PySpark in 7 Days (Day 2)

Learn PySpark in 7 Days – Day 2

Welcome to Day 2 of the PySpark in 7 Days series. Today is one of the most important days because DataFrames are the core abstraction used in real-world Spark pipelines.

Day 2 Focus:
• Understanding PySpark DataFrames
• Reading CSV and JSON files
• Schema inference vs manual schema
• select, filter, withColumn operations


What Is a PySpark DataFrame?

A PySpark DataFrame is a distributed table-like structure with rows and columns, similar to a SQL table or Pandas DataFrame, but designed to work on large-scale data.

  • Immutable (cannot be changed in place)
  • Schema-aware
  • Optimised using Spark Catalyst engine

Creating SparkSession (Recap)

from pyspark.sql import SparkSession spark = SparkSession.builder \ .appName("Day2-PySpark") \ .getOrCreate()

Reading CSV Files

df = spark.read \ .option("header", "true") \ .option("inferSchema", "true") \ .csv("/path/employees.csv") df.show() df.printSchema()
Interview Tip:
inferSchema is convenient but expensive for large files. In production, prefer explicit schemas.

Reading JSON Files

json_df = spark.read.json("/path/employees.json") json_df.show()

Defining a Manual Schema

from pyspark.sql.types import StructType, StructField, IntegerType, StringType schema = StructType([ StructField("emp_id", IntegerType(), True), StructField("name", StringType(), True), StructField("department", StringType(), True), StructField("salary", IntegerType(), True) ]) df = spark.read \ .option("header", "true") \ .schema(schema) \ .csv("/path/employees.csv")

Common DataFrame Operations

Select Columns

df.select("name", "salary").show()

Filter Rows

df.filter(df.salary > 50000).show()

Add or Modify Column (withColumn)

from pyspark.sql.functions import col df = df.withColumn("salary_bonus", col("salary") * 1.10) df.show()
Important:
withColumn always returns a new DataFrame.

Rename Columns

df = df.withColumnRenamed("emp_id", "employee_id")

Key Concepts You Learned Today

  • What a PySpark DataFrame is
  • Reading CSV and JSON data
  • Schema inference vs manual schema
  • select, filter, withColumn usage

What’s Coming on Day 3?

Day 3 – PySpark SQL & Expressions
  • Spark SQL vs DataFrame API
  • createOrReplaceTempView
  • SQL queries on DataFrames
  • Case when, aggregations
Series: Learn PySpark in 7 Days
Author: TrySpark
Target Audience: Data Engineers & SQL Professionals

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