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Time    : 2023-05-04 17:51:40 +0000
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Author: actions-user

01-introduction.md

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+---
+title: Introduction to Python
+teaching: 15
+exercises: 0
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Examine the Python interpreter
+- Recognize the advantage of using the Python programming language
+- Understand the concept and benefits of using notebooks for coding
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- Why learn Python?
+- What are Jupyter notebooks?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Introducing the Python programming language
+
+Python is a general purpose programming language. It is an interpreted language,
+which makes it suitable for rapid development and prototyping of programming segments or complete
+small programs.
+
+Python's main advantages:
+
+- Open source software, supported by [Python Software
+  Foundation](https://www.python.org/psf/)
+- Available on all major platforms (Windows, macOS, Linux)
+- It is a good language for new programmers to learn due to its straightforward,
+  object-oriented style
+- It is well-structured, which aids readability
+- It is extensible (i.e. modifiable) and is supported by a large community who
+  provide a comprehensive range of 3rd party packages
+
+## Interpreted vs. compiled languages
+
+In any programming language, the code must be translated into "machine code"
+before running it. It is the machine code which is executed and produces
+results. In a language like C++ your code is translated into machine code and
+stored in a separate file, in a process referred to as **compiling** the code.
+You then execute the machine code from the file as a separate step. This is
+efficient if you intend to run the same machine code many times as you only have
+to compile it once and it is very fast to run the compiled machine code.
+
+On the other hand, if you are experimenting, then your
+code will change often and you would have to compile it again every time before
+the machine can execute it. This is where **interpreted** languages have the
+advantage. You don't need a complete compiled program to "run" what has been
+written so far and see the results. This rapid prototyping is helped further by
+use of a system called REPL.
+
+## REPL
+
+**REPL** is an acronym which stands for Read, Evaluate, Print and Loop.
+
+REPL allows you to write single statements of code, have them executed, and if
+there are any results to show, they are displayed and then the interpreter loops
+back to the beginning and waits for the next program statement.
+
+![](fig/Python_repl_3.png){alt='Python\_Repl'}
+
+In the example above, two variables `a` and `b` have been created, assigned to values
+`2` and `3`, and then multiplied together.
+
+Every time you press <kbd>Return</kbd>, the line is interpreted. The assignment statements don't produce any
+output so you get only the standard `>>>` prompt.
+
+For the `a*b` statement (it is more of an expression than a statement), because
+the result is not being assigned to a variable, the REPL displays the result of
+the calculation on screen and then waits for the next input.
+
+The REPL system makes it very easy to try out small chunks of code.
+
+You are not restricted to single line statements. If the Python interpreter
+decides that what you have written on a line cannot be a complete statement it
+will give you a continuation prompt of `...` until you complete the statement.
+
+## Introducing Jupyter notebooks
+
+[**Jupyter**](https://jupyter.org/) originates from IPython, an effort to make Python
+development more interactive. Since its inception, the scope of the project
+has expanded to include **Ju**lia, **Pyt**hon, and **R**, so the name was changed to "Jupyter"
+as a reference to these core languages. Today, Jupyter supports even more
+languages, but we will be using it only for Python code. Specifically, we will
+be using **Jupyter notebooks**, which allows us to easily take notes about
+our analysis and view plots within the same document where we code. This
+facilitates sharing and reproducibility of analyses, and the notebook interface
+is easily accessible through any web browser. Jupyter notebooks are started
+from the terminal using
+
+```bash
+$ jupyter notebook
+```
+
+Your browser should start automatically and look
+something like this:
+
+![](fig/Python_jupyter_6.png){alt='Jupyter\_notebook\_list'}
+
+When you create a notebook from the *New* option, the new notebook will be displayed in a new
+browser tab and look like this.
+
+![](fig/Python_jupyter_7.png){alt='Jupyter\_notebook'}
+
+Initially the notebook has no name other than 'Untitled'. If you click on 'Untitled' you will be
+given the option of changing the name to whatever you want.
+
+The notebook is divided into **cells**. Initially there will be a single input cell marked by `In  [ ]:`.
+
+You can type Python code directly into the cell. You can split the code across
+several lines as needed. Unlike the REPL we looked at before, the code is not
+interpreted line by line. To interpret the code in a cell, you can click the
+*Run* button in the toolbar or from the *Cell* menu option, or use keyboard
+shortcuts (e.g., <kbd>Shift</kbd>\+<kbd>Return</kbd>). All of the code in that cell will then be
+executed.
+
+The results are shown in a separate `Out [1]:` cell immediately below. A new input
+cell (`In [ ]:`) is created for you automatically.
+
+![](fig/Python_jupyter_8.png){alt='Jupyter\_notebook\_cell'}
+
+When a cell is run, it is given a number along with the corresponding output
+cell. If you have a notebook with many cells in it you can run the cells in any
+order and also run the same cell many times. The number on the left hand side of
+the input cells increments, so you can always tell the order in which they were
+run. For example, a cell marked `In [5]:` was the fifth cell run in the sequence.
+
+Although there is an option to do so on the toolbar, you do not have to manually
+save the notebook. This is done automatically by the Jupyter system.
+
+Not only are the contents of the `In [ ]:` cells saved, but so are the `Out [ ]:` cells.
+This allows you to create complete documents with both your code and the output
+of the code in a single place.  You can also change the cell type from
+Python code to Markdown using the *Cell > Cell Type* option. [**Markdown**](https://en.wikipedia.org/wiki/Markdown) is
+a simple formatting system which allows you to create documentation for your
+code, again all within the same notebook structure.
+
+The notebook itself is stored as specially-formatted text file with an `.ipynb`
+extension. These files can be opened and run by others with Jupyter installed. This allows you to
+share your code inputs, outputs, and
+Markdown documentation with others. You can also export the notebook to HTML, PDF, and
+many other formats to make sharing even easier.
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Python is an interpreted language
+- The REPL (Read-Eval-Print loop) allows rapid development and testing of code segments
+- Jupyter notebooks builds on the REPL concepts and allow code results and documentation to be maintained together and shared
+- Jupyter notebooks is a complete IDE (Integrated Development Environment)
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+