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Mastering Python Data Analysis
Mastering Python Data Analysis

Mastering Python Data Analysis: Become an expert at using Python for advanced statistical analysis of data using real-world examples

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Mastering Python Data Analysis

Chapter 1. Tools of the Trade

This chapter gives you an overview of the tools available for data analysis in Python, with details concerning the Python packages and libraries that will be used in this book. A few installation tips are given, and the chapter concludes with a brief example. We will concentrate on how to read data files, select data, and produce simple plots, instead of delving into numerical data analysis.

Before you start

We assume that you have familiarity with Python and have already developed and run some scripts or used Python interactively, either in the shell or on another interface, such as the Jupyter Notebook (formerly known as the IPython notebook). Hence, we also assume that you have a working installation of Python. In this book, we assume that you have installed Python 3.4 or later.

We also assume that you have developed your own workflow with Python, based on needs and available environment. To follow the examples in this book, you are expected to have access to a working installation of Python 3.4 or later. There are two alternatives to get started, as outlined in the following list:

Tip

Even if you have a working Python installation, you might want to try one of the prepackaged distributions. They contain a well-rounded collection of packages and modules suitable for data analysis and scientific computing. If you choose this path, all the libraries in the next list are included by default.

We also assume that you have the libraries in the following list:

  • numpy and scipy: These are available at http://www.scipy.org. These are the essential Python libraries for computational work. NumPy defines a fast and flexible array data structure, and SciPy has a large collection of functions for numerical computing. They are required by some of the libraries mentioned in the list.
  • matplotlib: This is available at http://matplotlib.org. It is a library for interactive graphics built on top of NumPy. I recommend versions above 1.5, which is what is included in Anaconda Python by default.
  • pandas: This is available at http://pandas.pydata.org. It is a Python data analysis library. It will be used extensively throughout the book.
  • pymc: This is a library to make Bayesian models and fitting in Python accessible and straightforward. It is available at http://pymc-devs.github.io/pymc/. This package will mainly be used in Chapter 6 Bayesian Methods, of this book.
  • scikit-learn: This is available at http://scikit-learn.org. It is a library for machine learning in Python. This package is used in  Chapter 7, Supervised and Unsupervised Learning.
  • IPython: This is available at http://ipython.org. It is a library providing enhanced tools for interactive computations in Python from the command line.
  • Jupyter: This is available at https://jupyter.org/. It is the notebook interface working on top of IPython (and other programming languages). Originally part of the IPython project, the notebook interface is a web-based platform for computational and data science that allows easy integration of the tools that are used in this book.

Notice that each of the libraries in the preceding list may have several dependencies, which must also be separately installed. To test the availability of any of the packages, start a Python shell and run the corresponding import statement. For example, to test the availability of NumPy, run the following command:

import numpy

If NumPy is not installed in your system, this will produce an error message. An alternative approach that does not require starting a Python shell is to run the command line:

python -c 'import numpy'

We also assume that you have either a programmer's editor or Python IDE. There are several options, but at the basic level, any editor capable of working with unformatted text files will do.

Using the notebook interface

Most examples in this book will use the Jupyter Notebook interface. This is a browser-based interface that integrates computations, graphics, and other forms of media. Notebooks can be easily shared and published, for example, http://nbviewer.ipython.org/ provides a simple publication path.

It is not, however, absolutely necessary to use the Jupyter interface to run the examples in this book. We strongly encourage, however, that you at least experiment with the notebook and its many features. The Jupyter Notebook interface makes it possible to mix formatted, descriptive text with code cells that evaluate at the same time. This feature makes it suitable for educational purposes, but it is also useful for personal use as it makes it easier to add comments and share partial progress before writing a full report. We will sometimes refer to a Jupyter Notebook as just a notebook.

To start the notebook interface, run the following command line from the shell or Anaconda command prompt:

jupyter notebook

The notebook server will be started in the directory where the command is issued. After a while, the notebook interface will appear in your default browser. Make sure that you are using a standards-compliant browser, such as Chrome, Firefox, Opera, or Safari. Once the Jupyter dashboard shows on the browser, click on the New button on the upper-right side of the page and select Python 3. After a few seconds, a new notebook will open in the browser. A useful place to learn about the notebook interface is http://jupyter.org.

Imports

There are some modules that we will need to load at the start of every project. Assuming that you are running a Jupyter Notebook, the required imports are as follows:

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

Enter all the preceding commands in a single notebook cell and press Shift + Enter to run the whole cell. A new cell will be created when there is none after the one you are running; however, if you want to create one yourself, the menu or keyboard shortcut Ctrl +M+A/B is handy (A for above, B for below the current cell). In Appendix, More on Jupyter Notebook and matplotlib Styles, we cover some of the keyboard shortcuts available and installable extensions (that is, plugins) for Jupyter Notebook.

The statement %matplotlib inline is an example of Jupyter Notebook magic and sets up the interface to display plots inline, that is, embedded in the notebook. This line is not needed (and causes an error) in scripts. Next, optionally, enter the following commands:

import os
plt.style.use(os.path.join(os.getcwd(), 'mystyle.mplstyle') )

As before, run the cell by pressing Shift +Enter. This code has the effect of selecting matplotlib stylesheet mystyle.mplstyle. This is a custom style sheet that I created, which resides in the same folder as the notebook. It is a rather simple example of what can be done; you can modify it to your liking. As we gain experience in drawing figures throughout the book, I encourage you to play around with the settings in the file. There are also built-in styles that you can by typing plt.style.available in a new cell.

This is it! We are all set to start the fun part!

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Key benefits

  • Clean, format, and explore data using graphical and numerical summaries
  • Leverage the IPython environment to efficiently analyze data with Python
  • Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data

Description

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want! Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.

Who is this book for?

If you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed.

What you will learn

  • Read, sort, and map various data into Python and Pandas
  • Recognise patterns so you can understand and explore data
  • Use statistical models to discover patterns in data
  • Review classical statistical inference using Python, Pandas, and SciPy
  • Detect similarities and differences in data with clustering
  • Clean your data to make it useful
  • Work in Jupyter Notebook to produce publication ready figures to be included in reports
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Table of Contents

9 Chapters
1. Tools of the Trade Chevron down icon Chevron up icon
2. Exploring Data Chevron down icon Chevron up icon
3. Learning About Models Chevron down icon Chevron up icon
4. Regression Chevron down icon Chevron up icon
5. Clustering Chevron down icon Chevron up icon
6. Bayesian Methods Chevron down icon Chevron up icon
7. Supervised and Unsupervised Learning Chevron down icon Chevron up icon
8. Time Series Analysis Chevron down icon Chevron up icon
A. More on Jupyter Notebook and matplotlib Styles Chevron down icon Chevron up icon

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Mher kazandjian Sep 18, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A book that provides a comprehensive set tools and techniques for real life dataanalysis and lots of examples.Unlike many mainstream books that usually use synthetic data, the examples in thisbook use actual data that is publicly available. The examples and code samplesof the various algorithms and techniques run out of the box requiring minimalsetup.The book starts with a good introduction on statistical distributions followedby modelling techniques to assess the quality and the significance of the models.These are essential tools for any researcher or scientist or developer that wantto make sense of models in academia or industry.The second part of the book is really exciting connecting clustering techniquesto machine learning and time-series analysis that is at the core of big datanowadays.
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