frompandastopyspark.tar.gz

PandasAndPyspark

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Pandas is a popular Python library used to manipulate data, but it has certain limitations in its ability to process large datasets. The Apache Spark analytics library offers significant performance improvements.

This course will help improve your Python-based data processing by leveraging Apache Spark’s multithreading capabilities through the PySpark library. You’ll start by reading data into a PySpark DataFrame before performing basic input/output functions, such as renaming attributes, selecting, and writing data. You’ll move onto transformation functions like aggregation, statistical analysis, and joins before creating custom, user-defined functions. At each step, you’ll get a quick Pandas review before being walked through leveraging the more robust PySpark library to unlock Apache Spark.

By the end of this course, you’ll be able to quickly and reliably process large amounts of data, even stored across multiple files, using PySpark.

From Pandas to PySpark DataFrame

**(True or False)** UDFs speed up Spark queries significantly.

(True or False) UDFs speed up Spark queries significantly.

What is the return type of PySpark UDF, if the return type of the Python function is a list of strings?


What is the return type of PySpark UDF, if the return type of the Python function is a list of strings?

`StructType` and `MapType` have the same features.

`StructType` can be stored in Parquet data format.

Any field of a `StructType` instance can be accessed using the `.` operation.


**(Select all that apply.)** What are the valid claims about `StructType`? 

(Select all that apply.) What are the valid claims about <code>StructType</code>?

User Defined Functions

User Defined Functions

Test your understanding of UDF in pandas and PySpark.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Quiz: User-defined Functions