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

# Date-time in pandas

In the data source, the review time is in the Unix date-time format. In pandas, we can implement the `applymap` method on the DataFrame column to convert it into the native Python timestamp datatype as follows:


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)



raw_pdf['reviewed_at'] = raw_pdf[['unix_review_time']].applymap(
     datetime.fromtimestamp
     )
print(raw_pdf.head())
print('Code Executed Successfully')

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GoJS React

Typescript React

Vue.js

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

# Date-time in PySpark
In PySpark, there are built-in functions available to achieve the same functionality. The `from_unixtime` function from the `pyspark.sql.functions` module can be used here.


Learn to handle date-time in pandas and PySpark.


Handling Date-time

Learn to handle date-time in pandas and PySpark.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Handling Date-time

Date-time in pandas