2025-10-12

pandas pyspark

pandas in simple words

• what it is: a python library to handle tabular or structured data easily on a single machine.

• real-world example:

  • you work at a small e-commerce company
  • you have a csv file of 10,000 orders
  • you want to calculate total sales per product or average order value
  • pandas lets you do this with a few lines of code

• applications / tasks:

  • data cleaning: remove missing values, fix dates
  • data transformation: filter, sort, group, aggregate
  • visualization: easy integration with matplotlib or seaborn
  • quick analysis on csv, excel, sql tables

pyspark in simple words

• what it is: a python interface for apache spark, which can process very large datasets distributed across multiple machines.

• real-world example:

  • you work at a social media platform with 1 billion user interactions per day
  • you want to calculate top trending posts, or user activity patterns
  • pandas would crash trying to load this much data, but pyspark can handle it because it processes data in parallel across multiple servers

• applications / tasks:

  • large-scale ETL pipelines (extract-transform-load)
  • aggregating logs or sensor data
  • building data lakes or feeding machine learning pipelines
  • processing real-time streams (with spark streaming)

pandas vs pyspark (key differences)

in short:

• use pandas when your dataset is small enough to fit in memory and you need fast, interactive analysis.
• use pyspark when your dataset is huge, needs parallel processing, or comes from distributed sources like logs, databases, or cloud storage.