Welcome to the Future – AI Hints Today
Keyword is AI– This is your go-to space to ask questions, share programming tips, and engage with fellow coding enthusiasts. Whether you’re a beginner or an expert, our community is here to support your journey in coding. Dive into discussions on various programming languages, solve challenges, and exchange knowledge to enhance your skills.
Python Built-in Iterables: Complete Guide with Use Cases & Challenges
Python Dictionary in detail- Wholesome Tutorial on Dictionaries
Dictionaries in Python are powerful and versatile, making them essential for advanced coding, including automation, configuration management, and dynamic variable manipulation. Below are some advanced and useful use cases: 1. Configuration Management (Alternative to INI, JSON, YAML) Dictionaries can be used to store configuration settings, eliminating the need for external files. 🔹 Use Case: Store…
Python Programming Language Specials
Useful Code Snippets in Python and Pyspark
In PySpark, select(), selectExpr(), and expr() are all used to manipulate and select columns from a DataFrame, but they have different use cases. Let’s break them down with examples. 1️⃣ select() 🔹 Example: ✅ Best when selecting columns and applying column-based transformations. 2️⃣ selectExpr() 🔹 Example: ✅ Best when you want to use SQL-like expressions…
What is indexing in SQL- Syntax, Types, Use Cases, Advantages, Disadv, and Scenarios
Here’s a comprehensive, logically structured, and interactive guide to SQL Indexing, consolidating and enhancing all the content you’ve shared, complete with real examples, platform-specific insights, and advanced use cases: 🧠 Mastering Indexing in SQL: A Complete Guide 🔍 What is Indexing in SQL? Indexing is a performance optimization technique that allows fast retrieval of rows…
Spark SQL- operators Cheatsheet- Explanation with Usecases
How to Write Perfect Pseudocode- Syntax , Standards, Terms
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…
Spark SQL windows Function and Best Usecases
For Better understanding on Spark SQL windows Function and Best Usecases do refer our post Window functions in Oracle Pl/Sql and Hive explained and compared with examples. Window functions in Spark SQL are powerful tools that allow you to perform calculations across a set of table rows that are somehow related to the current row.…
PySpark architecture cheat sheet- How to Know Which parts of your PySpark ETL script are executed on the driver, master (YARN), or executors
confused between driver, driver program, master node, yarm.. is master node is the one which initiates driver code or master node is resource manager Here’s a breakdown of the different components: Driver Program Driver Node Master Node YARN (Yet Another Resource Negotiator) Spark Standalone Here’s a high-level overview of how the different components interact: so…
Scientists find a ‘Unique’ Black Hole that is hungrier than ever in the Universe
Quick Spark SQL reference- Spark SQL cheatsheet for Revising in One Go
Here’s an enhanced Spark SQL cheatsheet with additional details, covering join types, union types, and set operations like EXCEPT and INTERSECT, along with options for table management (DDL operations like UPDATE, INSERT, DELETE, etc.). This comprehensive sheet is designed to help with quick Spark SQL reference. Category Concept Syntax / Example Description Basic Statements SELECT SELECT col1, col2 FROM table WHERE…
Functions in Spark SQL- Cheatsheets, Complex Examples
Pyspark, Spark SQL and Python Pandas- Collection of Various Useful cheatsheets, cheatcodes for revising
Item PySpark Spark SQL Pandas Read CSV spark.read.csv(“file.csv”) SELECT * FROM csv.file.csv` pd.read_csv(“file.csv”) spark.read.csv SELECT * FROM csv. pd.read_csv Read JSON spark.read.json(“file.json”) SELECT * FROM json.file.json` pd.read_json(“file.json”) Read Parquet spark.read.parquet(“file.parquet”) SELECT * FROM parquet.file.parquet` pd.read_parquet(“file.parquet”) Data Creation PySpark Spark SQL Pandas From List spark.createDataFrame([(1, ‘A’), (2, ‘B’)], [“col1”, “col2”]) INSERT INTO table VALUES (1,…
Python Pandas Series Tutorial- Usecases, Cheatcode Sheet to revise