ICOCIMS_workshop_day1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction"
      ],
      "metadata": {
        "id": "IFCjpcx5TE2s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Welcome to the python workshop for biomedical data science with python at the ICOCIMS conference.\n",
        "\n",
        "As you can see, we use the google colab service as our programming environment. This has the advantage, that we can write code and analyse data independently from a computer. All we need is a browser and a google account to start. All calculations are performed on servers of google, therefore, the computer we are using does not matter any further.\n",
        "\n",
        "Google colab notebooks are designed after the famour Jupyter notebooks, and are even saved in the same format.\n",
        "The most essential building block is a cell. This text, and any upcoming code is typed into a cell. We can determine, if a cell is supposed to carry informative text, or code to be executed.\n",
        "\n",
        "However, let us start with the basics."
      ],
      "metadata": {
        "id": "9vw5PuXmwHUc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Python, like any programming language, is basically a big calculator. We can simply perform any mathematical operation we want:"
      ],
      "metadata": {
        "id": "t01AFt6_xRBm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Addition\n",
        "print(1 + 2)\n",
        "\n",
        "# Subtraction\n",
        "print(5 - 3)\n",
        "\n",
        "# Multiplication\n",
        "print(4 * 6)\n",
        "\n",
        "# Division\n",
        "print(10 / 2)\n",
        "\n",
        "# Exponentiation\n",
        "print(2 ** 3)\n",
        "\n",
        "# Modulo (remainder)\n",
        "print(10 % 3)\n"
      ],
      "metadata": {
        "id": "0JdnvNVHxd5D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see, we can perform basic and advanced mathematical operations. But in programming, we don´t type in the numbers directly to do calculations, they are stored in a variable."
      ],
      "metadata": {
        "id": "EzZlbgj4W4Gh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Basic data types\n",
        "name = \"Alice\"      # String variable\n",
        "age = 25            # Integer variable, a whole number\n",
        "height = 5.9        # Float variable, has a decimal\n",
        "\n",
        "print(\"Name:\", name)\n",
        "print(\"Age:\", age)\n",
        "print(\"Height:\", height)\n"
      ],
      "metadata": {
        "id": "vcXBowxgXMuS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once stored in a variable we can do calculations the same way we did above:"
      ],
      "metadata": {
        "id": "RtdBUj_TZD5D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weight = 60.5 # weight in kilogram, kg\n",
        "height = 1.70 #height in meters, m\n",
        "bmi = weight / (height ** 2)\n",
        "print(\"BMI:\", bmi)"
      ],
      "metadata": {
        "id": "XyWxV_9aZKaQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to do more complex calculations, python offers not only numbers, but also more complex data types:"
      ],
      "metadata": {
        "id": "t7K_3iIqZ0oU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = 10               # Integer, a whole number\n",
        "y = 3.5              # Float, has a decimal\n",
        "name = \"Data\"        # String\n",
        "is_active = True     # Boolean\n"
      ],
      "metadata": {
        "id": "wWLYTLBFaBdo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "These are some of the basic data types we will use. Whole and decimal numbers we have seen already.\n",
        "Any text in python is called a \"string\", and we can perform operations on them.\n",
        "\n",
        "And last but not least, logical values.\n",
        "Logical values, called \"Booleans\", can take the value of either \"True\", or \"False\". True is, in mathematical operations equal to 1, and False to 0.\n",
        "\n",
        "However, let´s look again at some examples of operations on the basic data types:"
      ],
      "metadata": {
        "id": "W90VM15PaHNF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = 5\n",
        "b = 2\n",
        "print(\"Adding numbers:\", a + b)         # 7\n",
        "\n",
        "greeting = \"Hello \" + \"World\"  # \"Hello World\"\n",
        "print(\"Adding strings together:\", greeting)\n",
        "\n",
        "is_true = True\n",
        "is_false = False\n",
        "print(\"Adding booleans together:\", is_true + is_false)  # 1"
      ],
      "metadata": {
        "id": "azNy4kf9bEFU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lists\n",
        "\n",
        "These are examples of working with some basic data types in python.\n",
        "For more advanced applications, it is necessary to store i.e. numbers together in a variable. One of the most important way to do this, is the list.\n",
        "\n",
        "Python lists are versatile, ordered collections of elements that can store different data types such as integers, floats, strings, and even other lists. Lists are mutable, meaning their contents can be changed after they are created. This flexibility makes them one of the most commonly used data structures in Python.\n",
        "\n"
      ],
      "metadata": {
        "id": "yrqbP81Pb-oT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# A list is created using square brackets [ ], with elements separated by commas.\n",
        "\n",
        "# Example: Creating a list with different data types\n",
        "my_list = [1, \"Hello\", 3.14, True]\n",
        "print(my_list)    # Output: [1, 'Hello', 3.14, True]"
      ],
      "metadata": {
        "id": "jn_w_WjDcVT3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accessing list elements:"
      ],
      "metadata": {
        "id": "yjclLHS2cgCs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fruits = [\"apple\", \"banana\", \"cherry\"]\n",
        "\n",
        "# Accessing elements\n",
        "print(fruits[0])   # Output: apple\n",
        "print(fruits[2])   # Output: cherry"
      ],
      "metadata": {
        "id": "_MKrrga5ch75"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This demonstrates an important principle in python: the zero-based indexing.\n",
        "Python starts counting from zero, and not like most people do at 1.\n",
        "So, when it comes to indexing in our example, we don´t have the elements 1, 2, and 3.\n",
        "Instead we have elements 0, 1, and 2.\n",
        "\n",
        "However, we can not only use indices fromthe beginning.\n",
        "Instead we can index from the end of the list, using negative indices:"
      ],
      "metadata": {
        "id": "fXmoXZV6clTu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accessing elements using negative indexing\n",
        "print(fruits[-1])  # Output: cherry"
      ],
      "metadata": {
        "id": "2Y14CgWOdN7j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sometimes, we don´t want to access only a single element in a list, but several. For this purpose, we use \"slicing\".\n",
        "\n",
        "Slicing allows you to access a range of elements in a list by specifying a start and end index. The syntax is list[start:end], and it includes elements from the start index up to (but not including) the end index."
      ],
      "metadata": {
        "id": "LVdRbkQGdR9p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numbers = [0, 1, 2, 3, 4, 5, 6]\n",
        "\n",
        "# Slicing to get elements from index 2 to 5 (excludes 5)\n",
        "print(numbers[2:5])   # Output: [2, 3, 4]"
      ],
      "metadata": {
        "id": "nh8SLGIIdca1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also omit the start or end index to get a slice from the beginning or to the end of the list."
      ],
      "metadata": {
        "id": "wXJQSg64dc49"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# From start to index 3 (excludes 3)\n",
        "print(numbers[:3])    # Output: [0, 1, 2]\n",
        "\n",
        "# From index 3 to the end\n",
        "print(numbers[3:])    # Output: [3, 4, 5, 6]"
      ],
      "metadata": {
        "id": "kovD21wxdkxk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Changing values in a list:\n",
        "\n",
        "Because lists are mutable, you can modify them directly by assigning new values to specific indices."
      ],
      "metadata": {
        "id": "mpbLPtTBdwBl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colors = [\"red\", \"green\", \"blue\"]\n",
        "colors[1] = \"yellow\"\n",
        "print(colors)         # Output: ['red', 'yellow', 'blue']"
      ],
      "metadata": {
        "id": "kOETZDnRdyQs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we look at basic functions for working with lists.\n",
        "\n",
        "#### Working with lists\n",
        "We will look at functions in more detail later, but here´s a short note for the following example:\n",
        "In python there are two types of functions. Literally functions, which are used with the structure\n",
        "function(argument1, argument2),\n",
        "\n",
        "and methods.\n",
        "Data types like lists are objects in python. And some functions are designed so they work only on this type of object. For more clarity, we don´t use such functions like we see above, instead the functions are called methods, and they belong to the object. They are used like\n",
        "object.function(argument2).\n",
        "We often use less arguments in object-methods, as the object itself is one of the inputs already.\n",
        "And this is what we will use now for working with lists.\n",
        "###\n",
        "\n",
        "Adding elements to a list:\n",
        "1. append(): Adds a single element to the end of the list.\n",
        "2. insert(): Adds an element at a specific index.\n",
        "3. extend(): Adds multiple elements to the end of the list.\n"
      ],
      "metadata": {
        "id": "klEYaf0Ld7oc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using append\n",
        "colors.append(\"purple\")\n",
        "print(colors)         # Output: ['red', 'yellow', 'blue', 'purple']\n",
        "\n",
        "# Using insert\n",
        "colors.insert(1, \"orange\")\n",
        "print(colors)         # Output: ['red', 'orange', 'yellow', 'blue', 'purple']\n",
        "\n",
        "# Using extend\n",
        "colors.extend([\"pink\", \"black\"])\n",
        "print(colors)         # Output: ['red', 'orange', 'yellow', 'blue', 'purple', 'pink', 'black']"
      ],
      "metadata": {
        "id": "7kOzMHxGffan"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing elements from a list:\n",
        "1. remove(): Removes the first occurrence of a specified value.\n",
        "2. pop(): Removes an element by index (if no index is specified, it removes the last item).\n",
        "3. clear(): Removes all elements from the list."
      ],
      "metadata": {
        "id": "ZAerCp6hfrDv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using remove\n",
        "colors.remove(\"yellow\")\n",
        "print(colors)         # Output: ['red', 'orange', 'blue', 'purple', 'pink', 'black']\n",
        "\n",
        "# Using pop\n",
        "popped_color = colors.pop(2)\n",
        "print(popped_color)   # Output: blue\n",
        "print(colors)         # Output: ['red', 'orange', 'purple', 'pink', 'black']\n",
        "\n",
        "# Using clear\n",
        "colors.clear()\n",
        "print(colors)         # Output: []\n"
      ],
      "metadata": {
        "id": "0sP3r9w8fyZx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sometimes we need to create long lists with calculations, for which we use list comprehensions.\n",
        "\n",
        "List comprehensions provide a concise way to create lists by embedding a loop inside square brackets. This is useful for generating lists based on existing lists or other conditions.\n",
        "\n",
        "This is using essentially a for loop, so please look at the example later, after learning about for-loops."
      ],
      "metadata": {
        "id": "cQkAq0g0hIyF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Creating a list of squares\n",
        "squares = [x**2 for x in range(1, 6)]\n",
        "print(squares)        # Output: [1, 4, 9, 16, 25]"
      ],
      "metadata": {
        "id": "fYvmSZhkhadK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sorting and Reversing Lists\n",
        "1. sort(): Sorts a list in ascending order. (Can also sort in descending order with reverse=True).\n",
        "2. reverse(): Reverses the order of the list."
      ],
      "metadata": {
        "id": "Rjb6i-erhkC5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sorting\n",
        "numbers = [4, 2, 9, 1]\n",
        "numbers.sort()\n",
        "print(numbers)        # Output: [1, 2, 4, 9]\n",
        "\n",
        "# Sorting in descending order\n",
        "numbers.sort(reverse=True)\n",
        "print(numbers)        # Output: [9, 4, 2, 1]\n",
        "\n",
        "# Reversing a list\n",
        "numbers.reverse()\n",
        "print(numbers)        # Output: [1, 2, 4, 9]"
      ],
      "metadata": {
        "id": "LBaKqQdohp6c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A list can contain other lists, which allows for creating nested structures, or \"lists of lists.\"\n",
        "\n",
        "This concept will be important later for matrices, as a matrix is essentially a nested list."
      ],
      "metadata": {
        "id": "WD_mSyMshvJR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a nested list\n",
        "matrix = [\n",
        "    [1, 2, 3],\n",
        "    [4, 5, 6],\n",
        "    [7, 8, 9]\n",
        "]\n",
        "\n",
        "# Accessing an element in a nested list\n",
        "print(matrix[1][2])   # Output: 6\n"
      ],
      "metadata": {
        "id": "iX6OdKTlhxq4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Python lists are powerful and flexible tools that are integral to data handling and manipulation. Their mutability, versatility in data types, and ease of access through indexing and slicing make them suitable for a wide range of programming tasks. Working with lists is foundational in Python, and mastering them is essential for data science, web development, and other Python applications.\n",
        "\n"
      ],
      "metadata": {
        "id": "Cv0Szt9Kicxj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dictionaries\n",
        "\n",
        "Now, we are getting to a second data type, the dictionary.\n",
        "Dictionaries in Python are powerful and flexible data structures that store data in key-value pairs. They are similar to a real-world dictionary where each word (key) is associated with a definition (value). Dictionaries are unordered, mutable, and can contain data of different types, making them extremely useful for various programming tasks.\n",
        "\n",
        "\n",
        "A dictionary is created using curly braces {}, where each key is separated from its corresponding value by a colon :, and key-value pairs are separated by commas."
      ],
      "metadata": {
        "id": "KkMO5E9QThc3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Creating a simple dictionary\n",
        "person = {\n",
        "    \"name\": \"Alice\",\n",
        "    \"age\": 30,\n",
        "    \"city\": \"New York\"\n",
        "}\n",
        "print(person)      # Output: {'name': 'Alice', 'age': 30, 'city': 'New York'}\n"
      ],
      "metadata": {
        "id": "BO5zJQGKh1CA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Keys in a dictionary must be unique and immutable (e.g., strings, numbers, tuples), while values can be of any data type and can be repeated."
      ],
      "metadata": {
        "id": "AdiM0T-Vixum"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accessign values in a dictionary:\n",
        "Values in a dictionary can be accessed by referencing their keys inside square brackets []."
      ],
      "metadata": {
        "id": "UJC_nQpoi_Mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accessing values\n",
        "print(person[\"name\"])   # Output: Alice\n",
        "print(person[\"age\"])    # Output: 30"
      ],
      "metadata": {
        "id": "CS1a5S8li7OJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Working with dictionaries"
      ],
      "metadata": {
        "id": "qALEk3meTla6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If a key doesn’t exist, accessing it directly will raise a KeyError. To avoid this, use the get() method, which returns None or a specified default value if the key is not found."
      ],
      "metadata": {
        "id": "jS5KRuz-jG-T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using get() to avoid KeyError\n",
        "print(person.get(\"city\", \"Unknown\"))  # Output: New York\n",
        "print(person.get(\"country\", \"Unknown\"))  # Output: Unknown"
      ],
      "metadata": {
        "id": "iZFWYjAajSmx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adding and Modifying Elements:\n",
        "\n",
        "Dictionaries are mutable, so you can add new key-value pairs or modify existing ones by assigning a value to a key."
      ],
      "metadata": {
        "id": "A1T_Pz9ejWGJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding a new key-value pair\n",
        "person[\"email\"] = \"alice@example.com\"\n",
        "print(person)      # Output: {'name': 'Alice', 'age': 30, 'city': 'New York', 'email': 'alice@example.com'}\n",
        "\n",
        "# Modifying an existing value\n",
        "person[\"age\"] = 31\n",
        "print(person)      # Output: {'name': 'Alice', 'age': 31, 'city': 'New York', 'email': 'alice@example.com'}\n"
      ],
      "metadata": {
        "id": "ItL1bRDGjWww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing Elements\n",
        "You can remove elements from a dictionary using several methods:\n",
        "\n",
        "1. pop(): Removes a specific key and returns its value.\n",
        "2. del: Deletes a specific key or the entire dictionary.\n",
        "3. popitem(): Removes the last inserted key-value pair (useful in Python 3.7+ where dictionaries maintain insertion order).\n",
        "4. clear(): Empties the dictionary.\n"
      ],
      "metadata": {
        "id": "mSDe1mo6jefn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using pop()\n",
        "removed_city = person.pop(\"city\")\n",
        "print(removed_city)   # Output: New York\n",
        "print(person)         # Output: {'name': 'Alice', 'age': 31, 'email': 'alice@example.com'}\n",
        "\n",
        "# Using del\n",
        "del person[\"email\"]\n",
        "print(person)         # Output: {'name': 'Alice', 'age': 31}\n",
        "\n",
        "# Using clear to empty the dictionary\n",
        "person.clear()\n",
        "print(person)         # Output: {}\n"
      ],
      "metadata": {
        "id": "7xFLCcRxjnWJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Also, we can loop through dictionaries.\n",
        "Again, we will still look at loops later, and you can go back to this section afterwards.\n",
        "\n",
        "Loops can access the keys of dictionaries, the values, or both at the same time:"
      ],
      "metadata": {
        "id": "2Ytb0i7Tk6AR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example dictionary\n",
        "fruit_colors = {\n",
        "    \"apple\": \"red\",\n",
        "    \"banana\": \"yellow\",\n",
        "    \"cherry\": \"red\"\n",
        "}\n",
        "\n",
        "print('Loop 1:')\n",
        "# Looping through keys\n",
        "for fruit in fruit_colors:\n",
        "    print(fruit)\n",
        "print(\"\")\n",
        "\n",
        "print('Loop 2:')\n",
        "# Looping through values\n",
        "for color in fruit_colors.values():\n",
        "    print(color)\n",
        "print(\"\")\n",
        "\n",
        "print('Loop 3:')\n",
        "# Looping through key-value pairs\n",
        "for fruit, color in fruit_colors.items():\n",
        "    print(f\"The color of {fruit} is {color}\")"
      ],
      "metadata": {
        "id": "Mi9Dg1P1k5nf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similar to list comprehensions, dictionary comprehensions provide a concise way to create dictionaries."
      ],
      "metadata": {
        "id": "HRqObmgVlhtp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a dictionary of squares\n",
        "squares = {x: x**2 for x in range(1, 6)}\n",
        "print(squares)      # Output: {1: 1, 2: 4, 3: 9, 4: 16, 5: 25}"
      ],
      "metadata": {
        "id": "MX9aqRYxliIB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, there are some useful methods for dictionaries:"
      ],
      "metadata": {
        "id": "9i9DhKPOlluf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example dictionary\n",
        "person = {\n",
        "    \"name\": \"Alice\",\n",
        "    \"age\": 30\n",
        "}\n",
        "\n",
        "# Getting keys, values, and items\n",
        "print(\"Get the keys of the dictionary\")\n",
        "print(person.keys())   # Output: dict_keys(['name', 'age'])\n",
        "print(\"\")\n",
        "\n",
        "print(\"Get the values of the dictionary\")\n",
        "print(person.values()) # Output: dict_values(['Alice', 30])\n",
        "print(\"\")\n",
        "\n",
        "print(\"Get the items of the dictionary\")\n",
        "print(person.items())  # Output: dict_items([('name', 'Alice'), ('age', 30)])\n",
        "print(\"\")\n",
        "\n",
        "# Checking if a key exists\n",
        "print(\"Check if a key exists\")\n",
        "print(\"name\" in person)  # Output: True\n",
        "print(\"city\" in person)  # Output: False\n",
        "print(\"\")\n",
        "\n",
        "# Updating a dictionary\n",
        "print(\"Update a dictionary\")\n",
        "person.update({\"city\": \"New York\", \"age\": 31})\n",
        "print(person)          # Output: {'name': 'Alice', 'age': 31, 'city': 'New York'}\n"
      ],
      "metadata": {
        "id": "UWPYJ5fslqCK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dictionaries are essential for managing data in key-value pairs. They provide efficient access, modification, and storage capabilities, making them fundamental in Python programming. Understanding dictionaries and their methods helps unlock the potential of data organization and manipulation in Python.\n",
        "\n"
      ],
      "metadata": {
        "id": "xX5Zwf34mIL-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conditional statements\n",
        "\n",
        "Conditional statements allow you to execute specific blocks of code based on certain conditions. Python’s main conditional statements are if, elif, and else, which allow for multiple branching based on logical conditions.\n",
        "\n",
        "Basic if Statement:\n",
        "An if statement executes a block of code if a specified condition is True."
      ],
      "metadata": {
        "id": "-C58Q8WPmddm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "age = 20\n",
        "if age >= 18:\n",
        "    print(\"You are an adult.\")"
      ],
      "metadata": {
        "id": "5nrIzMa4mKiG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "if-else Statement:\n",
        "\n",
        "The if-else structure allows one block of code to run if a condition is True and another if it’s False."
      ],
      "metadata": {
        "id": "JvIuIRH8mq__"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "age = 16\n",
        "if age >= 18:\n",
        "    print(\"You are an adult.\")\n",
        "else:\n",
        "    print(\"You are a minor.\")\n",
        "\n",
        "### Since age is less than 18, the program prints \"You are a minor.\""
      ],
      "metadata": {
        "id": "kdU1hxmkmndx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "if-elif-else Statement:\n",
        "\n",
        "The if-elif-else statement is used when there are multiple conditions to check. Only the first condition that evaluates to True will execute."
      ],
      "metadata": {
        "id": "0b6pJ9g8m4pQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "score = 85\n",
        "\n",
        "if score >= 90:\n",
        "    print(\"Grade: A\")\n",
        "elif score >= 80:\n",
        "    print(\"Grade: B\")\n",
        "elif score >= 70:\n",
        "    print(\"Grade: C\")\n",
        "else:\n",
        "    print(\"Grade: D\")\n",
        "\n",
        "### The score of 85 is greater than 80 but less than 90,\n",
        "### so it meets the elif score >= 80 condition,\n",
        "### and the program outputs \"Grade: B\"."
      ],
      "metadata": {
        "id": "umQGsTtMm7lJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Nested if-statements:\n",
        "\n",
        "You can also nest if-statements within other if-statements to handle more complex conditions."
      ],
      "metadata": {
        "id": "XiHfeLQpm9IX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "age = 20\n",
        "is_student = True\n",
        "\n",
        "if age >= 18:\n",
        "    if is_student:\n",
        "        print(\"You are an adult student.\")\n",
        "    else:\n",
        "        print(\"You are an adult.\")\n",
        "else:\n",
        "    print(\"You are a minor.\")"
      ],
      "metadata": {
        "id": "BxBEqQOunP-R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multiple Conditions with Logical Operators:\n",
        "You can use logical operators like and, or, and not to combine multiple conditions within a single if statement."
      ],
      "metadata": {
        "id": "gZzjMS2QnWZ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "age = 25\n",
        "is_employed = True\n",
        "\n",
        "if age >= 18 and is_employed:\n",
        "    print(\"You are an employed adult.\")"
      ],
      "metadata": {
        "id": "D49W6IRlnQkI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "One-Line if Statements (Conditional Expressions):\n",
        "\n",
        "Python supports a shorthand for if-else known as a conditional expression, which can be written on one line."
      ],
      "metadata": {
        "id": "Co9MSkYDneu9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "age = 17\n",
        "status = \"Adult\" if age >= 18 else \"Minor\"\n",
        "print(status)     # Output: Minor"
      ],
      "metadata": {
        "id": "M7iuXEgJnduL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is a small example using conditional statements."
      ],
      "metadata": {
        "id": "eok9O-IDnwR-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Welcome to our restaurant!\")\n",
        "print(\"Choose your meal preference: 1) Vegetarian  2) Non-Vegetarian  3) Vegan\")\n",
        "\n",
        "choice = int(input(\"Enter your choice (1, 2, or 3): \"))\n",
        "\n",
        "if choice == 1:\n",
        "    print(\"You selected Vegetarian. Here are your options:\")\n",
        "    print(\"- Grilled Vegetable Sandwich\")\n",
        "    print(\"- Vegetable Stir Fry\")\n",
        "elif choice == 2:\n",
        "    print(\"You selected Non-Vegetarian. Here are your options:\")\n",
        "    print(\"- Grilled Chicken Sandwich\")\n",
        "    print(\"- Beef Stir Fry\")\n",
        "elif choice == 3:\n",
        "    print(\"You selected Vegan. Here are your options:\")\n",
        "    print(\"- Vegan Buddha Bowl\")\n",
        "    print(\"- Tofu Stir Fry\")\n",
        "else:\n",
        "    print(\"Invalid choice. Please enter 1, 2, or 3.\")"
      ],
      "metadata": {
        "id": "c_re5kjWn2zn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "These examples demonstrate the flexibility of Python’s conditional statements, which are essential for controlling program flow and making dynamic decisions based on user input or other conditions."
      ],
      "metadata": {
        "id": "S5smzyB5oSRd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loops\n",
        "\n",
        "Loops are essential structures in Python that allow repetitive tasks, making code efficient and manageable. The two main types of loops in Python are for loops and while loops. Below, we provide a brief overview of while loops (because they are conceptually important) and then focus in detail on for loops (Because they are more used in practice).\n",
        "\n",
        "\n",
        "While-loops (brief overview):\n",
        "\n",
        "A while loop runs as long as a specified condition is True. It is useful when the number of iterations isn’t known in advance. However, to avoid infinite loops, the condition must eventually become False."
      ],
      "metadata": {
        "id": "OxRbvTBQoTA-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: While loop that runs until count is 5\n",
        "count = 0\n",
        "while count < 5:\n",
        "    print(\"Count:\", count)\n",
        "    count += 1\n",
        "\n",
        "### Here, count starts at 0 and increments by 1 on each loop iteration\n",
        "### until it reaches 5, at which point the condition count < 5 becomes False,\n",
        "### stopping the loop."
      ],
      "metadata": {
        "id": "QhjzlPynom6e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For-loops\n",
        "\n",
        "A for loop iterates over a sequence (e.g., a list, tuple, string, or dictionary, as we have seen earlier), performing the same action for each element. It is commonly used when the number of iterations is known in advance."
      ],
      "metadata": {
        "id": "RdmCOj3Zo260"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Syntax\n",
        "for item in sequence:\n",
        "    # Code to execute on each item"
      ],
      "metadata": {
        "id": "tsSdl9yDo4ov"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Loop with Lists:\n",
        "\n",
        "A common use of for loops is iterating through lists. Each iteration applies the code block to the current item."
      ],
      "metadata": {
        "id": "0UPU3CIOpB33"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Iterating over a list of fruits\n",
        "fruits = [\"apple\", \"banana\", \"cherry\"]\n",
        "for fruit in fruits:\n",
        "    print(\"I like\", fruit)"
      ],
      "metadata": {
        "id": "oPafcK1tpECZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For-loops with range():\n",
        "\n",
        "The range() function generates a sequence of numbers, making it ideal for looping a specific number of times."
      ],
      "metadata": {
        "id": "SIMZXhrbpIMN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Looping with range to print numbers from 0 to 4\n",
        "for i in range(5):\n",
        "    print(\"Number:\", i)\n",
        "\n",
        "print(\"\")\n",
        "print(\"\")\n",
        "\n",
        "### You can also specify a starting and ending point, and even a step value\n",
        "\n",
        "for i in range(1, 10, 2):\n",
        "    print(\"Odd number:\", i)"
      ],
      "metadata": {
        "id": "IQ30mkM_pCyg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Strings are iterable, so you can loop through each character individually."
      ],
      "metadata": {
        "id": "ryH_j0CkpmL8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for char in \"Hello\":\n",
        "    print(char)"
      ],
      "metadata": {
        "id": "f_KPD5z1pnP_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Loops with Dictionaries:\n",
        "\n",
        "For dictionaries, you can loop through keys, values, or both. The items() method allows access to both key and value in each iteration."
      ],
      "metadata": {
        "id": "iR-FFAl_ptCn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "person = {\"name\": \"Alice\", \"age\": 30}\n",
        "for key, value in person.items():\n",
        "    print(f\"{key}: {value}\")"
      ],
      "metadata": {
        "id": "5w88heuoppyI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nested For Loops:\n",
        "\n",
        "A for loop inside another for loop is known as a nested for loop. It’s often used when working with matrices or nested data structures."
      ],
      "metadata": {
        "id": "ehTXS4Awp51z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Nested loop for a matrix\n",
        "matrix = [\n",
        "    [1, 2, 3],\n",
        "    [4, 5, 6],\n",
        "    [7, 8, 9]\n",
        "]\n",
        "\n",
        "for row in matrix:\n",
        "    for item in row:\n",
        "        print(item, end=\" \")\n",
        "    print()\n",
        "###The end=\" \" in print() keeps items in the same row on one line."
      ],
      "metadata": {
        "id": "59TzuFSmpYaz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "List comprehensions: For loops are often used within list comprehensions, a concise way to generate lists.\n",
        "\n"
      ],
      "metadata": {
        "id": "obDvPR9GqLIZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Creating a list of squares\n",
        "squares = [x**2 for x in range(5)]\n",
        "print(squares)     # Output: [0, 1, 4, 9, 16]"
      ],
      "metadata": {
        "id": "8SGd7I2gqNCq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Summary\n",
        "1. While Loops: Run as long as a condition is true; useful when the end condition isn’t predetermined.\n",
        "2. For Loops: Ideal for iterating through sequences when the number of iterations is known. They’re versatile and work with lists, dictionaries, strings, and more, including list comprehensions for concise code generation."
      ],
      "metadata": {
        "id": "NiNo24HyqQoP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Functions in Python\n",
        "\n",
        "Functions in Python are reusable blocks of code that perform a specific task. They allow code to be modular, organized, and easy to debug and reuse. Functions are defined using the def keyword, followed by the function name and parentheses () containing any parameters. Here are some examples:\n",
        "\n",
        "Basic function:\n",
        "A function definition includes the function name, parameters (if any), and a return statement (if the function needs to return a value)."
      ],
      "metadata": {
        "id": "ntjZw2SksD0r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def greet():\n",
        "    print(\"Hello, World!\")\n",
        "\n",
        "# Calling the function\n",
        "greet()\n"
      ],
      "metadata": {
        "id": "iqifXAZEsCLG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Functions with parameters:\n",
        "\n",
        "Parameters allow you to pass data into functions, making them flexible and adaptable for different use cases."
      ],
      "metadata": {
        "id": "t5h2G9zosXbk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def greet_user(name):\n",
        "    print(f\"Hello, {name}!\")\n",
        "\n",
        "# Calling the function with an argument\n",
        "greet_user(\"Alice\")  # Output: Hello, Alice!"
      ],
      "metadata": {
        "id": "Gfq8lJlJqOqw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Functions with a return value:\n",
        "\n",
        "Functions can return a value using the return keyword, making the output available for further use."
      ],
      "metadata": {
        "id": "WSAwm14osgDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add(a, b):\n",
        "    return a + b\n",
        "\n",
        "# Using the returned value\n",
        "result = add(5, 3)\n",
        "print(\"Sum:\", result)"
      ],
      "metadata": {
        "id": "rHY9oVlPsdnS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Default values in functions:\n",
        "\n",
        "Functions can have default values for parameters, making them optional when calling the function."
      ],
      "metadata": {
        "id": "tgKUTFJXsqo5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def greet_user(name=\"Guest\"):\n",
        "    print(f\"Hello, {name}!\")\n",
        "\n",
        "greet_user()\n",
        "greet_user(\"Alice\")"
      ],
      "metadata": {
        "id": "BTJAEbNdstKE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multiple Return Values:\n",
        "\n"
      ],
      "metadata": {
        "id": "mS7NFtCIs0cB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def divide_and_remainder(dividend, divisor):\n",
        "    quotient = dividend // divisor\n",
        "    remainder = dividend % divisor\n",
        "    return quotient, remainder\n",
        "\n",
        "# Unpacking the returned tuple\n",
        "q, r = divide_and_remainder(10, 3)\n",
        "print(\"Quotient:\", q)     # Output: Quotient: 3\n",
        "print(\"Remainder:\", r)    # Output: Remainder: 1"
      ],
      "metadata": {
        "id": "st-HQfY2s3_z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Keyword Arguments:\n",
        "\n",
        "When calling a function, you can specify parameters by name using keyword arguments, improving code readability and flexibility."
      ],
      "metadata": {
        "id": "wOuvMeqJs9ri"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def introduce(name, age, city):\n",
        "    print(f\"{name} is {age} years old and lives in {city}.\")\n",
        "\n",
        "introduce(age=30, name=\"Alice\", city=\"New York\")"
      ],
      "metadata": {
        "id": "EpKzwEBYtBfp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lambda Functions (Anonymous Functions)\n",
        "\n",
        "Lambda functions are small anonymous functions defined with the lambda keyword. They are often used for simple tasks, especially when a quick function is needed without defining it formally."
      ],
      "metadata": {
        "id": "xck8z08wtJ_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "double = lambda x: x * 2\n",
        "print(double(5))"
      ],
      "metadata": {
        "id": "sqKj_cDftE3s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Small example:"
      ],
      "metadata": {
        "id": "Vdh4-zNStTCx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "def calculate_area(radius):\n",
        "    return math.pi * radius ** 2\n",
        "\n",
        "def calculate_circumference(radius):\n",
        "    return 2 * math.pi * radius\n",
        "\n",
        "# Calling the functions\n",
        "r = 5\n",
        "area = calculate_area(r)\n",
        "circumference = calculate_circumference(r)\n",
        "\n",
        "print(\"Area:\", area)\n",
        "print(\"Circumference:\", circumference)\n"
      ],
      "metadata": {
        "id": "syggMhnctUZD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Frames with the pandas library\n",
        "\n",
        "Finally, we have arrived at Data Frames. Data frames are the most important way to represent data and use data for statistics.\n",
        "\n",
        "You can imagine data frames like an excel table.\n",
        "Each observation is a row, and each variable in our data is a column.\n",
        "\n",
        "In pandas data frames, every row and every column has a name, making it easy to access data, subset the data frame, and so on.\n",
        "\n",
        "We will now look at some basic examples:\n",
        "\n",
        "First, we have to import the pandas library.\n",
        "(If an error message comes up saying that pandas is not installed, remove the hashtag and run the cell again. Then, pandas will be installed before being imported.)"
      ],
      "metadata": {
        "id": "VpIwfOfXwECt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install pandas\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "07eVaz6JwFCT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Internally, data frames are like dictionaries. Each column has a name, like a key in a dictionary, and then a list as the value of the dictionary.\n",
        "\n",
        "We must look out, that all lists in the dictionary have the same number of elements, but then we can create a data frame from a dictionary:"
      ],
      "metadata": {
        "id": "rZd_CFHCxL8C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = {'Name': ['Alice', 'Bob'], 'Age': [24, 27]}\n",
        "df = pd.DataFrame(data)\n",
        "df"
      ],
      "metadata": {
        "id": "MVIIZxVTxneG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pandas data frames are accessed using columns as keys in square brackets, similar to dictionaries. You can access a single column by passing its name in brackets or multiple columns by using a list of column names."
      ],
      "metadata": {
        "id": "-Zs_F4uNyGCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accessing a single column\n",
        "df['Name']"
      ],
      "metadata": {
        "id": "YZu7sf9GyWNW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Accessing multiple columns\n",
        "df[['Name', 'Age']]"
      ],
      "metadata": {
        "id": "2200UfUZxq-0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see here, that the index is called 0 and 1. But in fact, these are also names. To demonstrate, lets change the name of the index"
      ],
      "metadata": {
        "id": "yPpfKMQZyhLd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.index = [\"observation1\", \"observation2\"]\n",
        "df"
      ],
      "metadata": {
        "id": "vcIkF90LygXg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This makes it easier to demonstrate how to access both columns and rows by name. Above, we have already seen one way to access columns by their name.\n",
        "\n",
        "A second important way is, to use the \".loc[]\" keyword:"
      ],
      "metadata": {
        "id": "fAVz2KxSyzFI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[:, \"Name\"]"
      ],
      "metadata": {
        "id": "e6cy54rLxI8_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[:, \"Age\"]"
      ],
      "metadata": {
        "id": "yb-mGZHJzLse"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This alternative way will sometimes be necessary, when you analyse data in more complex ways.\n",
        "However, the same way, we can now acces rows by name as well:"
      ],
      "metadata": {
        "id": "ZWEzKQ-fwD23"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[\"observation1\", :]"
      ],
      "metadata": {
        "id": "p9HySfzSzfQg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[\"observation2\", :]"
      ],
      "metadata": {
        "id": "Fidg1ft4zhFS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "And again, we can use lists to access several rows or columns together:"
      ],
      "metadata": {
        "id": "9e7N9OTnzm1L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[[\"observation1\", \"observation2\"], [\"Name\"]]"
      ],
      "metadata": {
        "id": "9jRxEN3ezwqP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[[\"observation1\", \"observation2\"], [\"Name\", \"Age\"]]"
      ],
      "metadata": {
        "id": "b4FaxHFJzryg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In contrast to the \".loc\" keyword, \".iloc[]\" uses the index numbers to access rows or columns in a dataframe:"
      ],
      "metadata": {
        "id": "WCggjKwGz0TO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[0, :]"
      ],
      "metadata": {
        "id": "_wVr8n-KztZP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[1, :]"
      ],
      "metadata": {
        "id": "sFl2-PfH0Em2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[:, 0]"
      ],
      "metadata": {
        "id": "U3fpNrcd0Hif"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we ever want to access a single value in our data frame, we can do this by specifying both the row and column, with either \".iloc\" or \".loc\":"
      ],
      "metadata": {
        "id": "eM0ALy7M0MAE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[\"observation1\", \"Name\"]"
      ],
      "metadata": {
        "id": "VLRYRiRw0ej6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kNHVbwPy0gMv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ucimlrepo"
      ],
      "metadata": {
        "id": "hkFD1G4A2RuF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "heart_disease = fetch_ucirepo(id=45)"
      ],
      "metadata": {
        "id": "xiSDRYPA2jDl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = ((heart_disease[\"data\"])[\"features\"]).copy()"
      ],
      "metadata": {
        "id": "cOMD9xUP4t6j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have now downloaded a dataset on heart disease.\n",
        "Before going on to do actual statistics, I would like to introduce additional features for pandas data frames which will be of use.\n",
        "\n",
        "First, the \".query()\" method.\n",
        "Let´s take a look at the data frame."
      ],
      "metadata": {
        "id": "9OVAoAAx5jPx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In many machine learning datasets, we have for one the features, the data frame we are looking at, and the target, which we will explore more later.\n",
        "\n",
        "In the features of the dataset, we have a 13 variables, and each row corresponds to one observation, or individual.\n",
        "\n",
        "Often, when analysing a dataset, we want to subset our dataset to contain only data from specific individuals. For example, let´s say that we want a second data frame, that only contains data from individuals that are 50 years old, or younger.\n",
        "For that, we use the query-method:"
      ],
      "metadata": {
        "id": "2ll6KTJA6J1p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.query(\"age <= 50\")\n",
        "df2.head(10)"
      ],
      "metadata": {
        "id": "0CIw8p0l2RfT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we could investigate only this subset of individuals.\n",
        "\n",
        "Secondly, lets look at method chaining.\n",
        "Often, we want to change our dataframe, using query, accessing specific columns, sorting the dataframe. So we can do this all at once, we could chaing several methods behind each other:"
      ],
      "metadata": {
        "id": "jbTsiOrr7Q19"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.query(\"age <= 50\")[\"sex\"]"
      ],
      "metadata": {
        "id": "JeYoEStX7Qnb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to make this a bit easier on the eyes, we can use parentheses, and distribute the method-calls among several lines."
      ],
      "metadata": {
        "id": "a19odHLJskw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(\n",
        "    df.query(\"age <= 50\")\n",
        "    [\"sex\"]\n",
        "    .head(10)\n",
        ")"
      ],
      "metadata": {
        "id": "Z7yT0sSEsgY6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A third note on pandas is the Series.\n",
        "When we subset a pandas data frame to a single column (when we don´t use .loc or .iloc), the result is no longer a data frame, but a Series. Let me show you:"
      ],
      "metadata": {
        "id": "5YZCnNH5p-Vq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(df))\n",
        "\n",
        "print(type(df[\"sex\"]))"
      ],
      "metadata": {
        "id": "qtmV4OCsqkOT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In other words, whenever we create a data frame, the list we declare as a column is converted to a Series.\n",
        "A Series is a lot like list. However, it has not only values, but also an index."
      ],
      "metadata": {
        "id": "jXkoU3lzqtpI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df[\"sex\"][0:5])\n",
        "\n",
        "print(df[\"sex\"].index)"
      ],
      "metadata": {
        "id": "RYcHydbkrCB9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The index identifies the values just like the row-wise index in the data frame from which the Series is taken."
      ],
      "metadata": {
        "id": "EhKvnpAXrQZR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.index = [\"observation\"+str(i+1) for i in range(df.shape[0])]\n",
        "\n",
        "df[\"sex\"][0:5].index"
      ],
      "metadata": {
        "id": "IdtwqHxfrcIk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the upcoming section, pandas.Series will become important.\n",
        "\n",
        "Just like a list, the contents of a Series can be declared as whole numbers or decimal numbers. But additionally, we can declare a Series as a category (if you have ever used R, you will know).\n",
        "\n",
        "A category takes factual observations (heart_disease, no_heart_disease), and encodes them as 0, 1, ...\n",
        "This happens mostly invisibly to the user, but is essential for statistical packages that we are going to use."
      ],
      "metadata": {
        "id": "ltqpDwl6rnOX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.sex = df.sex.astype(\"category\")\n",
        "df.sex.dtype"
      ],
      "metadata": {
        "id": "KwjcgJ8AsSGq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load your own data\n",
        "If you ever want to load your own data inot google colab:\n",
        "\n",
        "https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92\n"
      ],
      "metadata": {
        "id": "cwBYEhW8VMFZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prepare the data frame\n",
        "\n",
        "For the upcoming section, we will need both the features, which we already have, and the target variable --heart disease-- in the same dataframe.\n",
        "Therefore, we access the data on heart disease and store it in a new column\n"
      ],
      "metadata": {
        "id": "mvho8P_3-B3L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"target\"] = heart_disease[\"data\"][\"targets\"].values"
      ],
      "metadata": {
        "id": "pOpkToBp-UMk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will change the dataframe in the next section, but will need the original afterwards. When that happens, we will use this function to get the original back:"
      ],
      "metadata": {
        "id": "LOGc_JZ6-qn0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_back_original_df(data_dict):\n",
        "    df = pd.DataFrame(data_dict[\"data\"][\"features\"])\n",
        "    df[\"target\"] = data_dict[\"data\"][\"targets\"].values\n",
        "    return df\n",
        "\n",
        "get_back_original_df(data_dict = heart_disease)"
      ],
      "metadata": {
        "id": "2u9ue47T-6hP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Simple statistics\n",
        "\n",
        "Many of you will have hear about machine learning. Machine learning is concerned with making predictions from data.\n",
        "\n",
        "For example, let´s take the dataset we already downloaded. If you go to the website https://archive.ics.uci.edu/dataset/45/heart+disease, you will discover that the purpose of collecting the dataset was to predict if an individual has heart disease, or not.\n",
        "The focus here is to train a model from data, like the one we downloaded, and then use the model to make predictions about previously unseen individuals to know if they have heart disease.\n",
        "This is certainly and important task. However, as researchers we are often concerned with the question \"WHY\" someone develops heart disease.\n",
        "\n",
        "In other words, we look at all the variables and ask, if this variable contributes to heart disease.\n",
        "\n",
        "This branch is called inferential statistics, recently also called statistical learning.\n",
        "\n",
        "Inferential statistics is a branch of statistics used in biomedical data science to make conclusions about a larger population based on a sample. By analyzing a subset of data, researchers can estimate characteristics like average values or variations within a broader population, helping to uncover trends and make predictions. Common inferential techniques include hypothesis testing, confidence intervals, and p-values, which allow researchers to determine if observed effects are likely to occur by chance or reflect true relationships in the biomedical field.\n",
        "\n",
        "As the dataset is geared towards machine learning tasks, we will convert it back into a form which is better handled by statistical algorithms. For now you shouldn´t be concerned why or how we do something, but in case, all information we need can be found at the website I mentioned above. The variable sex is coded as 0 for male and 1 for female, which we will convert to the categorical values female and male.\n",
        "\n",
        "\"cp\" is the chest pain type.\n",
        "-- Value 1: typical angina\n",
        "-- Value 2: atypical angina\n",
        "-- Value 3: non-anginal pain\n",
        "-- Value 4: asymptomatic\n",
        "We will convert each code to the categorical value.\n",
        "\n",
        "fbs: (fasting blood sugar > 120 mg/dl)  (1 = true; 0 = false)\n",
        "\n",
        "If the variable \"target\" for heart disease is 0, it indicates no heart disease. All non-zero values indicate heart disease."
      ],
      "metadata": {
        "id": "fgQsod6s9V6X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The heart dataset"
      ],
      "metadata": {
        "id": "BIOl2psnUnUh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### We load all the libraries and data entirely again, so this\n",
        "### section can be run indipendently from the rest of the notebook\n",
        "!pip install ucimlrepo\n",
        "!pip install pandas\n",
        "!pip install seaborn\n",
        "!pip install scipy\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "heart_disease = fetch_ucirepo(id=45)\n",
        "df = ((heart_disease[\"data\"])[\"features\"]).copy()\n",
        "\n",
        "\n",
        "def get_back_original_df(data_dict):\n",
        "    df = pd.DataFrame(data_dict[\"data\"][\"features\"])\n",
        "    df[\"target\"] = data_dict[\"data\"][\"targets\"].values\n",
        "    return df\n",
        "\n",
        "df = get_back_original_df(heart_disease)"
      ],
      "metadata": {
        "id": "RCNZVahfaANX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = get_back_original_df(heart_disease)\n",
        "\n",
        "def convert_df_back(df):\n",
        "    df[\"sex\"] = df[\"sex\"].map({0: \"female\", 1: \"male\"})\n",
        "    df[\"fbs\"] = df[\"fbs\"].map({0: \"high_fbs\", 1: \"low_fbs\"})\n",
        "    df[\"cp\"] = df[\"cp\"].map({1: \"typical angina\", 2: \"atypical angina\", 3: \"non-anginal pain\", 4: \"asymptomatic\"})\n",
        "    df[\"cp\"] = df[\"cp\"].apply(lambda x: x.replace(\" \", \"_\"))\n",
        "    df[\"target\"] = [\"heart disease\" if x != 0 else \"no heart disease\" for x in df[\"target\"]]\n",
        "    df[\"target\"] = df[\"target\"].apply(lambda x: x.replace(\" \", \"_\"))\n",
        "\n",
        "convert_df_back(df)\n",
        "df\n"
      ],
      "metadata": {
        "id": "X0BQ40XcZ84-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The easiest way to do inference on some data is to compare two groups.\n",
        "\n",
        "Imagine we did an experiment. We went into the clinic and randomly selected individuals with heart disease. These are our experimental group.\n",
        "\n",
        "Then we asked the clinic for a list with all individuals without heart disease. From these, we randomly selected people, too. These are our control group.\n",
        "\n",
        "As an example, we may ask if age is a contributing factor for developing heart disease.\n",
        "Therefore, we want to compare the age of the two groups.\n",
        "We do this using the Wilcoxon Rank-sum test, also called the Mann-Whitney-U test. We do NOT use the famous t-test here, because age is not a continuous variable, i.e. it does not contain decimal numbers."
      ],
      "metadata": {
        "id": "dqWyV5BXlhFn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind\n",
        "from scipy.stats import mannwhitneyu\n",
        "\n",
        "group1 = df.query(\"target == 'heart_disease'\")[\"age\"]\n",
        "group2 = df.query(\"target == 'no_heart_disease'\")[\"age\"]\n",
        "\n",
        "mannwhitneyu(group1, group2)"
      ],
      "metadata": {
        "id": "TcSPOUpQlTWg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we see, people with heart disease are on average older than people without heart disease.\n",
        "\n",
        "Let´s visualize this.\n",
        "For now, please don´t worry about how visualization works, we will get to this later."
      ],
      "metadata": {
        "id": "OxgOVKwtmV1B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "df = get_back_original_df(heart_disease)\n",
        "convert_df_back(df)\n",
        "\n",
        "sns.histplot(df, x = \"age\", hue = \"target\", bins = 75, kde=True)"
      ],
      "metadata": {
        "id": "TxAaMb9wpME5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In histograms and/or KDE plots, we look at the distributions of data. Basically, we ask how often a value occurs in a dataset.\n",
        "\n",
        "In the previous example we plotted both histogram and KDE plot, but did so independently for the two groups, \"heart_disease\" and \"no_heart_disease\".\n",
        "\n",
        "The peak of the group \"no_heart_disease\" is lower than the peak of the group \"heart_disease\". And therefore, we can hypothesize that the probability to have a heart disease is higher, the older you are."
      ],
      "metadata": {
        "id": "-xzU7DW2G7ih"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6x3j0NfXUbZ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting with Python"
      ],
      "metadata": {
        "id": "1D1ezgiRUKIz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cancer dataset\n",
        "\n",
        "Let´s look at another dataset from the UC Irvine Machine Learning Repository.\n",
        "I will provide you with the pre-processing for now.\n",
        "\n",
        "https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic"
      ],
      "metadata": {
        "id": "T--fZvF_IBW3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer_wisconsin_diagnostic = fetch_ucirepo(id=17)\n",
        "breast_cancer_wisconsin_diagnostic[\"data\"].keys()"
      ],
      "metadata": {
        "id": "Q-cKy_NFIePM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer_wisconsin_diagnostic[\"data\"].keys()"
      ],
      "metadata": {
        "id": "toODAyU5Irx4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(breast_cancer_wisconsin_diagnostic[\"data\"][\"features\"]).copy()\n",
        "df[\"diagnosis\"] = breast_cancer_wisconsin_diagnostic[\"data\"][\"targets\"].values.ravel()"
      ],
      "metadata": {
        "id": "KAn5i2JXIApg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "id": "PtmPu-wdXCwT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"diagnosis\"] = df[\"diagnosis\"].map({2: \"benign\", 4: \"malignant\"})"
      ],
      "metadata": {
        "id": "5urHvddM8dqP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "aNL-frcH9jAc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let´s first talk about what some of the parameters mean, which we are interested in.\n",
        "\n",
        "The dataset at hand is about breast cancer. The target variable encodes benign (B) and malignant (M) tumors. Benign cancers usually have an outer surface which contains them. They grow only slowly, and may not even be a danger to a persons' health.\n",
        "\n",
        "In contrast, malignant tumors invade neighboring tissues or metastasize. Therefore, they are a severe burden to health.\n",
        "\n",
        "Accordingly, it is of interest for health care to be able to distinguish them.\n",
        "\n",
        "Relevant parameters that we will look at are the radius of the tumor, the area and the smoothness.\n",
        "\n",
        "The tumors were examined with imaging. In three-dimensional imaging, any imaged space is seperated by three planes, sagittal, coronal and transverse. Basically a plane is put through the body from the front, from the side, and from the top. Just look at this example:\n",
        "\n",
        "However, for each of the three planes, we can determine variables like the radius (radius1, radius2, radius3).\n",
        "Further, we have variables like the area, and the concavity.\n",
        "While the area for a plane should be quite self-explaining, the concavity indicated if the surface of a tumor is rather smooth, or has concave growings. This is important, as benign tumors have a rather smooth surface.\n",
        "\n",
        "While it is easy to make such claims, let´s look at the actual data.\n",
        "\n",
        "We begin with the radius. I mentioned above that benign tumors grow slower. Therefore, we can hypothesize that the bigger a tumor is, the higher the chance that it is malignant.\n",
        "However, in this dataset we have three measurements for the radius, one for each of the imaging planes.\n",
        "Therefore, to take into account all the data, we calculate the mean of the three radius-measurements.\n",
        "\n",
        "Then we conduct a t-test for this radius-mean. Group 1 will be the malignant tumors, group 2 the benign tumors."
      ],
      "metadata": {
        "id": "cIy9i121PeMe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![The-three-perspective-planes-used-in-medical-imaging-are-Axial-or-Transversal-Coronal.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "tBEUSqmOy3EU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "However, for each of the three planes, we can determine variables like the radius (radius1, radius2, radius3).\n",
        "Further, we have variables like the area, and the concavity.\n",
        "While the area for a plane should be quite self-explaining, the concavity indicated if the surface of a tumor is rather smooth, or has concave growings. This is important, as benign tumors have a rather smooth surface.\n",
        "\n",
        "While it is easy to make such claims, let´s look at the actual data.\n",
        "\n",
        "We begin with the radius. I mentioned above that benign tumors grow slower. Therefore, we can hypothesize that the bigger a tumor is, the higher the chance that it is malignant.\n",
        "However, in this dataset we have three measurements for the radius, one for each of the imaging planes.\n",
        "Therefore, to take into account all the data, we calculate the mean of the three radius-measurements.\n",
        "\n",
        "Then we conduct a t-test for this radius-mean. Group 1 will be the malignant tumors, group 2 the benign tumors."
      ],
      "metadata": {
        "id": "rSM4MAJ9y6zJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer_wisconsin_diagnostic = fetch_ucirepo(id=17)\n",
        "breast_cancer_wisconsin_diagnostic[\"data\"].keys()\n",
        "\n",
        "df = pd.DataFrame(breast_cancer_wisconsin_diagnostic[\"data\"][\"features\"]).copy()\n",
        "df[\"diagnosis\"] = breast_cancer_wisconsin_diagnostic[\"data\"][\"targets\"].values.ravel()"
      ],
      "metadata": {
        "id": "vjtXzyG473bQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind\n",
        "\n",
        "df[\"radius_mean\"] = df[[\"radius1\", \"radius2\", \"radius3\"]].mean(axis=1)\n",
        "\n",
        "group1 = df.query(\"diagnosis == 'M'\")[\"radius_mean\"]\n",
        "group2 = df.query(\"diagnosis == 'B'\")[\"radius_mean\"]\n",
        "\n",
        "ttest_ind(group1, group2)"
      ],
      "metadata": {
        "id": "Yy_q1hBsT0RF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the test-statistic and the p-value, we can conclude that the mean radius of malignant tumors is larger than the mean radius of benign tumors.\n",
        "\n",
        "Let´s put this into a plot."
      ],
      "metadata": {
        "id": "v2Kp2U9nVRxU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df, x = \"radius_mean\", hue = \"diagnosis\", bins = 75, kde=True)"
      ],
      "metadata": {
        "id": "V5BlLMIZVg31"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is already a nice test and conclusion. But imagine you get a new dataset and have no idea about which metrics could differ between the two groups, you just don´t know where to begin.\n",
        "\n",
        "This is when we perform exploratory data analysis. Exploratory data analysis means that we visualize data and perform basic statistical test to see if the data makes sense, which variables could be relevant for upcoming statistical tests or machine learning, and much more.\n",
        "\n",
        "But first, we will summarize some variables like we did with radius."
      ],
      "metadata": {
        "id": "qvKQOtQ0-W0Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"texture_mean\"] = df[[\"texture1\", \"texture2\", \"texture3\"]].mean(axis=1)\n",
        "df[\"perimeter_mean\"] = df[[\"perimeter1\", \"perimeter2\", \"perimeter3\"]].mean(axis=1)\n",
        "df[\"area_mean\"] = df[[\"area1\", \"area2\", \"area3\"]].mean(axis=1)\n",
        "df[\"smoothness_mean\"] = df[[\"smoothness1\", \"smoothness2\", \"smoothness3\"]].mean(axis=1)\n",
        "df[\"compactness_mean\"] = df[[\"compactness1\", \"compactness2\", \"compactness3\"]].mean(axis=1)\n",
        "df[\"concavity_mean\"] = df[[\"concavity1\", \"concavity2\", \"concavity3\"]].mean(axis=1)\n",
        "df[\"concave_points_mean\"] = df[[\"concave_points1\", \"concave_points2\", \"concave_points3\"]].mean(axis=1)\n",
        "df[\"symmetry_mean\"] = df[[\"symmetry1\", \"symmetry2\", \"symmetry3\"]].mean(axis=1)\n",
        "df[\"fractal_dimension_mean\"] = df[[\"fractal_dimension1\", \"fractal_dimension2\", \"fractal_dimension3\"]].mean(axis=1)"
      ],
      "metadata": {
        "id": "cPZH1DVK_PrK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then we take a first look with correlation.\n",
        "\n",
        "Correlation is a very basic statistical test.\n",
        "The values of correlation go from -1 to 1. If a correlation coefficient for two variables is high, it means the higher the value of the first variable, the higher is the value of the second variable. The same thing applies to correlation coefficients that approximate -1: The lower the higher the value of the first variable, the lower the value of the second variable."
      ],
      "metadata": {
        "id": "2tRrzQOV_wFS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#HEATMAP\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "import matplotlib.pylab as plt\n",
        "plt.figure(figsize =(30,30))\n",
        "sns.heatmap(df.drop(\"diagnosis\", axis = 1).corr(), vmin=-1, vmax=1, annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "GS6vPsWm8MP_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: calculate the correlation from the df dataframe. If necessary, drop all columns which cannot be used for correlation.\n",
        "# then make a beautiful heatmap plot from the correlation coefficients.\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming 'df' is already defined as in your provided code.\n",
        "\n",
        "# Identify columns that are not numeric\n",
        "non_numeric_cols = df.select_dtypes(exclude=['number']).columns\n",
        "\n",
        "# Drop non-numeric columns, including the \"diagnosis\" column\n",
        "df_numeric = df.drop(columns=non_numeric_cols)\n",
        "\n",
        "\n",
        "# Calculate the correlation matrix\n",
        "correlation_matrix = df_numeric.corr()\n",
        "\n",
        "# Create a heatmap of the correlation coefficients\n",
        "plt.figure(figsize=(30, 30))  # Adjust figure size as needed\n",
        "sns.heatmap(correlation_matrix, vmin=-1, vmax=1, annot=True, cmap='coolwarm') # Use 'coolwarm' or other suitable colormap\n",
        "plt.title(\"Correlation Matrix Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Hy-Y425Ldywc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install numpy"
      ],
      "metadata": {
        "id": "n5suVJeu837c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "sns.set_theme(style=\"white\")\n",
        "corr = df.drop(\"diagnosis\",axis=1).corr()\n",
        "# Generate a mask for the upper triangle\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "# Generate a custom diverging colormapcmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "sns.heatmap(corr, mask=mask, cmap=cmap,square=True, linewidths=.5, cbar_kws={\"shrink\": .6})"
      ],
      "metadata": {
        "id": "70a-1WrM8MMO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "When plots become too large and convoluted, we subset our data to include only the columns with the summarized values:"
      ],
      "metadata": {
        "id": "JXHbmk9bBcQz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_columns = [x for x in df.columns if \"mean\" in x]\n",
        "df_means = df[mean_columns]\n",
        "df_means.head(2)"
      ],
      "metadata": {
        "id": "20_AtEn2Bjwk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme(style=\"white\")\n",
        "corr = df_means.corr()\n",
        "# Generate a mask for the upper triangle\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "# Generate a custom diverging colormapcmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "sns.heatmap(corr, mask=mask, cmap=cmap,square=True, linewidths=.5, cbar_kws={\"shrink\": .6})"
      ],
      "metadata": {
        "id": "oU6btYSe96da"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_means[\"diagnosis\"] = df[\"diagnosis\"]\n",
        "sns.pairplot(df_means, hue = \"diagnosis\", diag_kind='kde')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "SejsItupBtf5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: In the df_means dataframe, make a seaborn pairplot with diagnosis as hue\n",
        "\n",
        "sns.pairplot(df_means, hue=\"diagnosis\", diag_kind='kde')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "9jHurI-rdF_s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.violinplot(data = df_means, x = \"diagnosis\", y = \"radius_mean\")"
      ],
      "metadata": {
        "id": "-L1vKdSzCfnI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_long = df_means.melt(value_vars = [\"radius_mean\", \"texture_mean\", \"perimeter_mean\", \"area_mean\", \"smoothness_mean\", \"compactness_mean\", \"concavity_mean\", \"concave_points_mean\", \"symmetry_mean\", \"fractal_dimension_mean\"],\n",
        "              id_vars = \"diagnosis\")"
      ],
      "metadata": {
        "id": "8FAwapf4DMZr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: in the df_means dataframe, make a seaborn violin plot for diagnosis on the x axis, and radius_mean on the y-axis\n",
        "\n",
        "sns.violinplot(data=df_means, x=\"diagnosis\", y=\"radius_mean\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "WkQzM2Wbcmlj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt_df = (\n",
        "    df_long.query('variable == \"radius_mean\"')\n",
        ")\n",
        "\n",
        "sns.violinplot(x= \"variable\" ,y = \"value\", hue= \"diagnosis\", data = plt_df)\n",
        "for tick in plt.gca().get_xticklabels():\n",
        "    tick.set_rotation(45)"
      ],
      "metadata": {
        "id": "TUXtHm8O8MEx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lmplot(df, x = \"radius_mean\", y = \"area_mean\")"
      ],
      "metadata": {
        "id": "8lV1SePvFUgc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lmplot(df, x = \"radius_mean\", y = \"fractal_dimension_mean\")"
      ],
      "metadata": {
        "id": "PiSDOa9DFv9S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now it´s time to do some plots yourself. Here are some examples of plot types. Use the generating feature to prepare the data and create some plots."
      ],
      "metadata": {
        "id": "_WlqR3nf2qRY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![scatterplot_matrix.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "LWlITlaFHHgP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![errorband_lineplots.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "dtb6zusiHPa-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "> Add blockquote![scatterplot_sizes.png](data:image/png;base64,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)\n",
        "\n"
      ],
      "metadata": {
        "id": "1sGYVB-BHTJw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![multiple_regression.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "TNo7wrbwHaOd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Statistical tests\n",
        "Not part of the course"
      ],
      "metadata": {
        "id": "2oim1JbvGUpt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "While a t-test is a simple and effective way to test if the mean of two groups is the same, or they are (statistically) significantly different from each other, linear regressions allow us to do the same and at the same time adjust for covariates.\n",
        "\n",
        "When performing statistical analysis (as opposed to predictive machine learning), python offers the statsmodels package.\n",
        "\n",
        "https://www.statsmodels.org/stable/index.html\n",
        "\n",
        "Statsmodels allows us to specify statistical models using the formula language, which was originally invented for the R programming language.\n",
        "\n",
        "Using the statsmodels package and specifically the formula API, we will now conduct a linear regression."
      ],
      "metadata": {
        "id": "Q-OBC08Z-PRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# install statsmodels\n",
        "!pip install statsmodels"
      ],
      "metadata": {
        "id": "9DtZDj-lVtgN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import the statsmodels formula api\n",
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "rgupNz8gVluC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mod = smf.ols(formula = \"radius_mean ~ target\", data = df)\n",
        "res = mod.fit()\n",
        "print(res.summary())"
      ],
      "metadata": {
        "id": "IpQ3LWKsW7fF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let´s discuss what happened here.\n",
        "\n",
        "The import is quite standard for python. We import a namespace which contains functions, which we can use by putting the namespace before functions.\n",
        "\n",
        "\"smf.ols\" is the function for \"Ordinary Least Squares\", the standard linear regression model.\n",
        "Explaining linear regression would be beyond the scope of this workshop.\n",
        "\n",
        "But in terms of our code, the smf.ols function returns apython-object, which contains the linear model. We call this object \"mod\". But the linear model has not yet been \"fit\", the calculations to learn slope and y-intersept have not been performed.\n",
        "\n",
        "We use \"mod.fit()\" to perform these calculations, and get a new results-object. The results-object contains everything we need to know, and could even perform some additional calculations. In order to get actual results, we use the \"res.summary()\" function of the results object.\n",
        "\n",
        "The most important table is the middle one. The coefficient tells us, in this case, how many millimeters bigger the mean radius of the malignant tumors was, as compared to the benign tumors. \"st err\" shows the standard error of this estimation.\n",
        "\n",
        "Then we have the t-value. Linear regression with categorical variables -- malignant and benign -- uses the t-distribution for the differential distribution. The t-value is equivalent to the t-test we performed earlier. And therefore, we can conclude that the linear regression using only the tumor type as predictive variable, is equivalent to the t-test.\n",
        "\n",
        "We do see that the p-value is different as compared to the t-test, but p-value estimation is a sensitive matter, and in the end the p-value should not be of too much concern.\n",
        "\n",
        "Finally, we have the limits of the 95% confidence interval \"0.025\" and \"0.975\"."
      ],
      "metadata": {
        "id": "NpnOMS3GXMiw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "And finally, let´s look at some ways to plot our results.\n",
        "\n",
        "But for that, we need to take a step back and learn about the plotting libaries matplotlib, and seaborn.\n",
        "\n",
        "Matplotlib is the python library that is the basis for most plotting in python. It allows you to plot anything, if you can provide the coordinates in a coordinate system.\n",
        "\n",
        "In statistics, a staple is the scatter plot, so let´s make one with out data.\n",
        "\n",
        "Matplotlib has a sublibrary called pyplot. Pyplot contains the actual functions that lets us use matplotlib. We import this sublibrary and do a scatter plot.\n",
        "\n"
      ],
      "metadata": {
        "id": "2RLTB9Zbafn5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(df[\"target\"], df[\"radius_mean\"])"
      ],
      "metadata": {
        "id": "KGxfwzWTXFDw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This example is informative, we see that the mean radius is higher in malignant tumors, but as a scatter plot it is quite boring.\n",
        "\n",
        "We read ealier, that malignant tumors are not only bigger, they are less smooth and therefore have higher concavity. We calculate the mean concavity across the three dimensions, and plot the mean radius against the mean concavity."
      ],
      "metadata": {
        "id": "D1k4E7IHbXXp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"concavity_mean\"] = df[[\"concavity1\", \"concavity2\", \"concavity3\"]].mean(axis=1)\n",
        "\n",
        "plt.scatter(df[\"radius_mean\"], df[\"concavity_mean\"])\n",
        "plt.xlabel(\"radius_mean\")\n",
        "plt.ylabel(\"concavity_mean\")"
      ],
      "metadata": {
        "id": "YlPpDeC-bUa1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "With some imagination we can see a trend here. The higher the mean radius of a tumor, the higher the concavity.\n",
        "\n",
        "However, we did not visualize which tumor belongs to which group.\n",
        "\n",
        "This would certainly be possible with matplotlib and pyplot, but it is quite tedious.\n",
        "\n",
        "Therefore, we will be using the seaborn library.\n",
        "Seaborn is an add-on to matplotlib with many useful functions specifically for statistical visualizations. As it is an add-on, we use it together with matplotlib and any plot produced by seaborn can be further edited with matplotlib.\n",
        "\n",
        "First, let us see how the scatter plot would be produced with seaborn."
      ],
      "metadata": {
        "id": "RAizPdvRcH0J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data = df, x = \"radius_mean\", y = \"concavity_mean\")"
      ],
      "metadata": {
        "id": "P3e8TnBkcDse"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In seaborn, the first argument to each function is the entire dataframe. The arguments x and y are then the names of the dataframe columns which we would like to plot on the x and y axis.\n",
        "\n",
        "Then, we would like to add the membership of each datapoint to one of the groups, malignant and benign tumors, as the color of the dot."
      ],
      "metadata": {
        "id": "r3jFqUMfdRon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data = df, x = \"radius_mean\", y = \"concavity_mean\", hue = \"target\")"
      ],
      "metadata": {
        "id": "0nj0GWfidPTM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see, that the combination of mean radius is a very good way to seperate benign from malignant tumors visually.\n",
        "\n",
        "Seaborn has functions that do additonal tasks, like plotting a regression line."
      ],
      "metadata": {
        "id": "44uVAMoHd3tU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lmplot(data = df, x = \"radius_mean\", y = \"concavity_mean\", hue = \"target\")"
      ],
      "metadata": {
        "id": "1zt0aK6Ld0Du"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "And from this regression plot we can truly see, that mean radius and mean concavity is very different between the groups.\n",
        "\n",
        "As the last steps in this workshop, let´s underline the visual conclusion with some statistics.\n",
        "\n",
        "First, we do a regression between malignant and beign tumors as the predictors, and mean concavity as the target variable."
      ],
      "metadata": {
        "id": "jv_TAhNUeMVK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mod = smf.ols(formula = \"concavity_mean ~ target\", data = df)\n",
        "res = mod.fit()\n",
        "print(res.summary())"
      ],
      "metadata": {
        "id": "zcQPkbgweI9X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "And finally, a logistic regression.\n",
        "\n",
        "The datasets we used are from a repository for machine learning datasets. Machine learning is concerned with predictions from data. And in contrast to many tasks in statistics, the target one wants to predict is often categorical, i.e. malignant or benign.\n",
        "\n",
        "Logistic regression is one of the most used algorithms for classification. However, it may also be used in statistics. But in statistics, we may not only use logistic regression for prediction purposes, but extract information from the predictors, if a variable is able to explain the predictions, or not."
      ],
      "metadata": {
        "id": "CqhWgZw8ev-L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"target\"] = [1 if x == \"M\" else 0 for x in df[\"target\"]]"
      ],
      "metadata": {
        "id": "GNsWxDQ2gRXz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mod = smf.logit(formula = \"target ~ radius1 + radius2 + radius3\", data = df)\n",
        "res = mod.fit()\n",
        "print(res.summary())"
      ],
      "metadata": {
        "id": "MOHrs8eseuPh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mod = smf.logit(formula = \"target ~ concavity1 + concavity2 + concavity3\", data = df)\n",
        "res = mod.fit()\n",
        "print(res.summary())"
      ],
      "metadata": {
        "id": "Tk26NLI1fwyF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D5lP2oMCiPos"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}