更新时间：2021-08-05 17:32:19
封面
版权页
Credits
About the Author
About the Reviewers
www.PacktPub.com
Support files eBooks discount offers and more
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. Getting Started with Python Libraries
Software used in this book
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
Chapter 2. NumPy Arrays
The NumPy array object
Creating a multidimensional array
Selecting NumPy array elements
NumPy numerical types
One-dimensional slicing and indexing
Manipulating array shapes
Creating array views and copies
Fancy indexing
Indexing with a list of locations
Indexing NumPy arrays with Booleans
Broadcasting NumPy arrays
Chapter 3. Statistics and Linear Algebra
NumPy and SciPy modules
Basic descriptive statistics with NumPy
Linear algebra with NumPy
Finding eigenvalues and eigenvectors with NumPy
NumPy random numbers
Creating a NumPy-masked array
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 8. Working with Databases
Lightweight access with sqlite3
Accessing databases from pandas
SQLAlchemy
