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Home > Data > Big Data > Big Data Analysis with Python
Big Data Analysis with Python
Big Data Analysis with Python

Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning

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Big Data Analysis with Python

Chapter 2. Statistical Visualizations

Note

Learning Objectives

We will start our journey by understanding the power of Python to manipulate and visualize data, creating useful analysis.

By the end of this chapter, you will be able to:

  • Use graphs for data analysis

  • Create graphs of various types

  • Change graph parameters such as color, title, and axis

  • Export graphs for presentation, printing, and other uses

Note

In this chapter, we will illustrate how the students can generate visualizations with Matplotlib and Seaborn.

Introduction

In the last chapter, we learned that the libraries that are most commonly used for data science work with Python. Although they are not big data libraries per se, the libraries of the Python Data Science Stack (NumPy, Jupyter, IPython, Pandas, and Matplotlib) are important in big data analysis.

As we will demonstrate in this chapter, no analysis is complete without visualizations, even with big datasets, so knowing how to generate images and graphs from data in Python is relevant for our goal of big data analysis. In the subsequent chapters, we will demonstrate how to process large volumes of data and aggregate it to visualize it using Python tools.

There are several visualization libraries for Python, such as Plotly, Bokeh, and others. But one of the oldest, most flexible, and most used is Matplotlib. But before going through the details of creating a graph with Matplotlib, let's first understand what kinds of graphs are relevant for analysis.

Types of Graphs and When to Use Them

Every analysis, whether on small or large datasets, involves a descriptive statistics step, where the data is summarized and described by statistics such as mean, median, percentages, and correlation. This step is commonly the first step in the analysis workflow, allowing a preliminary understanding of the data and its general patterns and behaviors, providing grounds for the analyst to formulate hypotheses, and directing the next steps in the analysis. Graphs are powerful tools to aid in this step, enabling the analyst to visualize the data, create new views and concepts, and communicate them to a larger audience.

There is a vast amount of literature on statistics about visualizing information. The classic book, Envisioning Information, by Edward Tufte, demonstrates beautiful and useful examples of how to present information in graphical form. In another book, The Visual Display of Quantitative Information, Tufte enumerates a few qualities that a graph...

Components of a Graph

Each graph has a set of common components that can be adjusted. The names that Matplotlib uses for these components are demonstrated in the following graph:

Figure 2.3: Components of a graph

The components of a graph are as follows:

  • Figure: The base of the graph, where all the other components are drawn.

  • Axis: Contains the figure elements and sets the coordinate system.

  • Title: The title gives the graph its name.

  • X-axis label: The name of the x-axis, usually named with the units.

  • Y-axis label: The name of the y-axis, usually named with the units.

  • Legend: A description of the data plotted in the graph, allowing you to identify the curves and points in the graph.

  • Ticks and tick labels: They indicate the points of reference on a scale for the graph, where the values of the data are. The labels indicate the values themselves.

  • Line plots: These are the lines that are plotted with the data.

  • Markers: Markers are the pictograms that mark the point data.

  • Spines: The lines that delimit the...

Seaborn

Seaborn (https://seaborn.pydata.org/) is part of the PyData family of tools and is a visualization library based on Matplotlib with the goal of creating statistical graphs more easily. It can operate directly on DataFrames and series, doing aggregations and mapping internally. Seaborn uses color palettes and styles to make visualizations consistent and more informative. It also has functions that can calculate some statistics, such as regression, estimation, and errors. Some specialized plots, such as violin plots and multi-facet plots, are also easy to create with Seaborn.

Which Tool Should Be Used?

Seaborn tries to make the creation of some common analysis graphs easier than using Matplotlib directly. Matplotlib can be considered more low-level than Seaborn, and although this makes it a bit more cumbersome and verbose, it gives analysts much more flexibility. Some graphs, which with Seaborn are created with one function call, would take several lines of code to achieve using Matplotlib.

There is no rule to determine whether an analyst should use only the pandas plotting interface, Matplotlib directly, or Seaborn. Analysts should keep in mind the visualization requirements and the level of configuration required to create the desired graph.

Pandas' plotting interface is easier to use but is more constrained and limited. Seaborn has several graph patterns ready to use, including common statistical graphs such as pair plots and boxplots, but requires that the data is formatted into a tidy format and is more opinionated on how the graphs should look. Matplotlib...

Types of Graphs

The first type of graph that we will present is the line graph or line chart. A line graph displays data as a series of interconnected points on two axes (x and y), usually Cartesian, ordered commonly by the x-axis. Line charts are useful for demonstrating trends in data, such as in time series, for example.

A graph related to the line graph is the scatter plot. A scatter plot represents the data as points in Cartesian coordinates. Usually, two variables are demonstrated in this graph, although more information can be conveyed if the data is color-coded or size-coded by category, for example. Scatter plots are useful for showing the relationship and possible correlation between variables.

Histograms are useful for representing the distribution of data. Unlike the two previous examples, histograms show only one variable, usually on the x-axis, while the y-axis shows the frequency of occurrence of the data. The process of creating a histogram is a bit more involved than the line...

Pandas DataFrames and Grouped Data

As we learned in the previous chapter, when analyzing data and using Pandas to do so, we can use the plot functions from Pandas or use Matplotlib directly. Pandas uses Matplotlib under the hood, so the integration is great. Depending on the situation, we can either plot directly from pandas or create a figure and an axes with Matplotlib and pass it to pandas to plot. For example, when doing a GroupBy, we can separate the data into a GroupBy key. But how can we plot the results of GroupBy? We have a few approaches at our disposal. We can, for example, use pandas directly, if the DataFrame is already in the right format:

Note

The following code is a sample and will not get executed.

fig, ax = plt.subplots()
df = pd.read_csv('data/dow_jones_index.data')
df[df.stock.isin(['MSFT', 'GE', 'PG'])].groupby('stock')['volume'].plot(ax=ax)

Or we can just plot each GroupBy key on the same plot:

fig, ax = plt.subplots()
df.groupby('stock').volume.plot(ax=ax)

For the following...

Changing Plot Design: Modifying Graph Components

So far, we've looked at the main graphs used in analyzing data, either directly or grouped, for comparison and trend visualization. But one thing that we can see is that the design of each graph is different from the others, and we don't have basic things such as a title and legends.

We've learned that a graph is composed of several components, such as a graph title, x and y labels, and so on. When using Seaborn, the graphs already have x and y labels, with the names of the columns. With Matplotlib, we don't have this. These changes are not only cosmetic.

The understanding of a graph can be greatly improved when we adjust things such as line width, color, and point size too, besides labels and titles. A graph must be able to stand on its own, so title, legends, and units are paramount. How can we apply the concepts that we described previously to make good, informative graphs on Matplotlib and Seaborn?

The possible number of ways that plots can...

Exporting Graphs

After generating our visualizations and configuring the details, we can export our graphs to a hard copy format, such as PNG, JPEG, or SVG. If we are using the interactive API in the notebook, we can just call the savefig function over the pyplot interface, and the last generated graph will be exported to the file:

df.plot(kind='scatter', x='weight', y='horsepower', figsize=(20,10))
plt.savefig('horsepower_weight_scatter.png')

Figure 2.26: Exporting the graphs

All plot configurations will be carried to the plot. To export a graph when using the object-oriented API, we can call savefig from the figure:

fig, ax = plt.subplots()
df.plot(kind='scatter', x='weight', y='horsepower', figsize=(20,10), ax=ax)
fig.savefig('horsepower_weight_scatter.jpg')

Figure 2.27: Saving the graph

We can change some parameters for the saved image:

  • dpi: Adjust the saved image resolution.

  • facecolor: The face color of the figure.

  • edgecolor: The edge color of the figure, around the graph.

  • format: Usually PNG...

Summary

In this chapter, we have seen the importance of creating meaningful and interesting visualizations when analyzing data. A good data visualization can immensely help the analyst's job, representing data in a way that can reach larger audiences and explain concepts that could be hard to translate into words or to represent with tables.

A graph, to be effective as a data visualization tool, must show the data, avoid distortions, make understanding large datasets easy, and have a clear purpose, such as description or exploration. The main goal of a graph is to communicate data, so the analyst must keep that in mind when creating a graph. A useful graph is more desirable than a beautiful one.

We demonstrated some kinds of graphs commonly used in analysis: the line graph, the scatter plot, the histogram, and the boxplot. Each graph has its purpose and application, depending on the data and the goal. We have also shown how to create graphs directly from Matplotlib, from pandas, or a combination...

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Key benefits

  • Get a hands-on, fast-paced introduction to the Python data science stack
  • Explore ways to create useful metrics and statistics from large datasets
  • Create detailed analysis reports with real-world data

Description

Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.

Who is this book for?

Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.

What you will learn

  • Use Python to read and transform data into different formats
  • Generate basic statistics and metrics using data on disk
  • Work with computing tasks distributed over a cluster
  • Convert data from various sources into storage or querying formats
  • Prepare data for statistical analysis, visualization, and machine learning
  • Present data in the form of effective visuals
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Table of Contents

8 Chapters
The Python Data Science Stack Chevron down icon Chevron up icon
The Python Data Science Stack
Introduction
Python Libraries and Packages
Using Pandas
Data Type Conversion
Aggregation and Grouping
Exporting Data from Pandas
Visualization with Pandas
Summary
Statistical Visualizations Chevron down icon Chevron up icon
Statistical Visualizations
Introduction
Types of Graphs and When to Use Them
Components of a Graph
Seaborn
Which Tool Should Be Used?
Types of Graphs
Pandas DataFrames and Grouped Data
Changing Plot Design: Modifying Graph Components
Exporting Graphs
Summary
Working with Big Data Frameworks Chevron down icon Chevron up icon
Working with Big Data Frameworks
Introduction
Hadoop
Spark
Writing Parquet Files
Handling Unstructured Data
Summary
Diving Deeper with Spark Chevron down icon Chevron up icon
Diving Deeper with Spark
Introduction
Getting Started with Spark DataFrames
Writing Output from Spark DataFrames
Exploring Spark DataFrames
Data Manipulation with Spark DataFrames
Graphs in Spark
Summary
Handling Missing Values and Correlation Analysis Chevron down icon Chevron up icon
Handling Missing Values and Correlation Analysis
Introduction
Setting up the Jupyter Notebook
Missing Values
Handling Missing Values in Spark DataFrames
Correlation
Summary
Exploratory Data Analysis Chevron down icon Chevron up icon
Exploratory Data Analysis
Introduction
Defining a Business Problem
Translating a Business Problem into Measurable Metrics and Exploratory Data Analysis (EDA)
Structured Approach to the Data Science Project Life Cycle
Summary
Reproducibility in Big Data Analysis Chevron down icon Chevron up icon
Reproducibility in Big Data Analysis
Introduction
Reproducibility with Jupyter Notebooks
Gathering Data in a Reproducible Way
Code Practices and Standards
Avoiding Repetition
Summary
Creating a Full Analysis Report Chevron down icon Chevron up icon
Creating a Full Analysis Report
Introduction
Reading Data in Spark from Different Data Sources
SQL Operations on a Spark DataFrame
Generating Statistical Measurements
Summary

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This book is a broken mess.The sections on theory, for example the discussion on the which graph to use where in chapter 2, are fine.But then, suddenly, you are asked to do activities by yourself which the textbook hasn't prepared you for and you have to resort to the appendices to see how it should be done. Sometimes (e.g. activity 7) examples just doesn't work - the code is completely broken and the book doesn't prepare you for how to resolve it. No amount of googling is able to put you on the right track either. Exercise 23 just ends in a "type error" as lambda takes 0 positional arguments but 1 was given which, if the forums are to be believed, is because the function is not longer supported by Python. Next your are doing exercises and can follow along the examples in the chapter and all is fine again, but then functionality is introduced which isn't explained and you end up parroting the text. The text makes you install Hadoop, which with the problem solving took me 3 hours, and then moves unceremoniously on to Spark, which with the errors in the text is its own can of worms without ever going into Hadoop at all.This books just comes across as poorly edited and with little to none quality control resulting in frustration. Give it a pass.
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About the 3 authors

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Profile icon Marin
Marin
Ivan Marin is a systems architect and data scientist working at Daitan Group, a Campinas-based software company. He designs big data systems for large volumes of data and implements machine learning pipelines end to end using Python and Spark. He is also an active organizer of data science, machine learning, and Python in So Paulo, and has given Python for data science courses at university level.
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VK
Sarang VK is a lead data scientist at StraitsBridge Advisors, where his responsibilities include requirement gathering, solutioning, development, and productization of scalable machine learning, artificial intelligence, and analytical solutions using open source technologies. Alongside this, he supports pre-sales and competency.
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Profile icon Shukla
Shukla
Vishal Shukla is the CEO of Brevitaz Systems (http://brevitaz.com) and a technology evangelist at heart. He is a passionate software scientist and a big data expert. Vishal has extensive experience in designing modular enterprise systems. Since his college days (more than 11 years), Vishal has enjoyed coding in JVM-based languages. He also embraces design thinking and sustainable software development. He has vast experience in architecting enterprise systems in various domains. Vishal is deeply interested in technologies related to big data engineering, analytics, and machine learning. He set up Brevitaz Systems. This company delivers massively scalable and sustainable big data and analytics-based enterprise applications to their global clientele. With varied expertise in big data technologies and architectural acumen, the Brevitaz team successfully developed and re-engineered a number of legacy systems to state-of-the-art scalable systems. Brevitaz has imbibed in its culture agile practices, such as scrum, test-driven development, continuous integration, and continuous delivery, to deliver high-quality products to its clients. Vishal is a music and art lover. He loves to sing, play musical instruments, draw portraits, and play sports, such as cricket, table tennis, and pool, in his free time. You can contact Vishal at vishal.shukla@brevitaz.com and on LinkedIn at https://in.linkedin.com/in/vishalshu. You can also follow Vishal on Twitter at @vishal1shukla2.
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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
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

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.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
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
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

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.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela