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

# Read data in the Pandas

There are many ways to read data in pandas, but in this lesson, we’ll focus on the following two ways:

1. Read with custom code then convert it to a pandas DataFrame.


2. Read with a built-in pandas function.


> **Note:** The codes discussed below are executable.

## Using custom code


import pandas as pd
from tqdm import tqdm
import json
PATH_BIGDATA = '/Toys_and_Games_5.json'
def read_json_to_pdf(path: str) -> pd.DataFrame: 
  data = []
  with open(path, 'r') as f: 
    for line in tqdm(f):
      data.append(json.loads(line))
  df = pd.DataFrame(data)
  return df
raw_pdf = read_json_to_pdf(PATH_BIGDATA)
print(raw_pdf.head())

print('Code Executed Successfully')

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;
}


React

Dart

GoJS React

Typescript React

Vue.js

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

### Explanation
- **Lines 1–3**: We import the required library for reading the dataset.

- **Line 4**: We set the path of our dataset.
- **Lines 5–11**: We create a function to read JSON data, `read_json_to_pdf`.
   - **Line 5**: We define the function.
   - **Line 6**: We create an empty list.
   - **Line 7**: We open the JSON file.
   - **Lines 8–9**: We iterate through the dataset and append it in the list we created earlier.
   - **Line 10**: We convert the list to a pandas DataFrame.
   - **Line 11**: We return the DataFrame.
- **Line 12**: We call the function to load the dataset.

## Using built-in library

>  In the case of PySpark, we’ll only display newly added and hide all the code that was used in previous chapters.



Learn to read data in pandas and PySpark.


Read Data into DataFrame

Learn to read data in pandas and PySpark.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Read Data into DataFrame

Read data in the Pandas

Using custom code

Explanation