Python data analysis : data manipulation and complex data analysis with python

Python data analysis data manipulation and complex data analysis with python

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Learn how to apply powerful data analysis techniques with popular open source Python modules About This Book Find, manipulate, and analyze your data using the Python 3.5 libraries Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code An...

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Detalles Bibliográficos
Otros Autores: Fandango, Armando, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England ; Mumbai, [India] : Packt Publishing 2017.
Edición:Second edition
Materias:
Electronic data processing.
Information visualization > Data processing.
Punched card systems.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630105106719
Tabla de Contenidos:
  • Cover
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
  • Customer Feedback
  • Table of Contents
  • Preface
  • Chapter 1: Getting Started with Python Libraries
  • Installing Python 3
  • Installing data analysis libraries
  • On Linux or Mac OS X
  • On Windows
  • 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
  • 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
  • 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
  • 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 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
  • Installing and setting up SQLAlchemy
  • Populating a database with SQLAlchemy
  • Querying the database with 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
  • scipy.fftpack
  • scipy.signal
  • scipy.stats
  • Appendix C: Online Resources
  • Index.

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