Pyspark training | Introduction to PySpark DataFrames
- 1. Introduction to PySpark DataFrames
- 2. What is PySpark? PySpark is the Python API for Apache Spark, a powerful distributed computing engine. Used for big data processing with Python. Handles large-scale data with speed and scalability.
- 3. What is a DataFrame in PySpark? A DataFrame is a distributed collection of data organized into named columns, like a table in a database. Similar to pandas DataFrames but optimized for big data.
- 4. Why Use PySpark DataFrames? Can process terabytes of data across multiple machines. SQL-like operations on large datasets. Integrated with many big data tools (e.g., Hadoop, Hive).
- 5. Starting with PySpark SparkSession is the entry point to PySpark. appName is just a name for your Spark job.
- 7. Loading Data from CSV Use header=True to treat the first row as column names. inferSchema=True automatically detects column types.
- 8. Common DataFrame Operations Perform select, filter, and groupBy just like SQL!
- 9. Writing Data to Files Can also write to JSON, Parquet, or Hive tables.
- 10. Feature Pandas PySpark Scale In-memory Distributed Speed Slower on big data Fast on big data Syntax Pythonic SQL + Python Comparing Pandas vs. PySpark
- 11. Summary & Next Steps PySpark DataFrames make big data processing easy and efficient. Supports SQL-like operations on massive datasets. Next topics: Spark SQL, Transformations, Actions, and Joins.
- 12. Contact & Online Training 📢We Provide Online Training on Databricks and Big Data Technologies! ✅Hands-on Training with Real-World Use Cases ✅Live Sessions with Industry Experts ✅Job Assistance ✅Certification Guidance 🌐Visit our website: https://www.accentfuture.com/ 📩For inquiries, contact us at: [email protected], 📞+91-96400 01789 (Call/WhatsApp)