Python Data Analysis Cookbook

封面

版权页

Credits

About the Author

About the Reviewers

www.PacktPub.com

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Preface

Why do you need this book?

Data analysis data science big data – what is the big deal?

A brief of history of data analysis with Python

A conjecture about the future

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Chapter 1. Laying the Foundation for Reproducible Data Analysis

Introduction

Setting up Anaconda

Installing the Data Science Toolbox

Creating a virtual environment with virtualenv and virtualenvwrapper

Sandboxing Python applications with Docker images

Keeping track of package versions and history in IPython Notebook

Configuring IPython

Learning to log for robust error checking

Unit testing your code

Configuring pandas

Configuring matplotlib

Seeding random number generators and NumPy print options

Standardizing reports code style and data access

Chapter 2. Creating Attractive Data Visualizations

Introduction

Graphing Anscombe's quartet

Choosing seaborn color palettes

Choosing matplotlib color maps

Interacting with IPython Notebook widgets

Viewing a matrix of scatterplots

Visualizing with d3.js via mpld3

Creating heatmaps

Combining box plots and kernel density plots with violin plots

Visualizing network graphs with hive plots

Displaying geographical maps

Using ggplot2-like plots

Highlighting data points with influence plots

Chapter 3. Statistical Data Analysis and Probability

Introduction

Fitting data to the exponential distribution

Fitting aggregated data to the gamma distribution

Fitting aggregated counts to the Poisson distribution

Determining bias

Estimating kernel density

Determining confidence intervals for mean variance and standard deviation

Sampling with probability weights

Exploring extreme values

Correlating variables with Pearson's correlation

Correlating variables with the Spearman rank correlation

Correlating a binary and a continuous variable with the point biserial correlation

Evaluating relations between variables with ANOVA

Chapter 4. Dealing with Data and Numerical Issues

Introduction

Clipping and filtering outliers

Winsorizing data

Measuring central tendency of noisy data

Normalizing with the Box-Cox transformation

Transforming data with the power ladder

Transforming data with logarithms

Rebinning data

Applying logit() to transform proportions

Fitting a robust linear model

Taking variance into account with weighted least squares

Using arbitrary precision for optimization

Using arbitrary precision for linear algebra

Chapter 5. Web Mining Databases and Big Data

Introduction

Simulating web browsing

Scraping the Web

Dealing with non-ASCII text and HTML entities

Implementing association tables

Setting up database migration scripts

Adding a table column to an existing table

Adding indices after table creation

Setting up a test web server

Implementing a star schema with fact and dimension tables

Using HDFS

Setting up Spark

Clustering data with Spark

Chapter 6. Signal Processing and Timeseries

Introduction

Spectral analysis with periodograms

Estimating power spectral density with the Welch method

Analyzing peaks

Measuring phase synchronization

Exponential smoothing

Evaluating smoothing

Using the Lomb-Scargle periodogram

Analyzing the frequency spectrum of audio

Analyzing signals with the discrete cosine transform