PySpark Tutorial: Read CSV, Filter, Group By & Sort (vs Pandas)

Master PySpark tutorial fundamentals! Learn how to work with PySpark DataFrames from creating a Spark session to filtering, grouping, and sorting data. This comprehensive PySpark for beginners guide compares PySpark syntax with Pandas and Polars, helping you understand the key differences in Python data processing libraries. Unlike Pandas and Polars, PySpark has completely different syntax that requires setting up a Spark session before processing data. We'll walk through essential PySpark operations using the same car sales dataset, making it easy to compare approaches across all three libraries. For the notes and material related to "Pandas vs Polars vs PySpark", please subscribe to our Newsletter. Here is the link to the article: https://itversity.substack.com/p/whic.... You can also get the material related to "Pandas vs Polars vs PySpark" as part of medium: https://medium.com/itversity/which-py... What You'll Learn: ✅ Import and create Spark session object in Python ✅ Understand PySpark's unique initialization requirements ✅ Read CSV files using session.read.csv() with proper configuration ✅ Set header=True and inferSchema=True parameters correctly ✅ Understand infer schema concept and why it matters in PySpark ✅ Use .count() to get record count and .show() to preview data ✅ Filter PySpark DataFrame using .filter() function ✅ Select specific columns with .select() method ✅ Import and use PySpark aggregation functions (sum, count, round, col) ✅ Group by and aggregate data using .groupBy().agg() ✅ Apply column aliases for aggregated results ✅ Sort data using .orderBy() or .sort() with .desc() ✅ Convert PySpark DataFrame to Pandas using .toPandas() ✅ Format scientific notation display properly Key PySpark Functions Covered: SparkSession.builder - Creating Spark session session.read.csv() - Reading CSV with header and inferSchema .filter() - Filtering DataFrame rows .select() - Selecting specific columns .groupBy() - Grouping data (note: capital B) .agg() - Aggregate functions sum(), count(), round() - PySpark SQL functions .alias() - Column aliasing .orderBy() / .sort() - Sorting data col() and .desc() - Column reference and descending order .toPandas() - Converting to Pandas for better formatting PySpark vs Pandas Key Differences: Initialization: PySpark requires Spark session creation; Pandas/Polars don't Schema inference: Must explicitly set inferSchema=True in PySpark Header handling: Must specify header=True in PySpark Function naming: .groupBy() with capital B vs Pandas .groupby() Data preview: .show() in PySpark vs .head() in Pandas Formatting: Use .toPandas() to avoid scientific notation in PySpark 🔔 SUBSCRIBE for the upcoming performance comparison and data engineering tutorials! Connect with Us: Newsletter: http://notifyme.itversity.com LinkedIn:   / itversity   Facebook:   / itversity   Twitter:   / itversity   Instagram:   / itversity   Join this channel to get access to perks:    / @itversity   #PySpark #Python #Spark #DataEngineering #Pandas