Pyspark training | Introduction to PySpark DataFrames

Pyspark training | Introduction to PySpark DataFrames

• 1.
  Introduction to PySparkDataFrames
• 2.
  What is PySpark? PySparkis 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 aDataFrame 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.
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  Why Use PySparkDataFrames? Can process terabytes of data across multiple machines. SQL-like operations on large datasets. Integrated with many big data tools (e.g., Hadoop, Hive).
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  Starting with PySpark SparkSessionis the entry point to PySpark. appName is just a name for your Spark job.
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  Creating a DataFrame Displays
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  Loading Data fromCSV Use header=True to treat the first row as column names. inferSchema=True automatically detects column types.
• 8.
  Common DataFrame Operations Performselect, filter, and groupBy just like SQL!
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  Writing Data toFiles Can also write to JSON, Parquet, or Hive tables.
• 10.
  Feature Pandas PySpark ScaleIn-memory Distributed Speed Slower on big data Fast on big data Syntax Pythonic SQL + Python Comparing Pandas vs. PySpark
• 11.
  Summary & NextSteps PySpark DataFrames make big data processing easy and efficient. Supports SQL-like operations on massive datasets. Next topics: Spark SQL, Transformations, Actions, and Joins.
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  Contact & OnlineTraining 📢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: contact@accentfuture.com, 📞+91-96400 01789 (Call/WhatsApp)