Python数据分析（影印版） PDF下载

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