Python data analysis : learn how to apply powerful data analysis techniques with popular open source Python modules / Ivan Idris.

Format:

Book

Author/Creator:

Idris, Ivan, author.

Series:

Community experience distilled.

Community experience distilled

Language:

English

Subjects (All):

Python (Computer program language).

Physical Description:

1 online resource (348 p.)

Edition:

1st edition

Other Title:

Learn how to apply powerful data analysis techniques with popular open source Python modules

Place of Publication:

Birmingham : Packt Publishing, 2014.

Language Note:

English

System Details:

text file

Summary:

This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.

Contents:

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

Populating a database with SQLAlchemy

Notes:

Includes bibliographical references and index.

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed November 11, 2014).

ISBN:

9781783553365

1783553367

OCLC:

894630079