Pyspark subset dataframe. fillna() and DataFrameNaFunctions.

Pyspark subset dataframe Select columns in PySpark dataframe – A Comprehensive Guide to Selecting Columns in different ways in PySpark dataframe Join thousands of students who advanced their careers with MachineLearningPlus. DataFrame(jdf, sql_ctx) [source] # A distributed collection of data grouped into named columns. If the Apr 30, 2021 · In this example, we are going to create our own custom dataset and use the drop () function to eliminate the rows that have null values. Understanding Duplicate Row Filtering in PySpark Filtering duplicates in PySpark means identifying and either keeping or removing rows that are identical based on all columns or a subset of columns. fill(). select ( columns_names ) Note: We are specifying our path to spark directory using the findspark. 5 hundred thousand records in to one data frame and next 2. java_gateway. substring(str, pos, len) [source] # Substring starts at pos and is of length len when str is String type or returns the slice of byte array that starts at pos in byte and is of length len when str is Binary type. Syntax: dataframe. A DataFrame with subset (or all) of columns. So theoretically their efficiency should be equivalent. In the below code, we have passed the subset='City' parameter in the dropna () function which is the column name in respective of City column if any of the NULL value present in that column then we are dropping that row from the Dataframe. Changed in version 3. write Oct 6, 2023 · This tutorial explains how to select rows based on column values in a PySpark DataFrame, including several examples. Whether you’re analyzing large datasets, preparing data for machine learning models, or performing transformations, you often need to isolate specific subsets of data based on certain conditions. To subset or filter the data from the dataframe we are using the filter () function. How do I select a Jul 4, 2017 · I have a pyspark data frame with more than one million records, I need to subset in to 4 datafames. It is similar in functionality to the SQL WHERE clause, enabling you to select a subset of rows from your DataFrame that meet specific criteria. drop_duplicates(subset=None) [source] # drop_duplicates() is an alias for dropDuplicates(). From the docs: The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive. Want to quickly create Data Visualization from Python Pandas Dataframe with No code? Jul 1, 2025 · Learn how Spark DataFrames simplify structured data analysis in PySpark with schemas, transformations, aggregations, and visualizations. Jul 12, 2017 · df. Sep 25, 2024 · In PySpark, pyspark. Jan 22, 2020 · The following is a toy example that is a subset of my actual data's schema. They allow to manipulate and analyze data in a structured way, using SQL-like operations. It provides a concise and efficient way to work with data by specifying the start, stop, and step parameters. For a streaming DataFrame, it will keep all data across triggers as intermediate state to drop RandomSplit Operation in PySpark DataFrames: A Comprehensive Guide PySpark’s DataFrame API is a powerful tool for big data processing, and the randomSplit operation is a key method for dividing a DataFrame into multiple random subsets based on specified proportions. Oct 6, 2023 · This tutorial explains how to select rows by index in a PySpark DataFrame, including an example. dropDuplicates ¶ DataFrame. Do I need to read in the full file or is there a different approach. fillna() and DataFrameNaFunctions. e, if we want to remove duplicates purely based on a subset of columns and retain all columns in the original dataframe. DataFrame — PySpark master documentationDataFrame ¶ Aug 19, 2025 · PySpark SQL contains () function is used to match a column value contains in a literal string (matches on part of the string), this is mostly used to filter rows on DataFrame. Sep 25, 2025 · PySpark sampling (pyspark. replace # DataFrame. repartition(1) . functions. Any idea on how to do this? Na. plot is both a callable method and a namespace attribute for specific plotting methods of the form DataFrame. select (parameter). Nov 4, 2016 · I am trying to filter a dataframe in pyspark using a list. subtract(yesterdaySchemaRDD) onlyNewData Aug 4, 2021 · In this article, we will discuss how to select columns from the pyspark dataframe. Creating a new, smaller pyspark. DataFrame ¶ Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. fill() are aliases of each other. DataFrame. dropDuplicates(subset=None) [source] # Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. g. I abbreviated it for brevity. Spark data frames are a powerful tool for working with large datasets in Apache Spark. There are multiple dataframe functions for data sampling, click on function name in the below list and it will take you to the respective section of the page. 0 one could use subtract with 2 SchemRDDs to end up with only the different content from the first one val onlyNewData = todaySchemaRDD. <kind>. In this article, I will explain different examples of how to select distinct values of a column from DataFrame. Like 1st 2. May 19, 2021 · In this article, we'll discuss 10 PySpark functions that are most useful and essential to perform efficient data analysis of structured data. What I would like to do is subset the second dataframe based on if the client numbers in it match with the ones in the first dataframe. For a streaming DataFrame, it will keep all data across triggers as intermediate state to drop duplicates rows. To do this we will use the select () function. , sum, count, average) to each group to produce Jul 2, 2020 · Pyspark - How to apply a function only to a subset of columns in a DataFrame? Asked 5 years, 4 months ago Modified 5 years, 4 months ago Viewed 1k times Oct 18, 2017 · I am looking for a way to select columns of my dataframe in PySpark. For example, we may want to Nov 14, 2025 · Learn how to load and transform data using the Apache Spark Python (PySpark) DataFrame API, the Apache Spark Scala DataFrame API, and the SparkR SparkDataFrame API in Azure Databricks. replace provides a flexible way to Jul 23, 2025 · In this article, we are going to learn how to split data frames based on conditions using Pyspark in Python. PySpark, the Python API for Apache Spark, provides robust tools for handling massive distributed datasets, primarily through its core structure: the DataFrame. New in version 1. replace in data engineering workflows. _NoValueType, None] = <no value>, subset: Optional [List [str]] = None) → DataFrame ¶ Returns a new DataFrame replacing a value with another value. 6. Nov 15, 2021 · how to use a variable in the where clause to subset the dataframe in pyspark Asked 4 years ago Modified 4 years ago Viewed 1k times Jul 23, 2025 · Output: PySpark Under the Hood The randomsplit () function in PySpark is used to randomly split a dataset into two or more subsets with a specified ratio. Aug 19, 2025 · In this PySpark article, you will learn how to apply a filter on DataFrame columns of string, arrays, and struct types by using single and multiple conditions and also using isin() with PySpark (Python Spark) examples. filter function allows you to filter rows in a Spark DataFrame based on one or more conditions. Syntax `` DataFrame. DataFrame ¶ class pyspark. replace ¶ DataFrame. A DataFrame is equivalent to a relational table in Spark SQL, and can be created using various functions in SparkSession: Nov 16, 2025 · Mastering PySpark DataFrame Manipulation As data volumes continue to grow exponentially, the ability to efficiently manipulate and subset large datasets becomes paramount for modern data engineers and scientists. Why is take(100) basically instant, whereas df. DataFrame(jdf: py4j. pyspark. It takes three parameters: the column containing the string, the starting index of the substring (1-based), and optionally, the length of the substring. Column. replace() are aliases of each other. replace(to_replace, value=<no value>, subset=None) [source] # Returns a new DataFrame replacing a value with another value. 1. 5 hundred thousand records Jun 2, 2024 · This article is a one-stop guide to numerous DataFrame operations in PySpark. By default, drop_duplicates() will verify every column in the DataFrame to look for duplicate rows. We are going to drop all the rows in that have Null values in the dataframe. You can pass column names as strings, col () expressions, or column objects, and even include expressions for computed columns. fillna ¶ DataFrame. drop_duplicates(subset=None) Let‘s examine the parameters: df – The PySpark DataFrame you want deduplicated subset – An optional list of column names to check for duplicate rows. limit(1000) However, now when I try to filter the DataFrame on the string value of one of the columns, I get different results every time I run the following: How to get this kind of subset from a DataFrame in Pyspark? Asked 5 years, 5 months ago Modified 5 years, 5 months ago Viewed 931 times Oct 12, 2023 · This tutorial explains how to use fillna() in PySpark to fill null values in specific columns, including several examples. replace Operation in PySpark DataFrames: A Comprehensive Guide PySpark’s DataFrame API is a powerful tool for big data processing, and the na. Apr 17, 2025 · The primary method for selecting specific columns from a PySpark DataFrame is the select () method, which creates a new DataFrame with the specified columns. Since we are creating our own data we need to specify our schema along with it in order to create the dataset. DataFrame. When replacing, the new Aug 11, 2022 · In PySpark, we can use select function to select a subset or all columns from a DataFrame. 3. _globals. Oct 11, 2023 · This tutorial explains how to select the top N rows in a PySpark DataFrame, including several examples. init () function in order to Apr 9, 2015 · In Spark version 1. The slice function in PySpark is a powerful tool that allows you to extract a subset of elements from a sequence or collection. Feb 20, 2018 · Initially I misunderstood and thought you wanted to slice the columns. drop_dupli pyspark. DataFrame pyspark. I have 5 co Mar 14, 2023 · I see you can use use sample to return a random sample of items but is there anyway when reading in a csv file as a dataframe for example we only read in a specified random selection of rows of a specific number? Is there anyway to read in the csv but pick 100 random rows from that csv. fillna # DataFrame. dropDuplicates(subset: Optional[List[str]] = None) → pyspark. This guide shows the key patterns and how to avoid common pitfalls. Grouping involves partitioning a DataFrame into subsets based on unique values in one or more columns—think of it as organizing employees by their department. Step-by-step guide to replacing null values efficiently in various data types including dates, strings, and numbers. SparkSession. 1 Apr 17, 2025 · Diving Straight into Showing the Schema of a PySpark DataFrame Need to inspect the structure of a PySpark DataFrame—like column names, data types, or nested fields—to understand your data or debug an ETL pipeline? Showing the schema of a DataFrame is an essential skill for data engineers working with Apache Spark. May 28, 2024 · The PySpark substring() function extracts a portion of a string column in a DataFrame. Sep 29, 2024 · Filtering data is one of the most common operations you’ll perform when working with PySpark DataFrames. dataframe. If the random number is less than the ratio May 8, 2025 · You can replace column values of PySpark DataFrame by using SQL string functions regexp_replace(), translate(), and overlay() with Python examples. This class provides methods to specify partitioning, ordering, and single-partition constraints when passing a DataFrame as a table argument to TVF (Table-Valued Function)s including UDTF (User-Defined Table Function)s. Sep 3, 2023 · In PySpark, a DataFrame is a distributed collection of data organized into named columns. Select all columns in the DataFrame. asTable returns a table argument in PySpark. In this blog, we’ll … Take Operation in PySpark DataFrames: A Comprehensive Guide PySpark’s DataFrame API is a powerful tool for big data processing, and the take operation is a key method for retrieving a specified number of rows from a DataFrame as a list of Row objects. Aggregation then applies functions (e. Jun 12, 2025 · (Spark with Python) PySpark DataFrame can be converted to Python pandas DataFrame using a function toPandas(), In this article, I will explain how to Mar 27, 2024 · How does PySpark select distinct works? In order to perform select distinct/unique rows from all columns use the distinct () method and to perform on a single column or multiple selected columns use dropDuplicates (). I am brand new to pyspark and want to translate my existing pandas / python code to PySpark. DataFrame # class pyspark. The select () function allows us to select single or multiple columns in different formats. Here is an example of PySpark DataFrame subsetting and cleaning: After the data inspection, it is often necessary to clean the data which mainly involves subsetting, renaming the columns, removing duplicated rows etc Mar 27, 2024 · Spark SQL provides a slice() function to get the subset or range of elements from an array (subarray) column of DataFrame and slice function is part of the Spark SQL Array functions group. If the length is not specified, the function extracts from the starting index to the end of the string. drop () The subset parameter inside the drop method accepts a list of column names (List [String]) such that the Null check happens only in the mentioned subset of columns. This can help in working with a smaller dataset that is representative of the original large dataset, making it easier to perform preliminary analysis or testing without processing the entire dataset. contains() function works in conjunction with the filter() operation and provides an effective way to select rows based on substring presence within a string column. replace() and DataFrameNaFunctions. when() as using filter or pyspark Oct 6, 2023 · This tutorial explains how to select multiple columns in a PySpark DataFrame, including several examples. Sometimes, we may want to split a Spark DataFrame based on a specific condition. dropDuplicates # DataFrame. If None, all columns are checked. Whether you’re cleaning data, extracting 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. plot. Row s, a pandas DataFrame and an RDD consisting of such a list. Syntax: dataframe_name. Whether you’re performing exploratory data analysis, testing algorithms on smaller datasets, or creating training samples, sample provides a flexible way to reduce May 15, 2015 · To add on, it may not be the case that we want to groupBy all columns other than the column (s) in aggregate function i. dropna(how='any', thresh=None, subset=None) [source] # Returns a new DataFrame omitting rows with null or NaN values. Columns id, col_2 and col_3 are directly selected from previous DataFrame, while column sqrt_col_4_plus_5 is generated by the math functions. We have hundreds of functions for column manipulation in pyspark. May 16, 2021 · So in this article, we are going to learn how ro subset or filter on the basis of multiple conditions in the PySpark dataframe. Created using Sphinx 4. Function used: In PySpark we can select columns using the select () function. sample()) is a mechanism to get random sample records from the dataset, this is helpful when you have a larger dataset and wanted to analyze/test a subset of the data for example 10% of the original file. 0. replace(to_replace: Union [LiteralType, List [LiteralType], Dict [LiteralType, OptionalPrimitiveType]], value: Union [OptionalPrimitiveType, List [OptionalPrimitiveType], pyspark. The pyspark. 0: Supports Spark Connect. You can use withWatermark() to limit Jul 8, 2024 · The sample() function in PySpark is used to create a new DataFrame by randomly sampling a subset of the rows from an existing DataFrame. show () where, dataframe is the dataframe name parameter is the column (s) to be selected show () function is used to display the selected column Let's create a sample dataframe May 25, 2025 · Learn how to handle missing data in PySpark using the fillna () method. Oct 19, 2017 · I want to access the first 100 rows of a spark data frame and write the result back to a CSV file. Apr 8, 2018 · If you want to modify a subset of your DataFrame and keep the rest unchanged, the best option would be to use pyspark. Setting this fraction to 1/numberOfRows leads to random results, where somet Mar 1, 2019 · I have loaded CSV data into a Spark DataFrame. Optimized by We create a DataFrame with 21 columns via a for loop, then we only select 4 columns by select. What is the Select Operation in PySpark? The select method in PySpark DataFrames is your key to customizing data—grabbing specific columns, creating new ones with calculations, or renaming them, all while spitting out a fresh DataFrame. first(), but not sure about columns given that they do not have column names. Feb 7, 2023 · In this article, we will learn how to select columns in PySpark dataframe. drop() are aliases of each other. Whether you’re cleaning datasets, simplifying analysis, or maintaining data quality, distinct provides an efficient way to remove redundancy. I want to subset my dataframe so that only rows that contain specific key words I'm looking for in ' Table Argument # DataFrame. I am working with Pyspark 1. JavaObject, sql_ctx: Union[SQLContext, SparkSession]) ¶ A distributed collection of data grouped into named columns. Plotting # DataFrame. DataFrameNaFunctions class provides several functions to deal with NULL/None values, among these drop() function is used to remove/drop rows with NULL values in DataFrame columns, alternatively, you can also use df. dropna() and DataFrameNaFunctions. Apr 17, 2025 · Let’s dive in with a natural, conversational tone to make the concepts approachable and actionable. na. It resembles a table in a relational database or a spreadsheet in which data is arranged in rows and columns. fillna(value: Union[LiteralType, Dict[str, LiteralType]], subset: Union [str, Tuple [str, …], List [str], None] = None) → DataFrame ¶ Replace null values, alias for na. Executed through spark. Value can have None. The above filter function chosen mathematics_score greater than 50. Feb 27, 2023 · According to spark official documentation, DataFrame. Among its powerful operations, the filter method stands out as a key tool for refining data by selecting rows that meet specific conditions. Go from Beginner to Data Science (AI/ML/Gen AI) Expert through a structured pathway of 9 core specializations and build industry grade projects. Subqueries in PySpark: A Comprehensive Guide Subqueries in PySpark SQL bring the power of nested queries to your big data toolkit, letting you filter, compare, and refine datasets with precision using SQL syntax within Spark’s distributed environment. select (*cols) ` This function returns a new DataFrame object based on the projection expression list. Distinct Operation in PySpark DataFrames: A Comprehensive Guide PySpark’s DataFrame API is a powerful framework for big data processing, and the distinct operation is a key method for eliminating duplicate rows to ensure data uniqueness. If you want to select a subset of rows, one method is to create an index column using monotonically_increasing_id(). For a static batch DataFrame, it just drops duplicate rows. Oct 9, 2023 · This tutorial explains how to create a PySpark dataframe from an existing dataframe, including several examples. Apr 17, 2025 · Understanding Grouping and Aggregation in PySpark Before diving into the mechanics, let’s clarify what grouping and aggregation mean in PySpark. I am looking to build a PySpark dataframe that contains 3 fields: ID, Type and TIMESTAMP pyspark. Sometimes, you need to filter rows based on conditions derived from another query, known as a subquery. Is there an equivalent in Spark Dataframes? Pandas: df. I want to either filter based on the list or include only those records with a value in the list. substring # pyspark. In addition, df. Sample Operation in PySpark DataFrames: A Comprehensive Guide PySpark’s DataFrame API is a powerful tool for big data processing, and the sample operation is a key method for extracting a random subset of rows from a DataFrame. Aug 12, 2023 · PySpark DataFrame's sample (~) method returns a random subset of rows of the DataFrame. limit(100) . Under the hood, the function first creates a random number generator, then for each element in the dataset, it generates a random number between 0 and 1, and compares it to the specified ratio. 5. replace operation is a key method for replacing specific values, including nulls or NaNs, in a DataFrame with other values. DataFrame Creation # A PySpark DataFrame can be created via pyspark. So all client numbers that are not also in the first dataframe should be deleted, in this case all rows where bc = 01234. It allows you to extract relevant data subsets efficiently, reducing processing Aug 1, 2016 · Question: in pandas when dropping duplicates you can specify which columns to keep. Oct 6, 2023 · This tutorial explains how to keep certain columns in a PySpark DataFrame, including several examples. sql. dropna(). Apr 17, 2025 · Filtering rows in a PySpark DataFrame is a cornerstone of data processing for data engineers working with Apache Spark in ETL pipelines, data cleaning, or analytics. dropna # DataFrame. sql, these queries within queries work on DataFrames registered as temporary views, leveraging the Catalyst optimizer Oct 16, 2025 · In PySpark, fillna() from DataFrame class or fill() from DataFrameNaFunctions is used to replace NULL/None values on all or selected multiple columns with either zero (0), empty string, space, or any constant literal values. createDataFrame takes the schema argument to specify the schema of the DataFrame. 4. fillna(value, subset=None) [source] # Returns a new DataFrame which null values are filled with new value. createDataFrame typically by passing a list of lists, tuples, dictionaries and pyspark. drop () can also specify a subset. drop_duplicates # DataFrame. I need to slice this dataframe into two different dataframes, where each one contains a set of columns from the original dataframe. PySpark Dataframe Sampling This tutorial will explain how to use different sample functions available in Pyspark to extract subset of dataframe from the main dataframe. sort_values('actual_datetime', ascending=False). Values to_replace and value must have the same type and can only be numerics, booleans, or strings. Whether you’re standardizing data, correcting errors, or handling missing values, na. function and pyspark. Jul 19, 2021 · Example 4: Cleaning data with dropna using subset parameter in PySpark. 2. Whether you’re previewing data, debugging transformations, or extracting a small sample for local analysis, take provides an efficient way This article summarises how data engineers and data teams can leverage pyspark. I would like to investigate if the transformations and joins succeed and if the datafram looks like it is intended, but how can I show a small subset of the dataframe. Nov 22, 2025 · PySpark’s select, filter/where, and withColumn APIs solve this by keeping transformations explicit, type-safe, and testable. So the dataframe is subsetted or filtered with mathematics_score greater than 50 Subset or filter data with multiple conditions in pyspark (multiple and) Subset or filter data with multiple conditions can be done using filter () function, by passing the conditions inside the filter functions, here we have used and operators Jul 23, 2025 · In this article, we are going to learn how to slice a PySpark DataFrame into two row-wise. Parameters valueint, float, string, bool or dict Value to replace null values with. Nov 20, 2019 · I have a big pyspark dataframe which I am performing a number of transformations on and joining with other dataframes. Slicing a DataFrame is getting a subset containing all rows from one index to another. Can we do something similar in spark dataframe? Nov 14, 2025 · Learn how to load and transform data using the Apache Spark Python (PySpark) DataFrame API, the Apache Spark Scala DataFrame API, and the SparkR SparkDataFrame API in Databricks. fillna(0, subset=['a', 'b']) There is a parameter named subset to choose the columns unless your spark version is lower than 1. For example, df1 = df[10:20]. May 20, 2024 · Handling Nulls in Spark DataFrame Dealing with null values is a common task when working with data, and Apache Spark provides robust methods to handle nulls in DataFrames. Dec 1, 2015 · How can I get a random row from a PySpark DataFrame? I only see the method sample() which takes a fraction as parameter. For the first row, I know I can use df. In this article, I will explain the syntax of the slice () function and it’s usage with a scala example. May 12, 2024 · In PySpark, select() function is used to select single, multiple, column by index, all columns from the list and the nested columns from a DataFrame, PySpark select () is a transformation function hence it returns a new DataFrame with the selected columns. Nov 6, 2023 · The syntax is simple: df. Select a column with other expressions in the DataFrame. Aug 11, 2017 · val df_small = df. It touches on important operations like data loading, data manipulation, filtering, aggregation, and joining, among Mastering the Spark DataFrame Filter Operation: A Comprehensive Guide The Apache Spark DataFrame API is a cornerstone of big data processing, offering a structured and efficient way to handle massive datasets. When it is omitted, PySpark infers the I'm trying to filter a PySpark dataframe that has None as a row value: 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. My code below does not work: # define a Jul 23, 2025 · Subset in dataframe. Jul 10, 2020 · In pandas dataframe df, one can extract a subset of rows and store it in another pandas data frame. Method 1: Using limit () and subtract () functions In this method, we first make a PySpark DataFrame with precoded data using createDataFrame (). . kzip sesot spmd chsf vwugydo mxmqgi jyshdg mmnr futt wswgxj tqr cxiz nannk gwu mezayl