PythonWorkshop_solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Python for Data Science\n",
    "\n",
    "A quickstart Python workshop..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. What you will learn\n",
    "\n",
    "Our aim in this workshop is to make you familiar with the basics of Python, focusing on those aspects that will jump-start your ability to write data-science programs. \n",
    "\n",
    "In other words, we want to make you **dangerous enough** soon :-).\n",
    "\n",
    "Accordingly, we will cover fundamental python concepts including variables, lists, dictionaries, iteration, and functions; moving on to useful list like objects such as Pandas Series and numpy `ndarray`s, and their use in analyzing and making plots of data.\n",
    "\n",
    "Here is the outline:\n",
    "\n",
    "1. Getting Started\n",
    "2. Libraries and Functions\n",
    "3. Dictionaries\n",
    "4. Listiness\n",
    "5. Plotting with matplotlib\n",
    "6. Putting it all together\n",
    "\n",
    "We will end up with the code shown below, which leads to a plot of unemployment rates in multiple American states. This example exercises all basic python concepts for data science..if you understand it you are golden! \n",
    "\n",
    "Finally, a homework problem will help cement your understanding, and make you even more dangerous!\n",
    "\n",
    "Here is what the unemployment data looks like:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "```\n",
    "DATE,FLUR\n",
    "1976-01-01,9.7\n",
    "1976-02-01,9.7\n",
    "1976-03-01,9.6\n",
    "1976-04-01,9.5\n",
    "1976-05-01,9.3\n",
    "1976-06-01,9.2\n",
    "1976-07-01,9.1\n",
    "1976-08-01,9.1\n",
    "1976-09-01,9.1\n",
    "1976-10-01,9.1\n",
    "1976-11-01,9.1\n",
    "1976-12-01,9.1\n",
    "1977-01-01,9.0\n",
    "```\n",
    "\n",
    "...And here is the code..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# various imports of libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ask matplotlib to plot in notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# setting up plot sizes\n",
    "width_inches = 9\n",
    "subplot_height_inches = 2\n",
    "\n",
    "# where is our data?\n",
    "baseurl = \"https://raw.githubusercontent.com/univai-ghf/ghfmedia/main/\"\n",
    "\n",
    "# defining a function to load and clean unemployment data\n",
    "def get_unemployment_data(state_abbrev):\n",
    "    # fetch and read csv file into a dataframe\n",
    "    data = pd.read_csv(baseurl+\"data/\"+state_abbrev+\"UR.csv\")\n",
    "    # convert date to a python datetime object\n",
    "    data['DATE'] = pd.to_datetime(data['DATE'])\n",
    "    return data\n",
    "\n",
    "# creating a dictionary the hold the data, with\n",
    "# the lookup keys being the state abbreviations\n",
    "states = ['AL', 'TN', 'TX', 'NY', 'CA']\n",
    "state_data=dict()\n",
    "for abbrev in states:\n",
    "    state_data[abbrev] = get_unemployment_data(abbrev)\n",
    "    \n",
    "# plotting the data    \n",
    "\n",
    "# create as many subplots as we have states\n",
    "fig, ax = plt.subplots(nrows=len(states), \n",
    "                       figsize = (width_inches, subplot_height_inches*len(states)))\n",
    "\n",
    "counter = 0\n",
    "for state_abbrev in states:\n",
    "    # get daraframe from dictionary\n",
    "    data = state_data[state_abbrev]\n",
    "    # plot the data\n",
    "    ax[counter].plot(data['DATE'], data[state_abbrev+'UR'])\n",
    "    # label which state it is\n",
    "    ax[counter].set_ylabel(state_abbrev)\n",
    "    counter = counter + 1\n",
    "# Year label only below last subplot\n",
    "ax[4].set_xlabel(\"Year\")\n",
    "\n",
    "# title only on first subplot\n",
    "ax[0].set_title(\"Percentage Un-employment\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4A7IZqAlgwIH"
   },
   "source": [
    "## 1. Getting Started\n",
    "\n",
    "Lets start at the very beginning. You can use Python as a calculator. Lets do some arithmetic in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4A7IZqAlgwIH"
   },
   "source": [
    "### Arithmetic Operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F0rZHyfEgc20"
   },
   "source": [
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/calculator.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "siqC1BNaggkD",
    "outputId": "4d2b00bf-b5c1-4160-a7e0-10deced5bd2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WWns1uqOhH-L",
    "outputId": "0fc197c2-e463-4d11-f213-c68f8a323c7f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**10 # 2 to the power of 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_9OTo_JWhKZx",
    "outputId": "9776ff9d-d782-4733-c6ec-84a687532b7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yyOpHZluhLrN",
    "outputId": "0077e001-d8d0-4388-8e8c-4c9b98b1c3c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5//2 # integer division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 % 2 # get the remainder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hIubnDszhNCF"
   },
   "source": [
    "### Variables\n",
    "\n",
    "Variables are labels for values\n",
    "\n",
    "- Start with a letter or underscore\n",
    "- Can contain only alpha-numeric characters and underscore\n",
    "- Are case sensitive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RvjT6_VchV9A",
    "outputId": "1d1d9075-a487-451a-cd4a-c62d5e072a35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Var = \"hello\" # CANT DO THIS 9variable = \"hello\"\n",
    "Var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "znklPOb_hxSJ"
   },
   "source": [
    "What happened above? In the computer's memory, a location was found, and then filled with the word \"hello\". Then a variable Var was created and was used to label this memory location.\n",
    "\n",
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/labelmem.jpg)\n",
    "\n",
    "A variable is literally a label. You can think of it as a post-it, or sticky note, or a pointer. Do not think of it as a box in which a value is stored.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "znklPOb_hxSJ"
   },
   "source": [
    "### Types\n",
    "\n",
    "Python is a **typed** language. That is, values in Python have types. The most general type is `Object`. But there are more specific ones, and you can define your own.\n",
    "\n",
    "Variables can point to values of different types. See below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "40Uwt7THhsy1",
    "outputId": "d24f0df9-abde-415d-bfb4-1992f2bf6e64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INTEGER 7 <class 'int'>\n",
      "FLOAT 7.01 <class 'float'>\n",
      "STRING Hello World! <class 'str'>\n",
      "BOOLEAN True <class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "var_integer = 7\n",
    "print (\"INTEGER\", var_integer, type(var_integer))\n",
    "var_float = 7.01\n",
    "print (\"FLOAT\", var_float, type(var_float))\n",
    "var_string = \"Hello World!\"\n",
    "print (\"STRING\", var_string, type(var_string))\n",
    "var_boolean = True\n",
    "print (\"BOOLEAN\", var_boolean, type(var_boolean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python, as you might expect, also has the concept of a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist = [1, 2, 3, 4, 5, 6, 7, 8]\n",
    "type(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 <class 'int'>\n",
      "2 <class 'int'>\n",
      "3 <class 'int'>\n",
      "4 <class 'int'>\n",
      "5 <class 'int'>\n",
      "6 <class 'int'>\n",
      "7 <class 'int'>\n",
      "8 <class 'int'>\n"
     ]
    }
   ],
   "source": [
    "for item in mylist:\n",
    "    print(item, type(item))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the indentation: python dispenses with brackets and uses a colon and indentation (here 4 spaces) to denote that the `print` statement runs in the context of our list.\n",
    "\n",
    "You can also get the length of the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mylist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Back to our example\n",
    "\n",
    "Now we can see that the code right at the beginning of our  example set us up with some integer variables that specified the size of some plots we want to create:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "width_inches = 9\n",
    "subplot_height_inches = 2\n",
    "baseurl = \"https://raw.githubusercontent.com/univai-ghf/ghfmedia/main/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `baseurl` simply sets up a path to our data files. We use remote data files (which are supported by Pandas) here since Google Colab does not have good support for files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nrjc0oIHizIv"
   },
   "source": [
    "### Comparisons\n",
    "\n",
    "Python has comparison operators, which can be used to make decisions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/condslide.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H7alzW88igui",
    "outputId": "f2acfd80-3053-4c2a-820b-cb04377821c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "a = \"hgi\"\n",
    "b = \"hello\"\n",
    "c = \"hi\"\n",
    "d = \"hello\"\n",
    "print (a==c)\n",
    "print (b==d)\n",
    "var1 = 5\n",
    "var2 = 3\n",
    "print (var1 < var2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IrYxzwOJjDAU"
   },
   "source": [
    "The first comparison compares the contents of the memory for a and c and finds that both are different, giving us False. Conversely, the second comparison gives us True. We can utilize such comparisons in \"decision statements\". The third is a numerical comparison. The fact that these comparisons give us a boolean value can be used in the decision-making:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kS9VtABSjUiE",
    "outputId": "1f7f74e8-ce71-4b12-aad0-3df2e616d2ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First variable is lesser than the second variable\n"
     ]
    }
   ],
   "source": [
    "var1 = 5\n",
    "var2 = 10\n",
    "\n",
    "if (var1 == var2):\n",
    "    print(\"The values are equal\")\n",
    "elif (var1 < var2):\n",
    "    print(\"First variable is lesser than the second variable\")  \n",
    "else:\n",
    "    print(\"Second variable is lesser than the first variable\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8rvW-j1QjYtU"
   },
   "source": [
    "Notice again how python dispenses with brackets, replacing them by a colon and an indented next line. The indentation tells us that the code below runs when the condition holds. Python uses this colon-indentation for many things, such as in for loops for iteration, for conditionals, for function and class definition, etc.\n",
    "\n",
    "Indeed you can nest conditionals inside iteration, with this colon-indentation syntax, to make very readable and usable code!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qEPSTJTqnX90",
    "outputId": "25f213e2-2e1a-40a7-d639-20070c9f6b09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "mylist = [1, 2, 3, 4, 5, 6, 7, 8]\n",
    "for ele in mylist:\n",
    "    if ele % 2 == 0: #even numbers only\n",
    "        print(ele)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A: EXERCISES\n",
    "\n",
    "\n",
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "927rJoinjjun"
   },
   "source": [
    "## 2. Libraries and Functions\n",
    "\n",
    "If all python did was to provide a calculator with variables that would not be very ineresting. We want to have access to pre-made **libraries** of functionality!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "927rJoinjjun"
   },
   "source": [
    "### Libraries\n",
    "\n",
    "At the beginning of this notebook we defined some strange looking symbols:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are **imports** of **libraries** in the python ecosystem. They are also called **modules**. Having to write all of our code ourselves is no fun! Whats the whole point if we cant use code others have written for us?\n",
    "\n",
    "These imports allow us to use existing libraries. More precisely they allow us to use functions defined by others in these libraries. \n",
    "\n",
    "For example, we can say:\n",
    "\n",
    "```python\n",
    "import pandas\n",
    "df = pandas.read_csv(\"somefile.csv\") # Use a function from the library\n",
    "```\n",
    "\n",
    "or use `as` to provide a short form for the library name:\n",
    "```python\n",
    "import pandas as pd\n",
    "df = pd.read_csv(\"somefile.csv\")\n",
    "```\n",
    "\n",
    "But what IS a function, exactly?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine with `if` to filter out some elements:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "927rJoinjjun"
   },
   "source": [
    "### Functions\n",
    "\n",
    "A function is an **encapsulated** set of statements that take input, do some specific computation and produce output.\n",
    "\n",
    "There are many ways to define functions:\n",
    "\n",
    "- Functions can be built-in to python\n",
    "- Functions can imported from an existing Python Library, including libraries you have installed\n",
    "- A function can be user defined\n",
    "- A function can be anonymous\n",
    "- functions can belong to objects. These functions are called **methods**.\n",
    "\n",
    "Properties of functions:\n",
    "\n",
    "- A function can be called from other functions\n",
    "- Can return data, or even other functions\n",
    "\n",
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/runfuncs.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zKtATFxFkJ4m",
    "outputId": "a0b84532-1f20-4f78-a419-1c786ad95183"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Built-in functions\n",
    "\n",
    "var1 = -15\n",
    "abs(var1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B92MLiNpkSou"
   },
   "source": [
    "Here are the two different ways of importing a function from a module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UgFRAMpmkWPh",
    "outputId": "577bf8c0-ab3e-45fb-aee5-2cebd0813d40"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.cpu_count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The special `from` syntax allows us to import just one function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x6RN4wQBkQIB",
    "outputId": "4915c52c-e590-4ca8-ef4b-cf1361277e75"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "sqrt(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions are not the only thing we can import: pre-defined variables representing constants or other objects (like a database connection) may be imported as well.\n",
    "\n",
    "```python\n",
    "import math\n",
    "print(math.pi)\n",
    "```\n",
    "\n",
    "One can even import a single variable from a library, using the `from` syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us **define our own function**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314.1592653589793"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def circle_area(radius):\n",
    "    area = pi*radius*radius\n",
    "    return area\n",
    "circle_area(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the **indentation** of the function body, just as we did with conditional blocks...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can define **anonymous functions** and assign them to variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "hypot = lambda x, y: sqrt(x*x + y*y) # imported from math\n",
    "hypot(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In data science these are often used to define one-line math functions..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Back to our example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point the following code should broadly make sense to you. We import the libraries we need, set up some variables, and define a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "width_inches = 9\n",
    "subplot_height_inches = 2\n",
    "baseurl = \"https://raw.githubusercontent.com/univai-ghf/ghfmedia/main/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unemployment_data(state_abbrev):\n",
    "    data = pd.read_csv(baseurl+\"data/\"+state_abbrev+\"UR.csv\")\n",
    "    data['DATE'] = pd.to_datetime(data['DATE'])\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is going on inside the function? It loads some data from a csv, does something with a date and returns some data...let us break it down.\n",
    "\n",
    "Let us focus on the string inside the `pd.read_csv` call and what is inside it first:\n",
    "\n",
    "`state_abbrev` is provided as an argument to `get_unemployment_data` and we use it in the string. Lets simulate this outside of the function to understand:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://raw.githubusercontent.com/univai-ghf/ghfmedia/main/data/CAUR.csv'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_abbrev = 'CA'\n",
    "baseurl+\"data/\"+state_abbrev+\"UR.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the addition operator on strings simply concatenates them together. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructors, Methods and Instance variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now to read in the data. The `pd.read_csv` function is a **constructor function** that creates a Pandas **dataframe object** from a CSV file. The file can be at a remote url. A dataframe object is like a spreadsheet in memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(baseurl+\"data/\"+state_abbrev+\"UR.csv\")\n",
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, various functions can be carried out on this dataframe. Functions that belong to objects are called **methods**. The `.head` method on dataframes shows us the first 5 rows of this spreadsheet, the column names, and row ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>CAUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1976-01-01</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1976-02-01</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1976-03-01</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976-04-01</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1976-05-01</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE  CAUR\n",
       "0  1976-01-01   9.2\n",
       "1  1976-02-01   9.2\n",
       "2  1976-03-01   9.1\n",
       "3  1976-04-01   9.1\n",
       "4  1976-05-01   9.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*lists* also have a list constructor and an append method to add things into them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.6]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alist = list() # or [], the square brackets invoke the list constructor\n",
    "alist.append(5.6)\n",
    "alist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objects can have **properties**, or **attributes**, also called **instance variables**, because they belong to an instance of the object. For example, if I want to know all the column types in a given dataframe object, I can use the `.dtypes` instance variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE     object\n",
       "CAUR    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is in the column? This is an interesting syntax, to be read as \"the `DATE` in the `data`\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1976-01-01\n",
       "1      1976-02-01\n",
       "2      1976-03-01\n",
       "3      1976-04-01\n",
       "4      1976-05-01\n",
       "          ...    \n",
       "545    2021-06-01\n",
       "546    2021-07-01\n",
       "547    2021-08-01\n",
       "548    2021-09-01\n",
       "549    2021-10-01\n",
       "Name: DATE, Length: 550, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['DATE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['DATE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its a Pandas Series: a series of dates to be precise, of length 550."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the `DATE` column has the generic `object` type. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE     object\n",
       "CAUR    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We really want a date. It turns out that a function defined in the pandas library can do this conversion for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE    datetime64[ns]\n",
       "CAUR           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['DATE'] = pd.to_datetime(data['DATE'])\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that I re-assigned the output of `pd.to_datetime` to `data['DATE']`. In other words I labeled a new piece of memory with the old post-it. What happened to the original piece of memory with the old series? Its been abandoned, and python will free its memory...this is called **garbage collection**.\n",
    "\n",
    "Reading in the data into a dataframe and then making a change to the `DATE` column is exactly what the function `get_unemployment_data` does:\n",
    "\n",
    "```python\n",
    "def get_unemployment_data(state_abbrev):\n",
    "    data = pd.read_csv(baseurl+\"data/\"+state_abbrev+\"UR.csv\")\n",
    "    data['DATE'] = pd.to_datetime(df['DATE'])\n",
    "    return data\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B. CODING BREAKOUT\n",
    "\n",
    "Here is a list of x-values, the deviations from the mean temperature of a mechanical device. The corresponding y-values are failures. Plotted, they look like this:\n",
    "\n",
    "![](images/failures.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "xvals = [-2.022945287808054, -1.8486091760122103, -1.8476481826866842, -1.7435854332654428, \n",
    "         -1.5282423673888716, -1.3945778557109074, -1.1403979924220742, -0.8009077275459544,\n",
    "         -0.7108560219444477, -0.5638678812593274, -0.43048994749879754, -0.4265240230310121,\n",
    "         -0.23456806947264375, -0.08933017942095312, 0.04091978204507629, 0.5542534388866378,\n",
    "         0.5558786314354511, 0.8006640938973202, 1.2144534202678483, 1.9118026057089639]\n",
    "y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Future you has run a logistic regression model and come up with the following parameters called slope and intercept:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intercept = (9.812037170985635, 5.175316482607568) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This syntax is called the destructuring of another kind of list, called a tuple.\n",
    "\n",
    "1. Implement an anonymous or other function to calculate the probability of failure $p(x)$:\n",
    "\n",
    "$$p(x) = \\frac{1}{1+e^{-(b + m x)}}$$\n",
    "\n",
    "where $m$ is the slope and $b$ is the intercept. Use the function you defined to create an array `probs` of the probability of failures at the 20 temperatures above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.2384487899544125e-07,\n",
       " 2.3448061801774165e-06,\n",
       " 2.367020582552928e-06,\n",
       " 6.571192923791013e-06,\n",
       " 5.4358541876421125e-05,\n",
       " 0.00020173914933983947,\n",
       " 0.002437575065306435,\n",
       " 0.06397080195941987,\n",
       " 0.14189620473359968,\n",
       " 0.41159487946671264,\n",
       " 0.7213831993772614,\n",
       " 0.7291366897897232,\n",
       " 0.9465322242527417,\n",
       " 0.9865972956193445,\n",
       " 0.9962296985932257,\n",
       " 0.9999754229236333,\n",
       " 0.9999758117230415,\n",
       " 0.9999978096835431,\n",
       " 0.9999999622235002,\n",
       " 0.9999999999596727]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "import math\n",
    "proba = lambda x: 1./(1 + math.exp(-(intercept + slope*x)))\n",
    "probs = list() # or just say []\n",
    "for ele in xvals:\n",
    "    probs.append(proba(ele))\n",
    "probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Use these probabilities to make classifications at each of these 20 x-values in a new list `classi`. A data point is classified as a failure if the probability of failure $p(x)$ is greater than or equal to the probability of not failing (which is $1 - p(x)$. Compare these to the $y$ values in the data set to report the fraction of correctly classified failures (an element in `classi` is equal to the corresponding element in $y$) from the model. (you will need to create a counter which you increment to track the total number of correctly classified data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "classi = []\n",
    "for p in probs:\n",
    "    if p >= 1-p:\n",
    "        classi.append(1)\n",
    "    else:\n",
    "        classi.append(0)\n",
    "accuracy_sum = 0\n",
    "for i in range(len(y)):\n",
    "    if y[i] == classi[i]:\n",
    "        accuracy_sum +=1\n",
    "accuracy = accuracy_sum/len(y)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2ScIB5ykqc5M"
   },
   "source": [
    "## 3. Dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This `data['DATE']` syntax says: look up whats in the dataframe `data` under the **key** `DATE`. Give us that column. The key is like a word in a dictionary: you look up a word and get its meaning...here you look up the key and get the data associated with it.\n",
    "\n",
    "Why is it like this?\n",
    "\n",
    "It is because python has a **data structure** called a **dictionary**. The creators of Pandas wanted a dataframe to feel like a dictionary...\n",
    "\n",
    "Let's learn a bit about dictionaries.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2ScIB5ykqc5M"
   },
   "source": [
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/dict.png)\n",
    "\n",
    "A dictionary is a \"bag\" of **value**s, each with its own label, called a **key**. \n",
    "\n",
    "A unique 'key' is associated with each 'value' . The 'key' can be any **immutable data type**: boolean, float, int, tuple, string (but it is often a string)\n",
    "\n",
    "Dictionaries themselves are \"Mutable\" (the values can be changed)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create a dictionary using the `{}` notation, which is an alias for calling the constructor `dict()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QPBdWy84qsFl",
    "outputId": "06fff31c-dfcc-46de-9401-94cab24e739e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "{'day': 5, 'venue': 'GJB', 'event': 'Python Carnival!'}\n"
     ]
    }
   ],
   "source": [
    "# Creating a dictionary:\n",
    "# 1. Using {}\n",
    "empty_dict = {} \n",
    "print (type(empty_dict))\n",
    "new_dict = { \"day\": 5, \"venue\": \"GJB\", \"event\": \"Python Carnival!\" }\n",
    "print(new_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "another_empty_dict = dict()\n",
    "another_empty_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `dict` constructor can take arguments to initialize the dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m2iT93clrDiM",
    "outputId": "d7d8713d-006b-4b85-ca5f-c9cb9927fe93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'wallet', 'material': 'leather'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. Using dict()\n",
    "purse = dict(type=\"wallet\", material=\"leather\")\n",
    "purse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GzH2VuEqry-o"
   },
   "source": [
    "Getting and Setting values from a dictionary is easy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 37
    },
    "id": "hUPBbJTgr2JY",
    "outputId": "768b237d-ce69-4290-b236-d4af69251d77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'wallet'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purse['type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IP3h7K8prF6T",
    "outputId": "2fc69c07-e80b-4760-a494-019f5f1a5f5b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'wallet', 'material': 'leather', 'make': 'Versace'}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purse['make'] = \"Versace\"\n",
    "purse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bdapLUbwrgmw"
   },
   "source": [
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/dictmethod.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C. EXERCISES\n",
    "\n",
    "\n",
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7mIkf6kRlvy5"
   },
   "source": [
    "## 4. Lists and Listiness.. or behaving like a list\n",
    "\n",
    "Python puts great stock in the idea of having protocols or mechanisms of behavior, and identifying cases in which this behavior is common.\n",
    "\n",
    "One of the most important ideas is that of things that behave like a list of items.\n",
    "\n",
    "These include lists, strings, dictionaries, pandas series, and even files! Many other data structures in Python are made to behave like lists as well, so that their content can be iterated through, in addition to their own native behavior.\n",
    "\n",
    "Here is an example, a **range** object, which is an un-materialized sequence..one that is not loaded into memory (why would you not materialize a sequence? ). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(1, 5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = range(1, 5)\n",
    "type(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for item in r:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7mIkf6kRlvy5"
   },
   "source": [
    "### Lists, again\n",
    "\n",
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/lists.png)\n",
    "\n",
    "Here is a list our initial code created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = ['AL', 'TN', 'TX', 'NY', 'CA']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have seen that you can create an empty list using the `list()` constructor, and add things to it using the `append` method. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the constructor can take arguments: a very useful one is the **range** object.. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sKftKsU5mKS8",
    "outputId": "42abc8ff-52b4-42d5-c1cf-5e8d1613ba64"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = list(range(1,5))\n",
    "lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MLZNK3_Rmao4"
   },
   "source": [
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/listops.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How would you access various elements of `lst` ? You can get the first and the last like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sKftKsU5mKS8",
    "outputId": "42abc8ff-52b4-42d5-c1cf-5e8d1613ba64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 4\n"
     ]
    }
   ],
   "source": [
    "print (lst[0],lst[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rememer Python is like buildings in India..you start at the ground or 0th floor: so `lst[0]` is the *first* item in the list. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or a **slice** of the list as so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "czQ1Vd07mhQr",
    "outputId": "a595ce2c-d2f0-4447-f256-1ff9fda91fa1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst[0:3] # from 0 upto 3, dont include whats at index 3. Can also say lst[:3], 0 is implied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tXi6tdKGmkNH",
    "outputId": "ac7b93e1-276e-4e54-b60f-fa15cd9caa1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst[-3:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do you know that something is in a list?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S3n0bTcUmt34",
    "outputId": "b06b407b-621c-4c15-9c0a-0a81c4627bf6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Membership (using 'in' operator)\n",
    "courses = ['PP', 'RD', 'JG']\n",
    "'PP' in courses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FLKWEot9nNFZ"
   },
   "source": [
    "### Comprehensions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QqUiaw7Xneav"
   },
   "source": [
    "There is a short-cut iteration syntax called a **list comprehension**, often used to construct new lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = [4, 7, 2, 6, 3, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uCDuB1PvncOP",
    "outputId": "7bad03d8-f06d-4a84-d5fc-c19aa5204e21"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 7, 2, 6, 3, 9]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_with_same_as_num = [e for e in num]\n",
    "list_with_same_as_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iFlgf3MmnmN-",
    "outputId": "7642798e-536a-4529-c432-34ccedb466c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16, 49, 4, 36, 9, 81]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squared_list = [e*e for e in num]\n",
    "squared_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qrTcWIwgnib-",
    "outputId": "a5275655-25f9-439f-dd33-b63e38dd359e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 2, 6]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_with_evens = [e for e in num if e % 2 == 0]\n",
    "list_with_evens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python has a *ternary* expression you can use in conjunction with a comprehension to bring in the power of *else* in a succint syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'good'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1\n",
    "b = \"good\" if a==1 else \"bad\"\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 2, 6, 5, 5]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[e if e % 2 == 0 else 5 for e in num ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Listiness\n",
    "\n",
    "How about other things that behave like lists?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dictionaries are listy, with the iteration going the keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'wallet', 'material': 'leather', 'make': 'Versace'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type : wallet\n",
      "material : leather\n",
      "make : Versace\n"
     ]
    }
   ],
   "source": [
    "for key in purse:\n",
    "    print(key,\":\",purse[key])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas Series are listy too: (Our series of dates in `data` is big so i print only the first 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1976-01-01 00:00:00\n",
      "1976-02-01 00:00:00\n",
      "1976-03-01 00:00:00\n",
      "1976-04-01 00:00:00\n",
      "1976-05-01 00:00:00\n",
      "1976-06-01 00:00:00\n",
      "1976-07-01 00:00:00\n",
      "1976-08-01 00:00:00\n",
      "1976-09-01 00:00:00\n",
      "1976-10-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "for date in data['DATE'][:10]:\n",
    "    print(date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This brings us to another listy construct, one that is critical in datascience..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy's `ndarray`s\n",
    "\n",
    "Pandas Series objects are actually are thin veneers around a different list type in Python, called the `ndarray`. Unlike regular Python lists, `ndarray`s cannot have heterogeneous elements: all elements must have the same type. This means that python can optimize how these arrays are stored and accessed, which leads to faster access.\n",
    "\n",
    "Look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.2,  9.2,  9.1,  9.1,  9. ,  9. ,  9. ,  9.1,  9.2,  9.3,  9.3,\n",
       "        9.3,  9.2,  9.1,  8.9,  8.7,  8.6,  8.4,  8.3,  8.1,  8. ,  7.9,\n",
       "        7.8,  7.7,  7.6,  7.5,  7.5,  7.4,  7.3,  7.2,  7.1,  7. ,  6.9,\n",
       "        6.8,  6.7,  6.6,  6.5,  6.4,  6.3,  6.2,  6.2,  6.1,  6.1,  6.2,\n",
       "        6.2,  6.2,  6.1,  6.1,  6.2,  6.3,  6.5,  6.7,  6.9,  7.1,  7.2,\n",
       "        7.3,  7.2,  7.2,  7.1,  7.1,  7.1,  7.1,  7.1,  7.1,  7. ,  7.1,\n",
       "        7.2,  7.3,  7.6,  7.9,  8.2,  8.5,  8.8,  9.1,  9.3,  9.6,  9.8,\n",
       "       10. , 10.2, 10.4, 10.6, 10.8, 11. , 11.1, 11.1, 11. , 10.8, 10.5,\n",
       "       10.3, 10. ,  9.7,  9.4,  9.2,  8.9,  8.6,  8.4,  8.3,  8.1,  8. ,\n",
       "        7.9,  7.8,  7.7,  7.7,  7.6,  7.6,  7.5,  7.5,  7.4,  7.3,  7.3,\n",
       "        7.3,  7.3,  7.3,  7.3,  7.3,  7.2,  7.1,  7. ,  6.9,  6.9,  6.9,\n",
       "        6.9,  6.9,  6.9,  6.9,  6.8,  6.8,  6.7,  6.7,  6.6,  6.6,  6.5,\n",
       "        6.4,  6.3,  6.2,  6. ,  5.9,  5.8,  5.7,  5.6,  5.6,  5.5,  5.5,\n",
       "        5.4,  5.4,  5.3,  5.3,  5.3,  5.3,  5.3,  5.3,  5.3,  5.3,  5.2,\n",
       "        5.2,  5.1,  5. ,  5. ,  5. ,  5. ,  5. ,  5.1,  5.1,  5.1,  5.2,\n",
       "        5.2,  5.2,  5.2,  5.2,  5.2,  5.2,  5.3,  5.4,  5.5,  5.7,  5.9,\n",
       "        6.1,  6.4,  6.7,  6.9,  7.1,  7.4,  7.6,  7.7,  7.8,  7.8,  7.9,\n",
       "        7.9,  8. ,  8.1,  8.2,  8.4,  8.6,  8.8,  8.9,  9.1,  9.2,  9.4,\n",
       "        9.5,  9.6,  9.7,  9.8,  9.8,  9.8,  9.8,  9.7,  9.7,  9.6,  9.6,\n",
       "        9.6,  9.5,  9.5,  9.4,  9.4,  9.3,  9.3,  9.3,  9.2,  9.1,  9. ,\n",
       "        8.9,  8.7,  8.6,  8.5,  8.4,  8.2,  8. ,  7.9,  7.8,  7.8,  7.8,\n",
       "        7.8,  7.9,  8. ,  8. ,  8. ,  7.9,  7.9,  7.9,  7.9,  7.8,  7.8,\n",
       "        7.7,  7.6,  7.5,  7.4,  7.2,  7.1,  7.1,  7. ,  7. ,  7. ,  6.9,\n",
       "        6.8,  6.7,  6.6,  6.5,  6.4,  6.3,  6.2,  6.2,  6.1,  6.1,  6.1,\n",
       "        6. ,  6. ,  6. ,  6. ,  5.9,  5.9,  6. ,  6. ,  6. ,  5.9,  5.8,\n",
       "        5.7,  5.6,  5.5,  5.5,  5.4,  5.3,  5.3,  5.2,  5.2,  5.1,  5.1,\n",
       "        5. ,  5. ,  5. ,  5. ,  5. ,  5. ,  5. ,  5. ,  5. ,  4.9,  4.8,\n",
       "        4.8,  4.8,  4.8,  4.8,  4.8,  4.9,  5. ,  5.1,  5.2,  5.4,  5.6,\n",
       "        5.9,  6.1,  6.3,  6.5,  6.6,  6.7,  6.7,  6.7,  6.8,  6.8,  6.8,\n",
       "        6.7,  6.8,  6.8,  6.8,  6.9,  6.9,  6.9,  6.9,  7. ,  7. ,  7. ,\n",
       "        7. ,  7. ,  6.9,  6.8,  6.7,  6.6,  6.6,  6.5,  6.5,  6.4,  6.4,\n",
       "        6.3,  6.2,  6.1,  6. ,  5.9,  5.9,  5.8,  5.8,  5.7,  5.6,  5.5,\n",
       "        5.4,  5.3,  5.3,  5.3,  5.2,  5.2,  5.2,  5.1,  5. ,  5. ,  4.9,\n",
       "        4.9,  4.9,  4.9,  4.9,  4.9,  4.9,  4.9,  4.9,  4.9,  4.9,  5. ,\n",
       "        5. ,  5.1,  5.1,  5.2,  5.3,  5.4,  5.5,  5.7,  5.7,  5.8,  5.9,\n",
       "        6. ,  6.2,  6.4,  6.7,  7. ,  7.3,  7.6,  8. ,  8.4,  8.8,  9.3,\n",
       "        9.9, 10.4, 10.8, 11.2, 11.4, 11.7, 11.8, 12. , 12.1, 12.3, 12.4,\n",
       "       12.5, 12.6, 12.6, 12.6, 12.5, 12.5, 12.4, 12.3, 12.4, 12.4, 12.4,\n",
       "       12.4, 12.4, 12.3, 12.2, 12.1, 12. , 12. , 12. , 12. , 11.9, 11.8,\n",
       "       11.6, 11.5, 11.3, 11.2, 11. , 11. , 10.9, 10.8, 10.7, 10.5, 10.3,\n",
       "       10.2, 10. ,  9.9,  9.8,  9.7,  9.5,  9.4,  9.3,  9.2,  9.1,  9. ,\n",
       "        8.9,  8.8,  8.6,  8.5,  8.4,  8.2,  8.1,  8. ,  7.8,  7.7,  7.6,\n",
       "        7.5,  7.4,  7.3,  7.2,  7.1,  6.9,  6.8,  6.7,  6.6,  6.5,  6.4,\n",
       "        6.3,  6.2,  6. ,  5.9,  5.8,  5.8,  5.7,  5.7,  5.6,  5.6,  5.5,\n",
       "        5.5,  5.5,  5.5,  5.5,  5.5,  5.5,  5.4,  5.4,  5.3,  5.2,  5.1,\n",
       "        5. ,  4.9,  4.8,  4.8,  4.7,  4.7,  4.6,  4.5,  4.5,  4.4,  4.4,\n",
       "        4.3,  4.3,  4.2,  4.2,  4.2,  4.2,  4.2,  4.3,  4.3,  4.3,  4.3,\n",
       "        4.3,  4.2,  4.1,  4.1,  4.1,  4.1,  4.1,  4.1,  4.1,  4.1,  4.2,\n",
       "        4.2,  4.3,  4.5, 16. , 15.6, 14.1, 13.2, 12.3, 10.6,  9.8,  8.7,\n",
       "        9.3,  9. ,  8.5,  8.3,  8. ,  7.7,  7.6,  7.6,  7.5,  7.5,  7.3])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caurvalues = data['CAUR'].values\n",
    "caurvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(caurvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caurvalues.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here all these floating point numbers are stored next to each other in memory.\n",
    "\n",
    "While you can iterate over `ndarray`s, you are advised not to. This is because the `numpy` library optimizes operations over entire arrays, the so called **vectorized** operations, to be very fast. It also re-purposes standard python syntax for this..\n",
    "\n",
    "Dont do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "squares = []\n",
    "for element in caurvalues:\n",
    "    squares.append(element*element)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But rather do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "squares = caurvalues*caurvalues "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this calculation the `*` operations work pairwise: each element of the `caurvalues` array is multiplied by itself to create the new array. Because there is no iteration and Python handles the multiplication internally, this is a fast operation. This is called a **vector operation**. Another one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.4, 18.4, 18.2, 18.2, 18. , 18. , 18. , 18.2, 18.4, 18.6, 18.6,\n",
       "       18.6, 18.4, 18.2, 17.8, 17.4, 17.2, 16.8, 16.6, 16.2, 16. , 15.8,\n",
       "       15.6, 15.4, 15.2, 15. , 15. , 14.8, 14.6, 14.4, 14.2, 14. , 13.8,\n",
       "       13.6, 13.4, 13.2, 13. , 12.8, 12.6, 12.4, 12.4, 12.2, 12.2, 12.4,\n",
       "       12.4, 12.4, 12.2, 12.2, 12.4, 12.6, 13. , 13.4, 13.8, 14.2, 14.4,\n",
       "       14.6, 14.4, 14.4, 14.2, 14.2, 14.2, 14.2, 14.2, 14.2, 14. , 14.2,\n",
       "       14.4, 14.6, 15.2, 15.8, 16.4, 17. , 17.6, 18.2, 18.6, 19.2, 19.6,\n",
       "       20. , 20.4, 20.8, 21.2, 21.6, 22. , 22.2, 22.2, 22. , 21.6, 21. ,\n",
       "       20.6, 20. , 19.4, 18.8, 18.4, 17.8, 17.2, 16.8, 16.6, 16.2, 16. ,\n",
       "       15.8, 15.6, 15.4, 15.4, 15.2, 15.2, 15. , 15. , 14.8, 14.6, 14.6,\n",
       "       14.6, 14.6, 14.6, 14.6, 14.6, 14.4, 14.2, 14. , 13.8, 13.8, 13.8,\n",
       "       13.8, 13.8, 13.8, 13.8, 13.6, 13.6, 13.4, 13.4, 13.2, 13.2, 13. ,\n",
       "       12.8, 12.6, 12.4, 12. , 11.8, 11.6, 11.4, 11.2, 11.2, 11. , 11. ,\n",
       "       10.8, 10.8, 10.6, 10.6, 10.6, 10.6, 10.6, 10.6, 10.6, 10.6, 10.4,\n",
       "       10.4, 10.2, 10. , 10. , 10. , 10. , 10. , 10.2, 10.2, 10.2, 10.4,\n",
       "       10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.6, 10.8, 11. , 11.4, 11.8,\n",
       "       12.2, 12.8, 13.4, 13.8, 14.2, 14.8, 15.2, 15.4, 15.6, 15.6, 15.8,\n",
       "       15.8, 16. , 16.2, 16.4, 16.8, 17.2, 17.6, 17.8, 18.2, 18.4, 18.8,\n",
       "       19. , 19.2, 19.4, 19.6, 19.6, 19.6, 19.6, 19.4, 19.4, 19.2, 19.2,\n",
       "       19.2, 19. , 19. , 18.8, 18.8, 18.6, 18.6, 18.6, 18.4, 18.2, 18. ,\n",
       "       17.8, 17.4, 17.2, 17. , 16.8, 16.4, 16. , 15.8, 15.6, 15.6, 15.6,\n",
       "       15.6, 15.8, 16. , 16. , 16. , 15.8, 15.8, 15.8, 15.8, 15.6, 15.6,\n",
       "       15.4, 15.2, 15. , 14.8, 14.4, 14.2, 14.2, 14. , 14. , 14. , 13.8,\n",
       "       13.6, 13.4, 13.2, 13. , 12.8, 12.6, 12.4, 12.4, 12.2, 12.2, 12.2,\n",
       "       12. , 12. , 12. , 12. , 11.8, 11.8, 12. , 12. , 12. , 11.8, 11.6,\n",
       "       11.4, 11.2, 11. , 11. , 10.8, 10.6, 10.6, 10.4, 10.4, 10.2, 10.2,\n",
       "       10. , 10. , 10. , 10. , 10. , 10. , 10. , 10. , 10. ,  9.8,  9.6,\n",
       "        9.6,  9.6,  9.6,  9.6,  9.6,  9.8, 10. , 10.2, 10.4, 10.8, 11.2,\n",
       "       11.8, 12.2, 12.6, 13. , 13.2, 13.4, 13.4, 13.4, 13.6, 13.6, 13.6,\n",
       "       13.4, 13.6, 13.6, 13.6, 13.8, 13.8, 13.8, 13.8, 14. , 14. , 14. ,\n",
       "       14. , 14. , 13.8, 13.6, 13.4, 13.2, 13.2, 13. , 13. , 12.8, 12.8,\n",
       "       12.6, 12.4, 12.2, 12. , 11.8, 11.8, 11.6, 11.6, 11.4, 11.2, 11. ,\n",
       "       10.8, 10.6, 10.6, 10.6, 10.4, 10.4, 10.4, 10.2, 10. , 10. ,  9.8,\n",
       "        9.8,  9.8,  9.8,  9.8,  9.8,  9.8,  9.8,  9.8,  9.8,  9.8, 10. ,\n",
       "       10. , 10.2, 10.2, 10.4, 10.6, 10.8, 11. , 11.4, 11.4, 11.6, 11.8,\n",
       "       12. , 12.4, 12.8, 13.4, 14. , 14.6, 15.2, 16. , 16.8, 17.6, 18.6,\n",
       "       19.8, 20.8, 21.6, 22.4, 22.8, 23.4, 23.6, 24. , 24.2, 24.6, 24.8,\n",
       "       25. , 25.2, 25.2, 25.2, 25. , 25. , 24.8, 24.6, 24.8, 24.8, 24.8,\n",
       "       24.8, 24.8, 24.6, 24.4, 24.2, 24. , 24. , 24. , 24. , 23.8, 23.6,\n",
       "       23.2, 23. , 22.6, 22.4, 22. , 22. , 21.8, 21.6, 21.4, 21. , 20.6,\n",
       "       20.4, 20. , 19.8, 19.6, 19.4, 19. , 18.8, 18.6, 18.4, 18.2, 18. ,\n",
       "       17.8, 17.6, 17.2, 17. , 16.8, 16.4, 16.2, 16. , 15.6, 15.4, 15.2,\n",
       "       15. , 14.8, 14.6, 14.4, 14.2, 13.8, 13.6, 13.4, 13.2, 13. , 12.8,\n",
       "       12.6, 12.4, 12. , 11.8, 11.6, 11.6, 11.4, 11.4, 11.2, 11.2, 11. ,\n",
       "       11. , 11. , 11. , 11. , 11. , 11. , 10.8, 10.8, 10.6, 10.4, 10.2,\n",
       "       10. ,  9.8,  9.6,  9.6,  9.4,  9.4,  9.2,  9. ,  9. ,  8.8,  8.8,\n",
       "        8.6,  8.6,  8.4,  8.4,  8.4,  8.4,  8.4,  8.6,  8.6,  8.6,  8.6,\n",
       "        8.6,  8.4,  8.2,  8.2,  8.2,  8.2,  8.2,  8.2,  8.2,  8.2,  8.4,\n",
       "        8.4,  8.6,  9. , 32. , 31.2, 28.2, 26.4, 24.6, 21.2, 19.6, 17.4,\n",
       "       18.6, 18. , 17. , 16.6, 16. , 15.4, 15.2, 15.2, 15. , 15. , 14.6])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caurvalues + caurvalues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Broadcasting** allows us to all kinds of nice things with numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.2, 14.2, 14.1, 14.1, 14. , 14. , 14. , 14.1, 14.2, 14.3, 14.3,\n",
       "       14.3, 14.2, 14.1, 13.9, 13.7, 13.6, 13.4, 13.3, 13.1, 13. , 12.9,\n",
       "       12.8, 12.7, 12.6, 12.5, 12.5, 12.4, 12.3, 12.2, 12.1, 12. , 11.9,\n",
       "       11.8, 11.7, 11.6, 11.5, 11.4, 11.3, 11.2, 11.2, 11.1, 11.1, 11.2,\n",
       "       11.2, 11.2, 11.1, 11.1, 11.2, 11.3, 11.5, 11.7, 11.9, 12.1, 12.2,\n",
       "       12.3, 12.2, 12.2, 12.1, 12.1, 12.1, 12.1, 12.1, 12.1, 12. , 12.1,\n",
       "       12.2, 12.3, 12.6, 12.9, 13.2, 13.5, 13.8, 14.1, 14.3, 14.6, 14.8,\n",
       "       15. , 15.2, 15.4, 15.6, 15.8, 16. , 16.1, 16.1, 16. , 15.8, 15.5,\n",
       "       15.3, 15. , 14.7, 14.4, 14.2, 13.9, 13.6, 13.4, 13.3, 13.1, 13. ,\n",
       "       12.9, 12.8, 12.7, 12.7, 12.6, 12.6, 12.5, 12.5, 12.4, 12.3, 12.3,\n",
       "       12.3, 12.3, 12.3, 12.3, 12.3, 12.2, 12.1, 12. , 11.9, 11.9, 11.9,\n",
       "       11.9, 11.9, 11.9, 11.9, 11.8, 11.8, 11.7, 11.7, 11.6, 11.6, 11.5,\n",
       "       11.4, 11.3, 11.2, 11. , 10.9, 10.8, 10.7, 10.6, 10.6, 10.5, 10.5,\n",
       "       10.4, 10.4, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.2,\n",
       "       10.2, 10.1, 10. , 10. , 10. , 10. , 10. , 10.1, 10.1, 10.1, 10.2,\n",
       "       10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.3, 10.4, 10.5, 10.7, 10.9,\n",
       "       11.1, 11.4, 11.7, 11.9, 12.1, 12.4, 12.6, 12.7, 12.8, 12.8, 12.9,\n",
       "       12.9, 13. , 13.1, 13.2, 13.4, 13.6, 13.8, 13.9, 14.1, 14.2, 14.4,\n",
       "       14.5, 14.6, 14.7, 14.8, 14.8, 14.8, 14.8, 14.7, 14.7, 14.6, 14.6,\n",
       "       14.6, 14.5, 14.5, 14.4, 14.4, 14.3, 14.3, 14.3, 14.2, 14.1, 14. ,\n",
       "       13.9, 13.7, 13.6, 13.5, 13.4, 13.2, 13. , 12.9, 12.8, 12.8, 12.8,\n",
       "       12.8, 12.9, 13. , 13. , 13. , 12.9, 12.9, 12.9, 12.9, 12.8, 12.8,\n",
       "       12.7, 12.6, 12.5, 12.4, 12.2, 12.1, 12.1, 12. , 12. , 12. , 11.9,\n",
       "       11.8, 11.7, 11.6, 11.5, 11.4, 11.3, 11.2, 11.2, 11.1, 11.1, 11.1,\n",
       "       11. , 11. , 11. , 11. , 10.9, 10.9, 11. , 11. , 11. , 10.9, 10.8,\n",
       "       10.7, 10.6, 10.5, 10.5, 10.4, 10.3, 10.3, 10.2, 10.2, 10.1, 10.1,\n",
       "       10. , 10. , 10. , 10. , 10. , 10. , 10. , 10. , 10. ,  9.9,  9.8,\n",
       "        9.8,  9.8,  9.8,  9.8,  9.8,  9.9, 10. , 10.1, 10.2, 10.4, 10.6,\n",
       "       10.9, 11.1, 11.3, 11.5, 11.6, 11.7, 11.7, 11.7, 11.8, 11.8, 11.8,\n",
       "       11.7, 11.8, 11.8, 11.8, 11.9, 11.9, 11.9, 11.9, 12. , 12. , 12. ,\n",
       "       12. , 12. , 11.9, 11.8, 11.7, 11.6, 11.6, 11.5, 11.5, 11.4, 11.4,\n",
       "       11.3, 11.2, 11.1, 11. , 10.9, 10.9, 10.8, 10.8, 10.7, 10.6, 10.5,\n",
       "       10.4, 10.3, 10.3, 10.3, 10.2, 10.2, 10.2, 10.1, 10. , 10. ,  9.9,\n",
       "        9.9,  9.9,  9.9,  9.9,  9.9,  9.9,  9.9,  9.9,  9.9,  9.9, 10. ,\n",
       "       10. , 10.1, 10.1, 10.2, 10.3, 10.4, 10.5, 10.7, 10.7, 10.8, 10.9,\n",
       "       11. , 11.2, 11.4, 11.7, 12. , 12.3, 12.6, 13. , 13.4, 13.8, 14.3,\n",
       "       14.9, 15.4, 15.8, 16.2, 16.4, 16.7, 16.8, 17. , 17.1, 17.3, 17.4,\n",
       "       17.5, 17.6, 17.6, 17.6, 17.5, 17.5, 17.4, 17.3, 17.4, 17.4, 17.4,\n",
       "       17.4, 17.4, 17.3, 17.2, 17.1, 17. , 17. , 17. , 17. , 16.9, 16.8,\n",
       "       16.6, 16.5, 16.3, 16.2, 16. , 16. , 15.9, 15.8, 15.7, 15.5, 15.3,\n",
       "       15.2, 15. , 14.9, 14.8, 14.7, 14.5, 14.4, 14.3, 14.2, 14.1, 14. ,\n",
       "       13.9, 13.8, 13.6, 13.5, 13.4, 13.2, 13.1, 13. , 12.8, 12.7, 12.6,\n",
       "       12.5, 12.4, 12.3, 12.2, 12.1, 11.9, 11.8, 11.7, 11.6, 11.5, 11.4,\n",
       "       11.3, 11.2, 11. , 10.9, 10.8, 10.8, 10.7, 10.7, 10.6, 10.6, 10.5,\n",
       "       10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.4, 10.4, 10.3, 10.2, 10.1,\n",
       "       10. ,  9.9,  9.8,  9.8,  9.7,  9.7,  9.6,  9.5,  9.5,  9.4,  9.4,\n",
       "        9.3,  9.3,  9.2,  9.2,  9.2,  9.2,  9.2,  9.3,  9.3,  9.3,  9.3,\n",
       "        9.3,  9.2,  9.1,  9.1,  9.1,  9.1,  9.1,  9.1,  9.1,  9.1,  9.2,\n",
       "        9.2,  9.3,  9.5, 21. , 20.6, 19.1, 18.2, 17.3, 15.6, 14.8, 13.7,\n",
       "       14.3, 14. , 13.5, 13.3, 13. , 12.7, 12.6, 12.6, 12.5, 12.5, 12.3])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caurvalues + 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember how we said that instead of explicit iteration, you should use built-in numpy functions? For example, the `numpy` library also gives us access to some simple statistics for these unemployment rates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.3090909090909095"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(caurvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.9"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(caurvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1758904323411414"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(caurvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many other ways of creating numpy arrays. You will learn these in time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array = np.array([1, 2, 3, 4])\n",
    "my_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Back to our example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create an empty dictionary `state_data` to hold all our data. Then we iterate over our states, reading the CSV file for each state and putting the resultant Pandas Dataframes as the value corresponding to the state abbreviation key in the dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = ['AL', 'TN', 'TX', 'NY', 'CA']\n",
    "state_data=dict()\n",
    "for abbrev in states:\n",
    "    state_data[abbrev] = get_unemployment_data(abbrev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AL':           DATE  ALUR\n",
       " 0   1976-01-01   6.6\n",
       " 1   1976-02-01   6.6\n",
       " 2   1976-03-01   6.6\n",
       " 3   1976-04-01   6.5\n",
       " 4   1976-05-01   6.4\n",
       " ..         ...   ...\n",
       " 545 2021-06-01   3.3\n",
       " 546 2021-07-01   3.2\n",
       " 547 2021-08-01   3.1\n",
       " 548 2021-09-01   3.1\n",
       " 549 2021-10-01   3.1\n",
       " \n",
       " [550 rows x 2 columns],\n",
       " 'TN':           DATE  TNUR\n",
       " 0   1976-01-01   6.0\n",
       " 1   1976-02-01   6.0\n",
       " 2   1976-03-01   6.0\n",
       " 3   1976-04-01   6.0\n",
       " 4   1976-05-01   5.9\n",
       " ..         ...   ...\n",
       " 545 2021-06-01   4.9\n",
       " 546 2021-07-01   4.7\n",
       " 547 2021-08-01   4.6\n",
       " 548 2021-09-01   4.4\n",
       " 549 2021-10-01   4.2\n",
       " \n",
       " [550 rows x 2 columns],\n",
       " 'TX':           DATE  TXUR\n",
       " 0   1976-01-01   5.8\n",
       " 1   1976-02-01   5.8\n",
       " 2   1976-03-01   5.9\n",
       " 3   1976-04-01   5.9\n",
       " 4   1976-05-01   5.9\n",
       " ..         ...   ...\n",
       " 545 2021-06-01   6.5\n",
       " 546 2021-07-01   6.2\n",
       " 547 2021-08-01   5.9\n",
       " 548 2021-09-01   5.6\n",
       " 549 2021-10-01   5.4\n",
       " \n",
       " [550 rows x 2 columns],\n",
       " 'NY':           DATE  NYUR\n",
       " 0   1976-01-01  10.3\n",
       " 1   1976-02-01  10.3\n",
       " 2   1976-03-01  10.2\n",
       " 3   1976-04-01  10.2\n",
       " 4   1976-05-01  10.1\n",
       " ..         ...   ...\n",
       " 545 2021-06-01   7.7\n",
       " 546 2021-07-01   7.6\n",
       " 547 2021-08-01   7.4\n",
       " 548 2021-09-01   7.1\n",
       " 549 2021-10-01   6.9\n",
       " \n",
       " [550 rows x 2 columns],\n",
       " 'CA':           DATE  CAUR\n",
       " 0   1976-01-01   9.2\n",
       " 1   1976-02-01   9.2\n",
       " 2   1976-03-01   9.1\n",
       " 3   1976-04-01   9.1\n",
       " 4   1976-05-01   9.0\n",
       " ..         ...   ...\n",
       " 545 2021-06-01   7.6\n",
       " 546 2021-07-01   7.6\n",
       " 547 2021-08-01   7.5\n",
       " 548 2021-09-01   7.5\n",
       " 549 2021-10-01   7.3\n",
       " \n",
       " [550 rows x 2 columns]}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## D. EXERCISES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/6.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Plotting using the library `matplotlib`\n",
    "\n",
    "The standard plotting library in python is `matplotlib`, which is a very powerful plotting library. Let us see how to plot the time series of unemployment numbers from California.\n",
    "\n",
    "We first import the library and instruct it to plot \"inline\" in the Jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first create a figure, and axes associated with this figure using the figure constructor. The relationship of the axes to the figure can be seen in this diagram from Brandon Rohrer's excellent free matplotlib course at https://end-to-end-machine-learning.teachable.com/p/navigating-matplotlib-tutorial-how-to/ .\n",
    "\n",
    "For us though, the main thing to know is that figures hold axes, and axes hold lines. This plot from the above course illustrates this well:\n",
    "\n",
    "![](https://github.com/univai-ghf/ghfmedia/raw/main/images/figax.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `subplots` constructor, in the `matplotlib.pyplot` module, returns us BOTH these objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (9,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.figure.Figure"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.axes._subplots.AxesSubplot"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The axes object has a method `plot` defined on it. \n",
    "\n",
    "It takes as its first argument something listy for the x-axis..a list, or a numpy `ndarray`, or a Pandas Series. \n",
    "\n",
    "The second argument represents the y-data, also something listy. So, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1160a67f0>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (9,6))\n",
    "ax.plot(data['DATE'], data['CAUR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use some other methods on the axes object to create a nicely labeled plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (9,6))\n",
    "ax.plot(data['DATE'], data['CAUR'])\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Percentage\")\n",
    "ax.set_title(\"State of California Un-employment\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## E. CODING BREAKOUT\n",
    "\n",
    "Here is a dictionary with the percentage of double vaccinated (against Covid-19) people in the population in 50 US States."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "vaxdict = {'Alaska': 52.3,\n",
    " 'Alabama': 45.6,\n",
    " 'Arkansas': 48.9,\n",
    " 'Arizona': 54.2,\n",
    " 'California': 63.3,\n",
    " 'Colorado': 63.3,\n",
    " 'Connecticut': 71.0,\n",
    " 'Delaware': 60.7,\n",
    " 'Florida': 59.1,\n",
    " 'Georgia': 49.2,\n",
    " 'Hawaii': 60.8,\n",
    " 'Iowa': 55.8,\n",
    " 'Idaho': 43.2,\n",
    " 'Illinois': 61.5,\n",
    " 'Indiana': 49.4,\n",
    " 'Kansas': 54.5,\n",
    " 'Kentucky': 51.2,\n",
    " 'Louisiana': 47.9,\n",
    " 'Massachusetts': 71.8,\n",
    " 'Maryland': 67.8,\n",
    " 'Maine': 69.9,\n",
    " 'Michigan': 54.5,\n",
    " 'Minnesota': 62.9,\n",
    " 'Missouri': 50.6,\n",
    " 'Mississippi': 46.7,\n",
    " 'Montana': 50.6,\n",
    " 'North Carolina': 53.5,\n",
    " 'North Dakota': 49.6,\n",
    " 'Nebraska': 57.5,\n",
    " 'New Hampshire': 62.5,\n",
    " 'New Jersey': 67.2,\n",
    " 'New Mexico': 63.3,\n",
    " 'Nevada': 52.7,\n",
    " 'New York': 67.9,\n",
    " 'Ohio': 52.7,\n",
    " 'Oklahoma': 51.2,\n",
    " 'Oregon': 62.9,\n",
    " 'Pennsylvania': 59.3,\n",
    " 'Rhode Island': 74.1,\n",
    " 'South Carolina': 50.6,\n",
    " 'South Dakota': 54.3,\n",
    " 'Tennessee': 49.3,\n",
    " 'Texas': 54.1,\n",
    " 'Utah': 55.6,\n",
    " 'Virginia': 65.2,\n",
    " 'Vermont': 74.0,\n",
    " 'Washington': 64.2,\n",
    " 'Wisconsin': 59.3,\n",
    " 'West Virginia': 52.8,\n",
    " 'Wyoming': 44.4}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot a histogram of percentage of double vaccinated (against Covid-19) people in the US states. Label the x and y axis and set a title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use a list comprehension to get all the values from the vaxdict\n",
    "\n",
    "# your code here\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (9,6))\n",
    "ax.hist([vaxdict[state] for state in vaxdict])\n",
    "ax.set_xlabel('Percentage of doubly vaccinated')\n",
    "ax.set_ylabel('Frequency (or count)')\n",
    "ax.set_title('Histogram of percentage of double vaccinated \\n(against Covid-19) people in different US states', fontsize=13);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Putting it all together\n",
    "\n",
    "We are now in the position of keeping our promise of being able to make a comparative plot of employment rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "width_inches = 9\n",
    "subplot_height_inches = 2\n",
    "baseurl = \"https://raw.githubusercontent.com/univai-ghf/ghfmedia/main/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unemployment_data(state_abbrev):\n",
    "    data = pd.read_csv(baseurl+\"data/\"+state_abbrev+\"UR.csv\")\n",
    "    data['DATE'] = pd.to_datetime(data['DATE'])\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now provide the `subplots` constructor function with an additional argument, the number of rows. We want to make a figure with multiple rows, each row corresponding to a state. The figure size needs to change too, as the height must keep up with the number of states in the `states` list. Then, the enumeration gives us the index for each state, and uses that index to match the axes for the subplot. We use that axes to plot the corresponding data, labeling the y-axis for each subplot with the state abbreviation. Finally we set an x-label on the last axes, and a title on the first one!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=len(states), \n",
    "    figsize = (width_inches, subplot_height_inches*len(states)))\n",
    "counter = 0\n",
    "for state_abbrev in states:\n",
    "    data = state_data[state_abbrev]\n",
    "    ax[counter].plot(data['DATE'], data[state_abbrev+'UR'])\n",
    "    ax[counter].set_ylabel(state_abbrev)\n",
    "    counter = counter + 1\n",
    "ax[-1].set_xlabel(\"Year\")\n",
    "ax[0].set_title(\"Percentage Un-employment\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. What did you learn?\n",
    "\n",
    "At the end of this workshop, you are now able to create reasonably complex plots from data! \n",
    "\n",
    "This is because you understand enough of Python to be dangerous: its libraries, its dictionaries, how many things behave like lists, and how these lists can be used to make plots.\n",
    "\n",
    "Go forth and have fun!"
   ]
  }
 ],
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   "name": "ml1-arm64"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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