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

## Count the reviews per product in pandas

We need to group the data by `asin` and count the number of reviews for each group. Both APIs provide a `groupby` method, but the syntax implementation is different.


# Count the reviews per product in pandas

We need to group the data by `asin` and count the number of reviews for each group. Both APIs provide a `groupby` method, but the syntax implementation is different.


Learn to calculate the total number of reviews for each product in pandas and PySpark.


Total Number of Reviews for Each Product

Learn to calculate the total number of reviews for each product in pandas and PySpark.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Total Number of Reviews for Each Product

Count the reviews per product in pandas