{ "cells": [ { "cell_type": "markdown", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "# Reminder: Do not rename file (or allow your computer to do that)\n", "\n", "Please overwrite the original exercise release file(s) when uploading your assignment. I still had to fix some of your submissions.\n", "\n", "# Update: Trouble with previous assignment due to `_` breaking.\n", "\n", "In the previous assignment, some of you had trouble because the assignment expected `_` to be the output of the previous cell, but the Jupyter output was increasing by \"2\" as described [here](https://stackoverflow.com/q/78765478). The solution to this problem [is to disable the Anaconda Assistant](https://stackoverflow.com/a/78766025/1633026), namely:\n", "\n", "Steps to fix:\n", "\n", "1. Disable Anaconda Assistant\n", "1. Run `jupyter labextension disable @anaconda/assistant`\n", "1. Restart the Jupyter notebook\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `()` and `[]`\n", "\n", "Remember that Python is very, very precise about how it understands your code.\n", "\n", "`()` have multiple meanings in python:\n", "\n", "- function call: `my_function(arg1)`\n", "- defining a function: `def my_function(arg1):`\n", "- specifying the order of operations: `4*(3+2)`\n", "- creating a tuple: `(1,2,3,4)` or `(1,)` or `()` (Note that `(1)` will not create a tuple. Also: you can create a tuple without parentheses...)\n", "\n", "`[]` similarly has a few meanings, based on context\n", "\n", "- creating a list: `[1,2,3,4]` or `[1,]` or `[1]` or `[]`\n", "- getting an item: `x = my_list[index]`\n", " - getting a slice: `x = my_list[start_index:end_index:increment]`\n", "- setting an item: `my_list[index] = x`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic plotting, revisited" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Below, we will use matplotlib, so we need to import it here.\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "x=[1,2,3,4,5,6,7,8,9,1]\n", "y=[0,4,0,3,3,0,3,4,5,2]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x,y,'--')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x=[1,2,3,4,5,6,7,8,9,10]\n", "y1=[0,4,0,3,3,0,3,4,5,2]\n", "y2=[3,2,4,4,2,4,4,2,4,2]\n", "plt.plot(x,y1)\n", "plt.plot(x,y2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# String formatting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Old-style string formatting with `%`\n", "\n", "When the operator `%` is used on a string, the string is used as a *format string* for old-style formatting.\n", "\n", "In these old-style format strings, `%d` means to print an integer, `%s` means to print a string." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'The numbers are 5, 10, 20'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"The numbers are %d, %d, %d\" % (5,10,20)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'The numbers are 5, 10, 20'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp = (5,10,20)\n", "\"The numbers are %d, %d, %d\" % tmp" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "not all arguments converted during string formatting", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m tmp \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m20\u001b[39m,\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mThe numbers are \u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m, \u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m, \u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtmp\u001b[49m\n", "\u001b[0;31mTypeError\u001b[0m: not all arguments converted during string formatting" ] } ], "source": [ "tmp = (5,10,20,100)\n", "\"The numbers are %d, %d, %d\" % tmp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tmp = (5,10)\n", "\"The numbers are %d, %d, %d\" % tmp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_string = \"The numbers are %d, %d, %d\"\n", "my_string" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_string%(7, 14, 21)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tuple1 = (100, 200, 300)\n", "my_string % tuple1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"The numbers are %d, %d, %d\"%(5,10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"The numbers are %d, %d, %d\"%(5,10,20,40)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"Hello %s\"%(\"world\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"Hello %s\"%1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"Hello %d, %d\"%(1, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"Hello %d, %d\"%(1, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"Hello %d\"%1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"Hello %d\"%'hello'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"Hello %s\"%'hello'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New-style (since Python 2.7) formatting with `.format()`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"The numbers are {}, {}, {}\".format(5,10,20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"Hello {}\".format(\"world\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Even newer (since Python 3.6) style formatting with f-strings." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "name=\"Andrew\"\n", "my_string = f\"Hello, my name is {name}.\"\n", "print(my_string)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f\"Hello, my name is {name}.\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f\"Hello, my name is {name}. {akjfhasd}\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f\"Hello, my name is {name}. {{akjfhasd}}\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"Hello, my name is {}.\".format(name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_template = \"hello {}\"\n", "print('my template is:', my_template)\n", "my_template.format(name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_template = \"hello {}\"\n", "my_template.format(name)\n", "print('my template is', my_template)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Control flow with `while`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = True\n", "y = 0\n", "while x:\n", " print(y)\n", " y = y + 100\n", " if y >= 1000:\n", " x = False\n", "print('done')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y = 0\n", "while True:\n", " print(y)\n", " y = y + 100\n", " if y >= 1000:\n", " break\n", "print('done')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y = 0\n", "while y < 1000:\n", " print(y)\n", " y = y + 100\n", "print('done')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `break` and `continue`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y = 0\n", "while True:\n", " print(y)\n", " y = y + 100\n", " if y >= 1000:\n", " break" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y = 0\n", "while True:\n", " print(y) \n", " y = y + 100\n", " if y >= 1000:\n", " break\n", " if y < 400:\n", " continue \n", " y = y + 10" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The `+=` (and `-=`, `*=`, .. ) operator." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y += 20" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y = y +20" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y -= 20" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running Python from the terminal\n", "\n", "We want to be able to run Python from the terminal. This is a low-level approach but will let us Python on all kinds of computers in all kinds of ways, not just interactively inside a Jupyter notebook.\n", "\n", "First, we need to be able to edit Python scripts. There are lots of programs that let us do that. I recommend [Visual Studio Code](https://code.visualstudio.com). To keep things simple, you can also use Jupyter Lab. In that case, you can click on the \"+\" in the tab list to open a Launcher tab, scroll down and create a \"Python File\". This will open an empty `untitled.py` file in the Jupyter lab editor. These `.py` files are *plain text* files which the `.py` extension tells the computer that this file should be Python source code. Let's create a file called `hello.py` which has the following contents.\n", "\n", "```python\n", "print(\"hello world\")\n", "```\n", "\n", "Now, let's run it in the terminal. Again, this can be done in multiple ways. One way is to use Jupyter Lab by clicking on the \"+\" in the tab list again. This type create a \"Terminal\". Another way is to use Anaconda Navigator as shown in this movie:\n", "\n", "