6_Automation_Filled.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/theheking/intro-to-python/blob/gh-pages/6_Automation_Filled.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WVQkBcmeoUXa"
      },
      "source": [
        "# Automation with loops\n",
        "\n",
        "We use a for loop when you need to iterate through every item in a sequence object, like a list. \n",
        "\n",
        "It is a quick way to eliminate bugs and make the code more readable!\n",
        "\n",
        "### Objectives\n",
        "- To be able to understand what a for loop is\n",
        "- Be able to automate a task with for loops\n",
        "- Be able to understand when you would use them\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBTk0kVMoRxt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cbcedc7-23c1-447d-8998-13f15cb557d6"
      },
      "source": [
        "#for instance we want to iterate through ever letter in the word garvan \n",
        "word = \"garvan\"\n",
        "#print every character in the word\n",
        "print(word[0])\n",
        "print(word[1])\n",
        "print(word[2])\n",
        "print(word[3])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "g\n",
            "a\n",
            "r\n",
            "v\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8KuM_bjLpbiF"
      },
      "source": [
        "This is a flawed approach.\n",
        "1. It does not scale\n",
        "  \t- If a word contained 100 letters, it would be more efficient to type the characters.\n",
        "\n",
        "2. This code is fragile and wouldn't be suitable depending on the length of the string. \n",
        "  - If it's a longer string, it would only print only a part of the word\n",
        "  - If it's a short string: it will give us an erro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJ_e8JXjpKqO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "b9d2d29a-d3a6-4cb8-f798-91c75e6874d4"
      },
      "source": [
        "# any short or long word\n",
        "word = \"nan\"\n",
        "\n",
        "# reuse the code above to print the letters of the word\n",
        "print(word[0])\n",
        "print(word[1])\n",
        "print(word[2])\n",
        "print(word[3])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n\n",
            "a\n",
            "n\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-22b10d67fc34>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: string index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8Uf133DpzSi"
      },
      "source": [
        "__Better approach is by using loops__"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word = 'garvanite'\n",
        "\n",
        "#iterate through every character in the word \n",
        "for i in word:\n",
        "  print(i) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn7aXmScF5GB",
        "outputId": "b01ef9a6-00a9-4d57-81f9-b18acc49ef31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "g\n",
            "a\n",
            "r\n",
            "v\n",
            "a\n",
            "n\n",
            "i\n",
            "t\n",
            "e\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43jTnv8xuWBs"
      },
      "source": [
        "![loops_image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FS_qwYc5u6QR"
      },
      "source": [
        "# For loops \n",
        "The structure of a for loop requires:\n",
        "+ A __variable__ \n",
        "  - We can assign any name to it.\n",
        "+ A __list or string to loop through__\n",
        "+ __Colon and the indentation__\n",
        "    - The indented content after the `for` statement belongs to the loop\n",
        "\n",
        "Each character (`char`) in the variable `word` is looped through and printed one character after another\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3TsZ5oysunYU",
        "outputId": "c1822f37-f778-4b9c-d9c4-ba50df1c3019"
      },
      "source": [
        "## We can give any name to loop variable\n",
        "word = \"oxygen\"\n",
        "\n",
        "for char in word:\n",
        "  print(char)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "o\n",
            "x\n",
            "y\n",
            "g\n",
            "e\n",
            "n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8KDSbGwp6Ql",
        "outputId": "41d53e32-16c1-4e53-b10e-594b919cce7a"
      },
      "source": [
        "## choose a meaningful name for the loop variable\n",
        "## updating other variables within the loop\n",
        "length = 0\n",
        "\n",
        "for vowel in \"aeiou\":\n",
        "  length = length + 1\n",
        "\n",
        "print(\"There are\", length, \"vowels\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 5 vowels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMeXl2Kxn5Q9"
      },
      "source": [
        "+ During the first loop, `vowel` = `a` and `length` = `1`\n",
        "+ During the second loop, `vowel` = `e` and `length` = `2`\n",
        "+ During the fifth loop, `vowel` = `u` and `length` = `5`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6kRQtrLconmq"
      },
      "source": [
        "### Challenge - scope of the variable\n",
        "1. In the loop over `\"aeiou\"` above, does the loop variable `vowel` exist after the loop has finished ?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for vowel in 'aeiou':\n",
        "  print(vowel)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDsCzNaaHld8",
        "outputId": "ac4c6999-96ed-44f0-80bf-d73fd621bdfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a\n",
            "e\n",
            "i\n",
            "o\n",
            "u\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKxGp15amrNb",
        "outputId": "a896b4fc-8016-43aa-c9cc-e8c74cd536b5"
      },
      "source": [
        "length = 0\n",
        "for vowel in 'aeiou':\n",
        "    length = length + 1\n",
        "\n",
        "print('After the loop, `vowel` exists and has the value: ' + vowel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After the loop, `vowel` exists and has the value: u\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJG9y0n-rEf-"
      },
      "source": [
        "+ There is an built-in function `len` which is much faster and has greater functionalities"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len('oxygen')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdL3FWvxIOrc",
        "outputId": "488ef1a9-9df7-4851-dbc1-9a131c5a7beb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len('aeiou')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qi01tPOqv6AN",
        "outputId": "cde4c6c4-f440-4020-fe85-c97e687e54ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHmMR3LcDMhW"
      },
      "source": [
        "__What if we need numbers as a loop variable?__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2q1y_OYqrkJ7"
      },
      "source": [
        "#### Creating sequence of numbers\n",
        "+ Using built-in function `range`- can accept 1, 2 or 3 parameters\n",
        "  1. If 1 parameter: `range` creates array of that length starting at zero and incrementing by 1\n",
        "    + Eg: `range(3)` produces the numbers `0, 1, 2`\n",
        "  2. If 2 parameters: `range` starts at the first and ends before the second, incrementing by 1.\n",
        "    + Eg: `range(2, 5)` produces `2, 3, 4`\n",
        "  3. If 3 parameters: `range` starts at the first and ends before the second, incrementing by the third parameter.\n",
        "    + Eg: `range(3, 10, 2)` produces `3, 5, 7, 9`\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1,4):\n",
        "  print(i)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1aoePTqInnv",
        "outputId": "fe462924-43d3-4d07-d119-7fef6873f74d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfdFnYzL1SCL",
        "outputId": "7f37b5ca-e719-44ce-effa-19d4a1deac06"
      },
      "source": [
        "for num in range(3, 10, 2):\n",
        "  print(num)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n",
            "5\n",
            "7\n",
            "9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Rrt9jgVEJ85"
      },
      "source": [
        "### Challenges (breakout session - 10 mins)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nhpsd9DQD7Fn"
      },
      "source": [
        "Challenge 1 - Use `range` to print first 3 natural numbers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZ6S-W_-rDxv",
        "outputId": "5fbf2acb-da8a-4eb0-d739-5ae7ac86b717"
      },
      "source": [
        "## Solution code for challenge 1\n",
        "for num in range(3):\n",
        "  print(num + 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAUNhDcTttBs"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "Challenge 2 - Write a loop that calculates exponentiation using loop that calculates the same result as `5**3`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfvdN0ifo3Uc",
        "outputId": "41552b1d-817b-46e5-b07e-ef549b069ab5"
      },
      "source": [
        "## exponentiational \n",
        "5**3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "125"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvSFvRnjuEgB",
        "outputId": "549d2a94-da7f-4a4f-e21c-4acdc9a368e2"
      },
      "source": [
        "## Solution code for challenge 2\n",
        "5*5*5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "125"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_-6dHVETVP5",
        "outputId": "8b992cca-3ce5-4c74-b91d-8ba01a348ed7"
      },
      "source": [
        "num = 1\n",
        "for i in range(3):\n",
        "  num = num * 5\n",
        "  print(\"i: \", i, \"num: \", num)\n",
        "\n",
        "print(num)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "i:  0 num:  5\n",
            "i:  1 num:  25\n",
            "i:  2 num:  125\n",
            "125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTyohs90upfV"
      },
      "source": [
        "#### Bonus challenge - reversing string using loop\n",
        "+ Reverse `Newton` to `notweN` using concatenation operator `+`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "js3GFVjtWP0P",
        "outputId": "9e01cccb-932a-4e51-ccb7-27b09d90add6"
      },
      "source": [
        "\"a\" + \"b\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ab'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Solution\n",
        "word = \"Newton\"\n",
        "word2 = \"\"\n",
        "\n",
        "for char in word:\n",
        "  word2 = char + word2\n",
        "\n",
        "print(word2)"
      ],
      "metadata": {
        "id": "IN-RU7EHD9d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e982e7b1-aff5-49ce-ad7e-0af5ef696386"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "notweN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmPR3ohqwlOV"
      },
      "source": [
        "### Enumerate in Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKz6nuasyLga"
      },
      "source": [
        "`enumerate` gives you two loop vairables using sequence (eg. list): index and the corresponding value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GwW482xquwR6",
        "outputId": "2c17e9c3-3306-43d2-df75-7e46efd078b1"
      },
      "source": [
        "values = [\"a\", \"b\", \"c\"]\n",
        "\n",
        "for count, value in enumerate(values):\n",
        "  print(count,value)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 a\n",
            "1 b\n",
            "2 c\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "weJYUXDV0y9Q"
      },
      "source": [
        "Computing the value of a polynomial using enumerate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gvmhxUNyDEt"
      },
      "source": [
        "x = 5\n",
        "cc = [2, 4, 3]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kC8I1Z9bRWCb",
        "outputId": "e0be49e5-8a8e-4431-a362-ebf0a1d5bb96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 4, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFMjYoXEy5Gn",
        "outputId": "3a6ed96a-f947-47eb-dfb2-43c6790ab4a1"
      },
      "source": [
        "y = cc[0] * x**0 + cc[1] * x**1 + cc[2] * x**2\n",
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "97"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ac0M6oSozCuF",
        "outputId": "278ff85b-ae8a-47a2-e25c-e4e79d7f8492"
      },
      "source": [
        "y = 0\n",
        "for indices, values in enumerate(cc):\n",
        "  print(indices, values)\n",
        "  y = y + values * x ** indices\n",
        " \n",
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 2\n",
            "1 4\n",
            "2 3\n",
            "97\n"
          ]
        }
      ]
    }
  ]
}