641 lines
88 KiB
Plaintext
641 lines
88 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"# Reminder: Do not rename file (or allow your computer to do that)\n",
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"\n",
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"Please overwrite the original exercise release file(s) when uploading your assignment. I still had to fix some of your submissions.\n",
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"\n",
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"# Update: Trouble with previous assignment due to `_` breaking.\n",
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"\n",
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"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",
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"\n",
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"Steps to fix:\n",
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"\n",
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"1. Disable Anaconda Assistant\n",
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"1. Run `jupyter labextension disable @anaconda/assistant`\n",
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"1. Restart the Jupyter notebook\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# `()` and `[]`\n",
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"\n",
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"Remember that Python is very, very precise about how it understands your code.\n",
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"\n",
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"`()` have multiple meanings in python:\n",
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"\n",
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"- function call: `my_function(arg1)`\n",
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"- defining a function: `def my_function(arg1):`\n",
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"- specifying the order of operations: `4*(3+2)`\n",
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"- 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",
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"\n",
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"`[]` similarly has a few meanings, based on context\n",
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"\n",
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"- creating a list: `[1,2,3,4]` or `[1,]` or `[1]` or `[]`\n",
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"- getting an item: `x = my_list[index]`\n",
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" - getting a slice: `x = my_list[start_index:end_index:increment]`\n",
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"- setting an item: `my_list[index] = x`"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Basic plotting, revisited"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Below, we will use matplotlib, so we need to import it here.\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"x=[1,2,3,4,5,6,7,8,9,1]\n",
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"y=[0,4,0,3,3,0,3,4,5,2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x10b899cd0>]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.plot(x,y,'--')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x10ba02490>]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGdCAYAAABO2DpVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABysklEQVR4nO3dd1hc55k3/u+ZThmG3gQCVEFCQtWSuxP32I573137TXJt8ntlx46STeIkm7aJlbbeFCfOOm82m91E7r3XWHKJuigSoAoCBIjOwMD08/vjmWdmQJQpZ+acOef+XJcujSSY84g293meuwiiKIoghBBCCJGATu4FEEIIIUQ9KLAghBBCiGQosCCEEEKIZCiwIIQQQohkKLAghBBCiGQosCCEEEKIZCiwIIQQQohkKLAghBBCiGQMyb6g3+9Hd3c3rFYrBEFI9uUJIYQQEgNRFDE2NobS0lLodLPvSyQ9sOju7kZ5eXmyL0sIIYQQCXR2dqKsrGzWf096YGG1WgGwhWVlZSX78oQQQgiJgd1uR3l5efB1fDZJDyz48UdWVhYFFoQQQkiKmS+NgZI3CSGEECIZCiwIIYQQIhkKLAghhBAiGQosCCGEECIZCiwIIYQQIhkKLAghhBAiGQosCCGEECIZCiwIIYQQIhkKLAghhBAimagCi+9///sQBGHKr+Li4kStjRBCCCEpJuqW3itXrsS7774b/LNer5d0QYQQQghJXVEHFgaDgXYpCCGEEDKjqHMsjh07htLSUlRVVeGOO+7AyZMn53x7l8sFu90+5RchhBCiJC8ePI2/tfbJvQxViCqw2LRpE/7nf/4Hb731Fv7whz+gt7cX5513HgYHB2d9n23btsFmswV/lZeXx71oQgghRCrP7e/Cg0/V44v/ux/jLq/cy0l5giiKYqzv7HA4sHjxYnz961/H1q1bZ3wbl8sFl8sV/DOf5z46Okpj0wkhhMjqeN8YrvvNx5j0+AAAf/7cObh4WYHMq1Imu90Om8027+t31DkW4TIyMrBq1SocO3Zs1rcxm80wm83xXIYQQgiR3KTbhy1/PYhJjw+CAIgisPvkIAUWcYqrj4XL5UJLSwtKSkqkWg8hhBCSFD945TCOnBlDfqYZX7+yGgCw6+TsR/skMlEFFl/72tewY8cOtLW1Yffu3bjllltgt9txzz33JGp9hBBCiOReqj+NJ/d2QhCAX92xBteuZjfIjV2jmHBTnkU8ogosurq6cOedd2L58uW46aabYDKZsGvXLlRUVCRqfYQQQoikTvaP41vPNwEA7v/0Upy/JB9lOWlYkJ0Gr1/E/lPDMq8wtUWVY/Hkk08mah2EEEJIwjk9PmzZfhAOtw+bF+XigUuXAgAEQcCmRbl4/sBp7Do5iAuXUp5FrGhWCCGEEM340WvNaOmxIy/DhF/dsRZ6nRD8t81VeQCA3SeH5FqeKlBgQQghRBNebezGX3Z1QBCA/7h9DYqyLFP+ffMiFlg0dI1QnkUcKLAghBCieqcGHfjmcyyv4v9eshgXzVBSWp6bhlKbBR6fiAOnRpK8QvWgwIIQQoiqubw+bNl+AOMuLzZW5uArly2b8e1YnkXgOKSNyk5jRYEFIYQQVdv2eisOnbYjJ92IX9+5Fgb97C99mxflAqB+FvGgwIIQQohqvXmoF//9STsA4JHb1qDEljbn228KJHDWd45g0u1L9PJUiQILQgghqtQ5NIGvP9sAAPjiRYvwqerCed+nIi8dxVksz+JgB/WziAUFFoQQQlTH7fXjvicOwu70Yt3CbHztyuURvZ8gCHQcEicKLAghhKjOz95sRUPnCGxpRvzmrnUwzpFXMR1P4NzVRv0sYkGBBSGEEFV5t/kM/t9HbQCAX9xahwXZc+dVTMf7WdR3jMDpoTyLaFFgQQghRDVOj0ziq8+wvIrPnV+Fy1cURf0clXnpKMoyw+3z4wDlWUSNAgtCCCGq4PH5cf/2Axid9KCuzIZvXl0d0/MIghCsDqH23tGjwIIQQogq/PvbR3GgYwRWiwGP3rUOJkPsL3H8OIQSOKNHgQUhhJCU97cjffj9jhMAgJ/fshrluelxPd+mQGXIwU7Ks4gWBRaEEEJSWu+oE199muVV3HNuBa6qLYn7ORflZ6DAaobb60d950jcz6clFFgQQghJWV6fH19+4iCGHG7ULsjCt66pkeR5WT8LOg6JBQUWhBBCUtYv3z2GPe1DyDQb8Oid62A26CV77k1V7DiEEjijQ4EFIYSQlPThsX789oPjAIBtN61CZX6GpM/PdywOdAxTnkUUKLAghBCScvrsTjz4ZD1EEbhr00JcV1cq+TUWF2QgP9MMl9ePBsqziBgFFoQQQlKKzy/igSfrMehwo7rYiu9euyIh1xEEIVgdspvae0eMAgtCCCEp5TfvH8PfTw4i3aTHb+9eB4tRuryK6SiBM3oUWBBCCEkZn5wYwK/eOwYAePjGVVhckJnQ620OJHAe6BiGy0t5FpGgwIIQQkhK6B9z4YFAXsXtG8pxw9oFCb/mksJM5GWY4PT40dg1mvDrqQEFFoQQQhTP7xex9el69I+5sKwoE9//7MqkXHdKP4sTdBwSCQosCCGEKN7vPjiOD48NIM2ox2/vWoc0U+LyKqajBM7oUGBBCCFE0XafHMQj7xwFAPzw+pVYWmRN6vX5jsW+U0Nwe/1JvXYqosCCEEKIYg2Ou/DlJw/CLwI3rVuAWzeUJ30NSwszkRvMsxhJ+vVTDQUWhBBCFInlVTTgjN2FxQUZ+Lfra2VZhyAIofbedBwyLwosCCGEKNLjH57EjqP9MBt0+O3d65BhNsi2FupnETkKLAghhCjO/lND+PlbRwAAP/jsSlQXZ8m6Hp7Aua99GB4f5VnMhQILQgghijLscOP+7Qfh84v4bF0pbt+Y/LyK6ZYVWpGTbsSkx0f9LOZBgQUhhBDFEEUR//JsA7pHnajKz8DDN62CIAhyLws6nYBNVXQcEgkKLAghhCjGHz9qw7stfTAZdHj0rrXIlDGvYjrqZxEZCiwIIYQowsGOYfzkjVYAwL9euwIrS20yr2iqYD+L9iHKs5gDBRaEEEJkNzrhwX3bD8LrF3HNqhL8w6aFci/pLMuLrMhON2LC7UPTacqzmA0FFoQQQmTF8ypOj0xiYW46tt2sjLyK6XQ6AedUBo5DTtJxyGwosCCEECKrP3/Sjrebz8CoF/Dbu9Yhy2KUe0mzon4W86PAghBCiGyaukbx8Ossr+Jbn6nBqjJl5VVMF+pnMQQv5VnMiAILQgghsrA7Pdiy/QDcPj+uXFmEe8+rlHtJ86opzoItzQiH24dD3Xa5l6NIFFgQQghJOlEU8dBzTegYmkBZThp+dnOdIvMqptPpBJwTmBtCxyEzo8CCEEJI0v1ldwdea+qBQSfgN3euhS1duXkV0wUHklFgMSMKLAghhCTV4e5R/NurzQCAb15djbULc2ReUXR4Aufe9mHKs5gBBRaEEEKSZtzlxX3bD8Lt9eOymkJ8/oIquZcUtZqSLFgtBoy7vGjuoTyL6SiwIIQQkhSiKOJbzzehbcCBUpsFv7g1NfIqptPrhOBxCOVZnI0CC0IIIUnx1N5OvNzQDb1OwG/uWovsdJPcS4pZqJ8FNcqajgILQgghCdfaa8f3Xj4MAPjaFcuxviJX5hXFh0863ds2BJ9flHk1ykKBBSGEkIRyuLzY8tcDcHn9uGR5Ab540SK5lxS3FaVZsJoNGHN50Uz9LKagwIIQQkhC/etLh3Ci34GiLDP+/dY66HSpl1cxnZ76WcyKAgtCCCEJ88y+Tjx/4DR0AvDrO9YiL9Ms95Ikw9t7726jwCIcBRaEEEIS4tiZMXz3JZZXsfXyZdgUSHhUC57AuZvyLKagwIIQQojkJt0+bNl+AJMeHy5Yko//75Ilci9JcitKspBpNmDM6UUL9bMIosCCEEKI5L7/8mEcPTOOAqsZ/3H7GuhVkFcxnUGvw8ZK1jWU8ixCKLAghBAiqRcPnsZT+zohCMCvbl+DAqt68iqmo34WZ6PAghBCiGRO9I/jWy80AQC+/OmlOG9JvswrSqxNwbkhQ/BTngU
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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",
|
||
|
|
"<video controls src=\"CLI-python.mp4\" />"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.11.7"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|