+
+
+ * To open a blank copy:
+
+
+
+2. [Day 2](APL_SURP_Python_course_Notebook_2.ipynb): 2-D arrays, tabular data, datetimes, and more
+
+
+
+ * To open a blank copy:
+
+
+
+3. [Day 3](APL_SURP_Python_course_Notebook_3_blank_copy.ipynb): Line and scatter plots, depth profiles, timeseries data, logical operations, if statements and for loops and more
+
+
+
+More lessons will be added to this page throughout the summer.
+
+To learn about Python topics in more detail, see the external lessons we've curated in the [Complementary lesssons page](../complementary_lessons) and the [Advanced examples page](../advancedexamples).
diff --git a/site/notebooks/projectpythia-quickstart.ipynb b/site/notebooks/projectpythia-quickstart.ipynb
deleted file mode 100644
index 435acd1..0000000
--- a/site/notebooks/projectpythia-quickstart.ipynb
+++ /dev/null
@@ -1,653 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "5d45c2b9",
- "metadata": {},
- "source": [
- "# Quickstart: Zero to Python"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a52c17ad",
- "metadata": {},
- "source": [
- "Brand new to Python? Here are some quick examples of what Python code looks like.\n",
- "\n",
- "This is **not** meant to be a comprehensive Python tutorial, just something to whet your appetite."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "84eecdbd",
- "metadata": {},
- "source": [
- "## Run this code from your browser!\n",
- "\n",
- "Of course you can simply read through these examples, but it's more fun to ***run them yourself***:\n",
- "\n",
- "- Find the **\"Rocket Ship\"** icon, **located near the top-right of this page**. Hover over this icon to see the drop-down menu.\n",
- "- Click the `Binder` link from the drop-down menu.\n",
- "- This page will open up as a [Jupyter notebook](jupyter.html) in a working Python environment in the cloud.\n",
- "- Press Shift+Enter to execute each code cell\n",
- "- Feel free to make changes and play around!"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e02bc566",
- "metadata": {},
- "source": [
- "## A very first Python program\n",
- "\n",
- "A Python program can be a single line:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8137f81a",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(\"Hello interweb\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4c7a126b",
- "metadata": {},
- "source": [
- "## Loops in Python"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "54deab97",
- "metadata": {},
- "source": [
- "Let's start by making a `for` loop with some formatted output:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ba931e6d",
- "metadata": {},
- "outputs": [],
- "source": [
- "for n in range(3):\n",
- " print(f\"Hello interweb, this is iteration number {n}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "42e3baf4",
- "metadata": {},
- "source": [
- "A few things to note:\n",
- "\n",
- "- Python defaults to counting from 0 (like C) rather than from 1 (like Fortran).\n",
- "- Function calls in Python always use parentheses: `print()`\n",
- "- The colon `:` denotes the beginning of a definition (here of the repeated code under the `for` loop).\n",
- "- Code blocks are identified through indentations.\n",
- "\n",
- "To emphasize this last point, here is an example with a two-line repeated block:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "787f8e09",
- "metadata": {},
- "outputs": [],
- "source": [
- "for n in range(3):\n",
- " print(\"Hello interweb!\")\n",
- " print(f\"This is iteration number {n}.\")\n",
- "print('And now we are done.')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cd787438",
- "metadata": {},
- "source": [
- "## Basic flow control\n",
- "\n",
- "Like most languages, Python has an `if` statement for logical decisions:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "682eb657",
- "metadata": {},
- "outputs": [],
- "source": [
- "if n > 2:\n",
- " print(\"n is greater than 2!\")\n",
- "else:\n",
- " print(\"n is not greater than 2!\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3a6b8582",
- "metadata": {},
- "source": [
- "Python also defines the `True` and `False` logical constants:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b9367b41",
- "metadata": {},
- "outputs": [],
- "source": [
- "n > 2"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a9164391",
- "metadata": {},
- "source": [
- "There's also a `while` statement for conditional looping:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "12b978f3",
- "metadata": {},
- "outputs": [],
- "source": [
- "m = 0\n",
- "while m < 3:\n",
- " print(f\"This is iteration number {m}.\")\n",
- " m += 1\n",
- "print(m < 3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d5b066ae",
- "metadata": {},
- "source": [
- "## Basic Python data types\n",
- "\n",
- "Python is a very flexible language, and many advanced data types are introduced through packages (more on this below). But some of the basic types include: "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ef3921ed",
- "metadata": {},
- "source": [
- "### Integers (`int`)\n",
- "\n",
- "The number `m` above is a good example. We can use the built-in function `type()` to inspect what we've got in memory:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "184f8c79",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(type(m))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "861da01b",
- "metadata": {},
- "source": [
- "### Floating point numbers (`float`)\n",
- "\n",
- "Floats can be entered in decimal notation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7153a927",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(type(0.1))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8545d891",
- "metadata": {},
- "source": [
- "or in scientific notation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9bc33c68",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(type(4e7))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0a4ceb83",
- "metadata": {},
- "source": [
- "where `4e7` is the Pythonic representation of the number $ 4 \\times 10^7 $."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "87da5d64",
- "metadata": {},
- "source": [
- "### Character strings (`str`)\n",
- "\n",
- "You can use either single quotes `''` or double quotes `\" \"` to denote a string:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d4cc9304",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(type(\"orange\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ba603039",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(type('orange'))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "597631d0",
- "metadata": {},
- "source": [
- "### Lists\n",
- "\n",
- "A list is an ordered container of objects denoted by **square brackets**:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "be5d040f",
- "metadata": {},
- "outputs": [],
- "source": [
- "mylist = [0, 1, 1, 2, 3, 5, 8]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "271edb6e",
- "metadata": {},
- "source": [
- "Lists are useful for lots of reasons including iteration:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f6d0be16",
- "metadata": {},
- "outputs": [],
- "source": [
- "for number in mylist:\n",
- " print(number)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f3f8a57e",
- "metadata": {},
- "source": [
- "Lists do **not** have to contain all identical types:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f1673dc5",
- "metadata": {},
- "outputs": [],
- "source": [
- "myweirdlist = [0, 1, 1, \"apple\", 4e7]\n",
- "for item in myweirdlist:\n",
- " print(type(item))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c0f192ef",
- "metadata": {},
- "source": [
- "This list contains a mix of `int` (integer), `float` (floating point number), and `str` (character string)."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cb662954",
- "metadata": {},
- "source": [
- "Because a list is *ordered*, we can access items by integer index:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8178ee98",
- "metadata": {},
- "outputs": [],
- "source": [
- "myweirdlist[3]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "87e841a7",
- "metadata": {},
- "source": [
- "remembering that we start counting from zero!"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2edf60e3",
- "metadata": {},
- "source": [
- "Python also allows lists to be created dynamically through *list comprehension* like this:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2acae924",
- "metadata": {},
- "outputs": [],
- "source": [
- "squares = [i**2 for i in range(11)]\n",
- "squares"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5c2663a4",
- "metadata": {},
- "source": [
- "### Dictionaries (`dict`)\n",
- "\n",
- "A dictionary is a collection of *labeled objects*. Python uses curly braces `{}` to create dictionaries:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f4dbacdd",
- "metadata": {},
- "outputs": [],
- "source": [
- "mypet = {\n",
- " \"name\": \"Fluffy\",\n",
- " \"species\": \"cat\",\n",
- " \"age\": 4,\n",
- "}\n",
- "type(mypet)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c8e869ca",
- "metadata": {},
- "source": [
- "We can then access items in the dictionary by label using square brackets:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "05c66d6e",
- "metadata": {},
- "outputs": [],
- "source": [
- "mypet[\"species\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b2027506",
- "metadata": {},
- "source": [
- "We can iterate through the keys (or labels) of a `dict`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "387dba3d",
- "metadata": {},
- "outputs": [],
- "source": [
- "for key in mypet:\n",
- " print(\"The key is:\", key)\n",
- " print(\"The value is:\", mypet[key])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fb82a430",
- "metadata": {},
- "source": [
- "## Arrays of numbers with NumPy\n",
- "\n",
- "The vast majority of scientific Python code makes use of *packages* that extend the base language in many useful ways.\n",
- "\n",
- "Almost all scientific computing requires ordered arrays of numbers, and fast methods for manipulating them. That's what [NumPy](../core/numpy.md) does in the Python world.\n",
- "\n",
- "Using any package requires an `import` statement, and (optionally) a nickname to be used locally, denoted by the keyword `as`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "53ba88cf",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fb237b33",
- "metadata": {},
- "source": [
- "Now all our calls to `numpy` functions will be preceeded by `np.`"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a3391f8d",
- "metadata": {},
- "source": [
- "Create a linearly space array of numbers:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ae024132",
- "metadata": {},
- "outputs": [],
- "source": [
- "# linspace() takes 3 arguments: start, end, total number of points\n",
- "numbers = np.linspace(0.0, 1.0, 11)\n",
- "numbers"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cc1c2474",
- "metadata": {},
- "source": [
- "We've just created a new type of object defined by [NumPy](../core/numpy.md):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c3f3642d",
- "metadata": {},
- "outputs": [],
- "source": [
- "type(numbers)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "83325c40",
- "metadata": {},
- "source": [
- "Do some arithmetic on that array:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "12cc225d",
- "metadata": {},
- "outputs": [],
- "source": [
- "numbers + 1"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fee4e53c",
- "metadata": {},
- "source": [
- "Sum up all the numbers:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3cef5412",
- "metadata": {},
- "outputs": [],
- "source": [
- "np.sum(numbers)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ebf2b669",
- "metadata": {},
- "source": [
- "## Some basic graphics with Matplotlib\n",
- "\n",
- "[Matplotlib](../core/matplotlib.md) is the standard package for producing publication-quality graphics, and works hand-in-hand with [NumPy](../core/numpy.md) arrays.\n",
- "\n",
- "We usually use the `pyplot` submodule for day-to-day plotting commands:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "76f3329e",
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "66033cbc",
- "metadata": {},
- "source": [
- "Define some data and make a line plot:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "32ca697e",
- "metadata": {},
- "outputs": [],
- "source": [
- "theta = np.linspace(0.0, 360.0)\n",
- "sintheta = np.sin(np.deg2rad(theta))\n",
- "\n",
- "plt.plot(theta, sintheta, label='y = sin(x)', color='purple')\n",
- "plt.grid()\n",
- "plt.legend()\n",
- "plt.xlabel('Degrees')\n",
- "plt.title('Our first Pythonic plot', fontsize=14)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "04615882",
- "metadata": {},
- "source": [
- "## What now?\n",
- "\n",
- "That was a whirlwind tour of some basic Python usage. \n",
- "\n",
- "Read on for more details on how to install and run Python and necessary packages on your own laptop."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "edbbb2ad-4544-4564-9750-6d738698fe2e",
- "metadata": {},
- "source": [
- "## Resources and references\n",
- "\n",
- "- [Official Python tutorial (Python Docs)](https://docs.python.org/3/tutorial/index.html)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/site/overview.md b/site/overview.md
index 99d59e8..24bf9b1 100644
--- a/site/overview.md
+++ b/site/overview.md
@@ -1,9 +1,7 @@
-# Quick overview
+# Quick Python overview
-Let's do a quick overview of basic Python features, which more broadly will also cover general programming themes. This overview will also cover, briefly, a few more advanced capabilities that are widely with scientific and engineering data.
+At the same time as the lessons, we recommend this quick overview of basic Python features that also covers general programming themes. The overview also presents, briefly, a few more advanced capabilities that are widely used with scientific and engineering data.
-Go to [Quickstart: Zero to Python — Pythia Foundations](https://foundations.projectpythia.org/foundations/quickstart.html). There, we will all click on the "Binder" link at the top (the rocket icon) to run the steps live, online! It will launch a Jupyter notebook on JupyterLab, online. So, this will serve also as an initial exposure to JupyterLab and Jupyter notebooks. This open-source overview is provided by [Project Pythia, https://projectpythia.org](https://projectpythia.org).
+Go to [Quickstart: Zero to Python — Pythia Foundations](https://foundations.projectpythia.org/foundations/quickstart.html). There, click on the "Launch kernel" icon  below the page title, to the right, to run the steps live, online! It will launch a `Jupyter` notebook on `JupyterLab`, online. This open-source overview will serve also as an initial exposure to `JupyterLab` and `Jupyter` notebooks. It is provided by [Project Pythia](https://projectpythia.org).
-I put a copy of that lesson (which is a Jupyter notebook) here, for reference: [notebooks/projectpythia-quickstart.ipynb](./notebooks/projectpythia-quickstart.ipynb).
-
-[https://learnpython.org](https://learnpython.org) can nicely complement the Project Pythia Quickstart. It is a nice and compact introduction featuring online interactive coding, organized by Python feature.
\ No newline at end of file
+[https://learnpython.org](https://learnpython.org) can nicely complement the Project Pythia Quickstart. It is a compact introduction featuring online interactive coding, organized by Python feature.
\ No newline at end of file
diff --git a/site/python.md b/site/python.md
new file mode 100644
index 0000000..7159c34
--- /dev/null
+++ b/site/python.md
@@ -0,0 +1,7 @@
+# Why Python?
+
+[Python](https://www.python.org/) is a free, open-source, general-purpose and high-level programming language. It's relatively easy to learn, and the syntax facilitates writing new code fairly quickly. In the last few years it has become one of the most widely used general programming languages (see [here](https://pypl.github.io/PYPL.html) and [here](https://www.tiobe.com/tiobe-index/)) and among the [most popular languages for data science applications](https://r4stats.com/articles/popularity/). It's also widely used in the scientific community in general, and in Oceanography specifically (see [here](https://foundations.projectpythia.org/foundations/why-python.html), [here](https://jose.theoj.org/papers/10.21105/jose.00037) and [here](https://doi.org/10.1080/10899995.2024.2384338)).
+
+Python runs on all major operating systems (including Windows, MacOS and Linux) and on computers from the smallest to the largest. It's often used in introductory programming classes, especially outside Computer Science majors.
+
+An **open-source** language, it is freely available to anyone for any purpose, and both the core language (Python proper) and a very large "ecosystem" of packages (libraries) engage a wide range of contributors. But this openness sometimes leads to a disorienting set of choices, including the Python version, the sources and software used to install Python and write code in Python, and specialized packages used to accomplish specific tasks. Compared to, say, Matlab, the entry points can be more confusing. But there are tons of helpful resources and communities online!