Live_Coding_EN.ipynb

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    "### Task 1: Take in input some data from the user and store them as numbers\n",
    "\n",
    "Theory related concepts:\n",
    "- variables\n",
    "- input function\n",
    "- print function\n",
    "- built-in data types\n",
    "- operations with data"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Code here: get 6 input values and transform them into float numbers"
   ]
  },
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    "### Task 2.a Read from the pokemon dataset all Pokemon data\n",
    "\n",
    "Theory related concepts:\n",
    "- lists and access to their elements\n",
    "- for loops to iterate through lists\n",
    "- matrices\n",
    "- CSV files\n",
    "- open and read a file in Python\n",
    "- dataset\n",
    "- introduction to f-strings (?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "de455a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code here: Open dataset, and assign reference to a single variable"
   ]
  },
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   "source": [
    "### Task 2.b Use a function to establish a similarity degree\n",
    "\n",
    "Theory related concepts:\n",
    "- complex mathematical operators and operators precedence\n",
    "- math module\n",
    "- functions\n",
    "  - input/output of a function\n",
    "  - parameters"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "# Code Here:\n",
    "# - function to calculate Euclidean Distance\n",
    "# - data normalisation\n",
    "\n",
    "# ++ Extract only numerical columns for each row in the dataset"
   ]
  },
  {
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   "source": [
    "### Task 2.c Find the `k` most similar pokemon using the similarity function\n",
    "\n",
    "Theory related concepts:\n",
    "- for loops\n",
    "- complex data structures (list of tuples?)\n",
    "- sort\n",
    "- lambda functions\n",
    "- array slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "41bae39e",
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   "source": [
    "# Code here: (following up from previous tasks)\n",
    "# - Get user input, and define an imaginary pokemon_ from the 6 given input values\n",
    "# - Add the imaginary pokemon to the dataset, and run normalisation\n",
    "# - Extract the imaginary pokemon from the dataset (last row)\n",
    "# - calculate the distance (similarity) of the imaginary pokemon from the other pokemon in the dataset\n",
    "# - sorting of similarities / distances (via lambda function)\n",
    "# - extract the top K results"
   ]
  },
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   "source": [
    "### Task 3 Visualize the outputs\n",
    "\n",
    "Theory related concepts:\n",
    "- f-strings\n",
    "- functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9694ecb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code here: create a function that derives k strings from a set of k indices in the dataset using f-strings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ba5162",
   "metadata": {},
   "source": [
    "### Bonus track - Visualize Pokemon Art\n",
    "Theory related concepts:\n",
    "- `list comprehension`\n",
    "\n",
    "Code snippet to use: \n",
    "\n",
    "```python\n",
    "from IPython.display import Image\n",
    "\n",
    "for n,p in enumerate(best_pokemon):\n",
    "    print('-'*50)\n",
    "    print(n+1)\n",
    "    print(pokemon_dataset[p[0]][1])\n",
    "    display(Image(f\"resized/{pokemon_dataset[n][0]}.jpg\")),\n",
    "    print('-'*50)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "329498eb",
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   "outputs": [],
   "source": []
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