Getting Started with Python Data Analysis电子书

Getting Started with Python Data Analysis

Table of Contents

Getting Started with Python Data Analysis

Credits

About the Authors

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. Introducing Data Analysis and Libraries

Data analysis and processing

An overview of the libraries in data analysis

Python libraries in data analysis

NumPy

Pandas

Matplotlib

PyMongo

The scikit-learn library

Summary

2. NumPy Arrays and Vectorized Computation

NumPy arrays

Data types

Array creation

Indexing and slicing

Fancy indexing

Numerical operations on arrays

Array functions

Data processing using arrays

Loading and saving data

Saving an array

Loading an array

Linear algebra with NumPy

NumPy random numbers

Summary

3. Data Analysis with Pandas

An overview of the Pandas package

The Pandas data structure

Series

The DataFrame

The essential basic functionality

Reindexing and altering labels

Head and tail

Binary operations

Functional statistics

Function application

Sorting

Indexing and selecting data

Computational tools

Working with missing data

Advanced uses of Pandas for data analysis

Hierarchical indexing

The Panel data

Summary

4. Data Visualization

The matplotlib API primer

Line properties

Figures and subplots

Exploring plot types

Scatter plots

Bar plots

Contour plots

Histogram plots

Legends and annotations

Plotting functions with Pandas

Additional Python data visualization tools

Bokeh

MayaVi

Summary

5. Time Series

Time series primer

Working with date and time objects

Resampling time series

Downsampling time series data

Upsampling time series data

Time zone handling

Timedeltas

Time series plotting

Summary

6. Interacting with Databases

Interacting with data in text format

Reading data from text format

Writing data to text format

Interacting with data in binary format

HDF5

Interacting with data in MongoDB

Interacting with data in Redis

The simple value

List

Set

Ordered set

Summary

7. Data Analysis Application Examples

Data munging

Cleaning data

Filtering

Merging data

Reshaping data

Data aggregation

Grouping data

Summary

8. Machine Learning Models with scikit-learn

An overview of machine learning models

The scikit-learn modules for different models

Data representation in scikit-learn

Supervised learning – classification and regression

Unsupervised learning – clustering and dimensionality reduction

Measuring prediction performance

Summary

Index