stronger programmer. 11, and 3. 9, 3. Aug 19, 2025 · Learn how to use pandas DataFrame. PySpark Features & Advantages The following are the main features of PySpark. inplace: Make changes in the original data frame if True kwargs: Other keyword arguments.


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