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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 2. Exploring Data

When starting to work on a new dataset, it is essential to first get an idea of what conclusions can be drawn from the data. Before we can do things such as inference and hypothesis testing, we need to develop an understanding of what questions the data at hand can answer. This is the key to exploratory data analysis, which is the skill and science of developing intuition and identifying statistical patterns in the data. In this chapter, we will present graphical and numerical methods that help in this task. You will notice that there are no hard and fast rules of how to proceed at each step, but instead, we give recommendations on what techniques tend to be suitable in each case. The best way to develop the set of skills necessary to be an expert data explorer is to see lots of examples and, perhaps more importantly, work on our own datasets. More specifically, this chapter will cover the following topics:

  • Performing the initial exploration and cleaning...

The General Social Survey

To present concrete data examples in this chapter, we will use the General Social Survey (GSS). The GSS is a large survey of societal trends conducted by the National Opinion Research Center (NORC- http://www3.norc.org ) at the University of Chicago. As this is a very complex dataset, we will work with a subset of the data, the compilation from the 2012 survey. With a size 5.5 MB, this is a small data size by the current standards, but still well-suited for the kind of exploration being illustrated in this chapter. (Smith, Tom W, Peter Marsden, Michael Hout, and Jibum Kim. General Social Surveys, 1972-2014 [machine-readable data file] /Principal Investigator, Tom W. Smith; Co-Principal Investigator, Peter V. Marsden; Co-Principal Investigator, Michael Hout; Sponsored by National Science Foundation. --NORC ed.-- Chicago: NORC at the University of Chiago [producer]; Storrs, CT: The Roper Center for Public Opinion Research, University of Connecticut [distributor...

Univariate data

We are now ready to start playing with the data. A good way to get an initial feeling of the data is to create graphical representations with the aim of getting an understanding of the shape of its distribution. The word distribution has a technical meaning in data analysis, but we are not concerned with this kind of detail now. We are using this word in the informal sense of how the set of values in our data is distributed.

To start with the simplest case, we look at the variables in the data individually without, at first, worrying about relationships between variables. When we look at a single variable, we say that we are dealing with univariate data. So, this is the case that we will consider in this section.

Histograms

A histogram is a standard way of displaying the distribution of quantitative data, that is, data that can be represented in terms of real numbers or integers. (Notice that integers can also be used to indicate some types of categorical data.) A histogram...

Relationships between variables – scatterplots

The real power in data analysis is realized when we study how different variables relate. At the end of the previous section, we related income and gender, that is, a quantitative variable with a categorical one. In this section, we will investigate scatterplots, which are a graphical representation of the relationship between two quantitative variables.

To illustrate how Pandas can be used to explore the relationship between two variables, we will use an important example from the history of astronomy. Astronomer Edwin Hubble, in 1929, published a very important paper where he discovered that there is an approximately linear relationship between the distance and velocity of extragalactic nebulae. This was the foundation that would come to be the big bang theory.

A reprint of the article is available at http://apod.nasa.gov/diamond_jubilee/d_1996/hub_1929.html , where the data was obtained from. Notice that the dataset is very small and...

Summary

In this chapter, you learned how to use Pandas to perform an initial exploration of the data. You learned about displays of data, including histograms, KDE plots and boxplots for univariate distributions, and scatterplots for bivariate relationships. We also discussed summaries of data, including mean, standard deviation, range, median, quartiles, and percentiles.

In the next chapter, you will learn about statistical models for data.

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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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Length: 284 pages
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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

Customer reviews

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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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