最新章節

• Index
• Appendix C. Online Resources
• SciPy
• Scikit-learn
• pandas
• NumPy

• Index 更新時間：2021-08-05 17:32:19
• Appendix C. Online Resources
• SciPy
• Scikit-learn
• pandas
• NumPy
• matplotlib
• Appendix B. Useful Functions
• Appendix A. Key Concepts
• Summary
• IPython Parallel
• Installing MPI for Python
• Performing MapReduce with Jug
• Comparing Bottleneck to NumPy functions
• Speeding up embarrassingly parallel for loops with Joblib
• Creating a process pool with multiprocessing
• Calling C code
• Installing Cython
• Profiling the code
• Chapter 12. Performance Tuning Profiling and Concurrency
• Summary
• Working with Wakari
• Running programs on PythonAnywhere
• Setting up Google App Engine
• Using Fortran code through f2py
• Integrating Boost and Python
• Integrating SWIG and NumPy
• Sending NumPy arrays to Java
• Interfacing with R
• Installing rpy2
• Exchanging information with MATLAB/Octave
• Chapter 11. Environments Outside the Python Ecosystem and Cloud Computing
• Summary
• Decision trees
• Neural networks
• Genetic algorithms
• Mean Shift
• Clustering with affinity propagation
• Support vector regression
• Regression with ElasticNetCV
• Classification with support vector machines
• Classification with logistic regression
• Preprocessing
• A tour of scikit-learn
• Chapter 10. Predictive Analytics and Machine Learning
• Summary
• Social network analysis
• Creating word clouds
• Sentiment analysis
• Naive Bayes classification
• Analyzing word frequencies
• The bag-of-words model
• Filtering out stopwords names and numbers
• Installing NLTK
• Chapter 9. Analyzing Textual Data and Social Media
• Summary
• Apache Cassandra
• Storing data in Redis
• PyMongo and MongoDB
• Dataset – databases for lazy people
• Pony ORM
• SQLAlchemy
• Accessing databases from pandas
• Lightweight access with sqlite3
• Chapter 8. Working with Databases
• Summary
• Filtering
• Spectral analysis
• Fourier analysis
• Generating periodic signals
• ARMA models
• Autoregressive models
• Autocorrelation
• Defining cointegration
• Window functions
• Moving averages
• statsmodels subpackages
• Chapter 7. Signal Processing and Time Series
• Summary
• Plot.ly
• Autocorrelation plots
• Lag plots
• Plotting in pandas
• Three-dimensional plots
• Legends and annotations
• Scatter plots
• Logarithmic plots
• Basic matplotlib plots
• matplotlib subpackages
• Chapter 6. Data Visualization
• Summary
• Parsing HTML with Beautiful Soup
• Parsing RSS and Atom feeds
• Reading and writing JSON with pandas
• Using REST web services and JSON
• Reading and writing to Excel with pandas
• Reading and writing pandas DataFrames to HDF5 stores
• Storing data with PyTables
• Comparing the NumPy .npy binary format and pickling pandas DataFrames
• Writing CSV files with NumPy and pandas
• Chapter 5. Retrieving Processing and Storing Data
• Summary
• Remote data access
• Pivot tables
• Dealing with dates
• Handling missing values
• Joining DataFrames
• Concatenating and appending DataFrames
• Data aggregation with pandas DataFrames
• Statistics with pandas DataFrames
• Querying data in pandas
• pandas Series
• pandas DataFrames
• Installing and exploring pandas
• Chapter 4. pandas Primer
• Summary
• Creating a NumPy-masked array
• NumPy random numbers
• Finding eigenvalues and eigenvectors with NumPy
• Linear algebra with NumPy
• Basic descriptive statistics with NumPy
• NumPy and SciPy modules
• Chapter 3. Statistics and Linear Algebra
• Summary
• Broadcasting NumPy arrays
• Indexing NumPy arrays with Booleans
• Indexing with a list of locations
• Fancy indexing
• Creating array views and copies
• Manipulating array shapes
• One-dimensional slicing and indexing
• NumPy numerical types
• Selecting NumPy array elements
• Creating a multidimensional array
• The NumPy array object
• Chapter 2. NumPy Arrays
• Summary
• Where to find help and references
• IPython notebooks
• Reading manual pages
• Using IPython as a shell
• A simple application
• NumPy arrays
• Installing with setuptools
• Building NumPy SciPy matplotlib and IPython from source
• Software used in this book
• Chapter 1. Getting Started with Python Libraries
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Support files eBooks discount offers and more
• www.PacktPub.com
• About the Reviewers
• About the Author
• Credits
• 版權頁
• 封面

• 封面
• 版權頁
• 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
• Summary
• 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
• 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 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
• 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
• SQLAlchemy
• 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
• Appendix C. Online Resources
• Index 更新時間：2021-08-05 17:32:19