Pyspark count rows with condition. While PySpark's DataFrame API offers powerful .

Pyspark count rows with condition Apr 17, 2025 · This guide dives into the syntax and steps for counting rows in a PySpark DataFrame, with examples covering essential scenarios. sql import functions as F df = spark. Feb 28, 2018 · It's just the count of the rows, not the count for certain conditions. They allow to manipulate and analyze data in a structured way, using SQL-like operations. count() [source] # Returns the number of rows in this DataFrame. orderBy() function, specifying only the ordering criteria. Aug 18, 2017 · Approach can be grouping the dataframe based on your timeline criteria. count () which extracts the number of rows from the Dataframe and storing it in the variable named as 'row' For counting the number of columns we are using df. Apr 17, 2025 · Diving Straight into Filtering Rows in a PySpark DataFrame Need to filter rows in a PySpark DataFrame—like selecting high-value customers or recent transactions—to focus your analysis or streamline an ETL pipeline? Filtering rows based on a condition is a core skill for data engineers working with Apache Spark. What is the GroupBy Operation in PySpark? The groupBy method in PySpark DataFrames groups rows by one or more columns, creating a GroupedData object that can be aggregated using functions like sum, count, or avg. Parameters axis{0 or ‘index’, 1 or ‘columns’}, default 0 If 0 or ‘index’ counts are Apr 17, 2025 · How to Filter Rows Based on a Dynamic Condition from a Variable in a PySpark DataFrame: The Ultimate Guide Diving Straight into Dynamic Filtering in a PySpark DataFrame Filtering rows in a PySpark DataFrame is a core skill for data engineers and analysts working with Apache Spark in ETL pipelines, data cleaning, or analytics. Currently I use count operation to extract values, which, obviously, sl Nov 29, 2023 · distinct() eliminates duplicate records (matching all columns of a Row) from DataFrame, count () returns the count of records on DataFrame. count ¶ DataFrame. count () to get the number of rows within each group. rowsBetween - only consider rows fulfilling a specific condition (e. The agg operation can incorporate conditional logic using when from pyspark. select(sf. This technique is essential for data quality checks, cleaning datasets, or isolating valid records in ETL pipelines, such as identifying missing data or Mar 27, 2024 · In PySpark DataFrame you can calculate the count of Null, None, NaN or Empty/Blank values in a column by using isNull() of Column class & SQL functions isnan() count() and when (). In PySpark Mar 27, 2024 · PySpark When Otherwise and SQL Case When on DataFrame with Examples – Similar to SQL and programming languages, PySpark supports a way to check multiple conditions in sequence and returns a value when the first condition met by using SQL like case when and when (). show() +------------------------------+ |count_if(startswith(fruit, a))| +------------------------------+ | 2 May 13, 2024 · Through various methods such as count() for RDDs and DataFrames, functions. Basically I want to count the rows that has negative stock number, so in order to organize I created a calculated column "Flag N" that represents if the current row is neg Aug 4, 2022 · PySpark Window function performs statistical operations such as rank, row number, etc. groupBy ('column_name_group'). count_distinct(col, *cols) [source] # Returns a new Column for distinct count of col or cols. agg (functions) where, column Jun 29, 2021 · In this article, we are going to see how to delete rows in PySpark dataframe based on multiple conditions. It’s a transformation operation, meaning it’s lazy—Spark plans the change but waits for an action like show to execute it. date, datetime. row_number # pyspark. window module provides functions like row_number(), rank (), and dense_rank () to add ranking-based columns to a DataFrame. index, and apply() with lambda functions. How do you use count in PySpark? In Pyspark, there are two ways to get the count of distinct values. sql. Jun 30, 2025 · Add Column with Row Number to DataFrame by Partition You can use the row_number () function to add a new column with a row number as value to the PySpark DataFrame. where("Cond = 1") I exclude the dates that cond is equal zero. count doesn't sum True s, it only counts the number of non null values. May 5, 2024 · Use groupBy (). GroupedData and agg () function is a method from the GroupedData class. I am looking for a general way to do multiple counts on arbitrary conditions, fast. pyspark. Assigning a unique row number to each row within a specified group or Nov 13, 2023 · This tutorial explains how to drop rows based on multiple conditions in a PySpark DataFrame, including an example. Sep 5, 2022 · f. In PySpark, would it be possible to obtain the total number of rows in a particular window? Right now I am using: Sep 19, 2018 · The best way to keep rows based on a condition is to use filter, as mentioned by others. Aug 10, 2022 · The problem is that sometimes, there are more than one Product_Number while it should be unique. I have a use-case to get counts from 1000's of delta tables and do some further processing based o Apr 17, 2025 · Master grouping by a column and counting rows in PySpark with Python SQL nested data minimal null handling and optimization tips for efficient ETL pipelines Apr 30, 2025 · Image by Author | Canva Did you know that 402. over(W)) Is there something wrong in how I have used the count function? What can I do so the values in column 'Actual' match with 'Expecting'? I see two issues with my output - the count starts at 1 when it should start from 0 for each group the Oct 22, 2022 · A Neat Way to Count Distinct Rows with Window functions in PySpark If you use PySpark you are likely aware that as well as being able group by and count elements you are also able to group by and count distinct elements. row_number() [source] # Window function: returns a sequential number starting at 1 within a window partition. Those rows are criteria for grouping the records and that rows will set the startime and endtime for each group. Sep 23, 2025 · PySpark Window functions are used to calculate results, such as the rank, row number, etc. Sep 23, 2025 · Similar to SQL GROUP BY clause, PySpark groupBy() transformation that is used to group rows that have the same values in specified columns into summary rows. Then find the count and max timestamp (endtime) for each group. PySpark is the go-to tool for that. I've a table named Sheet1 with a colunm named Stock Quantity. To answer the question as stated in the title, one option to remove rows based on a condition is to use left_anti join in Pyspark. Apr 17, 2025 · Filtering rows after a group-by operation in PySpark is a powerful technique for analyzing aggregated data. alias('y_cnt'), . For example, we may want to . For this, we are going to use these methods: Using where () function. We have to use any one of the functions with groupby while using the method Syntax: dataframe. Dec 28, 2020 · Just doing df_ua. In PySpark, you can use the filter and count functions to count values based on a specific condition in a DataFrame. Using filter () function. This guide dives For counting the number of rows we are using the count () function df. where() is an alias for filter(). That gets to the result: Jul 13, 2018 · pySpark count IDs on condition Asked 6 years, 10 months ago Modified 6 years, 10 months ago Viewed 4k times Jan 26, 2021 · I have a pyspark application running on EMR for which I'd like to monitor some metrics. Here, we will check for the column value in a conditional statement and pass it to the filter() method. To filter rows where a column value is greater than a threshold, use a comparison expression with col () or direct column syntax. We will be considering most common conditions like dropping rows with Null values, dropping duplicate rows, etc. We keep only rows where rn = 1, effectively deduplicating by email. Apr 17, 2025 · How to Filter Rows Using SQL Expressions in a PySpark DataFrame: The Ultimate Guide Diving Straight into Filtering Rows with SQL Expressions in a PySpark DataFrame Filtering rows in a PySpark DataFrame is a cornerstone of data processing for data engineers and analysts working with Apache Spark in ETL pipelines, data cleaning, and analytics. You can also create UDF to Dec 19, 2021 · Output: In PySpark, groupBy () is used to collect the identical data into groups on the PySpark DataFrame and perform aggregate functions on the grouped data. May 16, 2024 · By using countDistinct () PySpark SQL function you can get the count distinct of the DataFrame that resulted from PySpark groupBy (). Step-by-step tutorial with examples and expected outputs. filter # DataFrame. May 12, 2024 · While working on PySpark SQL DataFrame we often need to filter rows with NULL/None values on columns, you can do this by checking IS NULL or IS NOT NULL conditions. In conclusion, PySpark’s GROUP BY COUNT operation offers a powerful mechanism for aggregating and analyzing data based on specified criteria. count() method is used to use the count of the DataFrame. This filters the DataFrame based on the condition and returns the count of matching rows. Method 1: Using Logical expression Here we are going to use the logical expression to filter the row. For example count loaded, saved rows. The condition is specified as a string that is evaluated for each row in the DataFrame. col('fruit'). count("B"). From basic array filtering to complex conditions, nested arrays, SQL expressions, and performance optimizations, you’ve got a versatile toolkit for processing complex datasets. The pyspark. functions to aggregate values based on specific conditions. count() for counting rows after grouping, PySpark provides versatile tools for efficiently computing counts at scale. groupBy () function returns a pyspark. Sometimes, you need to filter data based on conditions that aren’t Oct 29, 2018 · Creating a row number of each row in PySpark DataFrame using row_number () function with Spark version 2. In this article, I’ve explained the concept of window functions, syntax, and finally how to use them with PySpark SQL and PySpark DataFrame API. not being null) How can I achieve my expected output using window functions I'm trying to group a data frame, then when aggregating rows, with a count, I want to apply a condition on rows before counting. May 21, 2025 · What are Missing or Null Values? In PySpark, missing values are represented as null (for SQL-like operations) or NaN (for numerical data, especially in floating-point columns). Once that is in place we can use window based calculations (that require sorting rows) to provide the cycle counts Oct 18, 2021 · I cannot group by length or do other tricks like that. countDistinct () is used to get the count of unique values of the specified column. We’ll tackle key errors to keep your pipelines robust. In this article, I will explain how to count the number of rows with conditions in DataFrame by using these functions with examples. count() is enough, because you have selected distinct ticket_id in the lines above. Apr 17, 2025 · Diving Straight into Filtering Rows with Multiple Conditions in a PySpark DataFrame Filtering rows in a PySpark DataFrame based on multiple conditions is a powerful technique for data engineers using Apache Spark, enabling precise data extraction for complex queries in ETL pipelines. , over a range of input rows. Aug 19, 2025 · 1. Jan 31, 2023 · In Apache Spark, the where() function can be used to filter rows in a DataFrame based on a given condition. By chaining these you can get the count distinct of PySpark DataFrame. To do this by using the rank() function along with Window. 7 million terabytes of data are created each day? This amount of data that has been collected needs to be aggregated to find hidden insights or discover trends. Feb 6, 2018 · I have a dataframe which contains null values: from pyspark. This tutorial provides a comprehensive guide on how to accurately count the number of values in a specific column of a PySpark DataFrame that meet one or more conditional requirements. This solution is close to the one by @pault, but when there are several rows with the maximum value, it only keeps one of them, which I find better. They allow computations like sum, average, count, maximum, Apr 17, 2025 · Diving Straight into Filtering Rows with Null or Non-Null Values in a PySpark DataFrame Filtering rows in a PySpark DataFrame based on whether a column contains null or non-null values is a critical skill for data engineers using Apache Spark. It is similar to Python’s filter () function but operates on distributed datasets. Apr 10, 2025 · Here is the output. Jul 23, 2025 · In this article, we are going to learn how to split data frames based on conditions using Pyspark in Python. count() but I want to use selectExpr. To count the True values, you need to convert the conditions to 1 / 0 and then sum: cnt_cond(F. Rows are ordered based on the condition specified, and the assigned numbers reflect the row’s position Jun 27, 2018 · I have a DataFrame with a column "Speed". Mar 27, 2024 · The spark. May 12, 2024 · 1. functions. 2 Asked 7 years ago Modified 1 year, 10 months ago Viewed 63k times Apr 17, 2025 · The primary method for filtering rows in a PySpark DataFrame is the filter () method (or its alias where ()), which selects rows meeting a specified condition. partitionBy("A"). count() for counting non-null values in columns, and GroupedData. Syntax: filter ( condition) Parameters: Condition: Logical condition or Feb 16, 2018 · Another solution is to number the rows via row_number() using a window partitioned by A in the order of B. We will cover the following topics: Drop rows with condition using where () and filter () keyword. Dec 19, 2021 · In this article, we will discuss how to do Multiple criteria aggregation on PySpark Dataframe. agg() in PySpark to calculate the total number of rows for each group by specifying the aggregate function count. With PySpark, you can group data by specific columns and apply functions like sum, average, or count for deeper analysis. col('z') > 230). The values None, NaN are considered NA. What is the WithColumn Operation in PySpark? The withColumn method in PySpark DataFrames adds a new column or replaces an existing one with values derived from expressions, calculations, or conditions. Data frame in use: In PySpark, groupBy () is used to collect the identical data into groups on the PySpark DataFrame and perform aggregate functions on the grouped data. Drop rows with NA or missing Mar 13, 2022 · What I would want is, instead of aggregating by interquartiles, to aggregate by a count of the number of rows per group that satisfy the condition of being below the outlier threshold. The row_number() function assigns a unique numerical rank to each row within a specified window or partition of a DataFrame. on a group, frame, or collection of rows and returns results for each row individually. I am guessing that the reason for the slow performance is that for every count call, my pyspark notebook starts some Spark processes that have significant overhead. In this article, I will explain how to get the count of Null, None, NaN, empty or blank values from all or multiple selected columns of PySpark DataFrame. This method counts the occurrences of each unique value in the specified column. Sometimes, we may want to split a Spark DataFrame based on a specific condition. Syntax: dataframe. Feb 26, 2025 · To count the number of rows that meet a specific condition in a Polars DataFrame, use the filter() method along with count(). Nov 19, 2025 · Aggregate functions in PySpark are essential for summarizing data across distributed datasets. Also it returns an integer - you can't call distinct on an integer. Filter () function is used to filter the rows from RDD/DataFrame based on the given condition or SQL expression. Nov 27, 2023 · Hello folks, Is there a way with sql query to get count from delta table metadata without doing count(*) on each of table? Wondering, if this information is stored in any of INFORMATION_SCHEMA tables. pandas. Apr 17, 2025 · How to Compute a Row Number Using a Window Function in a PySpark DataFrame: The Ultimate Guide Introduction: The Power of Row Numbers in PySpark Computing row numbers using a window function is a fundamental operation for data engineers and analysts working with Apache Spark in ETL pipelines, data ranking, or analytics. col('y') > 12453). While PySpark's DataFrame API offers powerful Mar 18, 2016 · PySpark count values by condition Asked 9 years, 8 months ago Modified 6 years, 11 months ago Viewed 28k times Jun 23, 2025 · Snapshot of the dataframe Pyspark groupBy with Count To count the number of rows in each group, we can use the count () function. This approach gives you fine-grained control, especially when you need to choose which duplicate to keep based on sorting criteria. count_distinct # pyspark. cnt_cond(F. You can create a dataframe with the rows breaking the 5 minutes timeline. filter(condition) [source] # Filters rows using the given condition. Whether you're selecting employees meeting specific salary and age criteria, identifying transactions within a Jul 24, 2023 · PySpark Filter DataFrame by Column Value To filter a pyspark dataframe by a column value, we will use the filter() method. Creating Dataframe for demonstration: Oct 31, 2023 · This tutorial explains how to count the number of values in a column that meet a condition in PySpark, including an example. countDistinct() is a SQL function that could be used to get the count distinct of the selected multiple columns. It allows you to perform aggregate functions on groups of rows, rather than on individual rows, enabling you to summarize data and generate aggregate statistics. sql import functions as sf >>> df = spark. This function allows you to filter the data based on a specific condition and then count the remaining values in the column. This is useful for summarizing subsets of data, like totals for rows meeting a threshold, within or without groups. here is an example : Oct 16, 2023 · This tutorial explains how to count the number of occurrences of values in a PySpark DataFrame, including examples. I found the following answer but didn't help me: Window. Nov 6, 2020 · Count Rows from a separate dataframe with conditions - PySpark Asked 4 years, 5 months ago Modified 4 years, 5 months ago Viewed 67 times I want to filter dataframe according to the following conditions firstly (d<5) and secondly (value of col2 not equal its counterpart in col4 if value in col1 equal its counterpart in col3). After execution, we will get a pyspark dataframe with rows satisfying the condition. Oct 21, 2020 · But I need to get the count also of how many rows had that particular PULocationID NOTE: I can't add any other imports other than pyspark. PySpark Groupby Aggregate Example Use DataFrame. From basic average-based filtering to multiple aggregations, nested data, SQL expressions, and performance optimizations, you’ve got a robust toolkit for group-based tasks. It is also popularly growing to perform data transformations. createDataFrame( [("apple",), ("banana",), ("cherry",), ("apple",), ("banana",)], ["fruit"]) >>> df. May 8, 2022 · I have a DataFrame with a column "age" and I want to count how many rows with age = 60, for example. These occur due to Jan 2, 2019 · Hello guys, I'm newbie to PowerBI and I need some logical help. I Apr 9, 2019 · Add a group count column and filter for where the count is equal to 1. count() returns the number of rows in the dataframe. So by this we can do multiple aggregations at a time. All these conditions use different functions and we will discuss them in detail. What I am trying to do is from the ones that are in the dataframe more than once, to take the ones whose Condition is New without touching the rest. df. For this there is a function monotonically_increasing_id() that can be used to assign a unique and "increasing value" for each row so that is we sort by that column the original row sequence is retained. count_if(sf. Grouping and Summing Data Let’s calculate the total listening hours and sessions Jul 3, 2025 · PySpark rank without Partition You can also use the rank () function to add a row number (rank) as a new column to a DataFrame without applying any partitioning. count # DataFrame. Jan 12, 2024 · Learn the syntax of the count\\_if aggregate function of the SQL language in Databricks SQL and Databricks Runtime. aggregate_operation ('column_name') Filter the data means removing some data based on the condition. startswith('a'))). createDataFrame( [(125, '2012-10-10', 'tv'), (20, '2012-10-10 Feb 1, 2024 · Row number that resets numbers based on a condition Asked 1 year, 7 months ago Modified 1 year, 7 months ago Viewed 101 times Count Operation in PySpark DataFrames: A Comprehensive Guide PySpark’s DataFrame API is a powerful tool for big data processing, and the count operation is a key method for determining the total number of rows in a DataFrame, returning an integer value. alias('z_cnt') Jun 29, 2021 · In this article, we will discuss how to count rows based on conditions in Pyspark dataframe. withColumn("cnt", F. Nov 2, 2023 · You need a way to retain the current row order so that you count the cycles. groupBy(). I tr Jul 23, 2025 · In this article, we are going to drop the rows in PySpark dataframe. I know how to solve this using select or df. rowsBetween(-2, -1)) ) But when I use . email values, ordered by employee_id. Spark Count is an action that results in the number of rows available in Apr 17, 2025 · Filtering PySpark DataFrame rows with array_contains () is a powerful technique for handling array columns in semi-structured data. Mar 9, 2021 · In the 5 rows we have only 3 different ID, so the sum must be of 3 elements: -1 for the ID 144 (forth row), -1 for the ID 015 (third row) and 1 for the ID 198 (fifth row) for a total of -1. datetime, None, Series] ¶ Count non-NA cells for each column. We will discover how you can use basic or advanced aggregations using actual interview datasets! Let’s get started! Basic Aggregation In this section Apr 3, 2024 · To count the number of values in a column in PySpark, while also applying a condition on the column’s values, you can use the “filter” function. The filter function allows you to specify a condition to filter rows, and the count function will give you the number of rows after the filter is applied. Introduction to PySpark DataFrame Filtering PySpark filter() function is used to create a new DataFrame by filtering the elements from an existing DataFrame based on the given condition or SQL expression. Can I efficiently add a column with, for each row, the number of rows in the DataFrame such that their "Speed" is within +/2 from the row "Speed"? results = Learn how to use the count () function in PySpark to count rows and records in DataFrames. over(windowSpec. Apr 17, 2025 · The ROW_NUMBER () function assigns a unique number to each row within groups of identical contact. Dec 19, 2023 · Count distinct values with conditions Asked 6 years, 11 months ago Modified 1 year, 11 months ago Viewed 12k times Jun 26, 2025 · How to get pandas count rows with a condition? To count the number of rows that satisfy single/multiple conditions in pandas DataFrame using shape(), len(), df. Any clue? Oct 6, 2023 · This tutorial explains how to select rows based on column values in a PySpark DataFrame, including several examples. functions import col Dec 23, 2020 · Week count_total_users count_vegetable_users 2020-40 2345 457 2020-41 5678 1987 2020-42 3345 2308 2020-43 5689 4000 This desired output should be the count distinct for 'users' values inside the column it belongs to. orderBy(col("C")) main_df = main_df. Aggregations After Filtering What if you wonder how user activity varies across countries? Aggregations help you summarize filtered data to uncover patterns and trends. Whether you’re assessing dataset size, validating data transformations, or monitoring data volume in a pipeline, count provides a Jul 19, 2022 · Only keep rows with specific condition in PySpark Asked 3 years, 4 months ago Modified 3 years, 4 months ago Viewed 1k times pyspark. It does not take any parameters, such as column names. >>> from pyspark. g. In this case, this function considers the entire DataFrame as a single group and adds row numbers based on the global Jul 11, 2023 · I have a pyspark dataframe with below data [ My code: W = Window. Sep 5, 2025 · In PySpark, the row_number () window function in PySpark is used to return a new column containing a unique sequential number to each row based on a specified order. It is analogous to the SQL WHERE clause and allows you to apply filtering criteria to DataFrame rows Jan 9, 2021 · Pyspark group by and count data with condition Asked 4 years, 10 months ago Modified 4 years, 10 months ago Viewed 1k times Apr 17, 2025 · Diving Straight into Counting Rows in a PySpark DataFrame Need to know how many rows are in your PySpark DataFrame—like customer records or event logs—to validate data or monitor an ETL pipeline? Counting the number of rows in a DataFrame is a core skill for data engineers working with Apache Spark. otherwise () expressions, these works similar to “ Switch" and "if then else" statements. DataFrame. count(axis: Union [int, str, None] = None, numeric_only: bool = False) → Union [int, float, bool, str, bytes, decimal. It provides a quick way to assess dataset size and ensure data integrity. Spark data frames are a powerful tool for working with large datasets in Apache Spark. Let’s see these two ways with examples. These are handy when making aggregate operations in a specific window frame on DataFrame columns. collect_list("Val"). DataFrame. Decimal, datetime. txi ljyx agenvps mcvuma tbkhj jplmoft yfqmk arbl lque urhhj wrij hfzuyj kkubkh dwj czv