camp_intro_to_python.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/cyneuro/ML_camp/blob/main/camp_intro_to_python.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_K6vlxN3vXSA"
      },
      "source": [
        "# Introduction to Python, Google Colab and Jupyter Notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QykigURuremG"
      },
      "source": [
        "Welcome! This tutorial will introduce you to fundamental programming tools we are going to be using during the camp."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZE8z2oTmr6OF"
      },
      "source": [
        "## Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtughqHar7OG"
      },
      "source": [
        "[Python](https://en.wikipedia.org/wiki/Python_(programming_language)) is a popular programming language that features simple syntax and interactive usage. Python is a general-purpose language which means that we can use it to develop a wide variety of things: simple utilities, scientific programs, or games. Learning Python fundamentals is essential for machine learning, because the majority of tools is written in this language.\n",
        "\n",
        "[Resources for learning Python](https://wiki.python.org/moin/BeginnersGuide/Programmers)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fGB-8G3lvXSC"
      },
      "source": [
        "## Google Colab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yHOAneU0uk_v"
      },
      "source": [
        "If you know how to write Python code, you can start coding in any text editor – a specialized development environment (IDE), notepad, or even Microsoft Word. However, to execute the code you wrote, you need Python installed on your computer. Installation process might be a little challenging for beginners, so we will be learning Python in a pre-set web-based environment called Google Colab (this is where you are at).\n",
        "\n",
        "Google Colab allows you to run Python interactively by writing code in **cells** such as this one:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoddDIuevtZe",
        "outputId": "f3a5b40d-95a2-4309-a716-efa7d0a732da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "This is a cell!\n"
          ]
        }
      ],
      "source": [
        "print(\"This is a cell!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Txg4-BqFvzKf"
      },
      "source": [
        "To execute the cell above (i.e., to run the code in it), put your mouse cursor onto the cell and press the \"play\" button on the left:\n",
        "\n",
        "![Screenshot 2024-07-27 at 10.12.18 AM.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XE5Pksa0wO6v"
      },
      "source": [
        "The output of the cell will be displayed below it – in this case, it is a line \"This is a cell!\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F8qXmFGLw3TM"
      },
      "source": [
        "### Adding code cells"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gtuDiCyPw-B3"
      },
      "source": [
        "To add a new code cell, press the \"+Code\" button on the top panel.\n",
        "\n",
        "![Screenshot 2024-07-27 at 10.17.13 AM.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GG_vvzg1xsai"
      },
      "source": [
        "### Adding text cells"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhgeLNoUxxFA"
      },
      "source": [
        "You can also add text cells by pressing the \"+Text\" button on the top panel. Text is formatted using the [Markdown](https://en.wikipedia.org/wiki/Markdown) language which allows for simple modifications such as making text **bold** or *italic*. It also provides functionality for adding equations such as this one: $3x + 2y = 5$. Markdown can be especially helpful for presenting and describing results.\n",
        "\n",
        "[Markdown Cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet), a guide to editing Markdown files.\n",
        "\n",
        "[LaTex Wiki Page](https://en.wikibooks.org/wiki/LaTeX/Mathematics#Symbols) provides guidance on typing math expressions in Markdown."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdt6JrFNy6d2"
      },
      "source": [
        "**This is a text cell**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5TNwnj1xMIq"
      },
      "source": [
        "## Jupyter Notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8H7b2kDzApm"
      },
      "source": [
        "Note that all the code and text cells are collected in one file (which you are looking at right now). This file is called a **Jupyter notebook**. While Google Colab displays, renders and provides tools to execute Jupyter notebooks, a notebook is just a regular file. You can download this notebook by pressing \"File\" –> \"Download\" -> \"Download .ipynb\" (although you will not be able to open it on your computer unless you have Python and [Jupyter](https://jupyter.org/) installed)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zHjlw2J0WFy"
      },
      "source": [
        "## Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctVKKQZP11wH"
      },
      "source": [
        "It is time to start learning Python basics! The rest of this notebook is organized as follows: each section provides a link to an article or new material which you should study followed by exercises. Complete as many exerciese as you can."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-d0b02FqvXSF"
      },
      "source": [
        "### Indentation and printing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "weWxgaR91oE2"
      },
      "source": [
        "[Article: Hello, World](https://www.learnpython.org/en/Hello%2C_World%21)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9h9t_8s2R6M"
      },
      "source": [
        "**Exercise:** Write code to print \"Computer science is great!\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "st7LrNXz2xfi"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IA6v_n1C2xBt"
      },
      "source": [
        "### Arithmetic operations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EuJU6NRO25_d"
      },
      "source": [
        "Python can be used as a basic calculator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "739SfwrhvXSF",
        "outputId": "1e069173-0bbc-491d-e367-c33b70c83d61"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "2 + 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eN0Fmegqk_m6",
        "outputId": "41e9577b-b99b-4282-ed45-b22d9e916493"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "2 - 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MMnJCxklA3r",
        "outputId": "ccf0b8fa-fbf1-40ac-b884-c8294dfbf027"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "2 * 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eO6Rd5UPlCDy",
        "outputId": "e331f70d-aefc-4658-c057-0a08cab54ef1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "2 / 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JgNA_Y8E2-BN"
      },
      "source": [
        "However, not all functions are supported by default."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "RZbdhtE2lyRq",
        "outputId": "b693405d-7938-4c6f-fd03-c60ac4a14a64"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'sin' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-2b5fc2868c51>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msin\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[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'sin' is not defined"
          ]
        }
      ],
      "source": [
        "sin(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1E1Co1F3Gjj"
      },
      "source": [
        "**Exercise:** Compute the following expression using Python:\n",
        "\n",
        "$$\n",
        "3.01 \\times 15 + 1395.32 - \\frac{2}{7} \\times 12.3 - (4 + 3.39 \\times 73.1) = \\text{ }?\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eY2MKyUX5-m7"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8z0rT5kZvXSH"
      },
      "source": [
        "###  Variables and types"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "up98Qk_P3ngu"
      },
      "source": [
        "[Article: Variables and types](https://www.learnpython.org/en/Variables_and_Types)\n",
        "\n",
        "[Article: Lists](https://www.learnpython.org/en/Lists)\n",
        "\n",
        "[Article: Basic operators](https://www.learnpython.org/en/Basic_Operators)\n",
        "\n",
        "**Extended info: Python datatypes:**\n",
        "1. None\n",
        "2. Boolean\n",
        "3. Numeric (integer, float)\n",
        "4. Sequence (indexable, non-unique elements): list (mutable); tuple, string (immutable)\n",
        "5. Set (non-indexable, unique elements): set (mutable); frozenset (immutable)\n",
        "6. Mapping (non-indexable, key:value): dict"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9BMViqs3_z2"
      },
      "source": [
        "**Exercise:** Create a variable `x = 25` and a variable `y = 4.09`. Find the the value of `x * y` and `x + y`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xTqU3Yb_59o8"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCs_VgMV4ROe"
      },
      "source": [
        "**Exercise:** Explain what the following command does:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZuf8Klm4Uy4",
        "outputId": "c16e3db7-868c-4abc-a3cb-cb7a845ba09f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = \"4\"\n",
        "type(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fj_W1U1w4gSC"
      },
      "source": [
        "**Exercise:** Create two variables, `t1` and `t2`, representing temperatures in two regions. Assign values `3` and `2` to these variables. Find the average temperature."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WlCJYN3W584w"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hidKFiC5UNL"
      },
      "source": [
        "**Exercise:** Create a string variable `a = \"hello\"` and make a second string, which repeats `a` 10 times."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Glu2deWa58b_"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBlfTUTp5Drr"
      },
      "source": [
        "**Exercise:** Create the following lists.\n",
        "1. A list containing natural numbers from 1 to 5.\n",
        "2. A list containing the first 3 letters of the English alphabet.\n",
        "3. A list containing two other lists.\n",
        "4. A list containing items of different types.\n",
        "\n",
        "Find the length of one of these lists (use Google to find out how to compute lengths of objects!). Display the second element of each list."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iKSS0ww956lv"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZir9deKvXSI"
      },
      "source": [
        "### Control Flow\n",
        "\n",
        "[Article: Conditions](https://www.learnpython.org/en/Conditions)\n",
        "\n",
        "[Article: Loops](https://www.learnpython.org/en/Loops)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSKDE9yZvXSI",
        "outputId": "40e355ee-1d51-49a8-f35b-97f025631f9a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x is not 3!\n"
          ]
        }
      ],
      "source": [
        "# Conditions: examples\n",
        "x = 90\n",
        "\n",
        "if x == 3:\n",
        "    print(\"x is 3!\")\n",
        "else:\n",
        "    print(\"x is not 3!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oGVj1aHXzCz7"
      },
      "outputs": [],
      "source": [
        "# if cond1:\n",
        "#     if cond2:\n",
        "#         if cond3:\n",
        "#             print(\"something\")\n",
        "#         else:\n",
        "#             print(\"X\")\n",
        "#     else:\n",
        "#         print(\"X\")\n",
        "# else:\n",
        "#     print(\"X\")\n",
        "\n",
        "# for i in []:\n",
        "#     for j in []:\n",
        "#         do something"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9cAKvGZ-1Lt",
        "outputId": "0bdfde5d-b57f-406f-99ad-f3f89345a2c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n"
          ]
        }
      ],
      "source": [
        "# For loop\n",
        "a = [1, 2, 3, 4, 5]\n",
        "for element in a:\n",
        "    print(element)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3AbF5lM_Mfq",
        "outputId": "e73bd405-67ef-4e04-c03d-f71494d5d322"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n"
          ]
        }
      ],
      "source": [
        "# While loop\n",
        "x = 0\n",
        "while x < 10:\n",
        "    print(x)\n",
        "    x = x + 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2LlOGLZvXSI"
      },
      "source": [
        "**Exercise:** Consider the following time series."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c2J4zYXovXSI"
      },
      "outputs": [],
      "source": [
        "v = [18, 20, 22, -1, 20, 4, 22, 5, 5, 27, -5, 9,\n",
        "     20, 8, 27, 3, 21, 90, 19, 25, 27, 0, 0, 4,\n",
        "     0, 18, 19, 22, 27, 24, -120, 20, 9, 20, 5, 8, 20, 22]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECMJDF4VvXSI"
      },
      "source": [
        "Find\n",
        "1. The number of observations.\n",
        "2. Maximum and minimum value.\n",
        "3. Mean value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cT-ZUv4D61G7"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgyinSKivXSJ"
      },
      "source": [
        "**Exercise:** Samples measured in real life are often corrupted by noise or errors and need to be corrected before further analysis. One possible indication of such corruption is atypical values called *outliers*. Look at the sample from **E1** and propose a criterion for detecting outliers. Remove the outliers using a `for` loop and recompute the statistics above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n2c4LkRj4dJC"
      },
      "outputs": [],
      "source": [
        "v = [18, 20, 22, -1, 20, 4, 22, 5, 5, 27, -5, 9,\n",
        "     20, 8, 27, 3, 21, 90, 19, 25, 27, 0, 0, 4,\n",
        "     0, 18, 19, 22, 27, 24, -120, 20, 9, 20, 5, 8, 20, 22]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UbTRCwCo6zJd"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBneif9ivXSJ"
      },
      "source": [
        "**Exercise:** A researcher applies an initial current injection of $I_1$ nA and wants to gradually increase it up to at least $I_2$ nA. Due to characteristics of the equipment, they can only increase the amplitude in discrete steps, each step providing an increase of $15\\%$ of the previous value. How many steps would take them to reach the goal?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "40bQGWdh6y0r"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L76pdanhvXSJ"
      },
      "source": [
        "## Functions\n",
        "\n",
        "[Article: Functions](https://www.learnpython.org/en/Functions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7i0HXQF2vXSJ"
      },
      "outputs": [],
      "source": [
        "def function_name():\n",
        "    print(\"I was called!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xTrcu3I17E1W",
        "outputId": "483cc788-94c1-44b4-e2c6-e8e83bcff43a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "I was called!\n"
          ]
        }
      ],
      "source": [
        "function_name()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aTc3TfQ07goL"
      },
      "outputs": [],
      "source": [
        "def sum_of_two_numbers(number1, number2):\n",
        "    computed_sum = number1 + number2\n",
        "    return computed_sum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7tyw_NP78xs",
        "outputId": "1a83e567-02b6-4564-d9ad-9c46711f8b1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sum_of_two_numbers(3, 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Me2Pxx1t8pN9",
        "outputId": "01100a21-06ba-41a4-825a-ccdc255516c4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'32'"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sum_of_two_numbers(\"3\", \"2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cP1sVKC5vXSJ"
      },
      "source": [
        "**Exercise:** Write a function which filters out all negative elements in a given list of floats."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sevf3DDi7U_E"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KY9Qv3c5vXSJ"
      },
      "source": [
        "**Exercise:** Write a function which finds the median and the mode of a given list of floats."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U9t39n-F7VQW"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Acs1zvr6vXSJ"
      },
      "source": [
        "**Exercise:** Write a function which counts the number of occurences of each unique value in a given list."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JEPSJkHH7VfV"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1NHaXy_vXSJ"
      },
      "source": [
        "## Libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMzxk_PB7dGD"
      },
      "source": [
        "[Article: Classes and objects](https://www.learnpython.org/en/Classes_and_Objects)\n",
        "\n",
        "[Article: Modules and packages](https://www.learnpython.org/en/Modules_and_Packages)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-sTYOMo8FdY8"
      },
      "outputs": [],
      "source": [
        "import math # math.py somewhere\n",
        "math.sin(3) # def sin() inside math.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wG4PLx-yFyC2"
      },
      "outputs": [],
      "source": [
        "from math import cos\n",
        "cos(7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EWUjqZj3GB_f"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot([1, 2, 3], [5, 7, 8]) # matplotlib.pyplot.plot([1, 2, 3], [5, 7, 8])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0b9-KZovXSK"
      },
      "source": [
        "**Exercise:** Use the `random` library to generate 1000 random points from the uniform on [0, 1] distribution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R-v-LqtB7xsZ"
      },
      "outputs": [],
      "source": [
        "# Your code here"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "simulations",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}