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

# Task
Save the data set as a distributed data set with proper bucketing and sorting.

# Solution

def create_spark_session():
    """Create a Spark Session"""
    _ = load_dotenv()
    spark = SparkSession.builder.appName("SparkApp").master("local[5]").getOrCreate()
    return spark


def read_sdf(spark,PATH_BIGDATA):
    """Read the dataset"""
    raw_sdf=spark.read.json(PATH_BIGDATA)
    return raw_sdf

def rename_columns(df, column_map):
    """Rename the columns"""
    for old, new in column_map.items():
        df = df.withColumnRenamed(old, new)

    return df

def select_subset(df,columns):
    """Select the required columns"""
    df=df.select(*columns)
    return df

def repartitioning_and_saving(df,PATH_SNAPSHOT):
    """Repartitioning and saving the snapshot"""
    df = df.repartition('reviewed_year', 'reviewed_month').sortWithinPartitions("asin")
    df.write.mode("overwrite").parquet(PATH_SNAPSHOT)


import React from 'react';
import { shallow, configure } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';

configure({ adapter: new Adapter() });

import HelloWorld from './app';

var TestResult = function() {
    this.succeeded = false;
    this.reason = "";
    this.input = "";
    this.expected_output = "";
    this.actual_output = "";
}

export const executeTests = function() {

  var results = [];

  result = new TestResult();
  result.input = 'HelloWorld Component';
  result.expected_output = "span containing text 'Hello World'"

  let wrapper = shallow(<HelloWorld />);

  // Call your Challenge function here.

  let type = wrapper.type();
  let testSuccessful = true;
  let failureReason;

  if (type !== 'span') {
    testSuccessful = false;
    failureReason = "You need to render exactly one span HTML element";
  } else if (wrapper.props().children != "Hello World") {
     testSuccessful = false;
     failureReason = "You have rendered wrong message in your span element";
  }

  result.actual_output = wrapper.html();

  if (testSuccessful) {
    result.succeeded = true;
    result.reason = "Succeeded"
  } else {
    result.succeeded = false;
    result.reason = failureReason;
  }

  results.push(result);

  return results;
}


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# Explanation
- **Line 3**: We read the key-value pair from `.env` and add them to the environment variable. Basically, we load the environment here.
- **Line 4**:  We create a PySpark session and name it `.appname()`. We use `.master()` to create five threads as logical cores on our machine locally.

- **Line 10**: We set the path for the dataset.

- **Line 15–16**: We map column names to existing ones.

- **Line 22**: We use the `select` function to select the columns.

- **Line 27**: We use `repartition` for bucketing. We sort the data by the `“asin”` column within each partition.

- **Line 28**: We write data in the Parquet format with the partition and also overwrite in case any older data exists.




Let's see the solution of the data input/output Challenge.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Solution: Data Input and Output

Task