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
Perform summary statistics on the `review_text” and `vote` columns.

# Solution

def impute_NAN_values(df,columnName,value):
    """Replace the NaN values"""
    df = df.fillna({columnName: value})
    return df

def show_vote_stat(df: SparkDf) -> None:
    """
    Show summary status about the vote
    :param df: A Dataframe having asin and vote column
    :return: No Return
    """
    summary_df = (
        df
        .groupby("asin")
        .agg(fn.mean(col("vote")).alias("mean_vote"))
        .select("mean_vote")
        .summary("count", "min", "25%", "75%", "max")
    )

    summary = summary_df.rdd.map(lambda row: row.asDict(recursive=True)).collect()
    pprint(summary)

def show_review_text_stat(df: SparkDf) -> None:
    """
    Show general Stats for review text length
    :param df: DataFrame
    :return: Nothing
    """
    summary_df = (
        df
        .filter(col("review_text_len") > 0)
        .select('review_text_len')
        .summary("count", "min", "25%", "75%", "max")
    )
    summary = summary_df.rdd.map(lambda row: row.asDict(recursive=True)).collect()
    print("Review Length Stat")
    pprint(summary)
    weired_reviews = df.filter(col('review_text_len') <= 1).count()
    print(f"Reviews with length one or less: {weired_reviews}")



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 use the `.fillna` method to impute the missing values.
- **Line 12–18:** We group the DataFrame by `asin` with `.agg` to have the flexibility of renaming columns and resetting index when needed. Next, we use `.select` to select a specific column and `.summary` to get a summary.
- **Line 20:** We use the `rdd.map` method to iterate through all the rows of the resulting DataFrame and convert them into python native `dict` type and finally put the result into a list.
- **Line 29–34:** We calculate the summary for those reviews whose length is more than `0`.
- **Line 35:** We use the `rdd.map` method to iterate through all the rows of the resulting DataFrame and convert them into Python native `dict` type and finally put the result into a list.


Let's see the solution to the data transformation Challenge.

Introduction

Data Input/Output

Data Transformation

User Defined Function (UDF)

Wrapping Up

Appendix

Solution: Data Transformation

Task