Welcome to the NumPy competency course material for ITSE-1302 Computer Programming: Scientific Python 1 at Austin Community College in Austin, TX. Click here for a course overview.
The college website for this course is: http://www.austincc.edu/baldwin/
This course does not use a conventional paper or electronic textbook. Instead, this online study guide and the Blackboard learning management system will guide you through a variety of free online resources on topics that you will need to learn in order to succeed in the course.
The course is structured into four major units: one review unit and three competency units.
This is the web page for the competency titled Programming with the NumPy library. See the other pages in this online study guide for material that deals with the other competencies.
NumPy is the fundamental package for scientific computing with Python. It contains among other things:
This competency deals mainly with the first two items in the above list. The random number capability shown in item 4 is also important for this competency. However once you understand the first two items, you should have no difficulty learning about the random number capabilities on your own.
The numpy library provides many capabilities that are not discussed in this tutorial. For further information on NumPy, see the Reference Manual. Also see the NumPy User Guide.
Some of those capabilities, such as sine, cosine, and exponential are also important for this competency. However, as above, once you understand the first two items in the above list, you should have no difficulty learning about those capabilities on your own.
You will often see a numpy array referred to as an ndarray. This name derives from the fact that an ndarray is an N-dimensional array.
This is not your first exposure to the NumPy library. We have been using certain elements of the library since the beginning of the course. For example, here are some the statements that have been used in the code in previous competencies that used the NumPy library:
Hopefully in those instances, you took the time to go to the NumPy library documentation to learn the meaning of the code. For example, you will find documentation for the last item in the above list (np.array) here.
Here are some useful online resources for the NumPy library. A Google search will expose many more.
The following web page was developed specifically for this course. It provides many examples and exercises designed to help you learn how to program using the N-dimensional array features of the NumPy library.
You are encouraged to study these programming examples and exercises. All of the homework assignments for this competency will deal with some aspect of the NumPy library.
The page listed above was developed using Jupyter Notebook in its interactive mode and then downloaded as a static HTML file for inclusion in this course material. If you are unfamiliar with the format of Jupyter Notebook, a quick (approximately 19 minutes) tour of the following three videos will teach you everything you need to know to understand the format of the pages listed above.
Assessments such as assignments, quizzes, and tests will be administered through Blackboard. Some of the free online resources may also include graded assessments such as exercises and tests. You are encouraged to take advantage of those exercises and tests to enhance your ability to learn and retain the material. However, grades and credits associated with those resources will not be integrated into your grade for this course. Your grade for this course will be based solely on your grades on assignments, quizzes, and tests administered by your ACC instructor through Blackboard.
A set of review questions for this competency is provided here. The questions are similar to the questions that you will find on the test for this competency. Therefore, it is strongly recommended that you study the material until you thoroughly understand the material covered by those questions.
Author: Prof. Richard G. Baldwin
Affiliation: Professor of
Computer Information Technology at Austin Community College in
Austin, TX.
File: Numpy.htm
Revised: 04/24/18
Copyright 2018 Richard G. Baldwin
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