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• Appendix C. Online Resources
• SciPy
• Scikit-learn
• Pandas
• NumPy
• Matplotlib

• Appendix C. Online Resources 更新時間：2021-07-09 19:04:29
• 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
• PythonAnywhere Cloud
• Using Fortran code through f2py
• Integrating Boost and Python
• Integrating SWIG and NumPy
• Sending NumPy arrays to Java
• Interfacing with R
• Installing rpy2 package
• 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
• 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
• About NLTK
• Installing NLTK
• Chapter 9. Analyzing Textual Data and Social Media
• Summary
• Apache Cassandra
• Storing data in memcache
• 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
• The statsmodels modules
• 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
• The matplotlib subpackages
• Chapter 6. Data Visualization
• Reference
• 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
• The binary .npy and pickle formats
• Writing CSV files with NumPy and Pandas
• Chapter 5. Retrieving Processing and Storing Data
• Summary
• Creating a NumPy masked array
• NumPy random numbers
• Finding eigenvalues and eigenvectors with NumPy
• Linear algebra with NumPy
• Basic descriptive statistics with NumPy
• Chapter 4. Statistics and Linear Algebra
• References
• Summary
• 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
• The Pandas Series
• The Pandas DataFrames
• Installing and exploring Pandas
• Chapter 3. The Pandas Primer
• References
• 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
• Visualizing data using Matplotlib
• Listing modules inside the Python libraries
• Where to find help and references
• A simple application
• NumPy arrays
• Jupyter Notebook
• Reading manual pages
• Using IPython as a shell
• Installing Python 3
• Chapter 1. Getting Started with Python Libraries
• Preface
• Customer Feedback
• www.PacktPub.com
• About the Reviewers
• About the Author
• Credits
• 版權信息
• 封面

• 封面
• 版權信息
• Credits
• About the Author
• About the Reviewers
• www.PacktPub.com
• Customer Feedback
• Preface
• Chapter 1. Getting Started with Python Libraries
• Installing Python 3
• Using IPython as a shell
• Reading manual pages
• Jupyter Notebook
• NumPy arrays
• A simple application
• Where to find help and references
• Listing modules inside the Python libraries
• Visualizing data using Matplotlib
• 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
• References
• Chapter 3. The Pandas Primer
• Installing and exploring Pandas
• The Pandas DataFrames
• The 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
• Summary
• References
• Chapter 4. Statistics and Linear Algebra
• 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 5. Retrieving Processing and Storing Data
• Writing CSV files with NumPy and Pandas
• The binary .npy and pickle formats
• 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
• Reference
• Chapter 6. Data Visualization
• The 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
• The statsmodels modules
• 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
• Storing data in memcache
• Apache Cassandra
• Summary
• Chapter 9. Analyzing Textual Data and Social Media
• Installing NLTK
• About 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
• 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 package
• Interfacing with R
• Sending NumPy arrays to Java
• Integrating SWIG and NumPy
• Integrating Boost and Python
• Using Fortran code through f2py
• PythonAnywhere Cloud
• 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 更新時間：2021-07-09 19:04:29