IntermediatePython.ipynb

{
 "cells": [
  {
   "attachments": {
    "libraries_short_color.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "id": "4BZ2I-hfyZgx"
   },
   "source": [
    "<div>\n",
    "<img src=\"attachment:libraries_short_color.png\" width=\"350\" align=\"right\"/>\n",
    "</div>\n",
    "\n",
    "# Intermediate Python\n",
    "\n",
    "**Rijul Nandy, Shreemayi Sonti, and Nicholas Wolf, NYU Data Services**\n",
    "\n",
    "Rijuls's ORCID: 0000-0000-0000-0000 | Nick's ORCID: 0000-0001-5512-6151\n",
    "\n",
    "This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.\n",
    "\n",
    "## Overview\n",
    "\n",
    "This session is designed to be a followup to the Data Services [Intro to Python](https://guides.nyu.edu/ds_class_descriptions/Introduction-to-Python). It picks up where that lesson leaves off by again focusing on practical research applications for Python, but introducing more advanced versions of previous concepts such as functions and basic objects such as lists and dictionaries. It also introduces new concepts such as Python custom classes.\n",
    "\n",
    "Goals of this class include:\n",
    "\n",
    " - Build on Intro to Python topics with some new skills:\n",
    "  - Build out knowlege of fundamental Python objects such as functions and lists/sets/tuples\n",
    "  - Error handling\n",
    "  - List and dictionary comprehensions\n",
    "  - Various shortcuts when using lists\n",
    "  - Basic starter on writing custom classes\n",
    "\n",
    "\n",
    "## Class Materials\n",
    "\n",
    "**Lesson Plan**\n",
    "\n",
    "This notebook is available [online here](https://github.com/NYU-DataServices/IntermediatePython/blob/main/IntermediatePython.ipynb). You can download the file by cloning the notebook as follows:\n",
    "\n",
    "<code>git clone https://github.com/NYU-DataServices/IntermediatePython.git</code>\n",
    "\n",
    "**Class JupyterHub**\n",
    "\n",
    "You can access this lesson plan to run the code live at [https://tutorials-1.rcnyu.org](https://tutorials-1.rcnyu.org).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. More on Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine-tuning input parameters\n",
    "\n",
    "In Intro to Python we introduced functions as re-usable short code blocks useful for performing certain short tasks, and that a function may have one or more input parameters: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testFunc1(input_param):\n",
    "    return input_param + 1\n",
    "\n",
    "testFunc1(5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Such functions don't consider any identity to those input parameters, the function relies on the order they are passed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stateColor(red_input, blue_input):\n",
    "    print(\"Fire is\", red_input)\n",
    "    print(\"Water is\", blue_input)\n",
    "    \n",
    "stateColor(\"red\", \"blue\")   # desired result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stateColor(\"blue\", \"red\") # but...the function can't identify inputs and sort them into the right order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stateColor(\"red\")  # furthermore, you have to have both input parameters every time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In other words, functions by default rely on <em>position</em> of input parameters to assign the variables used in the function's code block. But what if you want to define input parameters explicitly? Or what if there are times when an input parameter is desired to be omitted? For this, we can use keyword arguments, which require you to use an \"=\" to explain the type of input you are using. We also set defaults in case no inputs are given:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testFunc1(key1 = 2, key2 = 3):    # Two keyword arguments, with default values assigned if no value is passed\n",
    "    return key1 + key2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testFunc1(key1 = 1, key2 = 4)  # explicitly define the two parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testFunc1()  # the pre-defined defaults are used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testFunc1(key1 = 10)  # the first parameter is defined, the second one is omitted and default will be used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wa46H42-CkFH"
   },
   "source": [
    "You can also mix and match:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s3cqkY1zIPK6"
   },
   "outputs": [],
   "source": [
    "def testFunc2(key1, key2, key3 = 2):\n",
    "    return key1 + key2 + key3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JVCeP_pfRTyk",
    "outputId": "68189e0e-2cda-4406-8d0b-3fe3b23cbe98"
   },
   "outputs": [],
   "source": [
    "print(testFunc2(1, 1)) #Purely Positional\n",
    "\n",
    "print(testFunc2(key1 = 1, key2 = 1)) #Purely Keyword\n",
    "\n",
    "print(testFunc2(1, 1, 1)) #Purely Positional \n",
    "\n",
    "print(testFunc2(1, key2 = 8)) #Positional and Keyword "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Positional arguments must be placed before keyword arguments.\n",
    "# This works\n",
    "\n",
    "print(testFunc2(5, key3=3, key2=1))\n",
    "\n",
    "print(testFunc2(5, 3, key3=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will fail\n",
    "\n",
    "testFunc2(key3=1, 1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aNGUFAPniM0p"
   },
   "source": [
    "### Lambda functions\n",
    "\n",
    "Python lambdas are anonymous functions typically defined only for the purpose of an immediate use, and designed to do a single short calculation or work step. They are particularly convenient when used in conjunction with other built-in Python functions that are designed to take a second function as an input parameter and apply it to an array of elements passed as a second parameter.\n",
    "\n",
    "First, we can look at how to define a lambda function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zWvs4mv8iOoA",
    "outputId": "8a49da8e-404d-473d-f339-3e08d70ed37b"
   },
   "outputs": [],
   "source": [
    "small_function = lambda x: x+1\n",
    "\n",
    "small_function(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to think about the syntax of a lambda function statement <code>lambda x: x+1</code> is \"I am defining here a function called lambda involving a single input parameter x such that for every input x, the return will be x + 1.\"\n",
    "\n",
    "Note that we can have multiple parameters as an input, but you can only have one expression (i.e. action to perform on the parameters) following the colon. Unlike a regular defined function, think of a lambda as a single-liner function defined exclusively locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WzwdBn7oiOqO",
    "outputId": "261a3cad-c753-496d-979d-9e384f073837"
   },
   "outputs": [],
   "source": [
    "small_function2 = lambda x, y: x + y\n",
    "\n",
    "small_function2(\"Hello \", \"world!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, this won't seem terribly useful. After all, we can easily do a similar process using a non-lambda function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "8pTEDYGtiOr_",
    "outputId": "8225dd94-9810-4c63-a7b8-29fa545bea5e"
   },
   "outputs": [],
   "source": [
    "def small_function3(x, y):\n",
    "    return x + y\n",
    "\n",
    "small_function3(\"Hello \", \"world!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But a big payoff happens for writing shorter code when lambda is used in conjunction with Python functions like <code>map()</code>. The function <code>map()</code> takes as its first parameter a function, and the second parameter an array of elements, and it applies the input function in turn to each element of the second parameter, constructing a map object of return values that can then be cast into a list.\n",
    "\n",
    "If we use lambda here instead of defining a full function, we can quickly use that lambda function for the purposes of that single line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_lambda = map(lambda x: 2**x, [0, 1, 2, 3])  # lambda function that raises 2 to the input as exponent\n",
    "\n",
    "list(map_lambda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As opposed to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exp_2(x):\n",
    "    return 2**x\n",
    "\n",
    "print(list(map(exp_2, [0, 1, 2, 3])))\n",
    "\n",
    "# Or, even less concise:\n",
    "input_list = [0, 1, 2, 3]\n",
    "output_list = []\n",
    "\n",
    "for i in input_list:\n",
    "    output_list.append(2**i)\n",
    "    \n",
    "output_list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concept of call by value and call by reference\n",
    "\n",
    "When you pass a variable to a python function, either its value or the reference (address) gets passed to the function depending on the type of data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Call by value: This happens for an immutable object - numbers, strings, tuples, etc.\n",
    "\n",
    "def change_value(x):\n",
    "    x = x * 2  # Changes the local copy of x within the context of the function only\n",
    "\n",
    "num = 5\n",
    "\n",
    "change_value(num)\n",
    "\n",
    "print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call by reference: This happens for a mutable object - lists, dictionaries, etc.\n",
    "\n",
    "def change_list(list1):\n",
    "    list1.append(3)  # Modifies the original list\n",
    "    list1[1] = 5\n",
    "\n",
    "my_list = [1, 2]\n",
    "\n",
    "change_list(my_list)\n",
    "\n",
    "print(my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus: More on the print() function\n",
    "\n",
    "The print function has additional, lesser-used parameters. The syntax is: <code>print(object(s), sep=' ', end='\\n', file=file, flush=flush)</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_name = 'Python Class'\n",
    "\n",
    "print(f\"Welcome back, {my_name}!\", \"I hope you're doing well.\", sep=\" - \", end=' :)' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cf5g8cXbjlGG"
   },
   "source": [
    "# 2. Lists vs Sets vs Tuples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iEtRC2yqBfc4"
   },
   "source": [
    "Python offers multiple data structures to store data. Lists, sets and tuples are among the most frequently used ones. \n",
    "\n",
    "Lists, sets, and tuples follow these principles:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GBvD6jK1kXsG"
   },
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yfYywx-vklbu"
   },
   "source": [
    "Image credit: Soner Yildirim, [*Towards Data Science: 15 Examples to Master Python Lists vs. Sets vs. Tuples*](https://towardsdatascience.com/15-examples-to-master-python-lists-vs-sets-vs-tuples-d4ffb291cf07)\n",
    "\n",
    "Let's look at each of these features.\n",
    "\n",
    "### Mutability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rTn-xzzdCZWC"
   },
   "source": [
    "Lists and sets are mutable in nature, meaning that the elements contained within them can be changed after creating and defining that list or set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BM1NhFYFkbjO",
    "outputId": "46949538-7058-4f07-e049-78ea7df021a4"
   },
   "outputs": [],
   "source": [
    "list1 = [1,2,3,4,5,'a']\n",
    "\n",
    "print(list1)\n",
    "\n",
    "list1[0] = 6   # setting a new value for the first element of the list\n",
    "\n",
    "print(list1)\n",
    "\n",
    "list1.append(6)  # adding a new element to the end of a list\n",
    "\n",
    "print(list1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SvCn94jCCisy"
   },
   "source": [
    "Sets are mutable as well. You can update the data after defining it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_slvS4Fw8-Zm",
    "outputId": "434a8b04-ed65-4e2c-a2b2-19e2757055e2"
   },
   "outputs": [],
   "source": [
    "set1 = {1,2,3,4,5}\n",
    "\n",
    "print(set1)\n",
    "\n",
    "set2 = {6,7}\n",
    "\n",
    "set1.update(set2)  # using .update() to concatenate a first set with a second\n",
    "\n",
    "print(set1)\n",
    "\n",
    "set1.add(8)   # using .add() to append a value to the end of a set\n",
    "\n",
    "print(set1)\n",
    "\n",
    "set1.remove(6)  # you can also remove by specifying an element by naming it (works because sets have unique elements)\n",
    "\n",
    "print(set1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "scExchWeCnfk"
   },
   "source": [
    "A tuple is not mutable. This adds a level of security to it and it can be used to store values that you don't want to change over the course of your code: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200
    },
    "id": "7gTL2-g28-cT",
    "outputId": "5ca0daaa-41d2-4e7b-d529-337c2ec4d343"
   },
   "outputs": [],
   "source": [
    "tuple1 = (1, 2, 3, 4, 5,'a')\n",
    "\n",
    "tuple1.update(1,2,3)  # error, you can't update a tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuple1[0] = 7  # same, you can't update the tuple by this method either"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 182
    },
    "id": "-d6vw-oS9vJo",
    "outputId": "6ee80c16-de15-44cd-b725-1e7b1a3bb657"
   },
   "outputs": [],
   "source": [
    "tuple1 = (1,2,3,4)\n",
    "\n",
    "tuple1.append(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HNh0m3y6n0yA"
   },
   "source": [
    "Similarly, this will return an error as well. However a mutable object within an unmutable structure can be updated as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IXpq0GLB9Jvc",
    "outputId": "55ff6bef-70b2-4067-81b8-8705c22a8e4e"
   },
   "outputs": [],
   "source": [
    "#mutable items within a tuple\n",
    "\n",
    "tuple2 = ([1,3], 'a', 'b', 8)\n",
    "\n",
    "print(tuple2)\n",
    "\n",
    "tuple2[0][0] = 99 # Modifying the list inside the tuple but not the tuple\n",
    "\n",
    "print(tuple2)\n",
    "\n",
    "\n",
    "# The following works, but it is not sample as modifying tuple2. It is creating a new tuple and pointing tuple2 to it.\n",
    "\n",
    "tuple2 += tuple2 + (0,)\n",
    "\n",
    "print(tuple2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ho_Zy1ksknr2"
   },
   "source": [
    "### Ordering, Indexing and Slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HVzUg0BID_KY"
   },
   "source": [
    "A list has an order in which the elements are stored. Hence the elements can then be called using the index. We can also slice the indexes to generate multiple elements from the list. This is review from introductory Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1yd3SrmIkptG",
    "outputId": "b6b29684-6feb-4ede-fecc-015f67e805d4"
   },
   "outputs": [],
   "source": [
    "text = \"Hello World!\"\n",
    "\n",
    "list2 = list(text)\n",
    "\n",
    "print(list2[0])  # accessing element by index location\n",
    "\n",
    "print(list2[:2]) # slicing using index location spans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7pw0yeTeEQ9J"
   },
   "source": [
    "A set has no order and thus cannot be indexed or sliced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vVgnMcp69deh",
    "outputId": "74fb8b2a-2c62-464d-e5e3-226a86d69368"
   },
   "outputs": [],
   "source": [
    "set2 = set(text)\n",
    "\n",
    "print(set2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165
    },
    "id": "tVB-6yzZ-fos",
    "outputId": "8101fa4e-9eeb-44fb-dab1-318b9e3e9a32"
   },
   "outputs": [],
   "source": [
    "print(set2[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165
    },
    "id": "fzHslWI9THwW",
    "outputId": "35dc41a9-da27-4801-9d09-129dd884416a"
   },
   "outputs": [],
   "source": [
    "print(set2[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fQU0e7lxEZx7"
   },
   "source": [
    "A tuple, however, follows the same rules as the list and has an ordered indexing system in-built, such that the elements can be called using the indexes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PkZ6yl8US8j5",
    "outputId": "f027c427-94ea-483d-a5ad-0ce4cf63b041"
   },
   "outputs": [],
   "source": [
    "tuple3 = tuple(text)\n",
    "\n",
    "print(tuple3[4])\n",
    "\n",
    "print(tuple3[:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8iec8Z3Dkp6z"
   },
   "source": [
    "### Duplicate Elements\n",
    "\n",
    "Only sets do not permit duplicate elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PW40uyRXkwLc",
    "outputId": "02d802bc-65de-407d-aca7-3848677df8b3"
   },
   "outputs": [],
   "source": [
    "text = \"NYU University\"\n",
    "\n",
    "print(list(text))\n",
    "\n",
    "print(tuple(text))\n",
    "\n",
    "print(set(text))  # Note in the output that there is only one \"i\" and one \"U\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nd7GrEObCiMc"
   },
   "source": [
    "# 3. Try/Except Error Handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vqkuGatj2945"
   },
   "source": [
    "Sometimes there are situations where you expect there will be cases where a line of code might fail and you need to account for that failure without causing the entire code to fail. In other cases, you want to debug a line of code and need it to run without failing in order to identify the error.\n",
    "\n",
    "The following code shows us the syntax for the 'try'-'except' functionalities which python uses for error handling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "01_OGjeBRT2R"
   },
   "outputs": [],
   "source": [
    "def testFunc3():\n",
    "    try:\n",
    "        print(var1)\n",
    "\n",
    "    except:\n",
    "        print(\"There was an error!\")\n",
    "        return None\n",
    "\n",
    "    print(\"Out of the block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZlX4mpzpUFd5",
    "outputId": "7b6ca3b4-c661-4a91-ab54-227d611eb10f"
   },
   "outputs": [],
   "source": [
    "testFunc3()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ULj6S5Pt_4GN"
   },
   "source": [
    "A line-by-line explanation:\n",
    "\n",
    "<b>try:</b> Python will attempt to run this code block. An error may or may not occur.\n",
    "\n",
    "<b>except:</b> If an error does occur, then this block will run instead. Error handling can happen in this block.\n",
    "\n",
    "Note that the final line of the function, a <code>print(\"Out of the block\")</code>, is not executed because the except block contained a return statement and once the return happens, the function's work is done. But you may also want a portion of your code to run regardless of how the try/except portion plays out. For this, you can use <b>finally</b>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WLqTn9fUUOZz"
   },
   "outputs": [],
   "source": [
    "def testFunc4():\n",
    "    try:\n",
    "        print(var1)\n",
    "\n",
    "    except:\n",
    "        print(\"There was an error!\")\n",
    "        return None\n",
    "\n",
    "    finally:\n",
    "        print(\"Out of the block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ugyc5GM3UOiO",
    "outputId": "8161daf8-44ae-4e2c-ed6b-d3a45190fc1e"
   },
   "outputs": [],
   "source": [
    "testFunc4()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RA4mWwIvVfq-"
   },
   "source": [
    "# 4. Comprehensions\n",
    "\n",
    "Comprehensions can be a compact way to instantiate an object such as a list while simultaneously controlling its membership. It can also replace a multi-line \"for loop\" with a single-line means of performing an operation on each element in an array object (list, dictionary). Let's start with lists:\n",
    "\n",
    "## List Comprehension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "upzB7xZzARGK"
   },
   "source": [
    "As learned previously, we can iterate through a structure such as a list to access every element stored in the structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eEIWmmxbVe9G",
    "outputId": "33c2339e-7d74-4173-8a3c-624ae912518d"
   },
   "outputs": [],
   "source": [
    "boroughs = [\"bronx\", \"brooklyn\", \"statenisland\", \"manhattan\", \"queens\"]\n",
    "newlist = [] # container for a filtered list\n",
    "\n",
    "for borough in boroughs:\n",
    "    if \"o\" in borough:\n",
    "        newlist.append(borough)\n",
    "\n",
    "print(newlist) #prints every borough that has an \"o\" in it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_NSl8kHmE_oK"
   },
   "source": [
    "But we can do this more compactly using an inline list comprehension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MOZS4yGfVe_o",
    "outputId": "90285e9c-24d8-429b-98e3-4e6c39fb969e"
   },
   "outputs": [],
   "source": [
    "boroughs = [\"bronx\", \"brooklyn\", \"statenisland\", \"manhattan\", \"queens\"]\n",
    "\n",
    "newlist = [b for b in boroughs if \"o\" in b]\n",
    "\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sP6qcvTOWc_x"
   },
   "source": [
    "#### Additional examples for list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-GrmO5NPVfCG",
    "outputId": "122c616e-d203-4021-b5b4-1f20497eb705"
   },
   "outputs": [],
   "source": [
    "newlist = [b for b in boroughs if b not in [\"manhattan\",\"bronx\"]]\n",
    "\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QllMu8-kbPZu",
    "outputId": "20396056-c5c6-4467-bc17-dad5053f562b"
   },
   "outputs": [],
   "source": [
    "newlist = [num for num in range(10) if num < 5]\n",
    "\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newlist = [num + 100 for num in range(10) if num == 5]  # modifying the resulting elment\n",
    "\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fr1k1Z4-wNAK"
   },
   "source": [
    "## Challenge 1: Given a list of numbers, remove all odd numbers from the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "M50fLZgNwP9o"
   },
   "outputs": [],
   "source": [
    "numbers = [3,5,45,97,32,22,10,19,39,43]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pzaBDXiZwSuO"
   },
   "outputs": [],
   "source": [
    "#Fill in your answer here:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZjhbFWnxbVXV"
   },
   "source": [
    "## Dictionary Comprehension"
   ]
  },
  {
   "attachments": {
    "noun-dictionary-2550890.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"attachment:noun-dictionary-2550890.png\" width=\"350\" align=\"center\"/>\n",
    "</div>\n",
    "\n",
    "Image credit: [Noun Project](https://thenounproject.com/icon/dictionary-2550890/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "23HqETkLPH4I"
   },
   "source": [
    "#### Quick Review\n",
    "\n",
    "Dictionaries provide a way to form custom 'key'-'value' pairs. While lists define the indexes in a pre-built index-based fashion, dictionaries allow you to have your own indexing system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sYeHRB31bYQn"
   },
   "outputs": [],
   "source": [
    "dict1 = {'a': 1, 'b': 2, 'c': 3, 'd': 4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dcq3u7dUbYSu",
    "outputId": "e59b60e6-f4cf-4141-8ea3-9a4b90ae8612"
   },
   "outputs": [],
   "source": [
    "print(dict1.keys()) # access the dictionary keys only\n",
    "\n",
    "print(dict1.values()) # access the dictionary values only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f-B554JSbYU7",
    "outputId": "7eb72b74-916a-40f0-f08e-59daa6ac6a13"
   },
   "outputs": [],
   "source": [
    "print(dict1.items())  # access both keys and values as an array of paired two-element items (key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we can also deploy a dictionary comprehension to work through a dictionary in a single line without using a for loop. Note the curly brackets denoting a dictionary (as opposed to the square brackets in a list comprehension):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BfU6xIyHeevT",
    "outputId": "07683537-03dd-457f-e3b8-9ab57d190ef2"
   },
   "outputs": [],
   "source": [
    "dict1_v3 = {key*2:value for key,value in dict1.items()}  # modifying the keys only, otherwise same dictionary\n",
    "\n",
    "print(dict1_v3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r37LqVHdhm9B"
   },
   "source": [
    "### Manipulating the structures\n",
    "\n",
    "A dictionary comprehension doesn't necessarily have to be altering a previously created dictionary. We can use it to create a dictionary derived from the contents of an array such as a list or some other group of elements. Here are two examples, one showing how this would be done with a more verbose for loop, and one as a dictionary comprehension.\n",
    "\n",
    "Here, we are building a list of even numbers into a dictionary consisting of their squares:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aOhkfmd5hl1l",
    "outputId": "00cbf5b8-4888-4dd3-9b80-303b447d2f37"
   },
   "outputs": [],
   "source": [
    "numbers = range(10)\n",
    "new_dict1 = {}\n",
    "\n",
    "# Add even values only from original list to 'new_dict1' using for loop. The \"long\" way to do this\n",
    "\n",
    "for num in numbers:\n",
    "    if num%2==0:    # this is the modulo. We check if the remainder of a number devided by 2 is 0 (if so, it is even)\n",
    "        new_dict1[num] = num**2\n",
    "\n",
    "print(new_dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "flxcKB3Ohx6V",
    "outputId": "d425a6ab-1207-40de-d312-18e26f9baf3a"
   },
   "outputs": [],
   "source": [
    "new_dict2 = {num:num**2 for num in numbers if num%2==0}  # Much shorter way to do this!\n",
    "\n",
    "print(new_dict2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M5vXTddmw6OG"
   },
   "source": [
    "## Challenge 2: Swap the keys for their values in the dictionary defined below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R360zQ-yxDE_"
   },
   "outputs": [],
   "source": [
    "swap_dict = {\"a\":10, \"b\":20, \"c\":30}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p7Qx6qSSxFW8"
   },
   "source": [
    "Output should be: {10: \"a\", 20: \"b\", 30: \"c\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in answer here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i1JjBpFixaLi"
   },
   "source": [
    "# 5. More on List and Dictionary Tools\n",
    "\n",
    "## Dictionary unpacking using ** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "baNBK8h6PyG2"
   },
   "source": [
    "The ** operator is used to \"unpack\" dictionary, used when we want to immediately access all values as a set of parameters. It is especially useful for sending those values to a function while retaining the dictionary's keys as the name of the input parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "r4zKgkmRRwfk"
   },
   "outputs": [],
   "source": [
    "def simple_funct(k1, k2, k3):\n",
    "    print(k2, k1, k3)           # Note that we switch the order; inputs are bound to variable names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ros8cb6_xfP4",
    "outputId": "cd1a5b50-9181-48a0-b29e-f025438c4b48"
   },
   "outputs": [],
   "source": [
    "d = {'k1':10,'k3':30,'k2':20 } #dictionary definition, note that the \"order\" here is not 1, 2, 3\n",
    "\n",
    "simple_funct(**d) # calling the function by unpacking the dictionary; keys are linked to variable names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GcYwLU-LSa1E"
   },
   "source": [
    "We can also use the ** to unpack dictionaries and merge them in a new dict object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dWTfZ8vAxfUb",
    "outputId": "d25d10ab-5411-4279-bee5-fdbf036fe366"
   },
   "outputs": [],
   "source": [
    "d = {'k1':10, 'k2':20, 'k3':30}\n",
    "\n",
    "new = {'k0':0, **d}\n",
    "\n",
    "print(new) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rRqOe4Px7Se-"
   },
   "source": [
    "## Enumerate, sorted, reverse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8rGDFURN_J-k"
   },
   "source": [
    "Often, when dealing with iterators, we also get need to keep a count of iterations. Python eases the programmer's task by providing a built-in function enumerate() for this task. Enumerate() method adds a counter to an iterable and returns it in a form of enumerating object. This enumerated object can then be used directly for loops or converted into a list of tuples using the list() function.\n",
    "\n",
    "The syntax involves an input iterable such as a list, tuple, or string (which will be iterated as an array of its characters), and an index start for the enumeration (default is 0):\n",
    "\n",
    "<code>enumerate(input_iterable, start=0)</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2xNoDEEV7VvA"
   },
   "outputs": [],
   "source": [
    "enum_item1 = [\"eat\", \"sleep\", \"repeat\"]\n",
    "\n",
    "enum_item2 = \"geek\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XINhhRcdABTO"
   },
   "source": [
    "Enumerating the two iterables formed above (list, then string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "isIRwq0H7Vw6",
    "outputId": "d28e2c2c-4af4-4e78-fa55-2c4576eaea97"
   },
   "outputs": [],
   "source": [
    "print(list(enumerate(enum_item1)))\n",
    "\n",
    "print(list(enumerate(enum_item2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UkiJudVDAZle"
   },
   "source": [
    "We can access the enumeration's count and the element separately by accessing each of the 2 elements in the resulting tuple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U3RvHg_IAZ9l",
    "outputId": "35cb8fdf-2956-411a-efb9-2f6b7fa6797b"
   },
   "outputs": [],
   "source": [
    "for count, ele in enumerate(enum_item1):\n",
    "    print(count)\n",
    "    print(ele)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UnebxAxBAhiO"
   },
   "source": [
    "### Reverse and sorted\n",
    "\n",
    "Recall that lists are mutable. We can therefore change them \"in place\" tools to perform tasks like sorting or reversing them. They do not need to be reassigned to a new variable when doing this--the list's order is simply changed:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O1_hkhi_BHzF"
   },
   "outputs": [],
   "source": [
    "enum_item1.reverse()\n",
    "\n",
    "enum_item1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note the difference!\n",
    "\n",
    "enum_item2 = [\"walk\", \"talk\", \"laugh\"]\n",
    "\n",
    "print(enum_item2[::-1])\n",
    "\n",
    "enum_item2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QYrFzdVFBP4j"
   },
   "outputs": [],
   "source": [
    "enum_item1.sort()   # Sorts elements by ASCII order, in this case initial string characters are considered\n",
    " \n",
    "enum_item1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wS29sjQTBXnc"
   },
   "source": [
    "See that the list elements are now sorted alphabetically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VlCCKmMS7V5L"
   },
   "source": [
    "## Using a list as a stack vs. a queue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "36J4M7GQBnan"
   },
   "source": [
    "In computing, a stack works on the principle of “last-in, first-out,” much like a stack of books for which the topmost book can easily be accessed, but the book on the bottom cannot.\n",
    "\n",
    "The in-built functions in Python related to managing such an approach with lists make the code short and simple.\n",
    "\n",
    "To add an item to the top (here, end) of the list (or stack), i.e., to \"push\" an item, we use the append() function. To pop out and access an element we use pop() function. Both result in modifications to the membership of the list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E0-PI3piDSrq"
   },
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Image Credit: Eric Jiang, [*Building a Modal Stack on React Native with Redux*](https://medium.com/@ericjiang97/building-a-modal-stack-on-react-native-with-redux-7f9fa51c4638)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-SAhTDf77ZBa",
    "outputId": "38a3faff-6ff5-418d-aab0-40101496e3c7"
   },
   "outputs": [],
   "source": [
    "stack = [\"Becky\", \"Tom\", \"Anthony\"]\n",
    "\n",
    "print(\"Initially the stack looks like:\",stack)\n",
    "\n",
    "\n",
    "stack.append(\"Leo\")\n",
    "\n",
    "stack.append(\"Ronald\")\n",
    "\n",
    "print(\"After two appends, the stack looks like: \", stack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pop() Removes the last item\n",
    "\n",
    "print(\"The first pop removes\",stack.pop(),\"from the stack\")\n",
    "\n",
    "print(\"Now the stack looks like:\",stack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removes the last item\n",
    "print(\"The second pop removes\",stack.pop(),\"from the stack\")\n",
    "\n",
    "print(\"Now the stack looks like:\",stack)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RelcN_R6CSI3"
   },
   "source": [
    "Alternatively, we can treat out list like a queue. A queue works on the principle of “first-in, first-out”. Below is list implementation of queue. We use pop(0), designating the front or beginning of the list, to remove the first item rather than the top/rear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YqNmehUJIsHO"
   },
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Image credit: Payal Adhikary, [*Data Structure and Algorithms*](https://medium.com/@payaladhikary2000/data-structure-and-algorithms-8d47c4bb6354)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ib70RSMf7ZDS",
    "outputId": "e1e81676-edb2-42a4-d131-865614ef5636"
   },
   "outputs": [],
   "source": [
    "queue = [\"Becky\", \"Tom\", \"Anthony\"]\n",
    "\n",
    "queue.append(\"Leo\")\n",
    "\n",
    "queue.append(\"Ronald\")\n",
    "\n",
    "print(\"Initially the queue looks like:\", queue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pop(0) Removes the first item\n",
    "\n",
    "print(\"The first pop removes\", queue.pop(0),\"from the queue\")\n",
    "\n",
    "print(\"Now the queue looks like:\", queue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removes the first item\n",
    "\n",
    "print(\"The second pop removes\",queue.pop(0),\"from the queue\")\n",
    "\n",
    "print(\"Now the queue looks like:\", queue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python lists can be used as stacks and queues as they have .append() and .pop() methods.\n",
    "\n",
    "Appending and popping elements to the right of a list takes O(1) time complexity. However, appending and popping to the left of a list are inefficient operations with O(n) speed. Moreover, python needs to reallocate memory to grow the list, which adds to its inefficiency when using as a stack or a queue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacks and Queues using deque() object from collections module\n",
    "\n",
    "Deque is pronounced as “deck.” The name stands for double-ended queue. It supports memory-efficient, fast append and pop, operations on both ends of the data structure.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "\n",
    "my_queue = deque([1, 2, 3, 4, 5])\n",
    "print(my_queue)\n",
    "\n",
    "my_stack = deque([1, 2, 3, 4, 5])\n",
    "my_stack "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert and Remove from a queue. Since it is a queue, always append from one end and pop from the other end\n",
    "print(my_queue.popleft())\n",
    "print(my_queue)\n",
    "print(my_queue.append(6))\n",
    "print(my_queue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert and Remove from a stack. Since it is a stack, always pop and append from the same end.\n",
    "print(my_stack.pop())\n",
    "print(my_stack)\n",
    "print(my_stack.append(6))\n",
    "print(my_stack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This won't work as deques are not random-access efficient, so we don't have that feature.\n",
    "\n",
    "my_stack.pop(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Also, deques support indexing but not slicing\n",
    "print(my_stack[3])\n",
    "my_queue[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: \n",
    "Unlike lists deques aren’t for random access as they traverse from one end to the other. So, accessing elements from the middle of a deque is less efficient than doing the same thing on a list. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X2QF52zS7ZLC"
   },
   "source": [
    "## A helper function called zip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wSzwq24dO2fx"
   },
   "source": [
    "The zip() function returns a zip object, which is an iterator of tuples where the first item in each passed iterator is paired together, and then the second item in each passed iterator are paired together, etc.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [\"John\", \"Charles\", \"Mike\", \"Jason\"]\n",
    "\n",
    "b = [\"Jenny\", \"Christy\", \"Monica\", \"Vicky\"]\n",
    "\n",
    "list(zip(a, b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UegcLNRFO7Vs"
   },
   "source": [
    "If the passed iterators have different lengths, the iterator with the least items decides the length of the new iterator. Items in the longer array that exceed the length of the other array will not be used.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QSJm4Ak77b8H"
   },
   "outputs": [],
   "source": [
    "c = (\"Ming\", \"Maria\", \"Bill\")       # Works with tuples as well\n",
    "d = (\"Ali\", \"Sara\", \"Art\", \"Troy\")\n",
    "\n",
    "list(zip(c, d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can start piecing together various options for quickly building data-holding objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GkLKc9LfPzOp",
    "outputId": "aa3990ef-5202-4612-ceee-e1c93b71edff"
   },
   "outputs": [],
   "source": [
    "player_numbers = [7, 9, 10]\n",
    "player_names = ['Cristiano Ronaldo', 'Gareth Bale', 'Lionel Messi']\n",
    "\n",
    "# Approach 1: zip() plus dictionary comprehension\n",
    "\n",
    "player_data = { number: name for number, name in zip(player_numbers, player_names) }\n",
    "\n",
    "print(player_data)\n",
    "\n",
    "# Approach 2: zip() plus dict()\n",
    "\n",
    "player_data = dict(zip(player_numbers, player_names))\n",
    "\n",
    "print(player_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cTG79mhv5F67"
   },
   "source": [
    "# 6. Building Strings Dynamically\n",
    "\n",
    "A common need in workflows is to dynamically modify text using dropped in variables. Two common ways to do this is to use .format() and (new in Python 3), Fstrings. These replace older methods that involved designating string variables using the % sign.\n",
    "\n",
    "First, using .format():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "50L--5Dq5LXJ",
    "outputId": "41ae4316-1f2f-4850-d30f-a92b86ad904f"
   },
   "outputs": [],
   "source": [
    "var1 = 'Washington Square'\n",
    "\n",
    "text_passage = \"NYU is located on {}. This is close to Greenwich Village.\".format(var1)\n",
    "\n",
    "text_passage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, using a structure called Fstrings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"One of the best-known locations in New York is {var1}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PnQjjr78pEhT"
   },
   "source": [
    "You can also call functions within the Fstrings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KbLEsi1io0Hh",
    "outputId": "2f5ca686-2202-4f05-b123-4b9ba278f44d"
   },
   "outputs": [],
   "source": [
    "def to_lowercase(input_text):\n",
    "    return input_text.lower()\n",
    "\n",
    "name = \"Chris Chen\"\n",
    "\n",
    "print(f\"{to_lowercase(name)} is smart.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0-rHLWGj5XiM"
   },
   "source": [
    "# 7. Custom Classes in Python\n",
    "\n",
    "There are times in your code when you will want to perform an extended analysis or data manipulation where you will want to define not a tool or action, but rather a \"type of thing\" that has certain characteristics and capabilities.\n",
    "\n",
    "Imagine, for example, that your data involves archaelogical objects, each with certain characteristics such as name, location found, time period it was created, and material. Let's say it also has a distance it was found from another object, based on the coordinates of its find location, and that we want to define a means to calculate that distance for any object.\n",
    "\n",
    "We could model this like this using Python's custom classes. Note that the convention in Python is to capitalize the names of classes, and lower case the names of functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ArchObject:\n",
    "    def __init__(self, name, location, time_period, material):\n",
    "        self.name = name\n",
    "        self.loc = location\n",
    "        self.time = time_period\n",
    "        self.mat = material\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python is entirely composed of classes. Even a fundamental like a list is a class, with certain attributes. We have now created a new class called ArchObject. Then we defined what happens when we create one (instatiate it), explained when we add this <code>def __init__()</code> part.\n",
    "\n",
    "Now we can instantiate a new ArchObject and assign it a variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_one = ArchObject(\"pottery shard\", (38.77009, 32.37245), \"Bronze Age\", \"clay\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we can retrieve these attributes of this specific instance of an ArchObject, called instance variables\n",
    "\n",
    "obj_one.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far so good. Let's say now that we want to have a more dynamic instance variable, namely a list of dates that the archaeological items--here, a pottery shard--has been accessed from its museum holding tray. We could modify our original class in this fashion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ArchObject:\n",
    "    def __init__(self, name, location, time_period, material):\n",
    "        self.name = name\n",
    "        self.loc = location\n",
    "        self.time = time_period\n",
    "        self.mat = material\n",
    "        self.access_dates = []\n",
    "        \n",
    "    def add_date(self, date):\n",
    "        self.access_dates.append(date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've added two things. A set of dates (held in a list) that records times the pottery shard was used. And we've also added a means, called an instance method, for updating that list. This feature looks and acts much like a function available only to your custom class object. Now we have this option:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_two = ArchObject(\"pottery shard\", (38.77009, 32.37245), \"Bronze Age\", \"clay\")\n",
    "\n",
    "obj_two.add_date(\"1999-05-03\")\n",
    "\n",
    "obj_two.access_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_two.add_date(\"1998-12-12\")\n",
    "\n",
    "obj_two.access_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, let's deploy an added instance method that calculates the distance between one archaeological ArchObject class and a second one. This uses a classis method of calculating distance between points on earth using 6371.01, the average radius of the earth in kilometers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LIoMT_2S5zjt"
   },
   "outputs": [],
   "source": [
    "from math import radians, sin, cos, acos\n",
    "\n",
    "class ArchObject:\n",
    "    def __init__(self, name, location, time_period, material):\n",
    "        self.name = name\n",
    "        self.loc = location\n",
    "        self.time = time_period\n",
    "        self.mat = material\n",
    "        self.access_dates = []\n",
    "        \n",
    "    def add_date(self, date):\n",
    "        self.access_dates.append(date)\n",
    "        \n",
    "    def calculate_distance(self, second_coordinates):\n",
    "        return 6371.01 * acos(sin(float(self.loc[0]))*sin(float(second_coordinates[0])) + \\\n",
    "                              cos(float(self.loc[0]))*cos(float(second_coordinates[0]))* \\\n",
    "                              cos(float(self.loc[1]) - float(second_coordinates[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_three = ArchObject(\"pottery shard\", (38.77009, 32.37245), \"Bronze Age\", \"clay\")\n",
    "\n",
    "obj_four = ArchObject(\"metal cuff\", (38.12, 33.7008), \"Bronze Age\", \"tin\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_three.calculate_distance(obj_four.loc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the difference between a class variable and object variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Employee:\n",
    "    # Class variable (shared by all instances)\n",
    "    org_name = \"NYU\"\n",
    "    employee_count = 0\n",
    "\n",
    "    def __init__(self, name, emp_id):\n",
    "        # Instance/object variables (unique to each instance)\n",
    "        self.name = name\n",
    "        self.emp_id = emp_id\n",
    "        Employee.employee_count += 1 # Incrementing a class variable for each object creation\n",
    "\n",
    "    def display_info(self):\n",
    "        print(\"Name:\", self.name)\n",
    "        print(\"Employee ID:\", self.emp_id)\n",
    "        print(\"Company:\", Employee.org_name) # Accessing class variable\n",
    "\n",
    "print(\"Current Number of employees:\", Employee.employee_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create two new Employee objects and set their instance variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create two employee objects\n",
    "\n",
    "emp1 = Employee(\"Alice\", 123)\n",
    "\n",
    "print(\"Current Number of employees:\", Employee.employee_count)\n",
    "\n",
    "emp2 = Employee(\"Bob\", 456)\n",
    "\n",
    "print(\"Current Number of employees:\", Employee.employee_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accessing object variables\n",
    "\n",
    "print(\"Employee 1 name:\", emp1.name)\n",
    "\n",
    "print(\"Employee 2 ID:\", emp2.emp_id)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All objects share the same class variable\n",
    "\n",
    "print(\"Company name:\", emp1.org_name)\n",
    "\n",
    "print(\"Company name:\", emp2.org_name) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge 3\n",
    "\n",
    "Create a custom class that can model the data for your own research project. What kind of class would be useful, and what features of that class do you need?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ub9NsJSjwaSg"
   },
   "source": [
    "# Challenge Answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d2pBS2wmwYh8",
    "outputId": "823ee4af-f28e-4e88-a85f-583125e56ac8"
   },
   "outputs": [],
   "source": [
    "result = [number for number in numbers if number % 2 == 0]\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H0p75PWRxOdb",
    "outputId": "c969b81f-5898-4fad-a59e-45f8633eaae1"
   },
   "outputs": [],
   "source": [
    "dict1_v3 = {value:key for key,value in swap_dict.items()}  # a quick way to swap keys for values!\n",
    "\n",
    "print(swap_dict)"
   ]
  }
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