week1_session1_grundkonzepte.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# AI in Medicine: Ground concepts\n",
    "\n",
    "## Python programming: introduction to the language\n",
    "\n",
    "* **Instructor**: Corey Taylor, AG Volkamer, Charité (corey.taylor@charite.de). \n",
    "* **Target audience**: Medical students from Charité\n",
    "* **Course date**: January 2022\n",
    "\n",
    "> Extended from previous contributions by Jan Philipp Albrecht and Jaime Rodríguez-Guerra."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Aims of this session\n",
    "\n",
    "This notebook will teach you the basic concepts necessary to understand and write basic Python code using practical examples based on the datasets you will use during the course!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Learning goals\n",
    "\n",
    "In general, the goal should be to understand the ground concepts behind the material as other lectures depend on it. Do not worry if you don't know the answer to every question. Try to understand as much as possible and to ask often to solve to your problems.\n",
    "\n",
    "### Theory\n",
    "\n",
    "- Python and Jupyter Notebooks\n",
    "- Variables\n",
    "- Flow control\n",
    "- Functions\n",
    "- Modules\n",
    "- Imports\n",
    "\n",
    "### Practical\n",
    "\n",
    "- How to use notebooks\n",
    "- Data structures:\n",
    "  - Assigning variables\n",
    "  - Perform operations on variables\n",
    "  - Indexing and slicing lists\n",
    "  - Create and alter dictionaries\n",
    "- Flow control:\n",
    "  - Taking decisions: `if-else` conditions\n",
    "  - Repeating actions: `for` loops\n",
    "- Reusing code:\n",
    "  - Defining and calling functions\n",
    "  - Importing modules\n",
    "\n",
    "Don't hesitate to interrupt and ask if concepts remain unclear or tasks are not understood!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. References\n",
    "\n",
    "The official documentation for Python:\n",
    "- https://docs.python.org/3/\n",
    "- https://docs.python.org/3/tutorial/modules.html\n",
    "\n",
    "Sources for datasets:\n",
    "- [RKI dataset](https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0/data?orderBy=Bundesland&where=Bundesland%20%3D%20%27Berlin%27)\n",
    "- [Berlin.de dataset](https://www.berlin.de/lageso/gesundheit/infektionsepidemiologie-infektionsschutz/corona/tabelle-bezirke/)\n",
    "- [PIMA Indians diabetes dataset](https://www.kaggle.com/uciml/pima-indians-diabetes-database)\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Contents\n",
    "\n",
    "This is a learning-by-doing notebook alternating between theory and practice. First, a short introduction to a particular topic is given, showing examples with code. You are then ask to perform similar tasks to the examples in the theory.\n",
    "\n",
    "### Before we start!\n",
    "\n",
    "Getting familiar with the data you will handle is one of the first steps in every data science pipeline. During the first three lessons we will work with the number of corona cases registered in Germany. RKI.de maintains a record of all CoViD-19 cases in Germany. Berlin.de also offers a dataset of corona cases split by neighborhood. We will use both to illustrate different aspects of Python while remaining relatable to the current events. The rest of the lessons will use the Pima Indians Diabetes Database."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Python and Jupyter Notebooks\n",
    "\n",
    "#### What is Python?\n",
    "\n",
    "Python is a widely used general-purpose high-level programming language.\n",
    "The term \"high-level\" means that the language has abstracted away most of the technical details and manages them for you (e.g. memory allocation).\n",
    "\n",
    "In this course we will use Python 3.6 (default in Colab as of Feb 2021).\n",
    "\n",
    "#### What is this interface?\n",
    "\n",
    "This web-based interface  is a so called **Jupyter notebook**, a web application that allows you to create and share documents which contain executable code, equations, visualizations and explanatory text. \n",
    "\n",
    "It allows you to define so called *cells* of different formats:\n",
    "\n",
    "- Rich-text or Markdown cells (like this cell)\n",
    "- Code cells\n",
    "\n",
    "You can *run* (execute) each cell seperately by pressing <kbd>Shift</kbd>+<kbd>Enter</kbd> or <kbd>Ctrl</kbd>+<kbd>Enter</kbd>. All names you define (variables, functions...) are available for **both** preceding _and_ following cells once executed. Careful with the order in which you execute your cells!\n",
    "\n",
    "Some code cells additionally produce an output (text, images, etc.). In that case, the output will appear underneath the code cell.\n",
    "\n",
    "\n",
    "> __Exercise__\n",
    ">\n",
    "> Try to execute the following cell and make sure the output appears."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"This is a cell with code.\")\n",
    "# This is a comment line. \n",
    "# Lines in a code cell starting with the '#' symbol are\n",
    "# ignored when you run a piece of code\n",
    "\n",
    "# print('This sentence will NOT appear underneath the cell!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Variables and Operations\n",
    "\n",
    "There are several **types** of objects depending on their contents.\n",
    "\n",
    "In the following cells, you will learn about:\n",
    "\n",
    "- scalar (one element) objects:\n",
    "    - integers (`int`)\n",
    "    - boolean (`bool`)\n",
    "    - floating-point numbers (`float`)\n",
    "    - strings (`str`)\n",
    "- collection objects:\n",
    "    - lists (`list`)\n",
    "    - dictionaries (`dict`)\n",
    "\n",
    "#### Assignment\n",
    "\n",
    "Regardless of the type, assignment can be done with the following syntax:\n",
    "\n",
    "```python\n",
    "name = value\n",
    "```\n",
    "\n",
    "You can think of _names_ as a label hanging from a _value_. As with real objects, you can attach several names to the same value:\n",
    "\n",
    "```python\n",
    "color = \"pink\"\n",
    "# add a label to the same object\n",
    "colour = color\n",
    "# you can also do this\n",
    "colour = color = \"pink\"\n",
    "```\n",
    "\n",
    "Two things to note:\n",
    "\n",
    "1. The _type_ of the value will be implicitly inferred from the value _contents_.\n",
    "2. You cannot use spaces in the name. Python style guide recommends using `lowercase_words_separated_by_underscores`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scalar objects\n",
    "\n",
    "##### Integers\n",
    "Integers (`int` for short) are numbers without decimal digits. In the following example, you see the definition of a variable named  `numeric_variable`. After its assigment, the name will be reassigned twice. After each operation the new value is shown with the function `print()`. Everything inside the `(...)` will be shown as output.\n",
    "\n",
    "```python\n",
    "# assigning the name 'numeric_variable' to a value of '3'.\n",
    "numeric_variable = 3\n",
    "# printing a variable will show its content underneath the cell\n",
    "print(numeric_variable)\n",
    "\n",
    "# assigning a different value to variable namend 'var1', thereby OVERWRITING the previous definition:\n",
    "numeric_variable = 4\n",
    "print(numeric_variable)\n",
    "\n",
    "# assigning a new value to variable namend 'var1', thereby setting a new value using the old one.\n",
    "numeric_variable = numeric_variable + 1\n",
    "print(numeric_variable)\n",
    "    \n",
    "```  \n",
    "\n",
    "The output will be:\n",
    "```python\n",
    "    3\n",
    "    4\n",
    "    5\n",
    "```\n",
    "\n",
    "Note that Python doesn't care about the actual _name_. You can use any word (e.g. `ugly_table = 4`), but it's often recommended to use **meaningful** names so we can get an idea on what the name refers to."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Boolean\n",
    "\n",
    "Boolean variables can only have two values: `True`  and `False`. They can be considered a very small subset of `int`: `True` is equivalent to `1` and `False` equivalent to `0`. Assignment works similarly, but you have to use the _Capitalized_ spelling!\n",
    "\n",
    "\n",
    "```python\n",
    "this_is_a_boolean = True\n",
    "```\n",
    "\n",
    "> *TASK:* <br>\n",
    "*Try to assign the value `False` to the variable from the example above.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "this_is_a_boolean = false\n",
    "print(this_is_a_boolean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Strings\n",
    " \n",
    "A `str` type is able to store _text_; i.e. they represent words, sentences or symbols. You need to use `\"\"` quotes to define them!\n",
    "\n",
    "```python\n",
    "# assigning the variable with the name 'my_string' the value \"This is a string\". \n",
    "my_string = \"This is a string\"\n",
    "```\n",
    "\n",
    "Note that a `str` can contain a the text representation of a number. They resulting type will depend on the presence of the quotes!\n",
    "\n",
    "```python\n",
    "also_a_string = \"1\"  # I am a string due to the \"\", not a number!\n",
    "i_am_an_int = 1  # I am an int due to the missing \"\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Float\n",
    "Variables of type `float` are numbers with floating decimal. Whenever you write a number that contains a `.`, the type of the variable is inferred to be a `float`.\n",
    "\n",
    "```python\n",
    "one_and_a_half = 1.5  # a typical float\n",
    "\n",
    "# The following is also a float. \n",
    "# Although this number mathematically has no floating decimal, it is written with a \".\"\n",
    "one_dot_zero = 1.0\n",
    "also_one_dot_zero = 1.  # and this one too!\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_string = \"This is a string\"\n",
    "# your lines of code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise time!\n",
    "\n",
    "> Let's go the [RKI.de dataset website](https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0/data?orderBy=Bundesland&where=Bundesland%20%3D%20%27Berlin%27) and let's try to identify the different _types_ contained in each row."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Collection objects\n",
    "\n",
    "##### lists\n",
    "\n",
    "Python has a datatype called `list`, where other variables (regardless of their type) can be positionally stored. In other words, `list`s are a sorted collection of elements. \n",
    "\n",
    "The syntax for list definition uses square brackes `[ ]`, which surround the values separated by commas `,`:\n",
    "\n",
    "![def_list.png](https://github.com/volkamerlab/ai_in_medicine/raw/update-2021.02/images/def_list.png)\n",
    "\n",
    "The code `my_list = [5, 10]` would therefore store `5` at first position, and `10` at second position.\n",
    "\n",
    "  \n",
    "> *TASK:*<br>\n",
    "> *Try to:*\n",
    "> - *define a list called `animals`*\n",
    "> - *at the first* **position**, *store the string `\"horse\"`.*\n",
    "> - *store the string `\"spider\"` TWICE at* **position** *2 and 3.*\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK contents here\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is another list\n",
    "neighborhoods = [\"mitte\", \"kreuzberg\", \"kreuzberg\"]\n",
    "print(neighborhoods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once defined, you can access the contained elements in different ways. The most common way is by using the **index** of the element. This is the position number, but the count starts from `0`.\n",
    "\n",
    "```python\n",
    "# print the first element of the list\n",
    "print(neighborhoods[0])\n",
    "```\n",
    "\n",
    "Note that the `index` itself could be a variable of the type `int`!\n",
    "\n",
    "```python\n",
    "index = 0\n",
    "print(neighborhoods[index])\n",
    "```\n",
    "\n",
    "\n",
    "> *TASK:*\n",
    "> *Now print out your favorite kiez in the list in two ways:*\n",
    "> - *by using an integer number corresponding to the `index` of your favorite kiez.*\n",
    "> - *by using a variable called `index`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighborhoods = [\"mitte\", \"kreuzberg\", \"neukölln\"]\n",
    "print(neighborhoods[0])\n",
    "\n",
    "index = 0\n",
    "print(neighborhoods[index])\n",
    "\n",
    "print(neighborhoods[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "Oh! Was that an error? \n",
    "\n",
    "<font color=red>Setting or accessing a value of an index of a list which is not defined results in an error!</font>\n",
    "\n",
    "```python\n",
    "list1[3]\n",
    "---------------------------------------------------------------------------\n",
    "IndexError                                Traceback (most recent call last)\n",
    "<ipython-input-9-831b15cbf272> in <module>()\n",
    "----> 1 list1[3]\n",
    "\n",
    "IndexError: list index out of range\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Slicing (range-indexing) a list\n",
    "\n",
    "In the following you find a definition of a list called `more_neighborhoods`. By giving a **range** of indices you have access to a contiguous subset of the list. A range can be set in the following way:\n",
    "\n",
    "```python\n",
    "more_neighborhoods = [\"mitte\", \"kreuzberg\", \"neukölln\", \"spandau\"]\n",
    "more_neighborhoods[start:stop]\n",
    "```\n",
    "where `start` and `stop` are numbers (integers). Thus, ` more_neighborhoods[1:3] ` would give an output of `[\"kreuzberg\", \"neukölln\"]`. Notice that the `stop` index is _not_ included in the output.\n",
    "\n",
    "> *TASK:*\n",
    ">\n",
    "> *print out all strings in the list `more_neighborhoods` ending in a consonant*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "more_neighborhoods = [\"mitte\", \"kreuzberg\", \"neukölln\", \"spandau\"]\n",
    "print(more_neighborhoods[1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# More examples\n",
    "print(more_neighborhoods[3:5])  # even if we are out of bounds, this will not raise an error\n",
    "print(more_neighborhoods[3])\n",
    "print(more_neighborhoods[-1])  # negative slicing can be used to count from the end; -1 == last one!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool trick: Python considers `str` types to be a _sequence_ of characters, which is very similar to the structure of a `list`. In fact, we can apply the same synthax to get a **substring** of our string. Keep that in mind, it will become necessary later on.\n",
    "\n",
    "```python\n",
    "far_away = \"spandau\"\n",
    "print(far_away[1:4])\n",
    "# will print:    \n",
    "\"pan\"\n",
    "```"
   ]
  },
  {
   "attachments": {
    "def_dict.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dictionaries\n",
    "\n",
    "We have learnt that different objects can be grouped in a sequential container called `list`. There's another useful container called a dictionary (type `dict`). \n",
    "\n",
    "With `dict` objects, you don't obtain a _sequence_ of variables, but a _mapping_ of variable _key-value_ pairs, like in real life dictionaries! (The word is the key, the value is the definition for that word). \n",
    "\n",
    "They can be useful, for example, to assign properties to variables. Let's say, classmates and their age:\n",
    "\n",
    "\n",
    "![def_dict.png](https://github.com/volkamerlab/ai_in_medicine/raw/update-2021.02/images/def_dict.png)\n",
    "\n",
    "\n",
    "Or, neighborhoods and the [number of cases as of 07.02.2021](https://www.berlin.de/lageso/gesundheit/infektionsepidemiologie-infektionsschutz/corona/tabelle-bezirke/):\n",
    "\n",
    "```python\n",
    "cases_by_neighborhood = {\"mitte\": 15672, \"kreuzberg\": 100569, \"neukölln\": 14630, \"spandau\": 9243}\n",
    "```\n",
    "\n",
    "The keys in this example are `\"mitte\"`, `\"kreuzberg\"`, `\"neukölln\"` and `\"spandau\"`. The values are `15672`, `100569`, `14630`, `9243` respectively.\n",
    "\n",
    "Values can be accessed by refering to the specific **key** by using `[]` brackets (same as lists!):\n",
    "\n",
    "```python\n",
    "cases_by_neighborhood[\"mitte\"]\n",
    "\n",
    "    1188\n",
    "```\n",
    "\n",
    "Variables of type `dict` can be easily extended by assigning a **key**-**value** pair to the dictionary. This looks like the following:\n",
    "\n",
    "```python\n",
    "cases_by_neighborhood[\"lichtenberg\"] = 419\n",
    "```\n",
    "\n",
    "In the same way already existent  **key**-**value** pairs can be overwritten.\n",
    "\n",
    "> **TASK:**\n",
    "> *We made a typo in the dict definition above. Kreuzberg (or more specifically, Friedrichshain-Kreuzberg) has 10569 cases, not 100569! Correct the **key**-**value** pair and set its value to `10569` without redefining the entire `cases_by_neighborhood` dictionary. \n",
    "> Check your correction by printing the new dictionary value.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_by_neighborhood = {\"mitte\": 15672, \"kreuzberg\": 100569, \"neukölln\": 14630, \"spandau\": 9243}\n",
    "# Your lines of code here\n",
    "\n",
    "print(cases_by_neighborhood)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "#### Operations\n",
    "\n",
    "Every object type (`int`, `str`, etc) defines some **operations** to do basic tasks.\n",
    "\n",
    "For example, a variable of type `int` defines, among others, the following operations:\n",
    "- Addition `+`\n",
    "- Substraction `-`\n",
    "- Multiplication `*`\n",
    "- Division `/`\n",
    "\n",
    "\n",
    "A division is allowed but **changes** the type of the _returned_ variable (from `int` to `float`). \n",
    "\n",
    "\n",
    "```python\n",
    "# applying basic operations on the cases registerd in Mitte and Friedrichshain-Kreuzberg\n",
    "print(cases_by_neighborhood[\"mitte\"] + cases_by_neighborhood[\"kreuzberg\"])  \n",
    "   7854 # type int\n",
    "print(cases_by_neighborhood[\"mitte\"] - cases_by_neighborhood[\"kreuzberg\"])  \n",
    "   -5478 # type int\n",
    "print(cases_by_neighborhood[\"mitte\"] * cases_by_neighborhood[\"kreuzberg\"])  \n",
    "   7919208 # type int\n",
    "print(cases_by_neighborhood[\"mitte\"] / cases_by_neighborhood[\"kreuzberg\"])  \n",
    "   0.1782178217821782 # type float\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "> *TASK:* <br>\n",
    "*Now try to use the addition-operation for the variable named `my_string` already defined in the cell below. <br>\n",
    "Save the addition in a variable called `res1` and print the result.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_string = \"grundkonzepte\"\n",
    "# ...\n",
    "# res1 = ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on the type of the object these operations are **contextually** defined.\n",
    "\n",
    "Note how mixing two types of variables with an operation can result in an error! For example the following is forbidden:\n",
    "\n",
    "```python\n",
    "var1 = 5\n",
    "res1 = \"test\"\n",
    "# this should produce an error, since the operator '+' can not combine 'int' and 'str' variables! \n",
    "var1 + res1\n",
    "\n",
    "---------------------------------------------------------------------------\n",
    "TypeError                                 Traceback (most recent call last)\n",
    "<ipython-input-6-05639e44053b> in <module>()\n",
    "      1 # this should produce an error, since the operant '+' can not combine 'int' and 'str' variables!\n",
    "----> 2 var1 + res1\n",
    "\n",
    "TypeError: unsupported operand type(s) for +: 'int' and 'str'\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to mathematical operations, you can apply logical operations, like **comparisons**.\n",
    "\n",
    "An **equality operation** (a comparison operation) can be performed by using the `==` operator. The result is always a `bool`-type having either the value `True` or `False`, depending on whether the content of two variables are indeed equal or not. Beside equality, we can also check for inquality be using the `!=` operation.\n",
    "\n",
    "Imagine this as a question you ask to the computer. \"Is the content of var1 **equal** to the content of var2?\"\n",
    "\n",
    "- Equality `==`\n",
    "- Inequality `!=`\n",
    "\n",
    "```python\n",
    "print(var1 == var1)  # type bool\n",
    "    True\n",
    "    \n",
    "print(var1 != var1)  # type bool\n",
    "    False\n",
    "```\n",
    "\n",
    "Note how comparisons _always_ produce a `bool` type!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 7\n",
    "y = 7\n",
    "print(\"Is x equal to y? The answer is...\", x == y)\n",
    "print(\"Is x not equal to y? The answer is...\", x != y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of equality (`==`), there are other comparison operations which return a `bool`-type value. Think of it as other questions to the computer than only asking it for equality. These \"questions\" can be used in a `while` loop:\n",
    "- `<` strictly less than\n",
    "- `>` strictly greater than\n",
    "- `<=` less than or equal\n",
    "- `>=` greater than or equal\n",
    "- `!=` not equal\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "⌚ Suggested break!\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Flow control\n",
    "\n",
    "As any programming language python gives flow control possibilities: `if, else, for, while`.\n",
    "\n",
    "#### Decisions: if, elif, else\n",
    "\n",
    "Conditionals allow you to you easily define automated decisions.\n",
    "\n",
    "We could have a piece of code checking the number of cases in our neighborhood every day so we can get an alert if the number of cases exceeds a certain threshold. For example:\n",
    "\n",
    "```python\n",
    "if cases_by_neighborhood[\"kreuzberg\"] > 10000:\n",
    "    print(\"The number of Friedrichshain-Kreuzberg is above ten thousand!!!!!\")\n",
    "    # here, more lines could follow\n",
    "else:\n",
    "    # If the `if` clause is not true, then this block gets executed instead.\")\n",
    "    print(\"Kreuzberg has not reached a 10000 cases yet.\")\n",
    "\n",
    "# These lines are not indented anymore.\n",
    "# They will be executed regardless of the value of variable 'status'. \n",
    "print(\"Number of cases in Kreuzberg:\", cases_by_neighborhood[\"kreuzberg\"])    \n",
    "```\n",
    "\n",
    "Notice the syntax: \n",
    "* Keyword `if` indicates the start of an ` if`-statement. \n",
    "* A **conditional expression** (here `cases_by_neighborhood[\"kreuzberg\"] > 1000`) follows, which will return (explicitly or implicitly) a `bool`-type. Thus, either `True` or `False`. \n",
    "* A `:` closes the ` if`-statement. \n",
    "* An **indented** block follows. All contiguous lines sharing the same level of indentation (or deeper) belong to the same block and are executed only if the condition is met.\n",
    "* After the `if` block, an `else` block is defined. This is optional!\n",
    "\n",
    "Now further think of a scenario, where we have to decide whether to run a certain code based on 3 or more different conditions. Alternative, exclusive conditions can be done with `elif`.\n",
    "\n",
    "```python\n",
    "if cases_by_neighborhood[\"kreuzberg\"] > 15000:\n",
    "    print(\"The number of Friedrichshain-Kreuzberg is above a 15000!!!!!\")\n",
    "    # here, more lines could follow\n",
    "elif cases_by_neighborhood[\"kreuzberg\"] > 7500:\n",
    "    print(\"The number of Friedrichshain-Kreuzberg is above a 7500!!!\")\n",
    "    # here, more lines could follow\n",
    "elif cases_by_neighborhood[\"kreuzberg\"] > 3750:\n",
    "    print(\"The number of Friedrichshain-Kreuzberg is above a 3750!!!\")\n",
    "    # here, more lines could follow\n",
    "else:\n",
    "    # If the `if` clause is not true, then this block gets executed instead.\")\n",
    "    print(\"Kreuzberg has not reached a 3750 cases yet.\")\n",
    "```\n",
    "***\n",
    "\n",
    "> **TASK**\n",
    ">\n",
    "> Copy the code above in a new cell. Before running it, think: What's the expected output of the cell below? Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy the if-elif-elif-else code here\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Repetition: for-loop\n",
    "\n",
    "When we want to repeat the same action for different parameters, we wouldn't like to write the same code N times, right? That's what computers are for, anyway.\n",
    "\n",
    "Let's check which neighborhoods are above ten thousand cases. You might be tempted to do this:\n",
    "\n",
    "```python\n",
    "if cases_by_neighborhood[\"kreuzberg\"] > 10000:\n",
    "    print(\"The number of cases in Friedrichshain-Kreuzberg is above ten thousand!!!!!\")\n",
    "if cases_by_neighborhood[\"mitte\"] > 10000:\n",
    "    print(\"The number of cases in Mitte is above ten thousand!!!!!\")\n",
    "if cases_by_neighborhood[\"neukölln\"] > 10000:\n",
    "    print(\"The number of cases in Neukölln is above ten thousand!!!!!\")\n",
    "if cases_by_neighborhood[\"spandau\"] > 10000:\n",
    "    print(\"The number of cases in Spandau is above ten thousand!!!!!\")\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is really boring because we have to copy and paste the same code N times, and then replace the _key_ of the dictionary, and the full name of the neighborhood. It also introduces a lot of duplication. What if we want to change the threshold from `1000` to `2000` in the future? That would be four replacements. Imagine this for all neighborhoods in Berlin, or for all cities in Germany! No way!\n",
    "\n",
    "That's what loops are for! Let's analyze each block. They are really similar because, well, they are copies:\n",
    "\n",
    "```python\n",
    "if cases_by_neighborhood[THIS_IS_THE_DICT_KEY] > 1000:\n",
    "    print(\"The number of cases in THIS_IS_THE_FULL_NAME is above a thousand!!!!!\")\n",
    "```\n",
    "\n",
    "We need to repeat _that_ action, using different dictionary keys.\n",
    "\n",
    "```python\n",
    "neighborhoods = [\"mitte\", \"kreuzberg\", \"neukölln\", \"spandau\"]\n",
    "\n",
    "for neighborhood_key in neighborhoods:\n",
    "    if cases_by_neighborhood[neighborhood_key] > 1000:\n",
    "        print(\"The number of cases in\", neighborhood_key, \"is above a thousand!!!!!\")    \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's happening here?\n",
    "\n",
    "1. `for NAME in COLLECTION:`. Just as `if`, a `:` is needed to end the line.\n",
    "2. `NAME` will be assigned to the first element in `COLLECTION`.\n",
    "3. The indented block(s) will be executed with `NAME = FIRST VALUE`.\n",
    "4. `NAME` will take the second element.\n",
    "5. The indented block(s) will be executed with `NAME = SECOND VALUE`\n",
    "6. ... and so on. The loop ends when there are no more elements in the list to assign.\n",
    "\n",
    "> **TASK**\n",
    ">\n",
    "> How many entries does `neighborhoods` contain? You can use `len(neighborhoods)` to guess the answer, but you can also compute it with a `for` loop, a reassignable `int` variable and the `+` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Try your best here\n",
    "count = 0\n",
    "for neighborhood in neighborhoods:\n",
    "    ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: There are more ways to _repeat actions_ in Python, like using `while` loops or recursion, but `for` is by far the most common!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Introduction to functions\n",
    "\n",
    "As you have seen, repeating a single operation is useful so you do not get bored writing the same lines again and again. In fact, avoiding the repetition of tasks is one of the main core concepts in programming! We are lazy and we want to do as little as possible. This leads to devising mechanisms to reuse the same code in a flexible way.\n",
    "\n",
    "This is what functions are for: **reusable pieces of code that can be parametrized**. This means they accept variables as arguments! They can optionally _return_ a result too. They are very similar to mathematical functions in that sense!\n",
    "\n",
    "```python\n",
    "# Define a function\n",
    "def square(number):\n",
    "    output = number ** 2\n",
    "    return output\n",
    "\n",
    "# Use the function\n",
    "result = square(2)\n",
    "```\n",
    "\n",
    "What's the result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions can be understood as tasks. A chef might be asked to perform a task, like \"cutting vegetables\". The function would contain the instructions to know **how** to move the knife, but not **what** to cut, since there is more than one vegetable for which this instruction is valid. You then give for example a carrot to the cook (call the function with a \"carrot\" as argument) and get the cut carrot back.\n",
    "\n",
    "Note that:\n",
    "1. Defining a function does not mean you are using or _calling_ the function. \n",
    "2. If there's a return value, you also need to collect it. Otherwise it will be produced and discarded. Think of the cook you asked to cut the carrot. He did it properly and hands you the cut carrot, but you don't take it.\n",
    "\n",
    "So, as a recap, in order to use a function, you first have to know:\n",
    "\n",
    "1. If the function **exists**, either because you have written it or because you have imported it from another file (see below).\n",
    "2. What **arguments** or **parameters** the function is expecting. A function can take arguments or not. Arguments can be required (positional arguments) or optional (keyword arguments).\n",
    "3. If the function **returns** something. If it does not return anything, it doesn't mean it didn't _do_ anything. It might have some kind of side effect (writing a file to disk, for example).\n",
    "\n",
    "\n",
    ">*TASK:*\n",
    "*You already know and even used one particular function. Do you know how it is named and what it does?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Methods and atributes\n",
    "\n",
    "Functions can exist on their own (like `len()` or `print()`), but they can also be attached to variables. When a variable contains methods, software scientists call them _objects_. And, surprise, _everything_ in Python is an object! Even the most basic data types like `int` or `str` define their own functions. Object-attached functions are called **methods**. In addition to methods, you can also find **attributes**. Attributes are like \"sub-variables\"; variables inside a variable. To access both of them you use a `.` symbol.\n",
    "\n",
    "A `float` datatype for example has an attribute called `real` and a function called `is_integer()`.\n",
    "\n",
    "```python\n",
    "\n",
    "x = 1.2  # a float value\n",
    "print(x.real)  # accessing attribute of variable \"x\" called \"real\". Notice: No () brackets!\n",
    "print(x.is_integer())  # calling function is_integer() of variable \"x\" with no argument.\n",
    "\n",
    "    1.2\n",
    "    True\n",
    "```\n",
    "\n",
    "The **attribute** `real` contains the real part of the floating point number (in case of a complex number this becomes important).<br>\n",
    "The **function** `is_integer()` does not use any arguments and returns a `bool` type having the value `True` if `x` can be written as `int` type.\n",
    "\n",
    "> TASK\n",
    "> \n",
    "> You can explore the list of attributes and methods in each object by typing `.` after their name. If you press <kbd>TAB</kbd> you will see an autocompletion list! Execute the cell below and explore these lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "number = 4\n",
    "string = \"Jaime\"\n",
    "dictionary = {\"python_version\": \"3.6\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place your cursor right after the period and press tab!\n",
    "number.\n",
    "string.\n",
    "dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Modules and imports\n",
    "\n",
    "Python let you reuse _objects_ defined in other _modules_ via the [`import` system](https://docs.python.org/3/reference/import.html). A module is a file containing Python definitions and statements. You can easily identify them thanks to the `*.py` file extension.\n",
    "\n",
    "> More info on the [Python documentation](https://docs.python.org/3/tutorial/modules.html)!\n",
    "\n",
    "Python ships with a large battery of modules that you can use right away. Since it would be unfeasible to load all definitions directly when starting Python (it simply would take too long and would consume too much memory), only a very small subset is available upon initialization (like `len()`, `print()`, `range()`). The other ones must be _explicitly_ invoked or **imported**. That can be achieved with the `import` keyword.\n",
    "\n",
    "For example, to get access to more scalar functions like the square root, you need to import the `math` module, which defines `sqrt`. As you can see, the functions defined in `math` can be accessed via `.`.\n",
    "\n",
    "```python\n",
    "import math\n",
    "help(math.sqrt)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try it here!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The import system can also be used to load modules developed by 3rd parties. One of the most popular modules are **numpy** and **pandas**, as you will see in the following dates. Import statements look like the following:\n",
    "\n",
    "```python\n",
    "import numpy\n",
    "import pandas\n",
    "```\n",
    "\n",
    "Definition\n",
    "\n",
    "```python\n",
    "# calling the function \"mean\" from the numpy module, giving the list \"[1,2,3,4]\" as argument.\n",
    "numpy.mean([1,2,3,4])\n",
    "\n",
    "```\n",
    "\n",
    "Since module names are sometimes long (and informaticians are lazy) we can alias them by using the keyword `as`.\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "```\n",
    "\n",
    "Whenever we need a function/definition from the `numpy` module we can further refer to the module as `np`.\n",
    "\n",
    "```python\n",
    "# calling the function \"mean\" from the numpy module, giving the list \"[1,2,3,4]\" as argument.\n",
    "np.mean([1,2,3,4])\n",
    "```\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 5. Discussion\n",
    "\n",
    "Python is a very expressive language that has risen in popularity in the recent years. The ecosystem of packages and modules for data science and, more specifically, data science is _vast_. Learning the basics can end up being one of your master assets down your career, especially if you decide to pursue an academic research path!\n",
    "\n",
    "Right now, all these concepts might be very spread and not make sense together. Hopefully, after seeing more application-oriented examples in the following days you will have a better understanding of the potential and power of Python.\n",
    "\n",
    "_Hello world, future Pythonista!_\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Exercises\n",
    "\n",
    "6.1 When executing the following code, will there be an error?\n",
    "\n",
    "```python\n",
    "x = 5\n",
    "y = \"5\"\n",
    "\n",
    "print(x + y)\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.2 What do we need to change it order to get the correct output \"10\"?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.3 What is the expected output of the following code?\n",
    "```python\n",
    "x = \"\"\n",
    "for i in \"python_is_amazing!\":\n",
    "    if i == \"_\":\n",
    "        x += \" \"\n",
    "    else:\n",
    "        x += i\n",
    "print(x)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Solutions\n",
    "\n",
    "Before you take a look at the solutions, try to solve the exercises yourself! All the information needed lives in 5. Practical - if you are stuck, first take a look at the material there. Talk to your fellow students. If you have a solution, then go ahead and take a look here.\n",
    "\n",
    "\n",
    "<details><summary>Exercise 6.1</summary>\n",
    "\n",
    "Integers and strings cannot be summed or concatenated together, so this will result in an error. In order to use `+` you have to decide the behavior you want by converting one of them to the expected type.\n",
    "    \n",
    "</details>\n",
    "\n",
    "\n",
    "***\n",
    "\n",
    "\n",
    "<details><summary>Exercise 6.2</summary>\n",
    "   \n",
    "```python\n",
    "# Without changing the assignment, use `int(  )` to cast string to integer\n",
    "print(x + int(y))    \n",
    "```\n",
    "    \n",
    "</details>\n",
    "\n",
    "***\n",
    "\n",
    "<details><summary>Exercise 6.3</summary>\n",
    "\n",
    "The solution would be:\n",
    "    \n",
    "```python\n",
    "\"python is amazing!\"\n",
    "\n",
    "```\n",
    "\n",
    "Why? Because we are iterating over the string contents, letter by letter, and adding them to a new one (`x`) one letter at a time. However, before adding to `x` we first chechk if the letter is a `_`, and in that case we replace it with a space ` `. In practice, this means we are replacing underscores with spaces. Of course there are simpler ways to do such a common operation! \n",
    "    \n",
    "```python\n",
    "x = \"python_is_amazing!\".replace(\"_\", \" \")\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  }
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