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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Use powerful industry-standard tools to unlock new, actionable insight from your existing data
Key Features
- Get up and running with the Jupyter ecosystem and some example datasets
- Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests
- Discover how you can use web scraping to gather and parse your own bespoke datasets
Book Description
Getting started with data science doesn’t have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.
Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You’ll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world.
We'll start with understanding the basics of Jupyter and its standard features. You'll be analyzing an example of a data analytics report. After analyzing a data analytics report, next step is to implement multiple classification algorithms. We’ll then show you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. Finish up by learning to visualize these data interactively.
What you will learn
- Identify potential areas of investigation and perform exploratory data analysis
- Plan a machine learning classification strategy and train classification models
- Use validation curves and dimensionality reduction to tune and enhance your models
- Scrape tabular data from web pages and transform it into Pandas DataFrames
- Create interactive, web-friendly visualizations to clearly communicate your findings
Who this book is for
This course is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.
Alex Galea has been professionally practicing data analytics since graduating with a Master’s degree in Physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks.Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 173
Veröffentlichungsjahr: 2018
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Table of Contents
Beginning Data Analysis with Python and Jupyter
Beginning Data Analysis with Python and Jupyter
Copyright © 2018 Packt Publishing
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First published: May 2018
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ISBN 978-1-78953-202-9
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Contributors
About the author
Alex Galea has been professionally practicing data analytics since graduating with a Master's degree in Physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks.
About the reviewer
Elie Kawerk likes to solve problems using the analytical skills he has accumulated over the years. He uses the data science process, including statistical methods and machine learning, to extract insights from data and get value out of it.
His formal training is in computational physics. He used to simulate atomic and molecular physics phenomena with the help of supercomputers using the good old FORTRAN language; this involved a lot of linear algebra and quantum physics equations.
You can find out more about Elie on his LinkedIn profile (https://www.linkedin.com/in/elie-kawerk-data-scientist/).
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Preface
Data science is becoming increasingly popular as industries continue to value its importance. Recent advancements in open source sofware have made this discipline accessible to a wide range of people. In this book, we show how Jupyter Notebooks can be used with Python for various data science applications. Aside from being an ideal "virtual playground" for data exploration, Jupyter Notebooks are equally suitable for creating reproducible data processing pipelines, visualizations, and prediction models. By using Python with Jupyter Notebooks, many challenges presented by data science become simple to conceptualize and implement. This is achieved by leveraging Python libraries, which offer abstractions to the more complicated underlying algorithms. The result is that data science becomes very approachable for beginners. Furthermore, the Python ecosystem is very strong and is growing with each passing year. As such, students who wish to continue learning about the topics covered in this book will fnd excellent resources to do so.
By the end of this book, you will be equipped to analyse data using Python and use Jupyter notebooks effectively.
What This Book Covers
Lesson 1, Jupyter Fundamentals, covers the fundamentals of data analysis in Jupyter. We will start with usage instructions and features of Jupyter such as magic functions and tab completion. We will then transition to data science specific material. We will run an exploratory analysis in a live Jupyter Notebook. We will use visual assists such as scatter plots, histograms, and violin plots to deepen our understanding of the data. We will also perform simple predictive modeling.
Lesson 2, Data Cleaning and Advanced Machine Learning, shows how predictive models can be trained in Jupyter Notebooks. We will talk about how to plan a machine learning strategy. This lesson also explains the machine learning terminology such as supervised learning, unsupervised learning, classification, and regression. We will discuss methods for preprocessing data using scikit-learn and pandas.
Lesson 3, Web Scraping and Interactive Visualizations, explains how to scrap web page tables and then use interactive visualizations to study the data. We will start by looking at how HTTP requests work, focusing on GET requests and their response status codes. Then, we will go into the Jupyter Notebook and make HTTP requests with Python using the Requests library. We will see how Jupyter can be used to render HTML in the notebook, along with actual web pages that can be interacted with. After making requests, we will see how Beautiful Soup can be used to parse text from the HTML, and used this library to scrape tabular data.
What You Need for This Book
This book will require the following minimum hardware requirements:
Throughout this book, we will be using Python and Jupyter Notebook to run our code. Additionally, Anaconda environment is needed to run Python and Jupyter notebook. Please ensure you have the following installed on your machine:
Python libraries included with Anaconda installation:
Python libraries that require manual installation:
Installation and Setup
Before you start with this book, we'll install Anaconda environment which consists of Python and Jupyter Notebook.
Installing Anaconda
Updating Jupyter and Installing Dependencies
Who This Book is for
This book will be most applicable to professionals and students interested in data analysis. The topics covered are relevant to a variety of job descriptions across a large range of industries. For the best experience, you should have knowledge of programming fundamentals and some experience with Python. In particular, having some familiarity with Python libraries such as Pandas, matplotlib, and scikit-learn will be useful.
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Chapter 1. Jupyter Fundamentals
Jupyter Notebooks are one of the most important tools for data scientists using Python. This is because they're an ideal environment for developing reproducible data analysis pipelines. Data can be loaded, transformed, and modeled all inside a single Notebook, where it's quick and easy to test out code and explore ideas along the way. Furthermore, all of this can be documented "inline" using formatted text, so you can make notes for yourself or even produce a structured report.
Other comparable platforms - for example, RStudio or Spyder - present the user with multiple windows, which promote arduous tasks such as copy and pasting code around and rerunning code that has already been executed. These tools also tend to involve Read Eval Prompt Loops (REPLs) where code is run in a terminal session that has saved memory. This type of development environment is bad for reproducibility and not ideal for development either. Jupyter Notebooks solve all these issues by giving the user a single window where code snippets are executed and outputs are displayed inline. This lets users develop code efficiently and allows them to look back at previous work for reference, or even to make alterations.
We'll start the lesson by explaining exactly what Jupyter Notebooks are and continue to discuss why they are so popular among data scientists. Then, we'll open a Notebook together and go through some exercises to learn how the platform is used. Finally, we'll dive into our first analysis and perform an exploratory analysis in Basic Functionality and Features.
Lesson Objectives
In this lesson, you will:
Note
All code from this book are available as lesson-specific IPython notebooks in the code bundle. All color plots from this book are also available in the code bundle.
Basic Functionality and Features
In this section, we first demonstrate the usefulness of Jupyter Notebooks with examples and through discussion. Then, in order to cover the fundamentals of Jupyter Notebooks for beginners, we'll see the basic usage of them in terms of launching and interacting with the platform. For those who have used Jupyter Notebooks before, this will be mostly a review; however, you will certainly see new things in this topic as well.
Subtopic A: What is a Jupyter Notebook and Why is it Useful?
Jupyter Notebooks are locally run web applications which contain live code, equations, figures, interactive apps, and Markdown text. The standard language is Python, and that's what we'll be using for this book; however, note that a variety of alternatives are supported. This includes the other dominant data science language, R:
Those familiar with R will know about R Markdown. Markdown documents allow for Markdown-formatted text to be combined with executable code. Markdown is a simple language used for styling text on the web. For example, most GitHub repositories have a README.md Markdown file. This format is useful for basic text formatting. It's comparable to HTML but allows for much less customization. Commonly used symbols in Markdown include hashes (#) to make text into a heading, square and round brackets to insert hyperlinks, and stars to create italicized or bold text:
Having seen the basics of Markdown, let's come back to R Markdown, where Markdown text can be written alongside executable code. Jupyter Notebooks offer the equivalent functionality for Python, although, as we'll see, they function quite differently than R Markdown documents. For example, R Markdown assumes you are writing Markdown unless otherwise specified, whereas Jupyter Notebooks assume you are inputting code. This makes it more appealing to use Jupyter Notebooks for rapid development and testing.
From a data science perspective, there are two primary types for a Jupyter Notebook depending on how they are used: lab-style and deliverable.
Lab-style Notebooks are meant to serve as the programming analog of research journals. These should contain all the work you've done to load, process, analyze, and model the data. The idea here is to document everything you've done for future reference, so it's usually not advisable to delete or alter previous lab-style Notebooks. It's also a good idea to accumulate multiple date-stamped versions of the Notebook as you progress through the analysis, in case you want to look back at previous states.
Deliverable Notebooks are intended to be presentable and should contain only select parts of the lab-style Notebooks. For example, this could be an interesting discovery to share with your colleagues, an in-depth report of your analysis for a manager, or a summary of the key findings for stakeholders.
In either case, an important concept is reproducibility. If you've been diligent in documenting your software versions, anyone receiving the reports will be able to rerun the Notebook and compute the same results as you did. In the scientific community, where reproducibility is becoming increasingly difficult, this is a breath of fresh air.
Subtopic B: Navigating the Platform
Now, we are going to open up a Jupyter Notebook and start to learn the interface. Here, we will assume you have no prior knowledge of the platform and go over the basic usage.
Introducing Jupyter Notebooks
Note
On Unix machines such as Mac or Linux, command-line navigation can be done using ls to display directory contents and cd to change directories.
On Windows machines, use dir to display directory contents and use cd to change directories instead. If, for example, you want to change the drive from C: to D:, you should execute d: to change drives.
A new window or tab of your default browser will open the Notebook Dashboard to the working directory. Here, you will see a list of folders and files contained therein.
Click on a folder to navigate to that particular path and open a file by clicking on it. Although its main use is editing IPYNB Notebook files, Jupyter functions as a standard text editor as well.Reopen the terminal window used to launch the app. We can see the NotebookApp being run on a local server. In particular, you should see a line like this:
