python-jupyter-intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Part 1 - Python and Jupyter\n",
    "\n",
    "An introduction to the Python language and using it in Jupyter notebooks.\n",
    "\n",
    "*Press `Spacebar` to go to the next slide (or `?` to see all navigation shortcuts)*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "# Python\n",
    "is a widely used programming language with many useful libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "# Jupyter Notebook\n",
    "a document format containing a mix of (Python) code and documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "# JupyterLab\n",
    "an interactive tool for editing and running Jupyter notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Jupyter notebook\n",
    "\n",
    "## Cells\n",
    "\n",
    "Notebook is separated into cells, which can be\n",
    "- **code** cells\n",
    "  - contain Python code to be executed\n",
    "- **markdown** cells\n",
    "  - contains text in markdown format\n",
    "\n",
    "To select a cell: click on it with the mouse\n",
    "\n",
    "To run the selected cell, click the `Run` button, or press `Ctrl+Enter`, or click \"Cell -> Run Cells\" on the menubar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# This is a code cell: press Ctrl+Enter to execute the code in it\n",
    "#\n",
    "print(\"Hello World!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "# Markdown cell\n",
    "This is a markdown cell, which can contain\n",
    "- headings, lists, *formatted* **text**, [links to websites](https://ssc.iwr.uni-heidelberg.de/).\n",
    "- math in latex format: $\\int_0^\\infty \\cos(x) dx$\n",
    "- images:\n",
    "![images can also be displayed](https://drupal-varnish-703-urz-webkit-webkit39-prd.apps.ocp-west.urz.uni-heidelberg.de/sites/default/files/site_logo/WEB%20Kopfgrafik%20Scientific-Software-Center%20LANG%20neu.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "# Mode\n",
    "\n",
    "Two *modes* of interacting with the active/selected cell\n",
    "- **edit** mode\n",
    "  - edit the text inside the cell\n",
    "- **command** mode\n",
    "  - use keyboard shortcuts to modify the cell or run commands\n",
    "\n",
    "- To enter edit mode: double click inside a cell, or press `Enter` with a cell selected\n",
    "- To enter command mode: click to the left of a cell inside the green outline, or press `Escape`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Commands\n",
    "\n",
    "Lots of keyboard shortcuts available. Press `Escape` to enter command mode, then `Ctrl+Shift+C` (or `H` in notebook) to see a list.\n",
    "\n",
    "Some commonly used shortcuts:\n",
    "- `A`: insert a cell above the current cell\n",
    "- `B`: insert a cell below the current cell\n",
    "- `M`: convert the current cell to a markdown cell\n",
    "- `Y`: convert the current cell to a code cell\n",
    "- `Shift+Enter`: run the current cell and advance to the next cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Order of Execution\n",
    "\n",
    "- you are free to execute / run cells in any order you choose\n",
    "- they can make use of and modify any objects, functions or variables that have already been created\n",
    "- however this can quickly get confusing and make reproducing results difficult!\n",
    "\n",
    "## Top to bottom\n",
    "\n",
    "- it is good practice to have a top-to-bottom flow of execution\n",
    "- i.e. write your notebook so that it can be executed in the order it is written\n",
    "- this makes it easier to understand what is going on"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Useful commands when things go wrong\n",
    "\n",
    "- menubar `Kernel -> Restart` (or command mode shortcut: `0 0`)\n",
    "  - fresh start (your code is still there, but all existing objects, functions and variables are cleared)\n",
    "- menubar `Kernel -> Restart and Clear Output`\n",
    "  - as above, but additionally clears all cell outputs\n",
    "- menubar `Kernel -> Restart and Run All`\n",
    "  - as above, but additionally executes all the cells in order\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python: Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# any lines starting with \"#\" are comments that Python ignores\n",
    "#\n",
    "# assign the number 12 to the variable \"a\":\n",
    "#\n",
    "a = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# any variable or object can be printed\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# display the type of an object\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# variables can be re-assigned, including to different types\n",
    "a = \"Hello!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python: Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# a list is an ordered container of objects (the objects don't have to have the same type)\n",
    "# create one by listing items inside square brackets, separated by commas:\n",
    "my_list = [1, 3, 88, -13, \"hello\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "print(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "type(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "len(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# can reference an item in the list by it's index: 0 is the first item\n",
    "print(my_list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# can also use negative indices: -1 is the last item, -2 the second-to-last, etc\n",
    "print(my_list[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# can use slicing to get a subset of the list: here elements with index 1 up to (but not including) index 3:\n",
    "print(my_list[1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# can omit starting element of slice: defaults to first element\n",
    "print(my_list[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# can omit end element of slice: defaults to number of elements\n",
    "print(my_list[3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# can omit start and end elements of slice: get all elements\n",
    "print(my_list[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# can add two lists together: this concatenates them into a single long list\n",
    "print(my_list + [5, 6, 7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# can iterate over the items in a list\n",
    "for item in my_list:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python: Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# a dictionary is an unordered set of key-value pairs\n",
    "# create one by listing key:value pairs inside curly brackets, separated by commas\n",
    "my_dict = {\"name\": \"Bob\", \"age\": 6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "print(my_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "type(my_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "len(my_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# can look up a value using its key\n",
    "print(my_dict[\"name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# can add a key-value pair to the dictionary by assinging a value to a key\n",
    "my_dict[\"sizes\"] = [1, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "print(my_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# adding an existing key overwrites the old value with the new one\n",
    "my_dict[\"sizes\"] = [5, 10, 24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "print(my_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# can iterate over dictionary items using dict.items()\n",
    "for key, value in my_dict.items():\n",
    "    print(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python: Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# functions are defined using the def keyword\n",
    "def my_function():\n",
    "    print(\"hi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "my_function()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# functions can take arguments\n",
    "def my_function(name):\n",
    "    print(\"hi\", name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "my_function(\"Liam\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python: Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# import a library, and (optionally) give it a shorter name\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "my_list = [1, 2, 3, 4, 5]\n",
    "# library functions accessed using library_name.function\n",
    "# here we create a numpy array from a list\n",
    "my_array = np.array(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "print(my_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "type(my_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# apply the numpy `sqrt` function to every element of the array\n",
    "np.sqrt(my_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# display help about this sqrt function\n",
    "?np.sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "np.mean(my_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "np.std(my_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Next\n",
    "\n",
    "- [Part 2: Pandas with toy data](https://ssciwr.github.io/jupyter-data-exploration/pandas-toy-data.html)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}