Cocalc Tutorial 02 Ipynb

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! As you might remember from worksheet_02, we practiced loading data from the Sustainable Development Solutions Network's World Happiness Report. That data was the output of their analysis that calculated each country's happiness score and how much each variable contributed to it. In the tutorial, we are going to look at the data at an earlier stage of the study - the aggregated/averaged values (per country and per year) for many different social and health aspects... The goal for today is to produce a plot of 2017 positive affect against healthy life expectancy at birth, where we plot healthy life expectancy at birth on the x-axis and positive affect on...

For this study, positive affect was defined as the average of three positive affect measures, specifically happiness, laugh and enjoyment. We would also like to convert the positive affect score from a scale of 0 - 1 to a scale from 0 - 10. use filter to subset the rows where the year is equal to 2017 use select to subset the "Healthy life expectancy at birth" and "Positive affect" columns This notebook loads some data, reports simple descriptive statistics (means, standard deviations etc) and shows a number of useful plots (scatter plots, histograms, time series plots). Most of the descriptive stats use the standard package Statistics.

The plots rely on the Plots.jl package and the pdf and quantiles are from the Distributions.jl package. For more stat functions, see the StatsBase.jl package. (Not used here.) Statistical calculations are often reported in tables. This notebook uses my own printmat() function. For more powerful alternatives, consider the PrettyTables.jl package.

The next cell displays the first few lines of a data file in a raw format. The subsequent cell loads the data into a matrix. By the end of this presentation, you will know how to input a module into Macaulay2 and compute its free resolution. In order for this to be useful, you need to understand how mathematical objects in commutative algebra correspond to values on the computer. The roadmap of how to get from 0 to free resolution is as follows. Rings.

You will learn how to construct and work with a finitely presented ring. Free Modules and Matrices. You will learn how to construct a free module and work with its elements. Maps between free modules are matrices, and you will learn how to work with these. Fancy Modules. You will learn to use images, kernels, and cokernels to construct more interesting modules.

These two libraries didn't actually make it into final video Real-world Data Analsys Problems w/ Python Pandas This lecture will consist of explaining what a function is in Python and how to create one. Functions will be one of our main building blocks when we construct larger and larger amounts of code to solve problems. Formally, a function is a useful device that groups together a set of statements so they can be run more than once. They can also let us specify parameters that can serve as inputs to the functions.

On a more fundamental level, functions allow us to not have to repeatedly write the same code again and again. If you remember back to the lessons on strings and lists, remember that we used a function len() to get the length of a string. Since checking the length of a sequence is a common task you would want to write a function that can do this repeatedly at command. Functions will be one of most basic levels of reusing code in Python, and it will also allow us to start thinking of program design (we will dive much deeper into the ideas of... Put simply, you should use functions when you plan on using a block of code multiple times. The function will allow you to call the same block of code without having to write it multiple times.

This in turn will allow you to create more complex Python scripts. To really understand this though, we should actually write our own functions! 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. 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! As you might remember from worksheet_02, we practiced loading data from the Sustainable Development Solutions Network's World Happiness Report. That data was the output of their analysis that calculated the happiness score and how much each variable contributed to each countries happiness score. In the tutorial, we are going to look at the data at an earlier stage of the study - the aggregated/averaged values (per country and per year) for many different social and health aspects...

This goal for today is to produce a plot of 2017 healthy life expectancy at birth against positive affect, where we plot healthy life expectancy at birth on the x-axis and positive affect on... For this study positive affect was defined as the average of three positive affect measures, specifically happiness, laugh and enjoyment. We would also like to covert the positive affect score from a scale of 0 - 1, to a scale from 0 - 10. use filter to subset the rows where the year is equal to 2017 use select to subset the "Healthy life expectancy at birth" and "Positive affect" columns Welcome to the first assignment of week 2.

In this assignment, you will: Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK. See how you can in a couple of hours build a deep learning algorithm. Keras was developed to enable deep learning engineers to build and experiment with different models very quickly. Just as TensorFlow is a higher-level framework than Python, Keras is an even higher-level framework and provides additional abstractions. 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

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