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Category: Dataframe Programming

  • Date and Time Functions- Pyspark Dataframes & Pyspark Sql Queries

    A quick reference for date manipulation in PySpark:– Function Description Works On Example (Spark SQL) Example (DataFrame API) to_date Converts string to date. String TO_DATE(‘2024-01-15’, ‘yyyy-MM-dd’) to_date(col(“date_str”), “yyyy-MM-dd”) to_timestamp Converts string to timestamp. String TO_TIMESTAMP(‘2024-01-15 12:34:56’, ‘yyyy-MM-dd HH:mm:ss’) to_timestamp(col(“timestamp_str”), “yyyy-MM-dd HH:mm:ss”) date_format Formats date or timestamp as a string. Date, Timestamp DATE_FORMAT(CURRENT_DATE, ‘dd-MM-yyyy’) date_format(col(“date_col”), “dd-MM-yyyy”)…

  • Window functions in PySpark on Dataframe programming

    Window functions in PySpark allow you to perform operations on a subset of your data using a “window” that defines a range of rows. These functions are similar to SQL window functions and are useful for tasks like ranking, cumulative sums, and moving averages. Let’s go through various PySpark DataFrame window functions, compare them with…

  • PySpark Control Statements Vs Python Control Statements- Conditional, Loop, Exception Handling

    Python control statements like if-else can still be used in PySpark when they are applied in the context of driver-side logic, not in DataFrame operations themselves. Here’s how the logic works in your example: Understanding Driver-Side Logic in PySpark Breakdown of Your Example This if-else statement works because it is evaluated on the driver (the main control point of…

  • Discuss Spark Data Types, Spark Schemas- How Sparks infers Schema?

  • String Manipulation on PySpark DataFrames

    String manipulation is a common task in data processing. PySpark provides a variety of built-in functions for manipulating string columns in DataFrames. Below, we explore some of the most useful string manipulation functions and demonstrate how to use them with examples. Common String Manipulation Functions Example Usage 1. Concatenation Syntax: 2. Substring Extraction Syntax: 3.…

  • Pyspark Dataframe programming – operations, functions, all statements, syntax with Examples

    Creating DataFrames in PySpark Creating DataFrames in PySpark is essential for processing large-scale data efficiently. PySpark allows DataFrames to be created from various sources, ranging from manual data entry to structured storage systems. Below are different ways to create PySpark DataFrames, along with interesting examples. 1. Creating DataFrames from List of Tuples (Manual Entry) This…