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✅ 30+ PySpark DataFrame Methods Crash Course for Data Engineers
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Hello PySpark Developers, Here I have listed some of the PySpark useful DataFrame methods that are very helpful in real-life PySpark applications.

Let's start! 👇

1. show()

The show() method is used to display the contents of the DataFrame. By default, it shows the top 20 rows.

df.show()

2. select():- The select() method allows you to select specific columns from a DataFrame.

new_df = df.select("first_name", "last_name", "age")
new_df.show()

3. filter() or where(): The filter() or where() method is used to filter rows that meet certain conditions.

from pyspark.sql.functions import col
new_df = df.filter(col("age") > 25)
new_df.show()

from pyspark.sql.functions import col
new_df = df.where(col("age") > 25)
new_df.show()

4. groupBy() and agg():- The groupBy() method is used to group data based on one or more columns, and agg() allows you to perform aggregation functions on grouped data.

from pyspark.sql.functions import avg
new_df = df.groupBy("department").agg(avg("salary").alias("average_salary"))
new_df.show()

5. withColumn(): The withColumn() method is used to add or modify a column in the DataFrame. For example, I want to add 5 to each employee’s age value.

from pyspark.sql.functions import col
new_df = df.withColumn("modified_age", col("age") + 5).select(
"first_name", "last_name", "modified_age"
)
new_df.show()

These are some Methods but you can get all 30+ PySpark DataFrame methods in the below tutorial.

💯Access this tutorial:- https://www.programmingfunda.com/top-30-pyspark-dataframe-methods-with-example/

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