explaingit

rvangenechten/pyspark_cheatsheet

★ 20HTMLAudience · dataComplexity · 1/5ActiveSetup · easy

TLDR

A single PDF cheat sheet for PySpark covering DataFrame operations, transformations, actions, Spark SQL, and common functions for daily reference.

Mindmap

mindmap
  root((Pyspark-cheatsheet))
    Inputs
      PDF download
    Outputs
      Printable reference card
    Use Cases
      Recall DataFrame syntax
      Look up Spark SQL functions
      Quick reference at the desk
    Tech Stack
      PDF
      PySpark
      Apache Spark

Things people build with this

USE CASE 1

Print a PySpark syntax reference and keep it next to your editor

USE CASE 2

Look up DataFrame transformation and action signatures without opening the Spark docs

USE CASE 3

Refresh memory on Spark SQL functions before writing a query

Tech stack

PySparkSparkPDF

Getting it running

Difficulty · easy Time to first run · 5min

Just a PDF download, no install or build.

In plain English

This repository is a one page reference document. It holds a PySpark cheat sheet in PDF form, and that is the whole project. PySpark is the Python interface to Apache Spark, which is a system for processing large amounts of data across a cluster of machines. People who work with Spark every day often need to look up the exact syntax for a transformation or a SQL function, and a cheat sheet is the printable summary that sits on their desk for that purpose. According to the README, the cheat sheet is meant as a quick reference for working with Apache Spark using Python. It covers a small set of essential topics: DataFrame operations, transformations, actions, Spark SQL, and common functions used in data processing workflows. The author describes the target reader as a data engineer or data scientist who already knows what Spark is and just wants to recall a piece of syntax without searching through the full Spark documentation. The README itself is very sparse. It is a single paragraph of about five sentences. It does not list which Spark version the sheet targets, does not include a table of contents, does not link to a preview image, does not specify a license, and does not say how the file was produced or how it can be regenerated. There are no installation instructions because there is no software to install: the deliverable is a PDF file. To use this repository you would download the PDF directly from the GitHub interface and open it in any PDF viewer. There is nothing to build, nothing to run, and no dependencies to install. The primary language label shown on GitHub is HTML, which usually means that GitHub is counting an auto generated preview or assets page rather than executable code. In short, treat this repository as a printable reference card. If you are looking for tutorials, runnable examples, or interactive notebooks, this repository does not provide them. If you want a single PDF you can keep open next to your editor while writing PySpark code, that is what it offers.

Copy-paste prompts

Prompt 1
Open the Pyspark_cheatsheet PDF and summarize which DataFrame transformations and actions it covers
Prompt 2
Turn this PySpark cheat sheet into a Markdown version I can search in my editor
Prompt 3
Walk me through the Spark SQL section of the PDF with one runnable example per function
Prompt 4
Compare this cheat sheet to the official PySpark docs and tell me which APIs are missing
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Generated 2026-05-22 · Model: sonnet-4-6 · Verify against the repo before relying on details.