Cocalc Tutorial Ipynb

This IPython / Jupyter notebook is an interactive tutorial in the use of Python for data analysis. First of all, the interface. The text you see in boxes like this is editable and is written in a format called Markdown similar to the syntax used to edit Wikipedia. If you double-click on this text you will convert it to the raw Markdown for editing. If you want to go back to the nicely-rendered form, click on the editable text and press shift-enter. Second, code to execute appears in editable textboxes, nicely syntax-highlighted for Python:

When you evaluate the cell above (click on it and press shift-enter), output should appear below the cell. Yay. Python is telling you 1+1=2. Not exactly earth-shattering, but that's the basic mode of interaction at work here: Edit the snippet or write another and repeat By the end of this lecture you will be able to:

Define variables and differentiate between global and local variables. Identify and use different object types in python. Use some of the python's default functions and define your own functions. Introduction to numpy and matplotlib libraries After completing this week's lecture and tutorial work, you will be able to: read data into R using a relative path and a url

compare and contrast the following functions: match the following tidyverse read_* function arguments to their descriptions: choose the appropriate tidyverse read_* function and function arguments to load a given plain text tabular data set into R Once you are at the python command line the first step is to import basic functionality from SymPy and the Mechanics module, otherwise you will only have basic python commands available to work with. We will import the symbols function from SymPy core and with the * method bring in all functionality from the mechanics package. You can now see what functions and variables that are available to you with::

This is a long list of available functions. Read about the python import statement to learn about better ways to import only what you need. One good explanation is http://effbot.org/zone/import-confusion.htm. To get started working with vectors we will need to create a reference frame, as all vectors need to be defined with respect to a reference frame in the mechanics package. If you know the name of the command that you want to use simply use the builtin help function to bring up the documentation for the function. In our case we need to use the ReferenceFrame class.

The ReferenceFrame class manages everything about rotations, angular velocities, and angular accelerations with respect to other reference frames. Now create an inertial reference frame called N for "Newtonian" as was described in the the docstring: Expand your data visualization knowledge and tool set beyond what we have seen and practiced so far. We will move beyond scatter plots and learn other effective ways to visualize data, as well as some general rules of thumb to follow when creating visualizations. All visualization tasks this week will be applied to real world data sets. Remember, it is an iterative process to answer questions and each step taken should have a good reason behind it.

After completing this week's lecture and tutorial work, you will be able to: Describe when to use the following kinds of visualizations: Given a dataset and a question, select from the above plot types to create a visualization that best answers the question Given a visualization and a question, evaluate the effectiveness of the visualization and suggest improvements to better answer the question This file demonstrates how to create plots in Julia by using the PyPlot package. PyPlot relies on the matplotlib library, which is part of Python.

If you have Python installed, then it will be used as is. Otherwise, see PyPlot's homepage for instructions on how to install. Collections of examples are available at Plot Examples and the Julia Plots Gallery. The subsequent figure adds a title, axis labels and legends. Text and font sizes are illustrated. The axis limits and the tick marks are set manually.

are easily constructed using the subplot() command. For a the first subplot in a 2x2 grid, use subplot(2,2,1). After completing this week's lecture and tutorial work, you will be able to: use a Jupyter notebook to execute provided R code edit code and markdown cells in a Jupyter notebook create new code and markdown cells in a Jupyter notebook

create new variables and objects in R using the assignment symbol This page represents a collection of notes and tutorials for common functions, methods, definitions, etc. used in our class. This is not comprehensive and is intended as an aid for the course. For additional help, use the SAGE cheat sheet, SAGE tutorial, or stackoverflow. There are many ways to define functions in SAGE.

The two main methods are: defining a function explictly in terms of variables or defining a variable and then using it in a function formula. Note that the show command simply produces a nicely formated output using LaTeX.\LaTeX.LATE​X. In this lesson, we'll learn about the Python programming language and the Jupyter notebook, as well as the Python packages numpy and matplotlib. This isn't a very thorough introduction -- we'll just learn the things that are essential for the course.

Some resources for learning more about Python and its use in scientific computing are linked at the end of this notebook. To run the code in this notebook, you'll need an installation of Python, numpy, and matplotlib. The easiest way to get them all is to use CoCalc -- just create a free account, start a new project, and upload this notebook file. Then open this notebook there and you're off. You can also install everything locally on your own machine. For local installation, Anaconda is convenient, or you can just use pip.

All of these are free. If you're new to Python, I recommend using Python version 3.7 or later. The code for this course is written in Python, which is a programming language designed to promote code that is easy to read and write. Python has become one of the most important languages in scientific computing, and is arguably the most popular programming language in the world. It is high-level like MATLAB, but unlike MATLAB it is free and is intended as a general-purpose language.

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