TReND_Python_Practical_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TReND-Python_Practical_3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/trendinafrica/python_workshop/blob/main/TReND_Python_Practical_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0yYS2_P3V1cp"
      },
      "source": [
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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vDqhfq7035p"
      },
      "source": [
        "# TReND Python Course: Day 2, Practical 3\n",
        "\n",
        "# Topic: While loops & Packages\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsL7KxkOAIDU"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "In this course we will practice what we have learned throughout the course. The idea is to train our understanding of the theory concepts. This tutorial provides you with problem sets and your goal is to formulate a solution to it. Note that in most cases different solutions can solve the problem. The problems are divided into levels: Think of levels as difficulty.\n",
        "\n",
        "For doing now:\n",
        "\n",
        "- Level 1: [tricky](#easy1)\n",
        "- Level 2: [trickier](#medium1)\n",
        "- Level 3: [trickiest](#difficult1)\n",
        "\n",
        "Take home\n",
        "\n",
        "- Level 1: [tricky](#easy)\n",
        "- Level 2: [trickier](#medium)\n",
        "- Level 3: [trickiest](#difficult)\n",
        "\n",
        "Note: Start with the \"For doing now\" part. If you do not finish in time, you can also do it during your free time."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exercises**"
      ],
      "metadata": {
        "id": "8upz1RRjEyX4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name=\"easy1\"></a>\n",
        "# 1. Tricky"
      ],
      "metadata": {
        "id": "p8iSmQizGqF-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "a)  Using a while loop, write a program to broadcast the message `Hello World!` 10 times.\n",
        "\n",
        "\n",
        "You can, if you like, as well try implementing the solution with a for loop.\n"
      ],
      "metadata": {
        "id": "op4JhmfqGqGA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "message = 'Hello World!'\n",
        "\n",
        "# Add your code here"
      ],
      "metadata": {
        "id": "0TMErIFjGqGB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "\n",
        "message = 'Hello World!'\n",
        "\n",
        "# Add your code here\n",
        "i = 0\n",
        "stop = 10\n",
        "\n",
        "while i <= stop:\n",
        "  print(message)\n",
        "  i += 1"
      ],
      "metadata": {
        "cellView": "form",
        "id": "IE5OHQDV_e_q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "b) Write a program that puts the numbers from 1 to 20 into a list and then prints the list. Try to do it with a for loop and a while loop.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "y6Ks8ewGEzdV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_number_list = []\n",
        "\n",
        "while:\n",
        "  \n"
      ],
      "metadata": {
        "id": "yMgawCHcGqGD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "my_number_list = []\n",
        "\n",
        "for i in range(1,21):\n",
        "  my_number_list.append(i)\n",
        "\n",
        "print(my_number_list)\n",
        "\n",
        "x=0\n",
        "while x>=20:\n",
        "  my_number_list.append(x)\n",
        "  x=x+1\n",
        "\n",
        "print(my_number_list)"
      ],
      "metadata": {
        "id": "z3KGLbbv_pTa",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "c) Create a vector using numpy that has the numbers from 5 to 55 in steps of 5. for this you can import the `numpy` package and use `np.linspace()` or `np.arange()`. If you are unsure what the functions do, remember the `help` function or search in the numpy documentation :).\n",
        "\n"
      ],
      "metadata": {
        "id": "Flnksp2hRWjU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " import numpy as np\n",
        " \n",
        " my_vector="
      ],
      "metadata": {
        "id": "15ZQqqFQP4Qh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        " import numpy as np\n",
        "\n",
        " my_vector=np.linspace(5,55,num=11)\n",
        " print(my_vector)\n",
        " my_vector=np.arange(5,60,5)\n",
        " print(my_vector)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "oEdszcuH_yR-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name=\"medium1\"></a>\n",
        "# 2. Trickier"
      ],
      "metadata": {
        "id": "36mCwfesPpm8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "a) You are tired and would like to have a short 30 minutes nap, then resume 30 minutes later, but you don't want to risk sleeping all day as that might cost you the amount of work done in a day. Luckily, you know some python programming and automation. You decide to write a simple program that counts the minutes and alarms you when 30 minutes are complete. Write such a program, and let the alarm is ring with a message \"Wake up!!\" after 30 mins!"
      ],
      "metadata": {
        "id": "CeHhVDxCGqGE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = 0\n",
        "end_time = 30\n",
        "nap = True\n",
        "\n",
        "while nap:\n",
        "  # Add your code here\n"
      ],
      "metadata": {
        "id": "54DeqMl6GqGG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "start_time = 0\n",
        "end_time = 30\n",
        "nap = True\n",
        "\n",
        "# Add your code here\n",
        "while nap:\n",
        "  if start_time == end_time:\n",
        "    print('Wake up!')\n",
        "    break\n",
        "  print(f'Napping: {nap}')\n",
        "  start_time += 1\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "51ldkqih_3cv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "b) Here we use numpy to create a vector and then perform some operations with it. \n",
        "\n",
        "1. First, we create a vector containing the numbers from 0 to 100 in steps of 2. Hint: You can use `np.linspace`."
      ],
      "metadata": {
        "id": "GefQMeQ6TyLk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Add your code here"
      ],
      "metadata": {
        "id": "RCryl41AAGUp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "my_vector=np.linspace(0,100,51)\n",
        "print(my_vector)\n"
      ],
      "metadata": {
        "id": "WNc80eS5TyLl",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Now:\n",
        "- Divide every element in my_vector by 3.\n",
        "- Multiply the vector by itself \n",
        "- Slice your vector using relational operators, and only keep the numbers bigger than 50.\n",
        "- Find the index of the maximum value in your array (index is the postion your number is within the vector). Clue: search in the internet if there is a numpy function to do this in a fast way ;) \n",
        "\n"
      ],
      "metadata": {
        "id": "oZhoDTBSM-cJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "division=\n",
        "print('division %s'%(division))\n",
        "\n",
        "multiplication=\n",
        "\n",
        "print('multiplication %s'%(multiplication))\n",
        "\n",
        "logical_slice= \n",
        "\n",
        "print('logical slice %s'%(logical_slice))\n",
        "\n",
        "position_of_maximum=\n",
        "\n",
        "print('position of maximum %s' %(position_of_maximum))"
      ],
      "metadata": {
        "id": "Mj24_lHBNRRt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "division=my_vector/3\n",
        "print('division %s'%(division))\n",
        "\n",
        "multiplication=my_vector*my_vector\n",
        "\n",
        "print('multiplication %s'%(multiplication))\n",
        "\n",
        "logical_slice= my_vector[my_vector>50]\n",
        "\n",
        "print('logical slice %s'%(logical_slice))\n",
        "\n",
        "position_of_maximum=np.argmax(my_vector)\n",
        "\n",
        "print('position of maximum %s' %(position_of_maximum))"
      ],
      "metadata": {
        "cellView": "form",
        "id": "f9dg0PehAmAA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name=\"difficult1\"></a>\n",
        "# 3. Trickiest"
      ],
      "metadata": {
        "id": "FADS-xAhG4Mt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "a) The following program, if run, will execute forever (until computer memory is exhausted). Fix it so that the execution finally reaches the end."
      ],
      "metadata": {
        "id": "M5LbsGWjG4Mv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Code\n",
        "start = 0\n",
        "answer = 42\n",
        "i = 'unaware'\n",
        "p = 'searching'\n",
        "\n",
        "while i == 'unaware' or start < 42:\n",
        "  value = str(start)\n",
        "  if start < 10:\n",
        "    value = '0' + value\n",
        "  print(value, i, 'that', p, 'is on')\n",
        "  \n",
        "  if start == 18:\n",
        "    print(start, ': Confused and', i, 'but', p,'is still on')\n",
        "    print()\n",
        "\n",
        "  if start == 25:\n",
        "    print(start, ' : Oh! I see.')\n",
        "    print()\n",
        "    i = 'aware'\n",
        "\n",
        "  # Add your solution here\n",
        "  \n",
        "if answer == start: \n",
        "  print()\n",
        "  print()\n",
        "  print(answer, '?')"
      ],
      "metadata": {
        "id": "IFpAbTuSG4Mw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "start = 0\n",
        "answer = 42\n",
        "i = 'unaware'\n",
        "p = 'searching'\n",
        "\n",
        "while i == 'unaware' or start < 42:\n",
        "  value = str(start)\n",
        "  if start < 10:\n",
        "    value = '0' + value\n",
        "  print(value, i, 'that', p, 'is on')\n",
        "  \n",
        "  if start == 18:\n",
        "    print(start, ': Confused and', i, 'but', p,'is still on')\n",
        "    print()\n",
        "\n",
        "  if start == 25:\n",
        "    print(start, ' : Oh! I see.')\n",
        "    print()\n",
        "    i = 'aware'\n",
        "\n",
        "  start += 1\n",
        "  \n",
        "if answer == start: \n",
        "  print()\n",
        "  print()\n",
        "  print(answer, '?')"
      ],
      "metadata": {
        "cellView": "form",
        "id": "tcCrvoWfA24t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "b) Write a programm that gives you a specific number of prime numbers as output. fill in the variable x to tell the programm how many primes do you want. \n",
        "\n",
        "  - take into account that a prime number can be divided by itself and by 1. non-prime numbers can be divided by themselves, by 1 and at least by a previous prime number\n",
        "\n",
        "  - take into account that 0 and 1 are not primer numbers.\n",
        "\n",
        "  - to find if a number is a prime you can use the modulo (%) operator:\n",
        "```\n",
        " 4%2=0  -> this means 4 is divisible by 2, hence 4 is not prime \n",
        " 3%2= 1, 3%3=0, 3%1=0  -> this means that 3 is not divisible by 2 but it is divisible by iself and by 1, hence 3 is prime \n",
        "```"
      ],
      "metadata": {
        "id": "XgTBmzwkFKoC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "number_of_primes_desired= ####fill in before running ####.\n",
        "\n",
        "count= 2 # this is the starting point for a loop to find the primes. why 2 and not 0?\n",
        "\n",
        "output=[] # a list that will contain the prime numbers that are found\n",
        "\n",
        "while len(output)<________:### fill in. until when should the loop run? ###   len() is a command that returns the lenght of an object\n",
        "\n",
        "  # test if the current number is a prime\n",
        "\n",
        "  if len(output)==0:\n",
        "    prime=True\n",
        "  else:\n",
        "    # write some code to find if the current count is a prime or not. for this you have to check if count is divisible by at least one of the numbers in the output list\n",
        "    for number in output: \n",
        "      \n",
        "  \n",
        "  if prime==True:\n",
        "    output.append() # if the current count is a prime number, add it to the output list\n",
        "  count=count+1\n",
        "\n",
        "print ('your prime numbers are:')\n",
        "print (output)\n",
        "  "
      ],
      "metadata": {
        "id": "g4fpa23YFIc7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "number_of_primes_desired= 10 #(this can be any number)\n",
        "\n",
        "count= 2 # 0 and 1 are not prime\n",
        "\n",
        "output=[] # a list that will contain the prime numbers that are found\n",
        "\n",
        "while len(output)<number_of_primes_desired:### fill in. until when should the loop run? ###:\n",
        "\n",
        "  # test if the current number is a prime\n",
        "\n",
        "  if len(output)==0:\n",
        "    prime=True # we can start by including 2 automatically in the list, since 2 is a prime\n",
        "  else:\n",
        "    prime=True\n",
        "    for number in output: \n",
        "      # write some code to find if the current count is a prime or not. for this you have to check if count is divisible by at least one of the numbers in the output list\n",
        "      if count%number==0:\n",
        "        prime=False\n",
        "        break\n",
        "      \n",
        "  if prime==True:\n",
        "    output.append(count) # if the current count is a prime number, add it to the output list\n",
        "  count=count+1\n",
        "\n",
        "print ('your prime numbers are:')\n",
        "print (output)\n",
        "  "
      ],
      "metadata": {
        "cellView": "form",
        "id": "rh5y8XLeBMe-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# c) In the next cell we use some packages to load an image. They seem to be a couple of faces upside down. Nevetheless, there seems to be something interesting about this image. \n",
        "\n",
        "- Your task is to investigate how can you turn this image upside down to watch it from different perspectives "
      ],
      "metadata": {
        "id": "_Zj33RMVo49z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- First, upload the image that we are going to use, and then upload here in colabs. Use files> upload\n",
        "\n",
        "https://drive.google.com/drive/folders/1mViJSwrGAbL-owc4gjtbxZjn9BTI7kf5?usp=sharing"
      ],
      "metadata": {
        "id": "-4ka_FRIo7YQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title import some packages  and show the image\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np \n",
        "\n",
        "\n",
        "data=plt.imread('ricky_face.jpeg')\n",
        "\n",
        "plt.imshow(data)\n",
        "plt.axis('off')"
      ],
      "metadata": {
        "id": "w8J7HwlIo49z",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "observe how big is this image with the `np.shape()` command and try to think about what the shape means."
      ],
      "metadata": {
        "id": "Ym_F9EH6Bf17"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# code here"
      ],
      "metadata": {
        "id": "6-FZmoeFo490"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "\n",
        "np.shape(data)\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "FVGFzZ2gBZL2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This image is an array with 3 dimensions. The first dimension represents the width of the image, second dimension represents the height, but if the image is flat, why is there a 3rd dimension? "
      ],
      "metadata": {
        "id": "lVNv0ex7Bnjd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To see what the third dimension is, take the mean of the third dimension of the data numpy array then execute the code to see what changed after doing this operation. "
      ],
      "metadata": {
        "id": "5gPcbQ3FBtIo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image2= # code here: take the mean of the 3rd dimension\n",
        "\n",
        "\n",
        "plt.imshow(image2,cmap='gray')\n",
        "plt.axis('off')\n"
      ],
      "metadata": {
        "id": "34KAEYzXBxzt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "\n",
        "image2=np.mean(data,axis=2)\n",
        "plt.imshow(image2,cmap='gray')\n",
        "plt.axis('off')"
      ],
      "metadata": {
        "cellView": "form",
        "id": "xXKVZAHdCdJ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " What kind of information is on the 3rd axis?"
      ],
      "metadata": {
        "id": "TyZ3e_hLBtQs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To find out the mistery about this image, use an online search engine to find how can you turn this image around and watch it in it's normal orientation. Hint: pay attention specially on how to put the first dimension upside down. Check out `np.flip` and `np.transpose`."
      ],
      "metadata": {
        "id": "0mx_hbsnCJoO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "changed_image= ## write your code here\n",
        "\n",
        "\n",
        "plt.figure()\n",
        "plt.axis('off')\n",
        "plt.imshow(changed_image)\n"
      ],
      "metadata": {
        "id": "9x7hpUzPo491"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Double click here for the solution\n",
        "turned_image=np.transpose(data,axes=[1,0,2])\n",
        "\n",
        "upside_down_image=np.flip(data, axis=0)\n",
        "\n",
        "\n",
        "plt.figure()\n",
        "plt.axis('off')\n",
        "plt.imshow(turned_image)\n",
        "plt.figure()\n",
        "plt.axis('off')\n",
        "plt.imshow(upside_down_image)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "XCyP6tikCaw3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-3CWDWr716E"
      },
      "source": [
        "---\n",
        "# Take home exercises!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAlC9T_PGCvO",
        "cellView": "form"
      },
      "source": [
        "\n",
        "# @title The code hidden here is for testing your code, do not modify it please :).\n",
        "\n",
        "def test_results(your_input, expected_input):\n",
        "  try:\n",
        "    assert your_input == expected_input\n",
        "\n",
        "  except AssertionError as e:\n",
        "    print(\"\\x1b[6;30;91m\", \"TEST FAILED - You are almost there. Give it another try :)\" + \"\\x1b[0m\")\n",
        "\n",
        "    if type(your_input) == type(expected_input):\n",
        "      raise AssertionError(f'\\x1b[6;30;91m {your_input} != {expected_input} \\x1b[0m')\n",
        "\n",
        "    elif type(your_input) != type(expected_input):\n",
        "      raise AssertionError(f'\\x1b[6;30;91m {type(your_input)} != {type(expected_input)} \\x1b[0m')\n",
        "    else:\n",
        "      raise e\n",
        "  else:\n",
        "    print('\\x1b[6;30;92m' + 'TEST PASSED! - Congratulations, you found a solution! Good Job!' + '\\x1b[0m')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dC_7rmdVu7_2"
      },
      "source": [
        "---\n",
        "<a name=\"easy\"></a>\n",
        "### **Level 1: tricky**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "872uWNEOEeQG"
      },
      "source": [
        "### 1.1 Lists"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Kg-oMCe88BE"
      },
      "source": [
        "a) Given a list `nums`, print the length of the list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DybiQJ01lDZA"
      },
      "source": [
        "nums = [73, 23, 45, 54, 29, 49, 2, -13, 48, 82, 49, 284, 204, 1337] # list to use"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qn2EbWF38NK_"
      },
      "source": [
        "# Write your code here!\n",
        "\n",
        "your_answer = ..."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "gP2lo1FCFvXT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bb3b366-aa1d-4a55-9955-80feece7c89f"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Tests\n",
        "test_results(your_answer, 14)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[6;30;92mTEST PASSED! - Congratulations, you found a solution! Good Job!\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ja6C_8BLIDJY",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "your_answer = len(nums)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tz9BKsZe8_Ei"
      },
      "source": [
        "b) Given `nums`, print the value of the last index."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tfpnUO0mSDzq"
      },
      "source": [
        "# Write your code here!\n",
        "\n",
        "your_answer = ..."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "k-vInv5u8H9n"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Tests\n",
        "test_results(your_answer, 1337)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzTQEz84AQea",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "your_answer = nums[-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rTMionSV9DC6"
      },
      "source": [
        "c) Given `nums`, select the last five elements of the list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfBfNddBmyJt"
      },
      "source": [
        "# Write your code here!\n",
        "\n",
        "your_answer = ..."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "7M_U3ffj29la"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Tests\n",
        "test_results(your_answer, [82, 49, 284, 204, 1337])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7HYz5RPAbXi",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "# solution to be added\n",
        "your_answer = nums[-5:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKsBn0Bopizv"
      },
      "source": [
        "d) Given `nums`, find the sum of all elements in the list using a `for loop`. Compare your result with the output found using the `sum`; python in-built function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKTg34mHpK-L"
      },
      "source": [
        "# Write your code here!\n",
        "\n",
        "# Using for loop\n",
        "total_using_forloop = 0\n",
        "\n",
        "\n",
        "\n",
        "# Using the sum in-built function\n",
        "total_using_sum = ...\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "n9Ycl5VQ3Cjz"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Test 1\n",
        "test_results(total_using_forloop, 2266)\n",
        "\n",
        "# Test 2\n",
        "test_results(total_using_sum == total_using_forloop, True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npb_sKZ1A1tn",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "# Using for loop\n",
        "total_using_forloop = 0\n",
        "\n",
        "for num in nums:\n",
        "  total_using_forloop += num\n",
        "\n",
        "# Using the sum in-built function\n",
        "total_using_sum = sum(nums)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOPCxj5trB3h"
      },
      "source": [
        "e) Given a list of integers `odds_evens` (below), loop through the list and using `append`, add all the even numbers in the list `evens` and all odds in list `odds`. Then print the result. Note: To check if a number is even in python, you do, `if number % 2 == 0:` otherwise it's odd"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FnRrM8cvri3-"
      },
      "source": [
        "odds_evens = [2, 4, 5, 6, 92, 54, 55, 53, 12, 32, 13, 95, 82, 77, 45, 65, 66, 22, 23, 15, 91, 100, 35, 88, 6, 8, 9, 11, 7]\n",
        "evens = []\n",
        "odds = []\n",
        "\n",
        "# Write your code here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "rEzmbwqj3I7L"
      },
      "source": [
        "# @title  ##### Run this. Have you found a solution?\n",
        "# Test even\n",
        "test_results(evens, [2, 4, 6, 92, 54, 12, 32, 82, 66, 22, 100, 88, 6, 8])\n",
        "# Test odd\n",
        "test_results(odds, [5, 55, 53, 13, 95, 77, 45, 65, 23, 15, 91, 35, 9, 11, 7])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItlxEetMBJnG",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "# Write your code here!\n",
        "\n",
        "for num in odds_evens:\n",
        "  if num & 1: # or num % 2 != 0\n",
        "    odds.append(num)\n",
        "  else:\n",
        "    evens.append(num)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J5k6Sje8sSrz"
      },
      "source": [
        "f) Using the above results, sort the resulting lists; `evens` and `odds`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kb007fNdsjTp"
      },
      "source": [
        "# Write your code here!"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2g4khxt3NmD",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Test even sorted\n",
        "test_results(evens, [2, 4, 6, 6, 8, 12, 22, 32, 54, 66, 82, 88, 92, 100])\n",
        "\n",
        "# Test odd sorted\n",
        "test_results(odds, [5, 7, 9, 11, 13, 15, 23, 35, 45, 53, 55, 65, 77, 91, 95])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybzmBSMsBucm",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "evens.sort()\n",
        "odds.sort()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o06uATiutNac"
      },
      "source": [
        "g) Concatenate `evens` and `odds` above and assert that your result is equally the same as the original list `odds_evens`. Note; you need to sort the list before asserting. Also, you can use both methods learned to concatenate the lists; not a requirement"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZgKX5s7t7D4"
      },
      "source": [
        "# Write your code here!\n",
        "\n",
        "new_nums = ..."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQZjbzKD3PkM",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Test\n",
        "test_results(new_nums == odds_evens, True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ejz54NFBCKrw",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "new_odds_evens = sorted(evens + odds)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siHhRNl-vjvf"
      },
      "source": [
        "h) Alice is attending a private party but she's not sure if her name is on the invited guests list. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wVCnc8I99r8",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. The list is hidden here. Don't peek!\n",
        "names = ['Olivia', 'Emma', 'Ava', 'Charlotte', 'Sophia', 'Amelia', 'Isabella', 'Mia', 'Evelyn', 'Harper', 'Camila', 'Gianna', 'Abigail', 'Luna', 'Ella', 'Elizabeth', 'Sofia', 'Emily', 'Avery', 'Mila', 'Scarlett', 'Eleanor', 'Madison', 'Layla', 'Penelope', 'Aria', 'Chloe', 'Grace', 'Alice', 'Ellie', 'Nora', 'Hazel', 'Zoey', 'Riley', 'Victoria', 'Lily', 'Aurora', 'Violet', 'Nova', 'Hannah', 'Emilia', 'Zoe', 'Stella', 'Everly', 'Isla', 'Leah', 'Lillian', 'Addison', 'Willow', 'Lucy', 'Paisley']\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAep046l_FPs"
      },
      "source": [
        "i) Alice doesn't like embarrassment and does not want to walk up to the gate not knowing, if her name is on the list or not. Alices hands you a list of names of the guests called `names`. Help her find out if she's on the list or not. Print the results. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Hc6kvLExQ99"
      },
      "source": [
        "my_name = 'Alice'\n",
        "# Write your code here!\n",
        "\n",
        "name_is_on_list =  ... # is the name on the list (True) or not (False)?"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyJFpBJZ-RLD",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this to know: Did you get it right?\n",
        "# Tests\n",
        "test_results(name_is_on_list, True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lprz3rw1CU-c",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "name_is_on_list = 'Alice' in names"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L0xlTTvSEvAw"
      },
      "source": [
        "### 1.2 Dictionaries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WshDk3dP_fIP"
      },
      "source": [
        "a) Given an (unkonw) dictionary `persons`, which contatins the name of a person and their age. Perform the following operations. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8vjPPTp-i2x",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. The unkown dictionary is hidden here. Don't peek!\n",
        "persons = {'Alice': 22, 'Bob': 26, 'Yoda': 123, 'Xii': 1, 'V': 28, 'Watson': 12}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AI4kks8M-s1Y"
      },
      "source": [
        "The hidden dictionary is called `persons`, print a list of all the keys of this dictionary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cExVOSw8_czl"
      },
      "source": [
        "# Write your code here!\n",
        "keys = ...\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "eNBeGKVnGnZ3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "e89879c4-6482-4460-c797-672223ddc15e"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Tests\n",
        "test_results(keys, ['Alice', 'Bob', 'Yoda', 'Xii', 'V', 'Watson'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-a2e1b2519a45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# @title ##### Run this. Have you found a solution?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Tests\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtest_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Alice'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Bob'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Yoda'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Xii'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'V'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Watson'\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;31mNameError\u001b[0m: name 'test_results' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfDHZj3pDQbn",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "keys = list(persons.keys())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tA2-nqHn-0Ja"
      },
      "source": [
        "b) Given the dictionary `persons`, print a list of all values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BLrdu6dAt2l"
      },
      "source": [
        "# Write your code here!\n",
        "values = ..."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "GpPpOrW53ZYG"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Test\n",
        "test_results(values, [22, 26, 123, 1, 28, 12])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-J7cbS7DfM_",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "values = list(persons.values())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBLEE4ce_cnf"
      },
      "source": [
        "c) Given the dictionary `persons`, print key-value pairs or items. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TG-0VqBFAyJU"
      },
      "source": [
        "# Write your code here!"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nq1IZ5hOBPaZ"
      },
      "source": [
        "d) Given the dictionary `persons`, return the age of the person mentioned below, \n",
        "if the person exists in the dictionary. If it does not exist, add the person with age 999 to the dictionary and print the new dict. Check, if you have successfully added the person. Add your own name and age to the dictionary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ciGXp-ggBe8V"
      },
      "source": [
        "person = 'Yoda'\n",
        "# Write your code here!\n",
        "age = ...\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPuVRzCV3av0",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Test\n",
        "test_results(age, 123)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHqq30TyDm47",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "age = persons[person]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfZML4OABhH1"
      },
      "source": [
        "person2 = 'Brian'\n",
        "\n",
        "# Check if person exists\n",
        "person_exits = ...\n",
        "\n",
        "# If person_exits is false, write your code here to add the person to the dictionary persons. Remember age is 999 if they aren't included!\n",
        "...\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdEuy6lM3cFt",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Test new persons\n",
        "test_results(persons, {'Alice': 22, 'Bob': 26, 'Yoda': 123, 'Xii': 1, 'V': 28, 'Watson': 12, 'Brian': 999})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBsKLQqiHBv8",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "# Check if person exists\n",
        "person_exits = person2 in persons\n",
        "\n",
        "# If person_exits is false, write your code here to add the person to the dictionary persons\n",
        "if not person_exits:\n",
        "  persons[person2] = 999\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z965jTIqGCSc"
      },
      "source": [
        "e) Add a new key called `'location'` with the value `'New Zealand'` to the dictionary `my_friend`. Print the new dictionary.\n",
        "Be sure to add it on a new line. Do not add the value in the first line where the dictionary is defined. Remember, capitalization matters!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-y8CU0nGBoP"
      },
      "source": [
        "my_friend = {'name': 'Fluffball', 'occupation': 'Cat'} # don't change anything here\n",
        "\n",
        "## Add your code here!"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXE6XUCZGuoT",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "test_results(my_friend, {'name': 'Fluffball', 'occupation': 'Cat', 'location': 'New Zealand'})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfXZE9vj1QFc",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "my_friend['location'] = 'New Zealand'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8T3e5tUwE2mh"
      },
      "source": [
        "### 1.3 Loops 'n Lists"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUwKldWfAuee"
      },
      "source": [
        "a) Given the top secret hidden list `top_secret`, use a for-loop to print each element in the given list on a new line."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "yzqgL_07A-rZ"
      },
      "source": [
        "# @title ##### Run this. Top secret hidden list. Don't peek!\n",
        "top_secret = [1,2,'woah',4,5,6]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgAO4PCqAxAI"
      },
      "source": [
        "## Add your code here!"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wt4_nEzkAy8G"
      },
      "source": [
        "b) Use a `while`-Loop to add one to the variable `counter` until it is greater than zero. Print the new value of `counter`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1q7ltoi1A1Ef"
      },
      "source": [
        "counter = -10\n",
        "\n",
        "## Add your code here!\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpsnh-nU4rFJ",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "test_results(counter, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuIg5HKx1ZLD",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "while counter < 1:\n",
        "  counter = counter + 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z7SouR_pA8DH"
      },
      "source": [
        "c) Insert the string `'owl'` into the list `animals_seen` at the first position using the `.insert()` function. Print the new list. You can read a short paragraph on the `insert()` function [here](https://www.programiz.com/python-programming/methods/list/insert).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5j_W1m1BAz5"
      },
      "source": [
        "animals_seen = ['cow', 'emu', 'crane']\n",
        "\n",
        "# Add your code here!\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "owOWNNVI4k4f",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "test_results(animals_seen, ['owl', 'cow', 'emu', 'crane'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eL6Hb0MN1upp",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "animals_seen.insert(0, 'owl')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "253wXXXsBJ4v"
      },
      "source": [
        "### 1.4 [SELF-STUDY] Tuples\n",
        "We have not introduced you to tuples in this year's Python course. You can study this on your own and test your understanding with the following study question."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create two lists `my_fav_food` and `my_fav_friends`. Make a tuple containing both lists. Then print the tuple."
      ],
      "metadata": {
        "id": "mhYbKlCPPiBw"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1zekdZ2BL9m"
      },
      "source": [
        "## TODO: create lists and tuple\n",
        "\n",
        "my_food_friends_tuple = ...\n",
        "\n",
        "## TODO print\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4KbKLLP4dRo",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "test_results([type(my_food_friends_tuple), type(my_food_friends_tuple[0]), type(my_food_friends_tuple[1])], [tuple, list, list])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVIMPFlL1zS7",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "my_food_friends_tuple = (['Pizza'], ['Pizza is also my favorite friend'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8UorEcREwxku"
      },
      "source": [
        "---\n",
        "<a name=\"medium\"></a>\n",
        "### **Level 2: trickier**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYfXKXWF6iLX"
      },
      "source": [
        "### 2.1 Lists\n",
        "a) Given the list of numbers `num`, perform the following operations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNiuwpn76bcw"
      },
      "source": [
        "# Given this list of numbers\n",
        "nums = [73, 23, 45, 54, 29, 49, 2, -13, 48, 82, 49, 284, 204, 1337] # list to use"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXd-vv7anbwU"
      },
      "source": [
        "# Operation 1: Change the value at index 7 to 147 in copy_nums. Do not change original nums.\n",
        "copy_nums = nums[::] # this is how copying a list is done in python. There other ways too, find link below\n",
        "\n",
        "# Write your code here!\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4KohAusI9im"
      },
      "source": [
        "- [Copying a list in python](https://www.programiz.com/python-programming/methods/list/copy)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSxGX7kD2_ZT",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Tests\n",
        "test_results(copy_nums, [73, 23, 45, 54, 29, 49, 2, 147, 48, 82, 49, 284, 204, 1337])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dV4arcM_ITHN",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "copy_nums[7] = 147\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caFOhOSIoNg4"
      },
      "source": [
        "# Operation 2: Take the value at index 2 and set it equal to 4 using the copy_nums1. Do not change original nums.\n",
        "copy_nums1 = nums[::]\n",
        "\n",
        "# Write your code here!\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lwx_0Sxy4Gs2",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Tests\n",
        "test_results(copy_nums1, [73, 23, 4, 54, 29, 49, 2, -13, 48, 82, 49, 284, 204, 1337])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9Zmh2P3JYlg",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "copy_nums1[2] = 4\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ih1-RsnVopBp"
      },
      "source": [
        "# Operation 3: Remove the first and last element from the list copy_nums2\n",
        "# Note: do not use the original list nums defined above, make a copy.\n",
        "\n",
        "copy_nums2 = nums[::]\n",
        "# Write your code here!\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvqotRrqKDd-",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Tests\n",
        "test_results(copy_nums2, [23, 45, 54, 29, 49, 2, -13, 48, 82, 49, 284, 204])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGSkxvylKieu",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "copy_nums2 = copy_nums2[1:-1]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2IpaGcY00kO"
      },
      "source": [
        "b) Given a sorted list of distinct integers; `sorted_nums` and a target value, the following function returns the index if the target is found. If not, it returns the index where it would be if it were inserted in order. Complete the function by writing your for loop where asked.\n",
        "\n",
        "For example:\n",
        "```\n",
        "Input: sorted_nums = [1, 3, 4, 5, 6, 8]\n",
        "       target = 2\n",
        "Output: 1\n",
        "\n",
        "input: sorted_nums = [1, 3, 4, 5, 6, 8]\n",
        "       target = 5\n",
        "Output: 3\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eiuqvnltzuqt"
      },
      "source": [
        "sorted_nums = [2, 3, 5, 6, 9, 10, 11, 14, 16, 18, 20, 23, 25, 28, 32, 44, 54, 67, 77, 88, 90, 95, 100, 117]\n",
        "target1 = 28\n",
        "target2 = 22\n",
        "target3 = 200\n",
        "target4 = 1\n",
        "\n",
        "\n",
        "def find_index(target):\n",
        "  found_index = -1\n",
        "  end = None # we set this when there is no such value in the list, this hold the next index\n",
        "\n",
        "  if target < sorted_nums[0]:\n",
        "    return 0\n",
        "\n",
        "  # Write your code here!\n",
        "  ...\n",
        "\n",
        "  if found_index < 0 and end:\n",
        "    return end - 1\n",
        "  elif found_index < 0:\n",
        "    return len(sorted_nums)\n",
        "  return found_index\n",
        "\n",
        "# target1\n",
        "result1 = find_index(target1)\n",
        "\n",
        "# target2\n",
        "result2 = find_index(target2)\n",
        "\n",
        "# target3\n",
        "result3 = find_index(target3)\n",
        "\n",
        "# target4\n",
        "result4 = find_index(target4)\n",
        "\n",
        "\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gi0471ar3lHC",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Test target1\n",
        "test_results(result1, 13)\n",
        "\n",
        "# Test target2\n",
        "test_results(result2, 10)\n",
        "\n",
        "# Test target3\n",
        "test_results(result3, 24)\n",
        "\n",
        "# Test target4\n",
        "test_results(result4, 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7IdEKpOK-Rr",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "def find_index(target):\n",
        "  found_index = -1\n",
        "  end = None\n",
        "\n",
        "  if target < sorted_nums[0]:\n",
        "    return 0\n",
        "\n",
        "  # Write your code here!\n",
        "  for index, num in enumerate(sorted_nums):\n",
        "    if num == target:\n",
        "      found_index = index\n",
        "      break\n",
        "    elif num > target:\n",
        "      end = index\n",
        "      break\n",
        "\n",
        "  if found_index < 0 and end:\n",
        "    return end - 1\n",
        "  elif found_index < 0:\n",
        "    return len(sorted_nums)\n",
        "  return found_index\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ri-zuaanVWw2"
      },
      "source": [
        "### 2.2 Dictionaries\n",
        "a) Given a dict of passwords, print password owner and the password, if valid. A valid password is one that does not have repeating characters.\n",
        "\n",
        "```\n",
        "Input: passwords = {1: 'ahsgdfk', 2: 'sbgri', 3: 'jjshhgs', 4: 'shggryyas'}\n",
        "Output: 1: ahsgdfk\n",
        "        2: sbgri\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2kbdRl8VrgU"
      },
      "source": [
        "passwords = {'A': 'aseddc', 'B': 'cdeesaq', 'C': 'lkkdjur', 'D': 'kksgrruee', 'E': 'jueysh', 'F': 'grteusi', 'G': 'uyttee', 'H': 'hfgtry', 'I': 'lkbyww', 'J': 'gfbtwsj'}\n",
        "\n",
        "# Write your code here!\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "EdDvwjOxMCCS"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "for owner, password in passwords.items():\n",
        "  dupes_removed = set(password)\n",
        "  valid = len(dupes_removed) == len(password)\n",
        "\n",
        "  if valid:\n",
        "    print(f'{owner}: {password}')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crY_fe4PZUBS"
      },
      "source": [
        "b) Using the previous code, loop through all passwords. Instead of printing the valid passwords, create a string formatted as `'Key: Value'`. Add this string to a set called `result`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OukfvxEIZhPr"
      },
      "source": [
        "# Write your code here!\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "HQCoj3Lm3jjs"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Test\n",
        "test_results(owners, {'J: gfbtwsj', 'F: grteusi', 'H: hfgtry', 'E: jueysh'})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzRw1WQsMDA0",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "for owner, password in passwords.items():\n",
        "  dupes_removed = set(password)\n",
        "  valid = len(dupes_removed) == len(password)\n",
        "\n",
        "  if valid:\n",
        "    owners.add(f'{owner}: {password}')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAil_PRFBRZt"
      },
      "source": [
        "### 2.3 Loops\n",
        "a) Write a nested for-loop that combines all letters in the variable given below. Set the variable `answer` equal to that string."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sx6zMS4fBT7D"
      },
      "source": [
        "prob_six_list = [['g','o'], ['o','d'], [' '], ['j','o','b','!']]\n",
        "answer = ''\n",
        "\n",
        "# Add your code here!\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouKYGoJk5I7U",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "test_results(answer, 'good job!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "19E5C6Zympev"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "for single_list in prob_six_list:\n",
        "  for element in single_list:\n",
        "    answer = answer + element"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBfDItjZv5kB"
      },
      "source": [
        "b) Complete a program that keeps asking an interger input from the user and print the sum of all the user unput so far until the user input `-1` "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yoqA5eoTv7su"
      },
      "source": [
        "# Insert your code here\n",
        "result = 0\n",
        "while True:\n",
        "    number = int(input(\"Enter an integer: \"))\n",
        "    \n",
        "    if #insert your code here:\n",
        "        break\n",
        "\n",
        "    result += # insert your code here\n",
        "    print(result)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlR9lKUOwAvC",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "result = 0\n",
        "while True:\n",
        "    number = int(input(\"Enter an integer: \"))\n",
        "    \n",
        "    if number == -1 :\n",
        "        break\n",
        "\n",
        "    result += number\n",
        "    print(result)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAKVTBaQw0t9"
      },
      "source": [
        "---\n",
        "<a name=\"difficult\"></a>\n",
        "### **Level 3: trickiest**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fH-awkym5joB"
      },
      "source": [
        "### 3.1 Lists\n",
        "You are given a list of lists of scores, the outer list represents the students and the inner list represents student scores for each subject the student undertake. Find the student with the highest score and return the score.\n",
        "\n",
        "For example;\n",
        "\n",
        "Given;\n",
        "```\n",
        "Input: students = [[23, 32, 41, 25], [43, 55, 45, 57], [12, 32, 43, 55]]\n",
        "Output: 200\n",
        "Explanation:\n",
        "  student 1: 23 + 32 + 41 + 25 = 121\n",
        "  student 2: 43 + 55 + 45 + 57 = 200\n",
        "  student 3: 12 + 32 + 43 + 55 = 142\n",
        "\n",
        "  You can use sum python in-built function, sum([23, 32, 41, 25])\n",
        "\n",
        "  The highest is student 2 with 200 score\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yBhKjl46nAo"
      },
      "source": [
        "students = [[23, 32, 41, 25], [43, 55, 45, 57], [12, 32, 43, 55], [21, 23, 34, 59], [32, 23, 14, 52], [34, 55, 54, 75], [93, 77, 65, 67]]\n",
        "\n",
        "# Write your code here!\n",
        "\n",
        "highest_score = ...\n",
        "\n",
        "..."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "_ejJb6zn3oIv"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Tests\n",
        "test_results(highest_score, 302)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rPjlP8CQMl7T",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "for student in students:\n",
        "  total = sum(student)\n",
        "\n",
        "  if total > highest_score:\n",
        "    highest_score = total\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWaHXKmQbLub"
      },
      "source": [
        "### 3.2 Lists & Dictionaries\n",
        "Given a list of integers, create a dictionary, call it `seen`, of key-value representing the amount of time a number is seen in the list. Print the dictionary.\n",
        "\n",
        "For example:\n",
        "\n",
        "```\n",
        "Input: numbers = [1, 2, 2, 7, 7, 7, 5, 4, 4, 8, 8]\n",
        "Output: {1: 1, 2: 2, 7: 3, 5: 1, 4: 2, 8: 2}\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PoDqWchacC01"
      },
      "source": [
        "numbers = [1, 2, 2, 7, 7, 7, 5, 4, 4, 8, 8]\n",
        "\n",
        "# Write your code here\n",
        "\n",
        "seen = {}\n",
        "\n",
        "...\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "b1Mvrucx3po2"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "# Test\n",
        "test_results(seen, {1: 1, 2: 2, 7: 3, 5: 1, 4: 2, 8: 2})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9p6VhybWMvl7",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "seen = {}\n",
        "for num in numbers:\n",
        "  if num in seen:\n",
        "    seen[num] += 1\n",
        "  else:\n",
        "    seen[num] = 1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyCmeWuBBYNE"
      },
      "source": [
        "### 3.3 Loops & Dictionaries\n",
        "Write a loop which goes through all keys in the dictionary below. If the key is an integer, pass without doing anything. Otherwise, sum the list corresponding to that key and append that sum to the variable answer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5vQvb2eBaZ0"
      },
      "source": [
        "prob_seven_list = {'a': [2,4,2], 1: [44,2], 2: [1,2,3], 'b': [0,0,10], 'c': [7]}\n",
        "answer = []\n",
        "\n",
        "## Add your code here!\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9CCa9SVqWFY",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for a hint (if needed)!\n",
        "\n",
        "# `type()` tells you the type of a variable. `pass` tells the loop to do nothing.*"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8J6hJvNM5Q7m",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "outputId": "3d4e3de1-a236-4ea5-afcc-2f8eccb4ea0e"
      },
      "source": [
        "# @title ##### Run this. Have you found a solution?\n",
        "test_results(answer, [8, 10, 7])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-2e170cc520d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# @title ##### Run this. Have you found a solution?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\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;31mNameError\u001b[0m: name 'test_results' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ac9NK54FoLvw",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "for key, value in prob_seven_list.items():\n",
        "  if type(k) == int:\n",
        "    pass\n",
        "  else:\n",
        "    total = sum(value)\n",
        "    answer.append(total)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Write a small program that starts counting from 1 and only stops counting when an x number of prime numbers that the user defines has been found.\n",
        "\n",
        "To do this, you should use a while loop. \n",
        "\n",
        "- Clue: a prime number is divisible only by one and by itself. \n",
        "- You need to find a way to see if your current number in the loop is divisible by any previous prime number that has already been counted"
      ],
      "metadata": {
        "id": "heqvzEizyvXT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# made by juan\n",
        "\n",
        "Quantity_of_primes=0 # we start with 0 prime numbers counted \n",
        "\n",
        "counter=1 # this variable needs to be changed witihn the while loop so a different number is analized in each repetition of the loop \n",
        "\n",
        "x= # write here the desired amount of prime numbers \n",
        "\n",
        "prime_numbers=[] # clue: we created this list for you, this means it will be useful to store prime numbers that you already found. you may need the append() command\n",
        "\n",
        "\n",
        "while Quantity_of_primes <= x:\n",
        "\n",
        "  # find if counter is divisible by any of the previous prime numbers, and store it it is a prime number\n",
        "\n",
        "  x= #the counter needs to change in every repetition, so the program doesn't run forever. fill in this here :)\n",
        "\n",
        "\n",
        "  #this program has a risk to run for too long , if x is too high. introduce a way to prevent this :)\n",
        "\n",
        "final_number= # create a way to pick the chosen number from your prime_numbers. \n",
        " \n",
        "print('your prime number is %d' %(final_number))"
      ],
      "metadata": {
        "id": "PmVoq-IakcXo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yrJcih-hWGL9"
      },
      "source": [
        "### 3.4 Operators"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-i38HkYotQlm"
      },
      "source": [
        "- Use relational operators, for loop, and if statements to complete the following problem.\n",
        "\n",
        "- Given a list of dictionaries that contain students' names as keys and values as the students' list of performance in several subjects, write a program to print a student's name followed by the student's performance score in each subject. See the example below.\n",
        "\n",
        "```\n",
        "\n",
        "Operations:\n",
        "\n",
        "- if score is below 30, print E\n",
        "- if score is greater than or equals to 30 and less than 50, print D\n",
        "- if score is greater than or equals to 50 and less than or equals to 70, print C\n",
        "- if score is greater than 70 and less than 80, print B\n",
        "- Otherwise, print A\n",
        "\n",
        "Input: students = [{'py': [23, 45, 56, 67, 89, 90], 'js': [32, 88, 77, 98, 89, 56]}]\n",
        "Output:\n",
        "        py\n",
        "        23 is E\n",
        "        45 is D\n",
        "        56 is C\n",
        "        67 is C\n",
        "        89 is A\n",
        "        90 is A\n",
        "\n",
        "        js\n",
        "        32 is D\n",
        "        88 is A\n",
        "        77 is B\n",
        "        98 is A\n",
        "        89 is A\n",
        "        56 is C\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drSFac8PtXyG"
      },
      "source": [
        "csstudents = [('py', [23, 45, 56, 67, 89, 90]), ('js', [32, 88, 77, 98, 89, 56]), ('cpp', [70, 60, 90, 80, 78, 85]), ('lisp', [90, 95, 77, 86, 97, 67, 65])]\n",
        "\n",
        "# Write your code here!\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OU3FRpdYNPKs",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "for student in csstudents:\n",
        "  name, scores = student\n",
        "  print(name)\n",
        "  print()\n",
        "\n",
        "  for score in scores:\n",
        "    if score < 30:\n",
        "      print(f'{score} is E')\n",
        "    elif score >= 30 and score < 50:\n",
        "      print(f'{score} is D')\n",
        "    elif score >= 50 and score <= 70:\n",
        "      print(f'{score} is C')\n",
        "    elif score > 70 and score < 80:\n",
        "      print(f'{score} is B')\n",
        "    else:\n",
        "      print(f'{score} is A')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6y6gbStqeu0"
      },
      "source": [
        "- Check for parity; parity refers to evenness or oddness of a number\n",
        "\n",
        "- Given a list of numbers or integers, print the number followed by `is even` if the number is even. Else, print the number followed by `is odd`.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YCZE1MAqisu"
      },
      "source": [
        "numbers = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
        "\n",
        "# Write your code here!"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUz6_JWTNfLm",
        "cellView": "form"
      },
      "source": [
        "# @title ##### Double click here for the solution \n",
        "\n",
        "\n",
        "for number in numbers:\n",
        "  if number % 2 == 0:\n",
        "    print(f'{number} is even')\n",
        "  else:\n",
        "    print(f'{number} is odd')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCffpBmpzohy"
      },
      "source": [
        "---\n",
        "*This notebook was created by Cameron Smith and Brian Mboya for the [TReND](https://trendinafrica.org/) Python course 2021 and is published under [MIT license](https://choosealicense.com/licenses/mit/).*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71rX1F3yBco5"
      },
      "source": [
        "---\n",
        "#Congrats! That's it for this tutorial - and for this course. \n",
        "## Thank you so much for being part of this adventure - keep up the good work! Hope to welcome you soon again!\n",
        "\n",
        "----\n",
        "<h1>About the Authors:</h1> \n",
        "\n",
        "## Brian Mboya\n",
        "\n",
        "Github: https://github.com/asheux\n",
        "\n",
        "<br>\n",
        "\n",
        "---\n",
        "\n",
        "## Juan Vargas \n",
        "\n",
        "Neuroscience PhD student in Mainz, Germany\n",
        "\n",
        "I study the sense of vision in insects :)\n",
        "\n"
      ]
    }
  ]
}