Cocalc Python Tutorial Ipynb
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 By the end of this lecture you will be able to:
Understand the concept of namespaces, and differentiate the 4 different namespaces in python Understand the meaning of Scope of a code Use classes to efficiently organize a code Differentiate between class variables and instances variables Online Supplement for "Numerical Methods in Physics with Python" (Cambridge University Press, 2020) We will assume you know how to launch Python and how to use a text editor (like vim or emacs), or even an integrated development environment (IDE), to save code into a file.
While some of the details vary based on which operating system (OS) you employ, most of what we have to say is OS-agnostic. You can run Python code interactively: once you’ve launched a Python shell you get the Python prompt >>> (also known as a chevron). Since this tutorial is in the form of a Jupyter Notebook, the Python prompt will not explicitly appear below. Here are a few examples of things you could type in. You should press Enter after each line you input at the Python interpreter (or Shift-Enter if you're working on the Notebook). This is similar to other interactive environments that you may have seen before, like Mathematica.
It is sometimes called Python’s read-evaluate-print loop (REPL). Note that the first two lines show us we could (if we wanted to) use Python as a calculator. The next line provided us with our first example of using a variable: in this case we created a variable x and assigned the value 42 to it. (Numbers like 3 or 42 are known as literals). We then used the print() function to print out the variable’s value to the screen. This is our first use of a function: as in mathematics, we use parentheses to show that we are passing in an argument.
We won’t go into the different versions of Python at this point, assuming for now that you are using Python 3, where the above example is legitimate. (We discuss Python 2 vs Python 3 in a section near the end of this tutorial). 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 This is a notebook. To run it you need Julia and Jupyter/VS Code. To run Julia without a local installation, use (for instance) CoCalc or JuliaHub. Also Colab works, but the support is still (as of mid 2025) a bit tentative. To install Julia on your machine, download and install it.
This link contains both instructions for the Microsoft Store and links to binaries for manual download. You may also want to use VS Code with the Julia extension. It is an IDE (editor and more) that can run script files and notebooks. It works well with windsurf and copilot. To run notebooks from your local installation, you need either (1) VS Code with the Julia extension (see above) or (2) Jupyter/JupyterLab. Link to the GitHub Repository pattern_classification
More information about the watermark magic command extension. In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern classification and machine learning tasks, and various... Downloading and saving CSV data files from the web Splitting into training and test dataset In the context of programming languages, a procedure is a collection of instructions to be performed by the computer to accomplish a given task, packaged as a unit. Procedures are also called functions or (sub)routines.
Examples: We may implement procedures to perform the following tasks: Take a positive real number (more precisely, a floating-point number) as argument and return an approximation to its square root. Take a positive integer as input and decide whether it is prime. Take two strings, representing a person's bank account ID and password, and a float, representing an amount to be withdrawn from that account, and if the password matches that associated with the client's as... In this lecture, we will cover methods for calculating derivatives and integrals numerically. While we will cover some of the theory behind these methods, the main practical takeaway is to gain familiarity with existing functions within the numpy and scipy libraries that implement these tasks.
In Python, most basic tasks have been coded up already in a very efficient way, so knowing your way around Python's libraries saves a lot of work. We have already introduced numpy. scipy is another important and vast library with many algorithms for scientific computing. By the end of this lecture you will be able to: Do numerical differentiation and understand the principles behind it. Perform basic numeric integration using python integration libraries
Use several integration techniques including: the rectangle, trapezoidal, and Simpson's rules.
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By The End Of This Lecture You Will Be Able
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 By the end of this lecture you will be able to:
Understand The Concept Of Namespaces, And Differentiate The 4 Different
Understand the concept of namespaces, and differentiate the 4 different namespaces in python Understand the meaning of Scope of a code Use classes to efficiently organize a code Differentiate between class variables and instances variables Online Supplement for "Numerical Methods in Physics with Python" (Cambridge University Press, 2020) We will assume you know how to launch Python and how to use ...
While Some Of The Details Vary Based On Which Operating
While some of the details vary based on which operating system (OS) you employ, most of what we have to say is OS-agnostic. You can run Python code interactively: once you’ve launched a Python shell you get the Python prompt >>> (also known as a chevron). Since this tutorial is in the form of a Jupyter Notebook, the Python prompt will not explicitly appear below. Here are a few examples of things ...
It Is Sometimes Called Python’s Read-evaluate-print Loop (REPL). Note That
It is sometimes called Python’s read-evaluate-print loop (REPL). Note that the first two lines show us we could (if we wanted to) use Python as a calculator. The next line provided us with our first example of using a variable: in this case we created a variable x and assigned the value 42 to it. (Numbers like 3 or 42 are known as literals). We then used the print() function to print out the varia...
We Won’t Go Into The Different Versions Of Python At
We won’t go into the different versions of Python at this point, assuming for now that you are using Python 3, where the above example is legitimate. (We discuss Python 2 vs Python 3 in a section near the end of this tutorial). 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 edita...
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