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Home > Data > Data Science > Apache Spark for Data Science Cookbook
Apache Spark for Data Science Cookbook
Apache Spark for Data Science Cookbook

Apache Spark for Data Science Cookbook: Solve real-world analytical problems

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Profile Icon Chitturi Profile Icon Nagamallikarjuna Inelu
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NZ$71.99
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Profile Icon Chitturi Profile Icon Nagamallikarjuna Inelu
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NZ$71.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (4 Ratings)
Paperback Dec 2016 392 pages 1st Edition
eBook
NZ$14.99 NZ$57.99
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NZ$71.99
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Apache Spark for Data Science Cookbook

Chapter 2. Tricky Statistics with Spark

In this chapter, you will learn the following recipes:

  • Working with Pandas
  • Variable identification
  • Sampling data
  • Summary and descriptive statistics
  • Generating frequency tables
  • Installing Pandas on Linux
  • Installing Pandas from source
  • Using IPython with PySpark
  • Creating Pandas DataFrames over Spark
  • Splitting, slicing, sorting, filtering and grouping DataFrames over Spark.
  • Implementing co-variance and correlation using DataFrames over Spark.
  • Concatenating and merging operations over DataFrames
  • Complex operations over DataFrames.
  • Sparkling Pandas

Introduction

Statistics refers to the mathematics and techniques with which we understand data. It is a vast field which plays a key role in the areas of data mining and artificial intelligence, intersecting with the areas of engineering and other disciplines. Statistics helps in describing data, that is, descriptive statistics reveals the distribution of the data for each variable. Also, statistics is widely used for the purpose of prediction.

In this chapter, we'll see how to apply various statistical measures and functions on large datasets using Spark.

Working with Pandas

Pandas is an open source Python library for highly specialized data analysis. It is the reference point that all professionals using the Python language need to study and analyze data sets for statistical purposes of analysis and decision-making. Pandas arises from the need to have a specific library for the analysis of the data which provides tools for data processing , data extraction and data manipulation...

Variable identification

In this recipe, we will see how to identify predictor (input) and target (output) variables for data at scale in Spark. Then the next step is to identify the category of the variables.

Getting ready

To step through this recipe, you will need Ubuntu 14.04 (Linux flavor) installed on the machine. Also, you need to have Apache Hadoop 2.6 and Apache Spark 1.6.0 installed.

How to do it…

  1. Let's take an example of student's data, using which we want to predict whether a student will play cricket or not. Here is what the sample data looks like:
    How to do it…
  2. The preceding data resides in HDFS and load the data into Spark as follows:
          import org.apache.spark._ 
      import org.apache.spark.sql._ 
        object tricky_Stats { 
         def main(args:Array[String]): Unit = { 
            val conf = new SparkConf() 
                .setMaster("spark://master:7077") 
                .setAppName("Variable_Identification") 
            val sc = new SparkContext...

Sampling data

In this recipe, we will see how to generate sample data from the entire population.

Getting ready

To step through this recipe, you need Ubuntu 14.04 (Linux flavor) installed on the machine. Also, have Apache Hadoop 2.6 and Apache Spark 1.6.0 installed. Readers are expected to have knowledge of sampling techniques.

How to do it…

Let's take an example of load prediction data. Here is what the sample data looks like:

How to do it…

Note

Download the data from the following location https://github.com/ChitturiPadma/datasets/blob/master/Loan_Prediction_Data.csv.

  1. Here is the code for sampling data from a DataFrame:
          import org.apache.spark._ 
      import org.apache.spark.sql.SQLContext 
      import org.apache.spark.sql.types.{StructType,
      StringType,DoubleType, StructField} 
     
      object Sampling_Demo { 
        def main(args:Array[String]): Unit = { 
          val conf = new SparkConf() 
            .setMaster("spark://master:7077") 
               .setAppName...

Summary and descriptive statistics

In this recipe, we will see how to get the summary statistics for data at scale in Spark. The descriptive summary statistics helps in understanding the distribution of data.

Getting ready

To step through this recipe, you need Ubuntu 14.04 (Linux flavor) installed on the machine. Also, have Apache Hadoop 2.6 and Apache Spark 1.6.0 installed.

How to do it…

Let's take an example of load prediction data. Here is what the sample data looks like:

How to do it…

Note

Download the data from the following location: https://github.com/ChitturiPadma/datasets/blob/master/Loan_Prediction_Data.csv.

  1. The preceding data contains numerical as well as categorical fields. We can get the summary of numerical fields as follows:
          import org.apache.spark._ 
      import org.apache.spark.sql._ 
      object Summary_Statistics { 
         def main(args:Array[String]): Unit = { 
             val conf = new SparkConf() 
              .setMaster("spark://master:7077") 
      ...

Generating frequency tables

In this recipe, we will see how to analyze the distribution of various variables in the data. Generally, we can take a histogram/boxplot of the variables to understand the distribution and also identify the outliers. But currently, Spark has no support for plotting the data. Let's see how we can perform analysis by generating frequency tables.

Getting ready

To step through this recipe, you need Ubuntu 14.04 (Linux flavor) installed on the machine. Also, have Apache Hadoop 2.6 and Apache Spark 1.6.0 installed.

How to do it…

Let's take an example of load prediction data. Here is what the sample data looks like:

How to do it…

Note

Download the data from the following location: https://github.com/ChitturiPadma/datasets/blob/master/Loan_Prediction_Data.csv.

The total record count is 614.

  1. Let us look at the chances of getting a loan-based on Credit_History. Here is the code to generate the frequency distribution of set of variables such as Loan_Status and Credit_History...

Introduction

Statistics refers to the mathematics and techniques with which we understand data. It is a vast field which plays a key role in the areas of data mining and artificial intelligence, intersecting with the areas of engineering and other disciplines. Statistics helps in describing data, that is, descriptive statistics reveals the distribution of the data for each variable. Also, statistics is widely used for the purpose of prediction.

In this chapter, we'll see how to apply various statistical measures and functions on large datasets using Spark.

Working with Pandas

Pandas is an open source Python library for highly specialized data analysis. It is the reference point that all professionals using the Python language need to study and analyze data sets for statistical purposes of analysis and decision-making. Pandas arises from the need to have a specific library for the analysis of the data which provides tools for data processing , data extraction and data manipulation.

It is designed...

Variable identification

In this recipe, we will see how to identify predictor (input) and target (output) variables for data at scale in Spark. Then the next step is to identify the category of the variables.

Getting ready

To step through this recipe, you will need Ubuntu 14.04 (Linux flavor) installed on the machine. Also, you need to have Apache Hadoop 2.6 and Apache Spark 1.6.0 installed.

How to do it…

  1. Let's take an example of student's data, using which we want to predict whether a student will play cricket or not. Here is what the sample data looks like:

  2. The preceding data resides in HDFS and load the data into Spark as follows:

          import org.apache.spark._ 
      import org.apache.spark.sql._ 
        object tricky_Stats { 
         def main(args:Array[String]): Unit = { 
            val conf = new SparkConf() 
                .setMaster("spark://master:7077") 
                .setAppName("Variable_Identification") 
            val sc = new SparkContext...

Sampling data

In this recipe, we will see how to generate sample data from the entire population.

Getting ready

To step through this recipe, you need Ubuntu 14.04 (Linux flavor) installed on the machine. Also, have Apache Hadoop 2.6 and Apache Spark 1.6.0 installed. Readers are expected to have knowledge of sampling techniques.

How to do it…

Let's take an example of load prediction data. Here is what the sample data looks like:

Note

Download the data from the following location https://github.com/ChitturiPadma/datasets/blob/master/Loan_Prediction_Data.csv.

  1. Here is the code for sampling data from a DataFrame:

          import org.apache.spark._ 
      import org.apache.spark.sql.SQLContext 
      import org.apache.spark.sql.types.{StructType,
      StringType,DoubleType, StructField} 
     
      object Sampling_Demo { 
        def main(args:Array[String]): Unit = { 
          val conf = new SparkConf() 
            .setMaster("spark://master:7077") 
    ...

Summary and descriptive statistics

In this recipe, we will see how to get the summary statistics for data at scale in Spark. The descriptive summary statistics helps in understanding the distribution of data.

Getting ready

To step through this recipe, you need Ubuntu 14.04 (Linux flavor) installed on the machine. Also, have Apache Hadoop 2.6 and Apache Spark 1.6.0 installed.

How to do it…

Let's take an example of load prediction data. Here is what the sample data looks like:

Note

Download the data from the following location: https://github.com/ChitturiPadma/datasets/blob/master/Loan_Prediction_Data.csv.

  1. The preceding data contains numerical as well as categorical fields. We can get the summary of numerical fields as follows:

          import org.apache.spark._ 
      import org.apache.spark.sql._ 
      object Summary_Statistics { 
         def main(args:Array[String]): Unit = { 
             val conf = new SparkConf() 
              .setMaster("spark://master:7077") 
...

Generating frequency tables

In this recipe, we will see how to analyze the distribution of various variables in the data. Generally, we can take a histogram/boxplot of the variables to understand the distribution and also identify the outliers. But currently, Spark has no support for plotting the data. Let's see how we can perform analysis by generating frequency tables.

Getting ready

To step through this recipe, you need Ubuntu 14.04 (Linux flavor) installed on the machine. Also, have Apache Hadoop 2.6 and Apache Spark 1.6.0 installed.

How to do it…

Let's take an example of load prediction data. Here is what the sample data looks like:

Note

Download the data from the following location: https://github.com/ChitturiPadma/datasets/blob/master/Loan_Prediction_Data.csv.

The total record count is 614.

  1. Let us look at the chances of getting a loan-based on Credit_History. Here is the code to generate the frequency distribution of set of variables such as Loan_Status and Credit_History :

          import org...

Installing Pandas on Linux

In this recipe, we will see how to install Pandas on Linux. Before proceeding with the installation, let's consider the version of Python we're going to use. There are two versions or flavors of Python, namely Python 2.7.x and Python 3.x. Although the latest version, Python 3.x, appears to be the better choice, for scientific, numeric, or data analysis work, Python 2.7 is recommended.

Getting ready

To step through this recipe, you need Ubuntu 14.04 (Linux flavor) installed on the machine. Python comes pre-installed. The python --version command gives the version of Python installed. If the version seems to be 2.6.x, upgrade it to Python 2.7 as follows:

sudo apt-get install python2.7

How to do it…

  1. Once Python version is available, make sure that the Python .dev files are installed. If not, install them as follows:

          sudo apt-get install python-dev

  2. Installing through pip:

          sudo apt-get install python-pip 
      sudo pip install numpy 
...

Installing Pandas from source

In this recipe, we will see how to install Pandas from Source on Linux. Before proceeding with the installation, let's consider the version of Python we're going to use. There are two versions or flavors of Python, namely Python 2.7.x and Python 3.x. Although the latest version, Python 3.x, appears to be the better choice, for scientific, numeric, or data analysis work, Python 2.7 is recommended.

Getting ready

To step through this recipe, you need Ubuntu 14.04 (Linux flavor) installed on the machine. Python comes pre-installed. The python --version command gives the version of Python installed. If the version seems to be 2.6.x, upgrade it to Python 2.7 as follows:

    sudo apt-get install python2.7

How to do it…

  1. Install the easy_install program:

           wget http://python-distribute.org/distribute_setup.pysudo python 
       distribute_setup.py

  2. Install Cython:

           sudo easy_install -U Cython

  3. Install from the source code as follows:

       ...

Using IPython with PySpark

As Python is the most preferred choice for data scientists due to its high-level syntax and extensive library of packages, Spark developers have considered it for data analysis. The PySpark API has been developed for working with RDDs in Python. IPython Notebook is an essential tool for data scientists to present the scientific and theoretical work in an interactive fashion, integrating both text and Python code.

This recipe shows how to configure IPython with PySpark and also focuses on connecting the IPython shell to PySpark.

Getting ready

To step through this recipe, you need Ubuntu 14.04 (Linux flavor) installed on the machine. Python comes pre-installed. The python --version command gives the version of the Python installed. If the version seems to be 2.6.x, upgrade it to Python 2.7 as follows:

    sudo apt-get install python2.7

How to do it…

  1. Install IPython as follows:

           sudo pip install ipython

  2. Create an IPython profile for use with PySpark...

Creating Pandas DataFrames over Spark

A DataFrame is a distributed collection of data organized into named columns. It is equivalent to a table in a relational database or a DataFrame in R/Python Python with rich optimizations. These can be constructed from a wide variety of sources, such as structured data files (JSON and parquet files), Hive tables, external databases, or from existing RDDs.

PySpark is the Python API for Apache Spark which is designed to scale to huge amounts of data. This recipe shows how to make use of Pandas over Spark.

Getting ready

To step through this recipe, you will need a running Spark cluster either in pseudo distributed mode or in one of the distributed modes, that is, standalone, YARN, or Mesos. Also, have Python and IPython installed on the Linux machine, that is, Ubuntu 14.04.

How to do it…

  1. Invoke ipython console -profile=pyspark  as follows:

          In [4]: from pyspark import SparkConf, SparkContext, SQLContext
      In [5]: import pandas as pd

  2. Creating...

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Key benefits

  • Use Apache Spark for data processing with these hands-on recipes
  • Implement end-to-end, large-scale data analysis better than ever before
  • Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data

Description

Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.

Who is this book for?

This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful.

What you will learn

  • Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning.
  • Solve real-world analytical problems with large data sets.
  • Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale.
  • Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package.
  • Learn about numerical and scientific computing using NumPy and SciPy on Spark.
  • Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models.
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Publication date : Dec 22, 2016
Length: 392 pages
Edition : 1st
Language : English
ISBN-13 : 9781785880100
Vendor :
Apache
Category :
Data
Concepts :
Data Science
Tools :
Apache Spark

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Product Details

Publication date : Dec 22, 2016
Length: 392 pages
Edition : 1st
Language : English
ISBN-13 : 9781785880100
Vendor :
Apache
Category :
Data
Concepts :
Data Science
Tools :
Apache Spark

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Table of Contents

10 Chapters
1. Big Data Analytics with Spark Chevron down icon Chevron up icon
1. Big Data Analytics with Spark
Introduction
Initializing SparkContext
Working with Spark's Python and Scala shells
Building standalone applications
Working with the Spark programming model
Working with pair RDDs
Persisting RDDs
Loading and saving data
Creating broadcast variables and accumulators
Submitting applications to a cluster
Working with DataFrames
Working with Spark Streaming
2. Tricky Statistics with Spark Chevron down icon Chevron up icon
2. Tricky Statistics with Spark
Introduction
Variable identification
Sampling data
Summary and descriptive statistics
Generating frequency tables
Installing Pandas on Linux
Installing Pandas from source
Using IPython with PySpark
Creating Pandas DataFrames over Spark
Splitting, slicing, sorting, filtering, and grouping DataFrames over Spark
Implementing co-variance and correlation using Pandas
Concatenating and merging operations over DataFrames
Complex operations over DataFrames
Sparkling Pandas
3. Data Analysis with Spark Chevron down icon Chevron up icon
3. Data Analysis with Spark
Introduction
Univariate analysis
Bivariate analysis
Missing value treatment
Outlier detection
Use case - analyzing the MovieLens dataset
Use case - analyzing the Uber dataset
4. Clustering, Classification, and Regression Chevron down icon Chevron up icon
4. Clustering, Classification, and Regression
Introduction
Supervised learning
Unsupervised learning
Applying regression analysis for sales data
Variable identification
Data exploration
Feature engineering
Applying linear regression
Applying logistic regression on bank marketing data
Variable identification
Data exploration
Feature engineering
Applying logistic regression
Real-time intrusion detection using streaming k-means
Variable identification
Simulating real-time data
Applying streaming k-means
5. Working with Spark MLlib Chevron down icon Chevron up icon
5. Working with Spark MLlib
Introduction
Working with Spark ML pipelines
Implementing Naive Bayes' classification
Implementing decision trees
Building a recommendation system
Implementing logistic regression using Spark ML pipelines
6. NLP with Spark Chevron down icon Chevron up icon
6. NLP with Spark
Introduction
Installing NLTK on Linux
Installing Anaconda on Linux
Anaconda for cluster management
POS tagging with PySpark on an Anaconda cluster
NER with IPython over Spark
Implementing openNLP - chunker over Spark
Implementing openNLP - sentence detector over Spark
Implementing stanford NLP - lemmatization over Spark
Implementing sentiment analysis using stanford NLP over Spark
7. Working with Sparkling Water - H2O Chevron down icon Chevron up icon
7. Working with Sparkling Water - H2O
Introduction
Features
Working with H2O on Spark
Implementing k-means using H2O over Spark
Implementing spam detection with Sparkling Water
Deep learning with airlines and weather data
Implementing a crime detection application
Running SVM with H2O over Spark
8. Data Visualization with Spark Chevron down icon Chevron up icon
8. Data Visualization with Spark
Introduction
Visualization using Zeppelin
Installing Zeppelin
Customizing Zeppelin's server and websocket port
Visualizing data on HDFS - parameterizing inputs
Running custom functions
Adding external dependencies to Zeppelin
Pointing to an external Spark Cluster
Creating scatter plots with Bokeh-Scala
Creating a time series MultiPlot with Bokeh-Scala
Creating plots with the lightning visualization server
Visualize machine learning models with Databricks notebook
9. Deep Learning on Spark Chevron down icon Chevron up icon
9. Deep Learning on Spark
Introduction
Installing CaffeOnSpark
Working with CaffeOnSpark
Running a feed-forward neural network with DeepLearning 4j over Spark
Running an RBM with DeepLearning4j over Spark
Running a CNN for learning MNIST with DeepLearning4j over Spark
Installing TensorFlow
Working with Spark TensorFlow
10. Working with SparkR Chevron down icon Chevron up icon
10. Working with SparkR
Introduction
Installing R
Interactive analysis with the SparkR shell
Creating a SparkR standalone application from RStudio
Creating SparkR DataFrames
SparkR DataFrame operations
Applying user-defined functions in SparkR
Running SQL queries from SparkR and caching DataFrames
Machine learning with SparkR

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RAG-Driven Generative AI
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Full star icon 4.5 (13)
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Hands-On Reinforcement Learning with Python
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Full star icon 2.6 (18)
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LLM Engineer's Handbook
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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(4 Ratings)
5 star 50%
4 star 0%
3 star 25%
2 star 0%
1 star 25%
pavan kumar jalla Sep 10, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As a big data engineer for 3 years in the industry, I was looking around for a solid hands on book for data science, this book has great content and well structred right from the beginning till the end, which takes you a deep dive into data science concepts, appreciate the author for sharing her knowledge.would recommend to anyone who is looking for practical data science approach.
Amazon Verified review Amazon
Brandon Jan 23, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book represents a useful resource to learn Spark programming model and how to employ it in several tasks. The approach followed is very practical, with code provided in every chapter, which guarantees a fast learning process. As technical reviewer of this book I feel to suggest it to people who want to understand how to perform data exploration, analysis and visualization tasks in Spark. With the many use cases covered in the book, it will represent a resource to inspire solutions for daily working tasks.
Amazon Verified review Amazon
Dimitri Shvorob Jun 01, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I would dismiss a five-star review by the book's technical reviewer - conflict of interest, anyone? - and "Apache Spark for Data Science Cookbook" is not a five-star book. It is, however, a decent book which compensates for the Packt-standard weakness of explanations with a thoughtful collection of (Scala) code, paying attention to the less glamorous but essential job of data manipulation. And yet, I hesitate to recommend it, and feel that a combo of "Machine Learning with Spark" by Pentreath and "Spark for Data Science" by Duvvuri and Singhal would be a better choice. I would suggest getting all three and deciding which one(s) to leave.
Amazon Verified review Amazon
Santanu Feb 25, 2017
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
This book does not improve you spark knowledge. Only bunch of code with input and output. No proper comments on code.
Amazon Verified review Amazon

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About the authors

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Profile icon Chitturi
Chitturi
Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey deans work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.
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Profile icon Nagamallikarjuna Inelu
Nagamallikarjuna Inelu
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Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


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