Pandas filter rows by value. Learn how to select rows from a dataframe by their column values using Python's pandas library. Whether you need the total size of a dataset, the number of non-null entries in a specific column, or the count of rows matching a particular condition, Pandas offers multiple approaches optimized for different use cases. Learn how to filter Pandas DataFrame by column values using practical examples. This complete guide covers boolean indexing, multiple conditions, string filters, dates, and real-world data analysis use cases. Let's start with a quick example to illustrate the concept: Mar 25, 2025 · This tutorial explains how to select rows based on column values in pandas, including several examples. Note that this routine does not filter based on content. Jul 15, 2025 · Filtering a Pandas DataFrame by column values is a common and essential task in data analysis. , 0, 3, 5, 7 instead of 0, 1, 2, 3), which can cause confusion and issues in downstream operations. isin () can be used to filter the DataFrame rows based on the exact match of the column values or being in a range. Working with dates and times is one of the most common tasks in data analysis. This tutorial provides several examples of how to use this Learn how to filter pandas dataframe by column using various methods. Using DataFrame. Example: Here we, drop players whose Salary is below 5,000,000. Complete guide with examples. query() function is the most used to filter rows based on a specified expression, pandas. Use . Filtering columns by conditions on another column: In addition to filtering rows based on column values, pandas also support filtering columns based on conditions in another column. To filter a DataFrame (df) by a single column, if we consider data with male and females we might: males = df[df[Gender]=='Male'] Question 1: But what if the data spanned multiple years and I want In this article, let's see how to filter rows based on column values. Use == to select rows where the column equals a value. The process allows to filter data, making it easier to perform analyses or visualizations on specific subsets. Query function can be used to filter rows based on column values. In this This tutorial explains how to filter a pandas Series by value, including several examples. Raw datetime values in a dataset often need to be transformed - extracting components like year, month, or hour, converting string formats, or filtering rows by time ranges - before they can be used effectively in analysis or modeling. loc, . filter # DataFrame. If you’re working with pandas in Python, filtering a DataFrame by column values is something you’ll do all the time. It works similarly to boolean indexing but allows selecting specific columns at the same time if needed. Pandas support several ways to filter by column value, DataFrame. Filter DataFrame Rows Based on the Date in Pandas To filter rows based on dates, first format the dates in the DataFrame to datetime64 type. In this article, we’ll cover eight different ways to filter a DataFrame. This can leave gaps in the index (e. Selecting rows from a Pandas DataFrame based on column values is a fundamental operation in data analysis using pandas. . Boost your pandas skills now! Output: Filter Pandas Dataframe by Column Value In this example, we filtered the DataFrame to show only rows where the "Age" column has values greater than 30. Consider below Dataframe: Filter Multiple Values using pandas Asked 10 years ago Modified 1 year, 8 months ago Viewed 112k times The question in other words: how to filter out only those columns of a pandas dataframe where the column's value in a chosen row meets a condition? You can filter the rows from Pandas DataFrame based on a single condition or multiple conditions using either loc[], query(), or apply() function. Python Pandas: How to Reset the Index of a Pandas DataFrame in Python When you manipulate a Pandas DataFrame - by dropping rows, filtering, sorting, or slicing - the original index values are preserved. and isin() and query() will still work. Pandas Boolean indexing multiple conditions standard way ("Boolean indexing" works with values in a column only) In this approach, we get all rows having Salary lesser or equal to 100000 and Age < 40 and their JOB starts with ‘P’ from the dataframe. When working with tabular data in Python, one of the most common tasks is filtering rows based on the values in one or more columns. Gain insights into the diverse filtering methods provided by Pandas. loc [] loc [] can filter rows based on a condition, dropping all rows not meeting the criteria. The Sales Data Analysis project is a Python application that uses the Pandas library to analyze product sales data. To filter the rows based on such a function, use the conditional function inside the selection brackets []. I have a Dataframe df Num1 Num2 one 1 0 two 3 2 three 5 4 four 7 6 five 9 8 I want to filter rows that have value bigger than 3 in Num1 To filter rows of Pandas DataFrame, you can use DataFrame. Fortunately, we can ultilise Pandas for this operation. We can also get rows from DataFrame satisfying or not satisfying one or more conditions. Unravel the mysteries of working with pandas in Python? Our comprehensive cheat sheet covers essential data manipulation, filtering, and analysis techniques. It’s a straightforward way to select rows with categorical variables or matching a list of values. This is useful when you need non-null values across the entire DataFrame for Pandas (stands for Python Data Analysis) is an open-source software library designed for data manipulation and analysis. isin () function or DataFrame. loc. It allows to extract specific rows based on conditions applied to one or more columns, making it easier to work with relevant subsets of data. It is similar to the WHERE clause in SQL or the filter feature in Excel. A common task is selecting rows from a DataFrame based on column values, similar to SQL’s SELECT * FROM table WHERE Learn all the ways in which to filter pandas dataframes in this tutorial, including filtering dates, multiple columns, using iloc, loc and query functions! A common operation in data analysis is to filter values based on a condition or multiple conditions. Pandas is an open source Python library for data analysis. sort_values('mpg') Order rows by values of a column (low to high). query () can be used with a boolean expression, where you can filter the rows based on a condition that involves one or more columns. Method – 8: Filtering DataFrame rows based on specific values using RegEx Here we want all the values in the column ‘Region’, which ends with ‘th’ in their string value and display them. isin () to select rows where the column value is in a list. But did you know there are multiple ways to do it? Today, we’ll break it down step by step —so the next time you need to filter data, you’ll know exactly which method to use! Pandas is a powerful library in Python for data manipulation and analysis. Maintaining Original Data Shape Retain the shape of the original data by setting other to a default value. This tutorial explains how to filter a pandas DataFrame for rows where a particular column contains a value in a list. Similar to the conditional expression, the isin() conditional function returns a True for each row the values are in the provided list. Pandas provides a variety of ways to filter data points (i. Master pandas value_counts() to analyze frequency distributions, count unique values, and explore categorical data. The simplest way to filter a pandas DataFrame by column values is to use the query function. The isin() method in Pandas is useful for filtering rows by checking if column values belong to a particular set. We may be presented with a Table, and want to perform custom filtering operations. Feb 2, 2024 · We can select rows of DataFrame based on single or multiple column values. Bookmark this pandas cheat sheet: 30 weekly commands to clean, filter, join, and summarize DataFrames. e. After groupby operations, filtering rows, or sorting data, your index can become fragmented with gaps, duplicates, or meaningless labels. Learn how to effectively filter Pandas DataFrames by column values. Now that we have the students in Class A, we need a plan to improve their performance. Quickly learn DataFrame operations, indexing, and performance tips with related keywords like data analysis, CSV handling, and time-series processing. query [] functions from the Pandas package to specify a filter condition. The filter is applied to the labels of the index Selecting and Filtering Data: This operation retrieves specific columns, rows or records that match a condition. filter(items=None, like=None, regex=None, axis=None) [source] # Subset the DataFrame or Series according to the specified index labels. The accepted answer shows how to filter rows in a pandas DataFrame based on column values using . Dive into Boolean indexing, the query method, string operations, lambda functions, and handling missing values for efficient and targeted data manipulation. Then use the DataFrame. To filter a DataFrame (df) by a single column, if we consider data with male and females we might: males = df[df[Gender]=='Male'] Question 1: But what if the data spanned multiple years and I want df. In this article, we will cover various methods to filter pandas dataframe in Python. This is useful when you need non-null values across the entire DataFrame for Master pandas value_counts() to analyze frequency distributions, count unique values, and explore categorical data. Choosing the right method depends on what exactly you need to count and how you plan to use the Selecting and Filtering Data: This operation retrieves specific columns, rows or records that match a condition. loc [] and DataFrame. DataFrame. As a result, acquire the subset of data, the filtered DataFrame. Jul 23, 2025 · Learn multiple efficient ways to filter Pandas DataFrames by column values, including . The filter is applied to the labels of the index pandas. where(df['A']>20,other=0) Explain Code In this case, rows where the condition is False will replace all values with 0 instead of NaN. Only rows satisfying the condition are kept; all others are dropped. Learn how to efficiently filter and select specific rows from a Pandas DataFrame based on conditions applied to column values in Python. Working with pandas DataFrames often leads to messy, non-sequential indexes. query (). python Copy result=df. It’s like a SQL WHERE clause but in Python. Most operations in pandas can be accomplished with operator chaining (groupby, aggregate, apply, etc), but the only way I've found to filter rows is via normal bracket indexing df_filtered = df[df[' This article provides multiple methods to accomplish such filtering using the query function in pandas with real-world code examples and discussions. The result is a smaller DataFrame containing only the rows that meet this condition. Whether you need to isolate records that meet a specific threshold, match a category, or satisfy a combination of conditions, Pandas offers several powerful and expressive ways to accomplish this. The program performs structured DataFrame operations to calculate revenue, filter high-revenue records, sort results, identify maximum and minimum revenue values, update product prices, and compute category-based totals. Boost your pandas skills now! Apr 19, 2019 5 min read Image Courtesy of Peter Oslanec via Unsplash Quite often it is a requirement to filter tabular data based on a column value. Data filtering is a common way to select specific rows from a dataset based on some conditions. pandas is a powerful Python library designed to work with structured data, meaning data organized in rows and columns—similar to what we see in Excel spreadsheets or SQL tables. rows). Built on top of NumPy, efficiently manages large datasets, offering tools for data cleaning, transformation, and analysis. query, boolean indexing, and performance tips. Apply the where () function with the other parameter. g. For DataFrame, filter rows or columns depending on axis argument. Practice faster with Code Labs Academy. list_of_values doesn't have to be a list; it can be set, tuple, dictionary, numpy array, pandas Series, generator, range etc. It allows precise extraction of required information. The code (Report_Card["Class"] == "A") returns a pandas. Series object of False and True values, which in turn is used to index our main data frame. Getting row counts is one of the most fundamental operations in data analysis. This can be accomplished using boolean indexing, positional indexing, label indexing, and query() method. Method 1: Basic query filtering The query function in Pandas DataFrame can be used to filter rows based on a condition string. ikhfz9, fjeiw, nc65x, lsp5o, gkdi, v3dww, cosak, pampcl, oodc2p, q6dw,