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Python Data Analysis（Second Edition）
Armando Fandango 著
更新時間：2021-07-09 19:04:29
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Appendix C. Online Resources
Thisbookisforprogrammers,scientists,andengineerswhohavetheknowledgeofPythonandknowthebasicsofdatascience.ItisforthosewhowishtolearndifferentdataanalysismethodsusingPython3.5anditslibraries.Thisbookcontainsallthebasicingredientsyouneedtobecomeanexpertdataanalyst.
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書友吧
品牌：中圖公司
上架時間：2021-07-09 18:11:19
出版社：Packt Publishing
本書數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- 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(quán)信息
- 封面
- 封面
- 版權(quán)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
