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Python Data Analysis.
Python is a multi-paradigm programming language well suited for both object-oriented application development as well as functional design patterns. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. It will give you velocity and promo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Python Libraries; Software used in this book; Installing software and setup; On Windows; On Linux; On Mac OS X; Building NumPY, SciPy, matplotlib, and IPython from source; Installing with setuptools; NumPy arrays; 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; The advantages of NumPy arrays.
- Creating a multidimensional arraySelecting 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; Chapter 3: Statistics and Linear Algebra; NumPy and SciPy modules; Basic descriptive statistics with NumPy.
- Linear algebra with NumPyInverting 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 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 DataFramesHandling 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 BeautifulSoup; Summary; Chapter 6: Data Visualization; matplotlib subpackages.
- Basic matplotlib plotsLogarithmic 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; Installing and setting up SQLAlchemy.