pm21-dragon/lectures/lecture-04/1 - For-loops-dicts-files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a795ba59-d48d-4f8d-934e-2cbc558071e4",
   "metadata": {},
   "source": [
    "# News: \n",
    "\n",
    "I enabled a `.ipynb` viewer at https://strawlab-rp2.zoologie.uni-freiburg.de/straw/pm21-dragon.\n",
    "\n",
    "# `exercise-04` review\n",
    "\n",
    "## Flow control Q3\n",
    "\n",
    "Now create a function called `simulate_generation` which takes two arguments, `a` and `Rt`. This function should return `None`. The first argument `a` will be a list with the number of infected individuals and `Rt` is the effective reproduction number, as before. The function should compute the number of newly infected individuals after one generation and append this to the list passed as the first argument.\n",
    "\n",
    "Your function signature should look like this:\n",
    "\n",
    "```python\n",
    "    def simulate_generation(a,Rt):\n",
    "```\n",
    "\n",
    "Here is an example that works:\n",
    "\n",
    "```python\n",
    "b = [1]\n",
    "simulate_generation(b,3)\n",
    "simulate_generation(b,3)\n",
    "simulate_generation(b,3)\n",
    "b\n",
    "```\n",
    "\n",
    "In this above example, `b` would be equal to `[1, 3, 9, 27]` at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5dbf80df-9df2-479b-bc33-fa5ee7465faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_generation(a,Rt):\n",
    "    previous = a[-1]\n",
    "    new_infected = previous*Rt\n",
    "    a.append(new_infected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13f871cc-57bd-466b-ab2c-335097d45871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 9, 27]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = [1]\n",
    "simulate_generation(b,3)\n",
    "simulate_generation(b,3)\n",
    "simulate_generation(b,3)\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4cbed4-d429-423c-9730-8eec2e7be271",
   "metadata": {},
   "source": [
    "## Flow control Q4\n",
    "\n",
    "Now create a function called `calculate_timeseries_to_1000` which takes one argument, `Rt`. This function should return a list containing the number of infected individuals after each cycle up to and including the first value over 1000 individuals.\n",
    "\n",
    "Your function signature should look like this:\n",
    "\n",
    "```python\n",
    "    def calculate_timeseries_to_1000(Rt):\n",
    "```\n",
    "\n",
    "Your code should work so that `calculate_timeseries_to_1000(2)` would return `[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]` and that `calculate_timeseries_to_1000(3)` would return `[1, 3, 9, 27, 81, 243, 729, 2187]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73d2d5a4-7697-4e91-9e0f-95f6ba36511b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is an incorrect answer from someone:\n",
    "\n",
    "def calculate_timeseries_to_1000(Rt):\n",
    "    infected_series = [1]\n",
    "    while infected_series[-1] <1000:\n",
    "        # Again, this is not correct:\n",
    "        print(infected_series)\n",
    "        infected_series = infected_series * Rt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eba13625-66b6-4910-8cdc-67b8cba4e6e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try to run it:\n",
    "\n",
    "calculate_timeseries_to_1000(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa76fadc-1ab5-42b3-9884-7542d5df0cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_timeseries_to_1000(Rt):\n",
    "    a = [1]\n",
    "    while a[-1] < 1000:\n",
    "        simulate_generation(a,Rt)\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56ea8eec-1128-4415-94ae-a1060cc2443c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_timeseries_to_1000(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b32e617-dc56-4f32-8953-ce4c4a12dce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 9, 27, 81, 243, 729, 2187]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_timeseries_to_1000(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef5d53f-d007-4727-8852-5d430eb7fef7",
   "metadata": {},
   "source": [
    "* I check your answers based on file name. Please keep the files names exactly as specified, i.e. `my_name.py`.\n",
    "\n",
    "* Example answers:\n",
    "\n",
    "```python\n",
    "print(\"Paolo\")\n",
    "\n",
    "\n",
    "a = 1001\n",
    "b = 22\n",
    "def onethousandandone_times_twentytwo(a,b):\n",
    "    print(a*b)\n",
    "```\n",
    "\n",
    "vs\n",
    "\n",
    "```python\n",
    "# Create a Python script called `my_name.py` which does two things:\n",
    "\n",
    "# 1) prints your name\n",
    "\n",
    "print(\"Paolo\")\n",
    "\n",
    "# 2) computes the value of 1001 * 22 and then prints this\n",
    "\n",
    "result = 1001*22\n",
    "print(result)\n",
    "```\n",
    "\n",
    "vs\n",
    "\n",
    "```python\n",
    "print (\"Paolo\")\n",
    "value1 = 1001*22\n",
    "print (value1)\n",
    "```\n",
    "\n",
    "Correct answer should look like this:\n",
    "\n",
    "```\n",
    "astraw@computer$ python my_name.py\n",
    "Paolo\n",
    "22022\n",
    "astraw@computer$\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba980f4b-8e46-41d7-940c-90910d939b81",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Andrew' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# What is wrong with this code?\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mAndrew\u001b[49m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;241m1001\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m22\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Andrew' is not defined"
     ]
    }
   ],
   "source": [
    "# What is wrong with this code?\n",
    "print(Andrew)\n",
    "print(1001 * 22)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df1eac5-e457-4d81-a1bf-fd151265ea9c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# For loops, iterators, Dictionaries, more operators, files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e19d742e-a9b2-4583-baf7-f3277157beb6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# We run this for use below\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1b23a3-eca2-475c-928d-74a9340c8948",
   "metadata": {},
   "source": [
    "## Control flow with `for` using `range` to produce an iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b083f041-81b4-4036-a074-777cca57b714",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for x in range(10):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d9b3d49-bfb1-4710-ad2d-2017c8f53f57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for x in range(0, 10):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ecaeef4-1e00-4ca7-9577-c17c5ea99da9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1a9fa26d-b496-4027-8284-296eeb03c458",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "800\n",
      "900\n"
     ]
    }
   ],
   "source": [
    "for y in range(0, 1000, 100):\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f255422b-b9f2-4cac-8416-e6e2873c0efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "myiter: range(0, 1000, 100)\n",
      "<class 'range'>\n",
      "0\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "800\n",
      "900\n"
     ]
    }
   ],
   "source": [
    "myiter = range(0, 1000, 100)\n",
    "print('myiter:', myiter)\n",
    "print(type(myiter))\n",
    "for y in myiter:\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8cde809-3a7c-4d47-95e0-ae92f4fbc32f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for y in range(10):\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "618eae3e-271e-4aa9-b960-2406e66f5dff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for y in range(4,10):\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1d9ce42-4b17-464b-890b-58fc3074451c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "6\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "for y in range(4, 10, 2):\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce930921-7f89-40c3-8ae1-86fa4bf76950",
   "metadata": {},
   "source": [
    "Note the symmetry between `range()` and slices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "21919ffa-13fa-44bb-b1d9-263e176c497c",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7418236e-f87c-4caf-9ae8-6e4f50fcda59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "72a30f71-a29c-4b5e-b18d-828c85598502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[4:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "84bcbdbf-4bc2-4099-ba18-3d1f88df47f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 8]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[4:10:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f170ad1-3f66-4dce-b838-08e9e6962fe4",
   "metadata": {},
   "source": [
    "## Control flow with `for` using a list as an iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5e8ad5d5-8310-44f7-b3a3-03efedd846c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "end\n"
     ]
    }
   ],
   "source": [
    "my_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "\n",
    "for y in my_list:\n",
    "    print(y)\n",
    "print(\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9d72823c-57da-443c-85b7-8beef3d785d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y: [5, 5, 6]\n",
      "5\n",
      "5\n",
      "6\n",
      "y: [6, 6, 7]\n",
      "6\n",
      "6\n",
      "7\n",
      "end\n"
     ]
    }
   ],
   "source": [
    "my_list = [[5,5,6], [6,6,7]]\n",
    "\n",
    "for y in my_list:\n",
    "    print('y:',y)\n",
    "    for z in y:\n",
    "        print(z)\n",
    "print(\"end\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8063e2bc-23a3-4364-bfa2-21d6da515555",
   "metadata": {},
   "source": [
    "# iterators\n",
    "\n",
    "We have seen now a couple examples of *iterators*.\n",
    "\n",
    "An iterator is not a type in Python but rather a behavior that some types have. Namely, you can iterate over them. This means you can use them as the source of data in a `for` loop. All items in the iterators do not need to be stored in memory at once, but rather they can be constructed one at a time.\n",
    "\n",
    "Iterators could run infinitely or they can end at a certain point.\n",
    "\n",
    "We can create a list from all values in an iterator in a couple different ways.\n",
    "\n",
    "The first you should be able to do by yourself already:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "04ae4602-76fe-4382-a9bc-946999dc9f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = []\n",
    "for x in range(10):\n",
    "    my_list.append(x)\n",
    "my_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7784431a-461e-4d0c-b3eb-21ab73d8da44",
   "metadata": {},
   "source": [
    "The second approach of creating a list from all values in an iterator relies on the `list()` function, which is the *constructor* of a list. This constructor function will iterate over the iterator and create a list with its contents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "67b35f94-b268-488b-a8ee-c0cb12bc708c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = list(range(10))\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb690611-4886-47fd-810a-048487ffa4bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = []\n",
    "x = \"my super important data\"\n",
    "# Note that we overwrite x here!\n",
    "for x in range(2):\n",
    "    my_list.append(x)\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0a7785b5-9a4d-438e-9cd6-b5fe38afa003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c93a418-83fd-459f-a277-d28384d7a0cf",
   "metadata": {},
   "source": [
    "`continue` and `break` work in for loops, too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "13d2a909-7a05-43f6-92b9-f9c09e51563f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = []\n",
    "for x in range(100):\n",
    "    if x > 5:\n",
    "        if x < 10:\n",
    "            continue\n",
    "    if x >= 20:\n",
    "        break\n",
    "    my_list.append(x)\n",
    "my_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6545f21f-1f66-41ec-9573-eabd95b0f0e8",
   "metadata": {},
   "source": [
    "# Methods\n",
    "\n",
    "Methods are a way of giving a type specific additional functions. You already know a few of them, which so far we have just used without discussing much. This includes `list.append` and `str.format`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "09c591ee-a2a6-475c-bfbd-b2fa52a3accc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "my_list.append(10)\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "82a767b3-d398-4f7e-b389-0b143511737c",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_str = \"Hello, my name is {}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8c459558-d7ce-4459-9195-1d197a1a23b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, my name is {}'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d987a1f3-4d33-405d-ac3e-2eb5b44f95c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, my name is Andrew'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str.format(\"Andrew\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3dcd5aba-8877-4ca5-9725-445870073529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, my name is {}'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d866caa5-8d01-4c36-9a0c-817cf5bb637f",
   "metadata": {},
   "source": [
    "Later, we will learn how to define our own methods. For now, it's just important that you know a method is like a function. Both can be *called* with input arguments, they return an output value, and they can have \"side effects\" -- changes to their inputs or something else."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e24fa7e-e8a7-42d9-9b74-7b82f18067b0",
   "metadata": {},
   "source": [
    "# Modules\n",
    "\n",
    "We have also used a number of modules without discussing this aspect much. There are built-in modules -- they come with Python as part of \"the standard library\" -- and there are modules which have to be installed separately. Matplotlib, for example, is a set of modules, (a \"library\") which we use a lot and which is not part of the Python language itself.\n",
    "\n",
    "Modules are a data type in Python like any other. They can have functions which have names like `module_name.function_name`. This is a very minor point, but the `.` makes a function in a module \"look like\" a method, but actually it is a normal function.\n",
    "\n",
    "Here we *import* the `random` module from the standard library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc64c0d3-8bee-4d5f-ace8-1fcd05b2ca85",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "39943249-daa9-49c3-bc95-d537ca76e025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1,2,3,4,5,'asdf','dalkfj']\n",
    "random.choice(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "885b9d19-4d21-400a-81f4-669460baa6f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.choice(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f424016-3f5a-4d51-ac50-bfbc1971f42d",
   "metadata": {},
   "source": [
    "As mentioned, there are modules which are not part of the Python language itself. In fact there are approximately zillions of libraries for doing many, many different things, and this is one of the reasons Python is so useful and so popular. There can be a positive feedback loop between language popularity and the availability of libraries, and Python has benefitted a lot from this - especially in the data science area.\n",
    "\n",
    "One place that distributes many Python modules: [PyPI, the python package index](https://pypi.org/) another is [Anaconda](https://www.anaconda.com).\n",
    "\n",
    "As an example, let's return to our previous use of matplotlib. Check, for example the [matplotlib gallery](https://matplotlib.org/stable/gallery/index.html) for example plots. Here is a simple usage of matplotlib to draw a simple plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "09231cae-e75e-463d-8b9b-b2c873d482d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117d65210>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Below, we will use matplotlib, so we need to import it here.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x=[1,2,3,4,5,6,7,8,9,10]\n",
    "y=[0,4,0,3,3,0,3,4,5,2]\n",
    "\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a417b0c-a52b-4a87-888e-322150bade72",
   "metadata": {},
   "source": [
    "To start with, there are a few simple things you can do to improve your plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "822633f1-a12b-4d5e-a577-937111d10914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y (unit 2)')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Below, we will use matplotlib, so we need to import it here.\n",
    "\n",
    "x=[1,2,3,4,5,6,7,8,9,10]\n",
    "y1=[0,4,0,3,3,0,3,4,5,2]\n",
    "plt.plot(x, y1, \"o-\", label=\"y1\")\n",
    "plt.plot(x, x, label=\"x\")\n",
    "y2=[3,2,4,4,2,4,4,2,4,2]\n",
    "plt.plot(x, y2, \"o:\", label=\"y2\")\n",
    "plt.legend()\n",
    "plt.xlabel('x (unit 1)')\n",
    "plt.ylabel('y (unit 2)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb1996b-1984-4b72-80a4-5d51f2c53dae",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Example: compute the Fibonacci sequence using *recursion*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf12416-91df-412f-948c-b65fc37dc0c1",
   "metadata": {
    "tags": []
   },
   "source": [
    "1, 1, 2, 3, 5, 8, 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2ea9d4a7-57e1-4a48-8314-8271e04baa35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 2]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fib(n):\n",
    "    \"\"\"Return the Fibonacci sequence up to position n.\n",
    "    \n",
    "    n is an integer\"\"\"\n",
    "    # Check that our assumptions are true\n",
    "    assert type(n)==int\n",
    "    assert n>0\n",
    "    \n",
    "    # special cases for short lists\n",
    "    if n == 1:\n",
    "        return [1]\n",
    "    if n == 2:\n",
    "        return [1,1]\n",
    "    \n",
    "    seq = fib(n-1)\n",
    "    a = seq[-2]\n",
    "    b = seq[-1]\n",
    "    seq.append( a+b )\n",
    "    return seq\n",
    "\n",
    "fib(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "884683c3-fc5b-4a7e-b9ea-3c5cf44d7e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 2, 3]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fib(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5c028087-c2d7-4db5-883d-28f8a7bedee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fib(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "073bfda5-113a-4d20-9862-97c81f8b0854",
   "metadata": {},
   "source": [
    "## More strings\n",
    "\n",
    "[`str`](https://docs.python.org/3/library/stdtypes.html#text-sequence-type-str)\n",
    "\n",
    "Useful function for strings:\n",
    "\n",
    "- `len`\n",
    "\n",
    "Useful methods:\n",
    "\n",
    "- `strip`\n",
    "- `split`\n",
    "- `startswith`\n",
    "- `endswith`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2e2a51f4-ba55-4da6-8c95-11d5d3b1c4ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"my string\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "51b609ce-a438-41dc-a708-3c29e616bd37",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'my string'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"   my string    \".strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "006f32e1-db2a-4358-a7e4-f78d23f1a382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"   my string    \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4c8ae22e-f44b-4465-a908-d116347a8343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"   my string    \".strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "014ef6e5-1a9c-42fc-a143-840fe2a0a992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'my string'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=\"   my string    \"\n",
    "a.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "967cb647-c24a-462c-88b9-a7b018ec448e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'   my string    '"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6993859c-1479-4fe8-8402-20b1281e077c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'def']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"a,b,c,def\".split(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "71684557-3b94-46e1-a0da-edbaf5752204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"hello world\".startswith(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "323f78f6-cad4-4522-a291-c3faa0d92edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"hello world\".endswith(\"world\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a9120ae-28a2-4d0b-8f90-7238f8dea8a2",
   "metadata": {},
   "source": [
    "## Dictionaries - Python's `dict` type\n",
    "\n",
    "`dict` construction is with either `{}` or `dict()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "13a7d7fc-704e-4ea6-b17e-353ac1b8ed5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = {'key1': 'value1',\n",
    "     'key2': 'value2',\n",
    "    'key3': 'value3',\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a5aa8b60-b195-400c-9cdd-99608c9b5b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6ee1492a-acc6-41a1-9751-66319d1585f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value1'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['key1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5abd5123-ff6c-468b-87c2-5d1a88749b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "key = \"key3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f48ab396-0797-484e-b812-f8ea25ec411a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value3'"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9d620659-6db4-4c50-b573-a6c0d9f4c94d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'key1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[68], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x[\u001b[43mkey1\u001b[49m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'key1' is not defined"
     ]
    }
   ],
   "source": [
    "x[key1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3025c22b-fb7c-4964-a0a0-f1d2a68ac910",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dict(   (('key1', 'value1'), ['key2', 'value2'], ('key3', 'value3'))    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3aee4625-a694-48ec-9627-89de9ad343df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b95d11fc-96c9-42fb-9ae5-79470f1d5a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff2b811-cfab-484c-9d12-35ae7308c42a",
   "metadata": {},
   "source": [
    "Keys in a `dict` can be any value that is *hashable*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "98c0c33d-c4cf-4912-87f7-37ff05f90217",
   "metadata": {},
   "outputs": [],
   "source": [
    "x={1:'value1', 2:'value2'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f2b9029e-1ba7-43fe-8447-39082e0972dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value1'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "34fc4694-1e93-4bb7-bc45-a9bfeb58cb9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(1, 2, 3): '456'}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x={(1,2,3): \"456\"}\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7130ab9e-83d7-4c88-a29b-d51db4090fc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'456'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[(1,2,3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "83a55976-33e5-4496-a069-d50d9b9079e3",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[76], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x\u001b[38;5;241m=\u001b[39m{[\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m]: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m456\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m      2\u001b[0m x\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "x={[1,2,3]: \"456\"}\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e6081d5f-f775-4cb1-a126-c1517a712f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = {'key1':1, 'key2':2, 'key3':123456, 'key4': [1,2,3], 'key5': {}, 1234: 4321, (1,2,3): '9845712345'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d453c135-5195-4fe3-8366-4799c0ea35e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 1,\n",
       " 'key2': 2,\n",
       " 'key3': 123456,\n",
       " 'key4': [1, 2, 3],\n",
       " 'key5': {},\n",
       " 1234: 4321,\n",
       " (1, 2, 3): '9845712345'}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "378490b3-1a1c-40eb-9fdd-49f373cfd311",
   "metadata": {},
   "source": [
    "Just like we can iterate over items in a list, we can iterate over the keys in a dict:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "4dc3830e-d4ca-41e8-8e7d-98cb583e1f61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key1\n",
      "key2\n",
      "key3\n",
      "key4\n",
      "key5\n",
      "1234\n",
      "(1, 2, 3)\n"
     ]
    }
   ],
   "source": [
    "for key in x:\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "19560e57-1815-48a8-95e8-f640d151e12b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key: key1, value: 1\n",
      "key: key2, value: 2\n",
      "key: key3, value: 123456\n",
      "key: key4, value: [1, 2, 3]\n",
      "key: key5, value: {}\n",
      "key: 1234, value: 4321\n",
      "key: (1, 2, 3), value: 9845712345\n"
     ]
    }
   ],
   "source": [
    "for key in x:\n",
    "    value = x[key]\n",
    "    print(f\"key: {key}, value: {value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "83e6c07c-dec3-4c86-9c0e-11954dce7b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['key5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "84b5bc6d-4a1f-4f64-a1f6-09ef906f838a",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'key does not exist'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[82], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mkey does not exist\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'key does not exist'"
     ]
    }
   ],
   "source": [
    "x['key does not exist']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "937a9759-bccb-4edc-85d1-4007a2e6bf07",
   "metadata": {},
   "outputs": [],
   "source": [
    "x['my new key'] = 9843059"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "71d14521-f025-440d-80e4-e2ac8f4f30d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 1,\n",
       " 'key2': 2,\n",
       " 'key3': 123456,\n",
       " 'key4': [1, 2, 3],\n",
       " 'key5': {},\n",
       " 1234: 4321,\n",
       " (1, 2, 3): '9845712345',\n",
       " 'my new key': 9843059}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "27f83073-2f88-4d7b-8ba8-5fc124cc78e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x['key5']['hello'] = 'world'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9305e9cf-b800-436f-8261-d0264c965e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 1,\n",
       " 'key2': 2,\n",
       " 'key3': 123456,\n",
       " 'key4': [1, 2, 3],\n",
       " 'key5': {'hello': 'world'},\n",
       " 1234: 4321,\n",
       " (1, 2, 3): '9845712345',\n",
       " 'my new key': 9843059}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f9b2afb6-9aba-41ed-8894-737eca4da4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = x['key5']\n",
    "tmp['hello'] = 'world 2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3933cbbd-fa69-4729-8a18-0a62d40a2d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 1,\n",
       " 'key2': 2,\n",
       " 'key3': 123456,\n",
       " 'key4': [1, 2, 3],\n",
       " 'key5': {'hello': 'world 2'},\n",
       " 1234: 4321,\n",
       " (1, 2, 3): '9845712345',\n",
       " 'my new key': 9843059}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7216abd8-d3af-4b2e-bac4-712c34cce195",
   "metadata": {},
   "outputs": [],
   "source": [
    "x['key4'].append( 4 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "746ed972-e427-4fa1-9fcf-99def813343c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 1,\n",
       " 'key2': 2,\n",
       " 'key3': 123456,\n",
       " 'key4': [1, 2, 3, 4],\n",
       " 'key5': {'hello': 'world 2'},\n",
       " 1234: 4321,\n",
       " (1, 2, 3): '9845712345',\n",
       " 'my new key': 9843059}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "de23284e-f7ce-442d-8e17-e9b2346256b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'key1' in x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3f86ace1-eb0c-4e9d-83d3-03e7b0610479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 in x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f1cc91eb-e228-48de-b797-ed1efa675d51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1234 in x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a2d6058-e5db-478c-9b18-ed4380a0d943",
   "metadata": {},
   "source": [
    "## More about functions: keyword arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "01cd5398-943c-49ee-a0bb-95f48dd52bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_function(x, z=1):\n",
    "    return x+z*z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b3419f92-8017-4656-b4cc-c9b29102e46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_function(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3a7c0dc4-5af9-493f-a7d1-b0f97d0e72fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_function(9,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c76e2e4c-9005-4cee-a27b-2e9decf954c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_function(9,z=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "debac7a1-0865-4c35-878a-e40626e7f485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_function(x=9,z=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f952135a-9211-484d-8159-0e8971cf3b23",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "my_function() missing 1 required positional argument: 'x'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[100], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmy_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: my_function() missing 1 required positional argument: 'x'"
     ]
    }
   ],
   "source": [
    "my_function(z=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4d5d297c-711b-4d54-bae4-923d95aa18f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_function(z=11,x=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "cf7db77e-fd81-4035-8bd8-66219ed478d9",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (690924689.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[102], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    my_function(z=11,9)\u001b[0m\n\u001b[0m                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "my_function(z=11,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "21f8f437-b093-47f0-a00a-e80fda8ade30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_function2(x, y, z=1, qq=0):\n",
    "    return x+z+qq+y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "fa8fe1f7-22aa-46f7-8f6a-4de9b354f7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_function2(0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d019400e-2aa2-4b42-85c6-27dea98ae5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-30"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_function2(0,1,qq=-32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6593546-0c45-4708-918a-6c215a1fb3a2",
   "metadata": {},
   "source": [
    "## The `+` operator on various data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3d6f8126-f40b-4733-9fe1-fd86af8a0d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7ea7cec7-9343-4f9a-b615-2755747ed983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 2.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "24a67ab4-d73f-48d0-be71-2eff2964ac48",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"int\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[108], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
     ]
    }
   ],
   "source": [
    "\"1\"+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f2ac0055-6908-43f4-b8c4-e749191641af",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[109], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
     ]
    }
   ],
   "source": [
    "1+\"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f37d2ace-962a-4c02-90f2-52bffd60592d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'11'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"1\"+\"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "81dc1b8a-0b49-47af-afb0-b5681dd9c045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1   x1   y'"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"1   x\" + \"1   y\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "bab16714-5a6b-4e4f-8ed4-8a16498923a6",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate list (not \"int\") to list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[112], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list"
     ]
    }
   ],
   "source": [
    "[1]+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "00d9ea96-be24-43a0-b2bd-6bcedf2c05ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1] + [1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "3bc11485-00de-40c3-8af6-3e063d2de489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=[1]\n",
    "y=[1]\n",
    "z=x+y\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5a100456-56a3-4134-ab9f-6017ec27f7b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list.__add__([1], [1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9533ce06-1ee9-4c98-9d7b-188774fb2c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=[1]\n",
    "x.append(1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d524a21a-7538-45be-8f92-7b7299bfd2cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int.__add__(1, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "3f8b6fbe-f757-490d-8689-93adf78ee211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e52366a-b96e-4602-8f50-0e2b1932adc2",
   "metadata": {},
   "source": [
    "Note: \"joining\" or \"combining\" one sequence to another is called *concatenating* the sequences. It works with lists of any length:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "568bdadd-2505-4972-9955-968fd8b959dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1,2,3] + [4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "5863946d-499d-406c-ac15-6e9d627d5d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1,2,3] + []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "91fcf9e5-a3a6-413f-a5aa-990ab97fd9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[] + [1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b6fdaf75-7185-4b9f-9c5d-178242b662e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1,) + (1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "cc30aba1-031e-4bec-9af0-910df0e8c74d",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate tuple (not \"int\") to tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[123], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate tuple (not \"int\") to tuple"
     ]
    }
   ],
   "source": [
    "(1,) + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bac25ee-d7ee-4501-bc94-9a372f889a19",
   "metadata": {},
   "source": [
    "## The `*` operator on various data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c99b7fb4-8d1a-464a-b2b6-b71c78053a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "14bc9a15-79c8-4840-a623-ff4267b874bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1xz1xz1xz1xz1xz'"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"1xz\"*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "b1ff7075-8a59-490a-9047-c2283a6f648e",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[126], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1xz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblah\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n",
      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'str'"
     ]
    }
   ],
   "source": [
    "\"1xz\"*\"blah\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "342b200b-8dae-4793-9140-597b88f142f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1,2,3]*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "6a5d7d47-2c74-4c88-87e0-cb9d18f70ea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 * [1,2,3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e4e558-5d84-44df-94d7-02d638a88ed5",
   "metadata": {},
   "source": [
    "## Special method: `object.__add__(other)`\n",
    "\n",
    "Many of the bits of Python we have already been using are defined as \"special methods\". The names of these methods start and end with a double underscore `__`. They are not usually called directly, but rather Python calls these methods \"secretly\" to acheive some task. As we saw above, the \"add\" special method is implemended with `__add__`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "d201089e-a0bc-44ae-b786-ca6c5b372c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "six = 6\n",
    "six.__add__(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "bd47841c-f7ea-4d41-bf8d-702c81677ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int.__add__(6,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f658de87-ace5-4863-b401-8c77e953af67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "six+4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7adb09-9380-47f3-8640-a839355322d0",
   "metadata": {},
   "source": [
    "## Special method: `object.__getitem__(index)`\n",
    "\n",
    "The special method `object.__getitem__(index)` is how python implements `object[index]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "1ba584d9-4454-4f92-8161-91db385765e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x={0:1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "48747698-be87-43c4-9b62-c4f2c01d3358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 1}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "8b329eca-9cc2-49b5-9f38-984e2a5d6082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "7d8c44c3-83a8-44fb-a2aa-662cec019329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.__getitem__(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "843c9276-15fa-4c37-8957-e65095bfd1ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "x={1:\"value1\",2:43}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ad181878-37d7-453b-9d82-0e0e87fa262e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value1'"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "078a64da-72e6-482a-bd10-6214b13b0259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value1'"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.__getitem__(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7700f265-438e-47f0-8374-bfaa04c37839",
   "metadata": {},
   "source": [
    "## Special method: `sequence.__len__()`\n",
    "\n",
    "Another special method is `__len__`, which returns the length of a sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "3f43db53-58aa-4fb7-844a-2a887f573a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'value1', 2: 43}"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "cbc1f087-1bd2-45c8-9769-b634ed0bf454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "340077e2-4976-4c35-ae40-875fc852bd7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.__len__()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e6adc3-9886-46b5-9648-7ce6cc848e5c",
   "metadata": {},
   "source": [
    "## Special methods: `object.__str__()` (and `object.__repr__()`)\n",
    "\n",
    "Another special method is `__str__`, which returns a string representation of the object. (`__repr__` does something very similar but can often be used to \"reproduce\" the original thing and is hence a little more exact if less \"nice\" or \"pretty\".)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "9af0f5db-21ac-4034-95ce-0f87d4f707fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4'"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "2a1fc7fb-d30b-4777-868f-6048d35a481c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4'"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 0.4\n",
    "x.__str__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "39c639fd-ef4f-4dee-9d0f-e7c1afa2e561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"{1: 'value1', 2: 43}\""
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x={1:\"value1\",2:43}\n",
    "x.__str__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "9378b32d-b53d-400a-b961-e71e856a190d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 'value1', 2: 43}\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "059a009a-b3f0-420e-b5f4-b8c38f0f1baa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "print(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "15846c60-857d-4d5c-a8af-359165665d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"hello\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "80199a3b-8e26-4ca2-b172-6acfb4c2cd58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"my value is: {1: 'value1', 2: 43}\""
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"my value is: {x}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "3d68b71b-21dd-4190-ba3e-266003959887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one = 1\n",
    "one.__str__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "e7a3c69e-437f-47dd-ac3c-e831af5a9efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'my value is: 1'"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"my value is: {1}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "742638d8-641e-4d44-9633-9984bb4032bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.30000000000000004'"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repr(0.1 + 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "bc52ee21-d3f2-44e7-a526-f8be942d2749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30000000000000004"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.1 + 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b701ab6-6fbb-4d03-b598-92010251ca94",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "49372fac-b68d-4937-8836-e143fbf35cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"{1: 'value1', 2: 43}\""
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.__repr__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "dc6ed135-8ee7-4aad-8e4b-331c042e16a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"'hello'\""
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"hello\".__repr__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "49c71868-c7b0-4ad3-bc95-0956dcce9883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"hello\".__str__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "8879819f-72ac-4c78-af40-a11c1e0466c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "print(\"hello\".__str__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ef18f2c3-690e-4633-a3ae-198800f37408",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'hello'\n"
     ]
    }
   ],
   "source": [
    "print(\"hello\".__repr__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "9f22f4d4-8ef1-4edb-9a78-0adff34c77d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<built-in function print>'"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print.__str__()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "981f1a73-7c79-4a0e-b91e-63a3d348a503",
   "metadata": {},
   "source": [
    "# Abstract *interfaces* in python\n",
    "\n",
    "`for` loops iterate over \"iterables\". You can construct a `list` (or a `dict`) from iterables.\n",
    "\n",
    "Functions and methods are \"callable\".\n",
    "\n",
    "Getting items with square brackets (e.g. `x[0]`) works by calling the `__getitem__` method (so, `x.__getitem__(0)`). Any type can define how this works for that type."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa6d28b-16cb-4d24-8a94-ec39f011d192",
   "metadata": {},
   "source": [
    "## More on iterators\n",
    "\n",
    "There are a couple of very handy functions which take an iterable and return a new iterator:\n",
    "\n",
    "- `enumerate(items)` - returns iterator with index of items. Each iteration produces a tuple with `(index, item)`.\n",
    "- `zip(a_items, b_items)` - returns iterator combining two other iterators. Each iteration produces a tuple with `(a_item, b_item)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "6efaf701-1c93-4029-9c66-26f4154a42ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: abc\n",
      "1: def\n",
      "2: ghi\n"
     ]
    }
   ],
   "source": [
    "my_list = ['abc', 'def', 'ghi']\n",
    "my_iterator = enumerate(my_list)\n",
    "for x in my_iterator:\n",
    "    idx, item = x\n",
    "    print(f\"{idx}: {item}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b727d87-2494-4834-8031-ef4c8cb936c4",
   "metadata": {},
   "source": [
    "Usually, the temporary iterator would be implicit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "d85af85e-6cd1-4004-9b25-e45e66e16e74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: abc\n",
      "1: def\n",
      "2: ghi\n"
     ]
    }
   ],
   "source": [
    "my_list = ['abc', 'def', 'ghi']\n",
    "for x in enumerate(my_list):\n",
    "    idx, item = x\n",
    "    print(f\"{idx}: {item}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ba404f2-d30f-44ed-b6ce-e174153e1340",
   "metadata": {},
   "source": [
    "We can directly assign the tuple to two variables for further elimination of temporary variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "d0bf3490-b600-4d44-b6c3-ff298237c3a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: abc\n",
      "1: def\n",
      "2: ghi\n"
     ]
    }
   ],
   "source": [
    "my_list = ['abc', 'def', 'ghi']\n",
    "for idx, item in enumerate(my_list):\n",
    "    print(f\"{idx}: {item}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01eaf24f-4556-4c2d-a980-9523557cec4a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "61709401-22b2-4419-b36f-e9b79e5909f4",
   "metadata": {},
   "source": [
    "Now, for `zip`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "3506da24-eb14-4e94-951e-70b599388fb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abc red\n",
      "def green\n",
      "ghi blue\n"
     ]
    }
   ],
   "source": [
    "my_list = ['abc', 'def', 'ghi']\n",
    "list2 = ['red', 'green', 'blue']\n",
    "my_iterator = zip(my_list, list2)\n",
    "for x in my_iterator:\n",
    "    (item, color) = x\n",
    "    print(f\"{item} {color}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "fce72964-cc76-44b6-95f3-732c07f032c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abc red\n",
      "def green\n",
      "ghi blue\n"
     ]
    }
   ],
   "source": [
    "my_list = ['abc', 'def', 'ghi']\n",
    "for (item, color) in zip(my_list, ['red', 'green', 'blue']):\n",
    "    print(f\"{item} {color}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "48c0294b-0d17-464f-bd2c-c4c8000f277f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abc 3\n",
      "def 4\n",
      "ghi 5\n"
     ]
    }
   ],
   "source": [
    "my_list = ['abc', 'def', 'ghi']\n",
    "for item, number in zip(my_list, range(3,6)):\n",
    "    print(f\"{item} {number}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3610ab7-a3fb-49b9-b147-4ae6570f2ed9",
   "metadata": {},
   "source": [
    "# Data Frames\n",
    "\n",
    "We are going to look at data in *tables* where each *row* of the table contains measurements or values about a single thing and each *column* is the measurement type. Such tables are very common in data science."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f6488f-694b-4ee6-8f28-f0f7b1ec691a",
   "metadata": {},
   "source": [
    "(Loading the iris data is hidden in this cell. You can ignore this.)\n",
    "<!--\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "df= pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
    "                 columns= iris['feature_names'] + ['target'])\n",
    "df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n",
    "df = df.drop('target',axis=1)\n",
    "def to_dict(df):\n",
    "    result = {}\n",
    "    for column_name in df.columns:\n",
    "        result[column_name] = []\n",
    "    for i,row in df.iterrows():\n",
    "        for column_name in df.columns:\n",
    "            result[column_name].append( row[column_name] )\n",
    "    return result\n",
    "iris_dict = to_dict(df)\n",
    "print(iris_dict)\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b60c8c3-2b6f-4ca4-b4b7-ec085fb1609b",
   "metadata": {},
   "source": [
    "Here is an example of the data we will be looking at. It is a subsampling of the very famous [Iris data set](https://en.wikipedia.org/wiki/Iris_flower_data_set).\n",
    "\n",
    "<table class=\"dataframe\" border=\"1\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: right;\">\n",
    "      <th></th>\n",
    "      <th>sepal length (cm)</th>\n",
    "      <th>sepal width (cm)</th>\n",
    "      <th>petal length (cm)</th>\n",
    "      <th>petal width (cm)</th>\n",
    "      <th>species</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <th>11</th>\n",
    "      <td>4.8</td>\n",
    "      <td>3.4</td>\n",
    "      <td>1.6</td>\n",
    "      <td>0.2</td>\n",
    "      <td>setosa</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>81</th>\n",
    "      <td>5.5</td>\n",
    "      <td>2.4</td>\n",
    "      <td>3.7</td>\n",
    "      <td>1.0</td>\n",
    "      <td>versicolor</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>97</th>\n",
    "      <td>6.2</td>\n",
    "      <td>2.9</td>\n",
    "      <td>4.3</td>\n",
    "      <td>1.3</td>\n",
    "      <td>versicolor</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>37</th>\n",
    "      <td>4.9</td>\n",
    "      <td>3.6</td>\n",
    "      <td>1.4</td>\n",
    "      <td>0.1</td>\n",
    "      <td>setosa</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>31</th>\n",
    "      <td>5.4</td>\n",
    "      <td>3.4</td>\n",
    "      <td>1.5</td>\n",
    "      <td>0.4</td>\n",
    "      <td>setosa</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>28</th>\n",
    "      <td>5.2</td>\n",
    "      <td>3.4</td>\n",
    "      <td>1.4</td>\n",
    "      <td>0.2</td>\n",
    "      <td>setosa</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>141</th>\n",
    "      <td>6.9</td>\n",
    "      <td>3.1</td>\n",
    "      <td>5.1</td>\n",
    "      <td>2.3</td>\n",
    "      <td>virginica</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>149</th>\n",
    "      <td>5.9</td>\n",
    "      <td>3.0</td>\n",
    "      <td>5.1</td>\n",
    "      <td>1.8</td>\n",
    "      <td>virginica</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf97605-aa5d-4f7a-8015-fd9e543c80c0",
   "metadata": {},
   "source": [
    "For now, the data are given as a `dict`. This `dict` is created in a special way, where each key is the column name and each value is a list of the entry for each row for that column. Later we will read this from a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "e212cde2-364f-4055-b5f3-9e58db575544",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_dataset = {'sepal length (cm)': [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, \n",
    "                                      4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, \n",
    "                                      4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0,\n",
    "                                      5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6,\n",
    "                                      5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2,\n",
    "                                      5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1,\n",
    "                                      6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0,\n",
    "                                      5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, \n",
    "                                      5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, \n",
    "                                      7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, \n",
    "                                      7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, \n",
    "                                      6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9], \n",
    "                'sepal width (cm)': [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0], 'petal length (cm)': [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1], 'petal width (cm)': [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8], 'species': ['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7ca47cc-1af9-485b-96f2-cd6bb54aad8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "378a0233-8278-41a7-b889-336a897cd7ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'petal width (cm)')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iris_dataset['sepal width (cm)'], iris_dataset['petal width (cm)'], 'o');\n",
    "plt.xlabel('sepal width (cm)')\n",
    "plt.ylabel('petal width (cm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "bc40c86c-9fb8-45f2-8159-bd3f3580d902",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm)\n",
      "sepal width (cm)\n",
      "petal length (cm)\n",
      "petal width (cm)\n",
      "species\n"
     ]
    }
   ],
   "source": [
    "for column_name in iris_dataset:\n",
    "    print(column_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4bc81fb-b355-441a-a95e-32c31ac4a08d",
   "metadata": {},
   "source": [
    "Let's double check that every column (the value of each key) has the same number of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "8eecef02-329d-42c8-a357-fc5735cd787e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'sepal length (cm)': 150 rows\n",
      "'sepal width (cm)': 150 rows\n",
      "'petal length (cm)': 150 rows\n",
      "'petal width (cm)': 150 rows\n",
      "'species': 150 rows\n"
     ]
    }
   ],
   "source": [
    "for column_name in iris_dataset:\n",
    "    column = iris_dataset[column_name]\n",
    "    print(\"'{}': {} rows\".format(column_name, len(column)))"
   ]
  }
 ],
 "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,
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