Gain insights into enhancing Python data processing with PySpark. Delve into reading, transforming, aggregating data, and creating user-defined functions, boosting efficiency with Apache Spark.

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

# Calculate the average review per product in pandas

To calculate the average review per product, we need to calculate the total number of products and the total number of reviews.


import pandas as pd
PANDAS_DISPLAY_SETTING = [
    ('display.max_rows', 500),
    ('display.max_columns', 500),
    ('display.width', 1000)
]


def set_pd_display(module: pd) -> bool:
    for item in PANDAS_DISPLAY_SETTING:
        module.set_option(*item)
    return True


# pathconfig.py
from pathlib import Path
import time
import yaml

ROOT = Path(__file__).parent
DATA_LAKE = ROOT.joinpath("data")
RAW_DATA = DATA_LAKE.joinpath("raw")
SNAPSHOT = DATA_LAKE.joinpath("snapshot")

PATH_TOY_GAME_DATA = "/Toys_and_Games_5.json"

def create_path_snapshot() -> Path:
    """
    Create path to shapshot wit time stamp
    :return: Path like object
    """
    path_fixed = 'snapshot_{}'.format(int(time.time()))
    return SNAPSHOT.joinpath(path_fixed)


# TODO: Store The Snapshot
def create_path_snapshot_spark():
    path_fixed = 'data/snapshot/pyspark/snapshot_{}'
    current_unix_time = int(time.time())
    return path_fixed.format(current_unix_time)


def load_latest_parquet_path() -> Path:
    """
    :return: path to latest parquet file storage
    """
    with open(SNAPSHOT.joinpath('pyspark', 'versions.yaml')) as f:
        content = yaml.safe_load(f)
        print(content)
        latest = content['latest']
        path = content[latest]['path']
        return SNAPSHOT.joinpath(path)



def average_review(df: pd.DataFrame) -> float: 
    """
    Calculate average review per product
    :param df: Pandas dataframe
    :return: Average
    """
    unique_asin = len(df['asin'].unique())
    total_review = len(df)
    avg_review = total_review / unique_asin 
    print(f"Average Review per product {avg_review:.2f}") 
    return avg_review

mean_over_all_review = average_review(raw_pdf)
print(mean_over_all_review)
print('Code Executed Successfully')


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After successful code execution, we’ll see the message “Code Executed Successfully” in the terminal.

We’ve written a function that takes a DataFrame and calculates the total number of reviews—assuming all the rows have valid reviews—and the total number of `unique_asin`. Then we calculate the average review per asin.
# Calculate the average review per product in PySpark

The same operation can be done in PySpark as follows:


Learn to calculate the average review per product in pandas and PySpark.


Average Review per Product


Learn to calculate the average review per product in pandas and PySpark.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Average Review per Product

Calculate the average review per product in pandas