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

# Save a snapshot in pandas
Below is how we can save a snapshot of our work for future use in pandas:

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)



# Drop The columns we Do not Need
column_list = ['unix_review_time', 'review_time', 'summary'] 
raw_pdf = raw_pdf.drop(columns=column_list, axis=1)
# Store The Snapshot
snapshot_path = SNAPSHOT.joinpath('pandas', 'snapshot.json')
raw_pdf.to_json(snapshot_path, orient='records')
print('Snapshot saved')
print('Code Executed Successfully')

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

First, we drop the columns that are not needed in the analysis anymore. Then we save the DataFrame as JSON, where each value in the JSON object array represents a row of the DataFrame.


# Save a snapshot in PySpark

Learn to save a filtered snapshot in pandas and PySpark.


Date Transformation: Save a Snapshot 

Learn to save a filtered snapshot in pandas and PySpark.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Date Transformation: Save a Snapshot

Save a snapshot in pandas