Mastering Python Data Analysis电子书

Mastering Python Data Analysis

Mastering Python Data Analysis

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Why subscribe?

Free access for Packt account holders

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