When I first started working with big data, I kept hearing about PySpark DataFrames — fast, distributed, and super powerful. But I also found the learning curve a bit steep.

In this guide, I’ll break down PySpark DataFrames in the simplest way possible using real-world examples I encountered in my role as a data engineer. I am going use Microsoft fabric notebooks for data transformation.

🧠 What Is a DataFrame in PySpark?

A DataFrame is a distributed collection of data organized into named columns, like a table in a database or a spreadsheet.

In PySpark, it’s optimized for parallel processing and can handle huge datasets that won’t fit in memory.

⚙️ Setting Up PySpark in Microsoft Fabric

If you’re using Microsoft Fabric, you’re already inside a notebook environment that supports Spark natively.

You don’t need to set up SparkSession manually. It's already available for you as spark.

Just start writing your code in a notebook inside your Lakehouse workspace.

But in local environments, you can create a session like this:

Creating session

📁 Loading Data into a DataFrame (from Lakehouse)

Use the default Files or Tables folder inside your Lakehouse:

df = spark.read.format(“csv”).option(“header”,”true”).load(“Files/Sales.csv”)

display(df.limit(5))

🔍 Inspecting the DataFrame

df.show(5)
df.printSchema()
df.describe().show()

df.show(5)

df.printSchema()

df.describe().show()

🛠 Common PySpark Transformations

✂️ Drop Nulls

df_cleaned = df.na.drop()

🔤 Trim Strings

from pyspark.sql.functions import trim

df_trimmed = df.select([trim(col).alias(col) for col in df.columns])

🔁 Rename Columns

📅 Convert to Date

from pyspark.sql.functions import to_date

df = df.withColumn(“OrderDate”, to_date(“OrderDate”, “yyyy-MM-dd”))

df.show(5)

OrderDate converted to date

🔄 Filtering Data

df_filtered = df.filter(df[“OrderDate”] == “2019–07–01”)

df_filtered.show(5)

🔧 Writing Data Back to Lakehouse

To save your cleaned data into the Files area:

df_cleaned.write.mode(“overwrite”).option(“header”, “true”).csv(“Files/cleaned_Sales_data.csv”)

Or you can use .parquet() or .format("delta") if you're using Delta Lake.

🧪 Real Example from My Work

In my work, I had to clean messy CSV files, filter Order date records, and standardize join dates. PySpark helped me:

• Trim whitespaces

• Convert date formats

• Drop Null values on specific columns

• Write results to a curated zone

It felt amazing to run that logic on thousands of records in seconds!

✅ Key Takeaways

• PySpark is perfect for working with big, messy, distributed datasets

• DataFrames are SQL-like and easy to learn

• You can chain transformations for a clean pipeline

🙌 Thanks for Reading!

If you’re just starting out, you’re not alone. I’m documenting everything I learn — the wins and the struggles.

👉 Follow me for beginner-friendly PySpark tutorials based on real experience in data engineering.

Feel free leave a comment if you want help setting up your own PySpark notebook!