Welcome to the Pandas courseware for ITSE-1372 Int Comp Program Python at Austin Community College in Austin, TX.
The college website for this course is: https://www.austincode.com/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.
This is the web page for the competency titled Introduction to Pandas. See the other pages in this online study guide for material that deals with the other competencies.
According to Introduction into Pandas,
Pandas is a software library written for the Python programming language. It is used for data manipulation and analysis. It provides special data structures and operations for the manipulation of numerical tables and time series.
There is often some confusion about whether Pandas is an alternative to Numpy, SciPy and Matplotlib. The truth is that it is built on top of Numpy. This means that Numpy is required by pandas. Scipy and Matplotlib on the other hand are not required by pandas but they are extremely useful.
There is no shortage of free online resources for this competency. An Internet search will likely reveal many more. If you are looking to find code samples for a large variety of circumstances in a "no frills" format, see 10 Minutes to pandas and Cookbook. Other resources in the following list provide more in the way of explanation.
The following web pages were developed specifically for this course. They provide many examples and exercises designed to help you learn how to use the Pandas library.
You can view a static HTML rendering of the notebook for each of these examples by clicking on the link in the following list. You can view the corresponding notebook file (filename.ipynb) by downloading this zip file, extracting the ipynb file into the file manager area of your Jupyter Notebook installation, and opening the ipynb file in Jupyter Notebook. The name of the ipynb file for each example notebook page is provided in the housekeeping material at the end of the notebook page.
You are encouraged to study these examples and exercises in parallel with your study of online resources for this competency. All of the homework assignments for this competency will deal with some aspect of the Pandas data structures: Series and DataFrame.
Assessments such as assignments, quizzes, and tests will be administered through Blackboard. Some of the free online resources will 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, tests, and projects administered by your ACC instructor through Blackboard.
The assessments for this competency will consist of several take-home assignments and one proctored test.
Author: Prof. Richard G. Baldwin
Affiliation: Professor of Computer
Information Technology at Austin Community College in Austin, TX.
File:
Pandas.htm
Revised: 09/03/18
Copyright 2018 Richard G.
Baldwin
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