Getting Started with Python Data Analysis: Learn to use powerful Python libraries for effective data processing and analysis

Vo.T.H

Czygan

€29.99

5 (1 Ratings)

Paperback Nov 2015 188 pages 1st Edition

Vo.T.H

Czygan

€29.99

5 (1 Ratings)

Paperback Nov 2015 188 pages 1st Edition

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Getting Started with Python Data Analysis

Chapter 2. NumPy Arrays and Vectorized Computation

NumPy is the fundamental package supported for presenting and computing data with high performance in Python. It provides some interesting features as follows:

Extension package to Python for multidimensional arrays (ndarrays), various derived objects (such as masked arrays), matrices providing vectorization operations, and broadcasting capabilities. Vectorization can significantly increase the performance of array computations by taking advantage of Single Instruction Multiple Data (SIMD) instruction sets in modern CPUs.
Fast and convenient operations on arrays of data, including mathematical manipulation, basic statistical operations, sorting, selecting, linear algebra, random number generation, discrete Fourier transforms, and so on.
Efficiency tools that are closer to hardware because of integrating C/C++/Fortran code.

NumPy is a good starting package for you to get familiar with arrays and array-oriented computing in data analysis...

Data processing using arrays

With the NumPy package, we can easily solve many kinds of data processing tasks without writing complex loops. It is very helpful for us to control our code as well as the performance of the program. In this part, we want to introduce some mathematical and statistical functions.

See the following table for a listing of mathematical and statistical functions:

Function	Description	Example
`sum`	Calculate the sum of all the elements in an array or along the axis	>>> a = np.array([[2,4], [3,5]]) >>> np.sum(a, axis=0) array([5, 9])
`prod`	Compute the product of array elements over the given axis	>>> np.prod(a, axis=1) array([8, 15])
`diff`	Calculate the discrete difference along the given axis	>>> np.diff(a, axis=0) array([[1,1]])
`gradient`	Return the gradient of an array	>>> np.gradient(a) [array([[1., 1.], [1., 1.]]), array([[2., 2.], [2., 2.]])]
`cross`	Return the cross product of two arrays	&gt...

Description

Data analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It’s often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis. With this book, we will get you started with Python data analysis and show you what its advantages are. The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems. Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples. Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn.

Who is this book for?

If you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.

What you will learn

Understand the importance of data analysis and get familiar with its processing steps
Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis
Create effective visualizations to present your data using Matplotlib
Process and analyze data using the time series capabilities of Pandas
Interact with different kind of database systems, such as file, disk format, Mongo, and Redis
Apply the supported Python package to data analysis applications through examples
Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python library

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Getting Started with Python Data Analysis: Learn to use powerful Python libraries for effective data processing and analysis

What do you get with Print?

Getting Started with Python Data Analysis

Chapter 2. NumPy Arrays and Vectorized Computation

NumPy arrays

Array functions

Data processing using arrays

Linear algebra with NumPy

NumPy random numbers

NumPy arrays

Page 1 of 7

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