commit 6c4b7e8f93b788bf72f550948d85d679e2c2ad35
Author: Luis Carvalho <lcarvalho@mailbox.org>
Date:   Thu Apr 28 13:54:18 2022 +0100

    Init repostiory

diff --git a/data-structures.ipynb b/data-structures.ipynb
new file mode 100644
index 0000000..043bfdd
--- /dev/null
+++ b/data-structures.ipynb
@@ -0,0 +1,1984 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Introduction to Programming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Basics "
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Advantages of Jupyter Notebooks\n",
+    "\n",
+    "* Very quick to test the code you are writing\n",
+    "* Allow us to work in a \"report mode\" mixing markdown and code\n",
+    "* No need for a big setup, you just need to open and start writing code\n",
+    "* You can test different concepts in different cells but still have a code structure that runs top to bottom (more on this later) \n",
+    "* Great for Data Scientists and Analytics \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Disadvantages of Jupyter Notebooks\n",
+    "\n",
+    "* Code can get messy very quickly\n",
+    "* The fact that is interactive (you can go above the line you are currently writing and run a piece of code) can lead to bugs. \n",
+    "* Not suitable for building software\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Why this matters? \n",
+    "\n",
+    "* Notebooks will be our base throughout the entire semester\n",
+    "* This is where you will write code and submit your assignments as well as test if what you are doing is going on the right direction.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## What is an expression?\n",
+    "\n",
+    "* An expression is a code statement\n",
+    "* In a natural language like English, a statement would be a sentence. It is complete and can be evaluated. Same thing with expressions in programming languages - they must be complete so that they can be evaluated.\n",
+    "* Expressions can be very complex or very simple.\n",
+    "\n",
+    "Let’s take a look at some simple ones!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Expressions can be very simple in a programming language. For example, the number “1” is a totally valid expression!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Or the expression can be a string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hello'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"hello\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Slightly less simple.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 1 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Concatenating two strings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hello world'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"hello \" + \"world\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Expressions can be made up of multiple expressions 🤯"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "calling a print function is an expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "calling a print function with an expression in it is also an expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1 + 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "But careful... an incomplete expression is not an expression! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-12-428cd044e81f>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-12-428cd044e81f>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1 +\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "1 + "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Hello errors! We will get to this, don't worry. Just one heads up: never be afraid of error, treat them as friends - because they are. They help you understanding what you are doing wrong and where that problem is. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Returning vs non-returning expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Expressions may or may not return values\n",
+    "* All of the expressions except for one that we have seen so far are returning values\n",
+    "* How do we know? Easy, there’s a red [out] that shows up on. \n",
+    "\n",
+    "Let's check it out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# returning\n",
+    "\"hello\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
+   "source": [
+    "# not-returning\n",
+    "print(\"hello\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### What is the difference? \n",
+    "\n",
+    "* Printing is for humans\n",
+    "    * Printing something shows it on the screen for us to look at it. \n",
+    "* Returning is for programs\n",
+    "    * Returning stuff allows it to be passed around different parts of the code\n",
+    "    * Returned values may be printed, but when using Jupyter Notebook most of the times that is not required"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Is there a difference? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "### Yes! A hugeeeeee one \n",
+    "\n",
+    "The difference between printing and returning something is very important\n",
+    "It’s okay if you don’t fully understand the difference right now, start paying attention to the red Outs and we will fully undertand it when we get to functions. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### What are variables? \n",
+    "\n",
+    "* Variables are symbols, not values\n",
+    "* When you write a mathematical function such as f(x) = x^2 you are using a variable to represent the value that will eventually be passed in and be squared\n",
+    "* A single variable can take on multiple values over its lifetime without any problem at all\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Compare values and variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* 5\n",
+    "    * is a number\n",
+    "    * It is a value! It is not abstract, it is very concrete - it is a number\n",
+    "* “ricardo”\n",
+    "    * Is a list of letters\n",
+    "    * It is a value - it is my name\n",
+    "    * It is concrete, it does not change over time\n",
+    "* x\n",
+    "    * Is a variable\n",
+    "    * It is abstract, it may take on different values over time\n",
+    "* x = 5\n",
+    "    * This expression sets the variable equal to the value 5\n",
+    "* x = “ricardo”\n",
+    "    * This expression sets the variable equal to the value “ricardo”\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Variables can have types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "\n",
+    "\n",
+    "* Variables can have different types. The basic types are: \n",
+    "    * Int\n",
+    "        * Whole numbers = 1, 2, etc\n",
+    "* Float\n",
+    "    * Decimals = 0.5, 1.1, 1.2, etc.\n",
+    "* String\n",
+    "    * Lists of characters = “here is a string”\n",
+    "* Boolean\n",
+    "    * True or False\n",
+    "* Null\n",
+    "* the absence of a value\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### What can we do with variables? \n",
+    "\n",
+    "* We can initialize them\n",
+    "* We can assign values to them\n",
+    "* We can print the value of them\n",
+    "* We can know the type of them\n",
+    "* We can test if they are equal to each other\n",
+    "* We can use them in conjunction with other variables to achieve cool things!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Variable Initialization - why we need it? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'name' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-324-40b2f89f25eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'name' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "A variable only exists when you initialize it, before that it doesn’t exist and therefore you can’t use it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = \"dobby\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "After a variable is initialized, it may be assigned different values.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dobby\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = \"cookie\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "After a variable is initialized, it may be assigned different values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cookie\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Two variables can be compared to each other using the “==” operator\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dobby_father = \"ricardo\"\n",
+    "cookie_father = \"ricardo\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(dobby_father == cookie_father)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "You can also combine variables with strings to produce dynamic text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The father of dobby is  ricardo\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The father of dobby is \", dobby_father)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The father of dobby is  Ricardo Pereira\n"
+     ]
+    }
+   ],
+   "source": [
+    "# re-assign\n",
+    "dobby_father = \"Ricardo Pereira\"\n",
+    "print(\"The father of dobby is \", dobby_father)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Important note\n",
+    "\n",
+    "When you assign a value to a variable, you are also implicitly setting the type as well!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "### Naming variables is super important\n",
+    "\n",
+    "* Variable naming and the conventions surrounding this is a hotly contested topic\n",
+    "* There are non-python specific rules\n",
+    "    * Don’t make them too long\n",
+    "    * Don’t make them too short\n",
+    "    * Make them descriptive\n",
+    "    * They cannot have spaces\n",
+    "    * They must begin with letters or an underscore “_”\n",
+    "* There are python specific rules\n",
+    "    * Use “snake case” when your variable names are long enough to include more than one word\n",
+    "        * a_variable_name instead of avariablename or aVariableName\n",
+    "* Think carefully about the name of your variable! It’s really important to name them well so that when you return to some code that you have not written for a while, you will be able to read it!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Live code 🚀"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Let's create a few variables, assign some values, check different types and make sure we are ok with this so far! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Your Pet's Information\n",
+    "\n",
+    "a) Create the following variables related to your pet: `pet_name`, `pet_age`, `pet_breed` and `pet_long_hair` (which is a boolean if the pet has long hair)\n",
+    "\n",
+    "b) Print the types of the variables you created.\n",
+    "\n",
+    "c) Create a variable `pet_year_of_birth` which is the result of subtracting the `pet_age` to the integer `2021`.\n",
+    "\n",
+    "d) Is your pet's year of birth the year 2020? Assign True or False to answer on a variable called born_2020\n",
+    "\n",
+    "e) Print a sentence with your pet's `pet_name` and `pet_year_of_birth`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Data Structures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "There's plenty of different data structures and it's important to understand a few of them to start.\n",
+    "\n",
+    "Let's start with understand what we can do with something as simple as a string and see a bit of what it can do! \n",
+    "\n",
+    "I don't want you to memorize, I just want you to get the intuitions behind what we are doing. Next week we will get our hands dirty on this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hello hello hello hello hello hello hello hello hello hello '"
+      ]
+     },
+     "execution_count": 268,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"hello \" * 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hello class'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"hello \" + \"class\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "We can access the different characters of the string and do all sorts of manipulations using the notation `<string>[index]`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'h'"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"hello world\"[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'e'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"hello world\"[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "We can access a subset of a string..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'o w'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"hello world\"[4:7]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "There's a pattern I want you to get used an just recognize / use in the beginning, until we fully understand what it is. \n",
+    "\n",
+    "The pattern is: \n",
+    "`<object> . <method>` \n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 276,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"hello world\".startswith(\"h\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"hello world\".startswith(\"H\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "You can also assign the string to a variable and have access to the same methods. Makes sense right? The variable itself it's a string! 🤠"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "string_variable = \"hello world\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# same thing as having \"hello world\".startswith(\"h\")\n",
+    "string_variable.startswith(\"h\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "We can do string interpolation to interpolate a string with values that we want"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My name is bruna and my age is 3!\n",
+      "My name is bruna and my age is 3!\n"
+     ]
+    }
+   ],
+   "source": [
+    "name = \"bruna\"\n",
+    "age = 3\n",
+    "print(\"My name is {} and my age is {}!\".format(name, age))\n",
+    "print(f\"My name is {name} and my age is {age}!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello your bank account balance is 1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "bank_account = 1000\n",
+    "print(f\"Hello your bank account balance is {bank_account}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Cool so we can do a lot with strings! \n",
+    "\n",
+    "But there's more in the world besides strings.. Values can be of many more types than the one we’ve learned so far!\n",
+    "**The types we’ve seen so far are called primitives - they are the most basic type of values** that we can have\n",
+    "Some of the more complicated types that variables can hold are:\n",
+    "* Lists\n",
+    "* Tuples\n",
+    "* Dictionaries\n",
+    "\n",
+    "These are usually referred to as data structures, they are structures that hold data - potentially a lot of it!\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Depending on your needs, you have a few cool built-in data structures in python\n",
+    "* Tuple\n",
+    "    * When you need to have a collection of things and don’t need to change it\n",
+    "* List\n",
+    "    * When you need a collection of things and want to be able to change it\n",
+    "* Dictionary\n",
+    "    * When you need to associate two things using a key-value store"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Let's start with a few use cases to deep dive into each one of the data structures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "1. I want to hold all the students that are in this class and be able to add and remove the students that will come in next week on the add and drop period\n",
+    "\n",
+    "For this, you can use a list! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "2. I want to hold the names of the teaching staff for the current semester\n",
+    "\n",
+    "For this you can use a tuple!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "3. I want to know the final grade of each student \n",
+    "\n",
+    "For this you can use a dictionary! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "🤯"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Tuples\n",
+    "* We need to learn the syntax. This is what will allow you to create different data structure as well as recognize by visually scanning your code\n",
+    "* Syntax is parenthesis and the different elements of the tuple inside, separated by commas! \n",
+    "\n",
+    "You must start to be aware: This is a computer looking for specific things. Commas are different than semi-colons, round parens are different than square, etc. \n",
+    "\n",
+    "Pay attention to the details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "teaching_staff = (\"Ricardo\", \"Angelo\", \"Claudio\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "What you can do with a tuple? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Access elements one at a time by their index. Python is 0-indexed which means that the first element is index nr 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Ricardo'"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# notice how the index starts at zero\n",
+    "teaching_staff[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Access the first two elements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "teaching_staff = (\"Ricardo\", \"Claudio\", \"Francisco\", \"Angelo\", \"Joao\", \"John\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('Ricardo',)"
+      ]
+     },
+     "execution_count": 290,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#[start:end:interval]\n",
+    "x = \"hello\"\n",
+    "teaching_staff[0:1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "* Slice of a tuple its still a tuple. Let's check the type in two lines of code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "teaching_slice = teaching_staff[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(teaching_slice)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* The syntax is the same as for tuples but with square brackets instead of parenthesis\n",
+    "* You can with a list everything you can do with a tuple and more! \n",
+    "* Lists are meant to be changed! You can add, remove, replace different elements, re-order, etc. \n",
+    "* They have a bunch of cool \"functionalities\" associated with them.\n",
+    "\n",
+    "\n",
+    "Remember, the pattern for cool functionalities: `<object> . <method>`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "list_of_students = [\"Francisco\", \"Antonio\", \"Maria\", \"Teresa\", \"John\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Francisco', 'Antonio', 'Maria', 'Teresa', 'John']"
+      ]
+     },
+     "execution_count": 326,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_of_students"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Access the value of the list elements using the index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Francisco'"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# just like we did it in the tuples\n",
+    "list_of_students[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "You can, for example, sort your list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "list_of_students.sort()"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "A bit of nomenclature: we called the method `sort()` on the `list_of_students`. Different data structure have different methods (the cool functionalities we have been talking about). You can always find these on the official Python documentation. Link [here](https://docs.python.org/3/tutorial/datastructures.html)\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Dictionaries are very cool, they are used to associate two things in a dynamic way\n",
+    "Most programming languages have them but they usually get different names (unfortunately). For example:\n",
+    "\n",
+    "* Javascript - anonymous object\n",
+    "* Ruby - hash\n",
+    "* Go - map\n",
+    "* PHP - Associative array\n",
+    "\n",
+    "In order to understand dictionaries, you need to wrap your head around one simple concept: that of a key-value store"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "**Key-value store**\n",
+    "\n",
+    "* A key-value store is a very abstract concept\n",
+    "* These exist in many forms in many different types of technologies but in python, they are called dictionaries\n",
+    "\n",
+    "A key-value store maps a key to a value. You can literally think of a dictionary! What the oxford dictionary does is to map a key (one word) to a value (description of that word)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Maybe get's easier with an example: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "student_grades = {\"Francisco\": 18, \"Terese\": [18, 20, 19]}\n",
+    "student_grades[\"Joana\"] = 20\n",
+    "student_grades[\"Terese\"].append(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "In here, Francisco and Terese are the keys and the values are the respective grades. The syntax for dictionaries is the following: \n",
+    "* `{key: value, another_key: another_value}` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Dictionary are **unordered** data structures. Therefore, you can't access by index! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "student_grades[\"Francisco\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "A common mistake that I see students wasting a lot of time. The keys that I am defining are **strings**! The most common mistake is when you try to use variables that are not defined. Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'dobby' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-79-08aedd0d63e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdogs_age_dictionary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mdobby\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'dobby' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "dogs_age_dictionary = {dobby: 3}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# option 1\n",
+    "dogs_age_dictionary = {\"dobby\": 3}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# option 2 - define dobby variable\n",
+    "dobby = \"dobby\"\n",
+    "dogs_age_dictionary = {dobby: 3}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "A few things we can do with dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['dobby'])"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# we can check all the keys and values - this will be useful later on\n",
+    "dogs_age_dictionary.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Adding keys and values. Syntax is `<dictionary>[<key we want to add>] = <value we want to add>` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# we can add keys and values as we want\n",
+    "# dictionary -> dogs_age_dictionary\n",
+    "# key we want to add -> cookie\n",
+    "# value we want to add -> 2\n",
+    "dogs_age_dictionary[\"cookie\"] = 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Live code 🚀"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### 1. Pet's Passport\n",
+    "a) Remember the variables you created for your pet's information? Organize all of that information in a dictionary.\n",
+    "\n",
+    "When you print the dictionary it should look like this `{'name': 'Snoopy', 'age': 5, ...}`\n",
+    "\n",
+    "b) Create a list with the names of the owners of your pet.\n",
+    "\n",
+    "c) Add a key `owners` to the dictionary with the list you created previously as its value.\n",
+    "\n",
+    "d) Add a key `initials` to the dictionary with the two first letters of your pet's name as the value (use slicing)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Nova Students\n",
+    "\n",
+    "names = [\"Francisca\", \"M@nel\", \"Jonas\", \"Kathryn\", \"Francisca\"]\n",
+    "\n",
+    "a) During the bidding period 'Jonas' decided to drop but another joined. Remove 'Jonas' and add 'Abel' to the list of names. \n",
+    "\n",
+    "b) Create a new list with the first 3 elements of the previous list and sort it alphabetically. \n",
+    "\n",
+    "`ages = [20, 25, 28, 25]`\n",
+    "\n",
+    "c) Above is a list with the age of the students (in the same order as the sorted list of names). Double check programatically that there is the same number of names as there are students.\n",
+    "\n",
+    "d) Put `names` and `ages` organised in a dictionary.\n",
+    "\n",
+    "e) In your final check you notice that there is a wrong character in the Manel's name. Correct his name in the dictionary. "
+   ]
+  }
+ ],
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