---
title: "PySpark Practice Guide: 10 Essential Examples for Data Analysis"
description: "This content provides 10 PySpark examples for starting with Apache Spark using Python, covering initializing a Spark session, loading and filtering data, aggregating, joining, writing to Parquet, UDFs, SQL queries, handling missing data, and machine learning."
url: https://srinimf.com/2023/09/05/how-to-practice-apache-pyspark-10-best-examples/
date: 2023-09-05
modified: 2026-10-10
author: "Srini"
image: https://i0.wp.com/srinimf.com/wp-content/uploads/2023/09/pexels-photo-3764544.jpeg?fit=1880%2C1253&ssl=1
categories: ["PySpark"]
tags: ["PySpark Examples"]
type: post
lang: en
---

# PySpark Practice Guide: 10 Essential Examples for Data Analysis

10 PySpark examples with answers to help you start with Apache Spark using Python.

![How to Practice Apache PySpark: 10 Best Examples](https://i0.wp.com/srinimf.com/wp-content/uploads/2023/09/pexels-photo-4271928.jpeg?resize=1880%2C1253&ssl=1)*Photo by alleksana on Pexels.com*

## **Initializing a Spark Session**

```
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("example").getOrCreate()
```

## **Loading Data from CSV**

```
df = spark.read.csv("data.csv", header=True, inferSchema=True)
df.show()
```

## **Filtering Data**

```
filtered_df = df.filter(df["age"] > 25)
filtered_df.show()
```

## **Grouping and Aggregating Data**

```
grouped_df = df.groupBy("department").agg({"salary": "avg"})
grouped_df.show()
```

Related[SQL Query: Extracting Employees Sal > Avg Salary](https://srinimf.com/2024/04/06/salavgsal-sql-query-how-to-write-in-pyspark-and-pandas/)

## **Joining DataFrames**

```
joined_df = df1.join(df2, "employee_id", "inner")
joined_df.show()
```

## **Writing Data to Parquet**

```
df.write.parquet("output.parquet")
```

## **Creating a User-Defined Function (UDF)**

```
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
def custom_function(name):
return "Hello, " + name

custom_udf = udf(custom_function, StringType())
df = df.withColumn("greeting", custom_udf(df["name"]))
df.show()
```

Related[PySpark Stored Procedure vs MySQL: UDF Equivalents Explained](https://srinimf.com/2025/09/10/pyspark-stored-procedure-alternative-explained-for-interview-prep/)

## **Running [SQL Queries](https://srinimf.com/2023/12/19/sql-user-defined-functions/)**

```
df.createOrReplaceTempView("employees")
result = spark.sql("SELECT * FROM employees WHERE age > 30")
result.show()
```

## **Handling Missing Data**

```
df = df.fillna(0, subset=["salary"]) # Fill NaN values with 0 in the "salary" column
df.show()
```

## **Machine Learning with PySpark**

```
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
def custom_function(name):
return "Hello, " + name

custom_udf = udf(custom_function, StringType())
df = df.withColumn("greeting", custom_udf(df["name"]))
df.show()
```

These are some basic examples to help you get started with PySpark. Your specific use case and data will decide the features to explore. You can investigate more advanced [features and](https://srinimf.com/2024/12/01/understanding-apache-cassandra-features-and-benefits/) operations that PySpark offers for data processing and analysis.

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