Python Data Analysis电子书

Python Data Analysis

Table of Contents

Python Data Analysis

Credits

About the Author

About the Reviewers

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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

Errata

Piracy

Questions

1. Getting Started with Python Libraries

Software used in this book

Installing software and setup

On Windows

On Linux

On Mac OS X

Building NumPy, SciPy, matplotlib, and IPython from source

Installing with setuptools

NumPy arrays

A simple application

Using IPython as a shell

Reading manual pages

IPython notebooks

Where to find help and references

Summary

2. NumPy Arrays

The NumPy array object

The advantages of NumPy arrays

Creating a multidimensional array

Selecting NumPy array elements

NumPy numerical types

Data type objects

Character codes

The dtype constructors

The dtype attributes

One-dimensional slicing and indexing

Manipulating array shapes

Stacking arrays

Splitting NumPy arrays

NumPy array attributes

Converting arrays

Creating array views and copies

Fancy indexing

Indexing with a list of locations

Indexing NumPy arrays with Booleans

Broadcasting NumPy arrays

Summary

3. Statistics and Linear Algebra

NumPy and SciPy modules

Basic descriptive statistics with NumPy

Linear algebra with NumPy

Inverting matrices with NumPy

Solving linear systems with NumPy

Finding eigenvalues and eigenvectors with NumPy

NumPy random numbers

Gambling with the binomial distribution

Sampling the normal distribution

Performing a normality test with SciPy

Creating a NumPy-masked array

Disregarding negative and extreme values

Summary

4. pandas Primer

Installing and exploring pandas

pandas DataFrames

pandas Series

Querying data in pandas

Statistics with pandas DataFrames

Data aggregation with pandas DataFrames

Concatenating and appending DataFrames

Joining DataFrames

Handling missing values

Dealing with dates

Pivot tables

Remote data access

Summary

5. Retrieving, Processing, and Storing Data

Writing CSV files with NumPy and pandas

Comparing the NumPy .npy binary format and pickling pandas DataFrames

Storing data with PyTables

Reading and writing pandas DataFrames to HDF5 stores

Reading and writing to Excel with pandas

Using REST web services and JSON

Reading and writing JSON with pandas

Parsing RSS and Atom feeds

Parsing HTML with Beautiful Soup

Summary

6. Data Visualization

matplotlib subpackages

Basic matplotlib plots

Logarithmic plots

Scatter plots

Legends and annotations

Three-dimensional plots

Plotting in pandas

Lag plots

Autocorrelation plots

Plot.ly

Summary

7. Signal Processing and Time Series

statsmodels subpackages

Moving averages

Window functions

Defining cointegration

Autocorrelation

Autoregressive models

ARMA models

Generating periodic signals

Fourier analysis

Spectral analysis

Filtering

Summary

8. Working with Databases

Lightweight access with sqlite3

Accessing databases from pandas

SQLAlchemy

Installing and setting up SQLAlchemy

Populating a database with SQLAlchemy

Querying the database with SQLAlchemy

Pony ORM

Dataset – databases for lazy people

PyMongo and MongoDB

Storing data in Redis

Apache Cassandra

Summary

9. Analyzing Textual Data and Social Media

Installing NLTK

Filtering out stopwords, names, and numbers

The bag-of-words model

Analyzing word frequencies

Naive Bayes classification

Sentiment analysis

Creating word clouds

Social network analysis

Summary

10. Predictive Analytics and Machine Learning

A tour of scikit-learn

Preprocessing

Classification with logistic regression

Classification with support vector machines

Regression with ElasticNetCV

Support vector regression

Clustering with affinity propagation

Mean Shift

Genetic algorithms

Neural networks

Decision trees

Summary

11. Environments Outside the Python Ecosystem and Cloud Computing

Exchanging information with MATLAB/Octave

Installing rpy2

Interfacing with R

Sending NumPy arrays to Java

Integrating SWIG and NumPy

Integrating Boost and Python

Using Fortran code through f2py

Setting up Google App Engine

Running programs on PythonAnywhere

Working with Wakari

Summary

12. Performance Tuning, Profiling, and Concurrency

Profiling the code

Installing Cython

Calling C code

Creating a process pool with multiprocessing

Speeding up embarrassingly parallel for loops with Joblib

Comparing Bottleneck to NumPy functions

Performing MapReduce with Jug

Installing MPI for Python

IPython Parallel

Summary

A. Key Concepts

B. Useful Functions

matplotlib

NumPy

pandas

Scikit-learn

SciPy

scipy.fftpack

scipy.signal

scipy.stats

C. Online Resources

Index