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Getting started with Python data analysis : learn to use powerful Python libraries for effective data processing and analysis /
Learn to use powerful Python libraries for effective data processing and analysisAbout This Book Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib Create, manipulate, and analyze your data to extr...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Preface; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Chapter 1: Introducing Data Analysis and Libraries; Data analysis and processing; An overview of the libraries in data analysis; Python libraries in data analysis; NumPy; Pandas; Matplotlib; PyMongo; The scikit-learn library; Summary; Chapter 2: NumPy Arrays and Vectorized Computation; NumPy arrays; Data types; Array creation; Indexing and slicing; Fancy indexing; Numerical operations on arrays; Array functions; Data processing using arrays; Loading and saving data; Saving an array.
- Loading an arrayLinear algebra with NumPy; NumPy random numbers; Summary; Chapter 3: Data Analysis with Pandas; An overview of the Pandas package; The Pandas data structure; Series; The DataFrame; The essential basic functionality; Reindexing and altering labels; Head and tail; Binary operations; Functional statistics; Function application; Sorting; Indexing and selecting data; Computational tools; Working with missing data; Advanced uses of Pandas for data analysis; Hierarchical indexing; The Panel data; Summary; Chapter 4: Data Visualization; The matplotlib API primer; Line properties.
- Figures and subplotsExploring plot types; Scatter plots; Bar plots; Contour plots; Histogram plots; Legends and annotations; Plotting functions with Pandas; Additional Python data visualization tools; Bokeh; MayaVi; Summary; Chapter 5: Time series; Time series primer; Working with date and time objects; Resampling time series; Downsampling time series data; Upsampling time series data; Time zone handling; Timedeltas; Time series plotting; Summary; Chapter 6: Interacting With Databases; Interacting with data in text format; Reading data from text format; Writing data to text format.
- Interacting with data in binary formatHDF5; Interacting with data in MongoDB; Interacting with data in Redis; The simple value; List; Set; Ordered set; Summary; Chapter 7: Data Analysis Application Examples; Data munging; Cleaning data; Filtering; Merging data; Reshaping data; Data aggregation; Grouping data; Summary; Chapter 8: Machine Learning Models with scikit-learn; An overview of machine learning models; The scikit-learn modules for different models; Data representation in scikit-learn; Supervised learning
- classification and regression.
- Unsupervised learning
- clustering and dimensionality reductionMeasuring prediction performance; Summary; Index.