Mastering Python Data Analysis by Persson, Magnus Vilhelm -- Read -- Imperial Library of Trantor

Index

Mastering Python Data Analysis

Mastering Python Data Analysis Credits About the Authors About the Reviewer www.PacktPub.com

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Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Downloading the color images of this book Errata Piracy Questions

1. Tools of the Trade

Before you start Using the notebook interface Imports An example using the Pandas library Summary

2. Exploring Data

The General Social Survey

Obtaining the data Reading the data

Univariate data

Histograms

Making things pretty Characterization

Concept of statistical inference Numeric summaries and boxplots

Relationships between variables – scatterplots Summary

3. Learning About Models

Models and experiments The cumulative distribution function Working with distributions The probability density function Where do models come from? Multivariate distributions Summary

4. Regression

Introducing linear regression

Getting the dataset Testing with linear regression

Multivariate regression

Adding economic indicators Taking a step back

Logistic regression

Some notes

Summary

5. Clustering

Introduction to cluster finding

Starting out simple – John Snow on cholera

K-means clustering

Suicide rate versus GDP versus absolute latitude

Hierarchical clustering analysis

Reading in and reducing the data Hierarchical cluster algorithm

Summary

6. Bayesian Methods

The Bayesian method

Credible versus confidence intervals Bayes formula Python packages

U.S. air travel safety record

Getting the NTSB database Binning the data Bayesian analysis of the data

Binning by month

Plotting coordinates

Cartopy Mpl toolkits – basemap

Climate change - CO2 in the atmosphere

Getting the data Creating and sampling the model

Summary

7. Supervised and Unsupervised Learning

Introduction to machine learning Scikit-learn Linear regression

Climate data Checking with Bayesian analysis and OLS

Clustering Seeds classification

Visualizing the data Feature selection Classifying the data

The SVC linear kernel The SVC Radial Basis Function The SVC polynomial K-Nearest Neighbour Random Forest

Choosing your classifier

Summary

8. Time Series Analysis

Introduction Pandas and time series data Indexing and slicing Resampling, smoothing, and other estimates Stationarity Patterns and components

Decomposing components Differencing

Time series models

Autoregressive – AR Moving average – MA Selecting p and q

Automatic function The (Partial) AutoCorrelation Function

Autoregressive Integrated Moving Average – ARIMA

Summary

A. More on Jupyter Notebook and matplotlib Styles

Jupyter Notebook

Useful keyboard shortcuts

Command mode shortcuts Edit mode shortcuts

Markdown cells Notebook Python extensions

Installing the extensions Codefolding Collapsible headings Help panel Initialization cells NbExtensions menu item Ruler Skip-traceback Table of contents

Other Jupyter Notebook tips

External connections Export Additional file types

Matplotlib styles Useful resources

General resources Packages Data repositories Visualization of data

Summary

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