NumPy 1.5 Beginner's Guide by Ivan Idris

By Ivan Idris

An motion packed advisor for the easy-to-use, excessive functionality, loose open resource NumPy mathematical library utilizing real-world examples

  • The first and in basic terms e-book that really explores NumPy practically
  • Perform excessive functionality calculations with fresh and effective NumPy code
  • Analyze huge information units with statistical functions
  • Execute advanced linear algebra and mathematical computations

In Detail

In ultra-modern global of technology and know-how, the hype is all approximately velocity and suppleness. by way of clinical computing, NumPy is at the most sensible of the record. NumPy is the elemental package deal wanted for clinical computing with Python. NumPy provides you with either pace and excessive productiveness. keep hundreds of thousands of greenbacks on pricey software program, whereas maintaining the entire flexibility and gear of your favorite programming language.

NumPy 1.5 Beginner's advisor will educate you approximately NumPy from scratch. It comprises every little thing from deploy, capabilities, matrices, and modules to checking out, all defined with applicable examples.

Numpy 1.5 Beginner's consultant will train you approximately fitting and utilizing NumPy and similar concepts.

This publication provides you with a fantastic origin in NumPy arrays and common features. on the finish of the ebook, we'll discover similar medical computing initiatives similar to Matplotlib for plotting and the SciPy venture via examples.

NumPy 1.5 Beginner's advisor can assist you be effective with NumPy and write fresh and speedy code.

What you'll examine from this book

  • Installing NumPy
  • Learn to load arrays from records and write arrays to files
  • Work with common functions
  • Create NumPy matrices
  • Use easy modules that NumPy offers
  • Write unit checks for NumPy code
  • Plot mathematical NumPy effects with Matplotlib
  • Integrate with Scipy, a excessive point Python medical computing framework equipped on best of NumPy

Approach

The e-book is written in beginner's advisor sort with each one element of NumPy validated via genuine global examples. there's applicable defined code with the necessary screenshots thrown in for the novice.

Who this ebook is written for

This publication is for the programmer, scientist or engineer, who has uncomplicated Python wisdom and want to be capable of do numerical computations with Python.

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Additional resources for NumPy 1.5 Beginner's Guide

Sample text

Time for action – installing NumPy on Mac OS X with a GUI installer We will install NumPy with a GUI installer. 1. net/projects/numpy/files/. Download the appropriate DMG file. Usually the latest one is the best: 2. mpkg. We will be presented with the welcome screen of the installer. ‰‰ Click on the Continue button to go to the Read Me screen, where we will be presented with a short description of NumPy: ‰‰ Continue to the License screen. [ 15 ] NumPy Quick Start 3. Accept the license: Read the license, click Continue and then the Accept button, when prompted to accept the license.

Py 4000 The last 2 elements of the sum [63920031996, 63968004000] PythonSum elapsed time in microseconds 2829 The last 2 elements of the sum [63920031996 63968004000] NumPySum elapsed time in microseconds 274 What just happened? Clearly, NumPy is much faster than the equivalent normal Python code. One thing is certain; we get the same results whether we are using NumPy or not. However, the result that is printed differs in representation. Notice that the result from the numpysum function does not have any commas.

Creates a Python list of 5 elements with values 0 to 4. ‰‰ Creates a NumPy array with values 1 to 5. ‰‰ Creates a NumPy array with values 0 to 4. ‰‰ None of the above. Have a go hero – continue the analysis The program we used here to compare the speed of NumPy and regular Python is not very scientific. We should at least repeat each measurement a couple of times. It would be nice to be able to calculate some statistics such as average times, and so on. Also, you might want to show plots of the measurements to friends and colleagues.

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