Cocalc Tutorial 04 Ipynb

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 Plots.jl package. The notebook uses the default backend (GR), but Plots.jl supports several alternatives. (See the package document for details.) See Plots Gallery for examples (with code snippets available by clicking on a figure).

The StatsPlots.jl package (not discussed here) defines various statistical plots. It is about to be integrated into Plots.jl. The next cell creates and shows a first plot. 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 📚 The CoCalc Library - books, templates and other resources This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!

< Further Resources | Contents | Simple Line Plots > We'll now take an in-depth look at the Matplotlib package for visualization in Python. Matplotlib is a multi-platform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack. It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot from the IPython command line. IPython's creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months. John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003.

It received an early boost when it was adopted as the plotting package of choice of the Space Telescope Science Institute (the folks behind the Hubble Telescope), which financially supported Matplotlib’s development and greatly... In the last notebook, we talked about modeling data with functions. A function is one of the most fundamental concepts in computing (and also in mathematics). A function is a piece of a program that receives input arguments, processes them by doing certain calculations on them, and returns outputs. For example, we might have a function g that takes a number as an input and returns the square of that number as an output. How can we define this function g on a computer?

Julia gives us a few different ways to do this. Alternatively, we could declare this function using the function and end keywords: The third way we could have declared this function is as an "anonymous" or "lambda" function. "Anonymous" functions are functions that truly don't need names! For example, we could have declared a function that squares its input as Originally published at https://sagemath.org/calctut and adapted as interactive worksheets on CoCalc.

01-review.ipynb: review of basics (trigonometry, ...) All material moved to the more comprehensive CoCalc Manual CoCalc is a cloud-based service that provides infrastructure and services that are useful for running courses based on Jupyter Notebooks. It is used for teaching by Universities around the world. All material moved to the more comprehensive CoCalc Manual For a list of authors see the contributors section.

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. Any place you see ..., you must fill in the function, variable, or data to complete the code. Replace fail() with your completed code and run the cell! Match the following definitions with the corresponding aesthetic mapping or function used in R: A. Prevents a chart from being stacked.

It preserves the vertical position of a plot while adjusting the horizontal position. B. In bar charts, this aesthetic fills in the bars by a specific colour or separates the counts by a variable different from the x-axis. C. In bar charts, it outlines the bars but in scatterplots, it fills in the points (colouring them based on a particular variable aside from the x/y-axis). 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

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