Cocalc Tutorial Complete 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 Note that many exercises are followed by a block with some assert statements.
These assertions may be preceded by some setup code. They are provided to give you feedback that you are on the right path -- receiving an AssertionError probably means you've done something wrong. Let's start with a very simple, undirected network. A path in a network is a sequence of edges connecting two nodes. In this simple example, we can easily see that there is indeed at least one path that connects nodes 3 and 4. We can verify this with NetworkX:
There can be more than one path between two nodes. Again considering nodes 3 and 4, there are two such "simple" paths: A simple path is one without any cycles. If we allowed cycles, there would be infinitely many paths because one could always just go around the cycle as many times as desired. 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: 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 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. 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 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
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This IPython / Jupyter Notebook Is An Interactive Tutorial In
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 for...
When You Evaluate The Cell Above (click On It And
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 Note that many exercises are followed by a block with some assert statements.
These Assertions May Be Preceded By Some Setup Code. They
These assertions may be preceded by some setup code. They are provided to give you feedback that you are on the right path -- receiving an AssertionError probably means you've done something wrong. Let's start with a very simple, undirected network. A path in a network is a sequence of edges connecting two nodes. In this simple example, we can easily see that there is indeed at least one path that...
There Can Be More Than One Path Between Two Nodes.
There can be more than one path between two nodes. Again considering nodes 3 and 4, there are two such "simple" paths: A simple path is one without any cycles. If we allowed cycles, there would be infinitely many paths because one could always just go around the cycle as many times as desired. By the end of this lecture you will be able to: Define variables and differentiate between global and loc...
Identify And Use Different Object Types In Python. Use Some
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: use a Jupyter notebook to execute provided R code edit code and markdown cells in a Jupyter notebook
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