Python数据分析（影印版）

Preface
Chapter 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
Chapter 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
Chapter 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
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
  Summary
Chapter 5: Retrieving, Processing, and Storing Data
  Writing CSV files withNumPy 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
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
  Summary
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
  Summary
Chapter 8: Working with Databases
  Lightweight access with sqlite3
  Accessing databases from pandas
  SQLAIchemy
    Installing and setting up SQLAIchemy
    Populating a database with SQLAIchemy
    Querying the database with SQLAIchemy
  Pony ORM
  Dataset - databases for lazy people
  PyMongo and MongoDB
  Storing data in Redis
  Apache Cassandra
  Summary
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Appendix A: Key Concepts
Appendix B: Useful Functions
  matplotlib
  NumPy
  pandas
  Scikit-learn
  SciPy
    scipy.fftpack
    scipy.signal
    scipy.stats
Appendix C: Online Resources
Index