When working with data, you often encounter scenarios where a single column contains values that need to be split into multiple columns for easier analysis or processing. PySpark provides flexible way to achieve this using the split() function. In this article, we’ll cover how to split a single column into multiple columns in a PySpark […]
Tag: pyspark basic
PySpark | How to Perform Data Type Casting on Columns in a DataFrame?
When working with data in PySpark, ensuring the correct data type for each column is essential for accurate analysis and processing. Sometimes, the data types of columns may not match your requirements. For example, a column containing numeric data might be stored as a string (string), or dates may be stored in an incorrect format.
PySpark | How to Remove Non-ASCII Characters from a DataFrame?
When working with text data in Spark, you might come across special characters that don’t belong to the standard English alphabet. These characters are called non-ASCII characters. For example, accented letters like é in “José” or symbols like emojis 😊. Sometimes, you may need to clean your data by removing these characters. This article will show you how to identify and remove non-ASCII characters from a Spark DataFrame.
PySpark | How to Handle Nulls in DataFrame?
Handling NULL (or None) values is a crucial task in data processing, as missing data can skew analysis, produce errors in data transformations, and degrade the performance of machine learning models. In PySpark, dealing with NULL values is a common operation when working with distributed datasets. PySpark provides several methods and techniques to detect, manage, and clean up missing or NULL values in a DataFrame.
In this blog post, we’ll explore how to handle NULL values in PySpark DataFrames, covering essential methods like filtering, filling, dropping, and replacing NULL values.
PySpark | How to remove duplicates from Dataframe?
When working with large datasets in PySpark, it’s common to encounter duplicate records that can skew your analysis or cause issues in downstream processing. Fortunately, PySpark provides some methods to identify and remove duplicate rows from a DataFrame, ensuring that the data is clean and ready for analysis. In this article, we’ll explore two methods to remove duplicates from a PySpark DataFrame: dropDuplicates() and distinct().
PySpark | How to Sort a Dataframe?
Sorting data is a fundamental task in data processing, whether for analysis, reporting, or data transformation. In PySpark, sorting a DataFrame is a common operation that allows you to organize your data based on one or more columns. PySpark provides multiple ways to sort data efficiently, even when dealing with large datasets distributed across clusters.
In this blog post, we’ll explore various methods to sort a DataFrame in PySpark, covering both ascending and descending orders, sorting by multiple columns, and handling null values during sorting.
PySpark | How to Filter Data in DataFrame?
Filtering data is one of the most common operations you’ll perform when working with PySpark DataFrames. 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. PySpark provides several methods for filtering DataFrames, and this article will explore the most widely used approaches.
PySpark | How to Add a New Column in a Dataframe?
In PySpark, adding a new column to a DataFrame is a common and essential operation, often used for transforming data, performing calculations, or enriching the dataset. PySpark offers 3 main methods for this: withColumn(),select() and selectExpr(). These methods allow you to create new columns, but they serve different purposes and are used in different contexts.
This article will guide you through adding new columns using both methods, explaining their use cases and providing examples.
PySpark | How to Create a Dataframe?
In PySpark, a DataFrame is a distributed collection of data organized into named columns, similar to a table in a relational database or an Excel spreadsheet. DataFrames provide a powerful abstraction for working with structured data, offering ease of use, high-level transformations, and optimization features like catalyst and Tungsten. This article will cover how to […]
PySpark | How to Create a RDD?
Resilient Distributed Datasets (RDDs) are the core abstraction in PySpark, offering fault-tolerant, distributed data structures that can be operated on in parallel. Although the DataFrame API is more popular due to its higher-level abstractions, RDDs are still fundamental for certain low-level operations and are the building blocks of PySpark.
In this article, you’ll learn how to create RDDs in PySpark, the different ways to create them, and when you should use RDDs over DataFrames.