c08c-python-petle-nested-zadania.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python: Pętle zagnieżdżone - Zadania"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop Nested Mean\n",
    "\n",
    "1. Użyj danych z sekcji \"Input\" (patrz poniżej)\n",
    "1. Odseparuj nagłówek od danych\n",
    "1. Wylicz średnią wartość `Sepal length`\n",
    "\n",
    "Output:\n",
    "```python\n",
    "result: float\n",
    "# 5.911111111111111\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = [\n",
    "    ('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),\n",
    "    (5.8, 2.7, 5.1, 1.9, 'virginica'),\n",
    "    (5.1, 3.5, 1.4, 0.2, 'setosa'),\n",
    "    (5.7, 2.8, 4.1, 1.3, 'versicolor'),\n",
    "    (6.3, 2.9, 5.6, 1.8, 'virginica'),\n",
    "    (6.4, 3.2, 4.5, 1.5, 'versicolor'),\n",
    "    (4.7, 3.2, 1.3, 0.2, 'setosa'),\n",
    "    (7.0, 3.2, 4.7, 1.4, 'versicolor'),\n",
    "    (7.6, 3.0, 6.6, 2.1, 'virginica'),\n",
    "    (4.6, 3.1, 1.5, 0.2, 'setosa'),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop Nested Unique Keys\n",
    "\n",
    "1. Użyj danych z sekcji \"Input\" (patrz poniżej)\n",
    "1. Zbierz klucze z wszystkich wierszy w jednej sekwencji ``result``\n",
    "1. Posortuj ``result``\n",
    "1. Wypisz unikalne klucze\n",
    "1. Porównaj wyniki z sekcją \"Output\" (patrz poniżej)\n",
    "\n",
    "Output:\n",
    "```python\n",
    "Petal length\n",
    "Petal width\n",
    "Sepal length\n",
    "Sepal width\n",
    "Species\n",
    "```\n",
    "\n",
    "Podpowiedź:\n",
    "* ``row.keys()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = [\n",
    "    {'Sepal length': 5.1, 'Sepal width': 3.5, 'Species': 'setosa'},\n",
    "    {'Petal length': 4.1, 'Petal width': 1.3, 'Species': 'versicolor'},\n",
    "    {'Sepal length': 6.3, 'Petal width': 1.8, 'Species': 'virginica'},\n",
    "    {'Sepal length': 5.0, 'Petal width': 0.2, 'Species': 'setosa'},\n",
    "    {'Sepal width': 2.8, 'Petal length': 4.1, 'Species': 'versicolor'},\n",
    "    {'Sepal width': 2.9, 'Petal width': 1.8, 'Species': 'virginica'},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop Unpacking Endswith\n",
    "\n",
    "1. Użyj danych z sekcji \"Input\" (patrz poniżej)\n",
    "1. Odseparuj nagłówek od danych\n",
    "1. Iterując po danych rozpakuj wiersz do ``*features`` oraz ``label``\n",
    "1. Wypisz nazwy gatunków kończące się na \"ca\" lub \"osa\"\n",
    "\n",
    "Podpowiedź:\n",
    "* ``str.endswith()``\n",
    "* ``set.pop()``\n",
    "* ``isinstance`` or ``type``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = [\n",
    "    ('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),\n",
    "    (5.8, 2.7, 5.1, 1.9, {'virginica'}),\n",
    "    (5.1, 3.5, 1.4, 0.2, {'setosa'}),\n",
    "    (5.7, 2.8, 4.1, 1.3, {'versicolor'}),\n",
    "    (6.3, 2.9, 5.6, 1.8, {'virginica'}),\n",
    "    (6.4, 3.2, 4.5, 1.5, {'versicolor'}),\n",
    "    (4.7, 3.2, 1.3, 0.2, {'setosa'}),\n",
    "    (7.0, 3.2, 4.7, 1.4, {'versicolor'}),\n",
    "    (7.6, 3.0, 6.6, 2.1, {'virginica'}),\n",
    "    (4.6, 3.1, 1.5, 0.2, {'setosa'}),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop Dict from Dict to Dict\n",
    "\n",
    "1. Użyj danych z sekcji \"Input\" (patrz poniżej)\n",
    "1. Przekonwertuj do ``result: Dict[str, str]``\n",
    "1. Porównaj wyniki z sekcją \"Output\" (patrz poniżej)\n",
    "\n",
    "Output:\n",
    "```python\n",
    "result: Dict[str, str]\n",
    "# {'Doctorate': '6',\n",
    "#  'Prof-school': '6',\n",
    "#  'Masters': '5',\n",
    "#  'Bachelor': '5',\n",
    "#  'Engineer': '5',\n",
    "#  'HS-grad': '4',\n",
    "#  'Junior High': '3',\n",
    "#  'Primary School': '2',\n",
    "#  'Kindergarten': '1'}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = {\n",
    "    6: ['Doctorate', 'Prof-school'],\n",
    "    5: ['Masters', 'Bachelor', 'Engineer'],\n",
    "    4: ['HS-grad'],\n",
    "    3: ['Junior High'],\n",
    "    2: ['Primary School'],\n",
    "    1: ['Kindergarten'],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop Dict from List to Dict\n",
    "\n",
    "1. Użyj danych z sekcji \"Input\" (patrz poniżej)\n",
    "1. Odseparuj nagłówek od danych\n",
    "1. Wypisz ``List[dict]``:\n",
    "\n",
    "    * klucz: nazwa z nagłówka\n",
    "    * wartość: wyniki pomiarów lub gatunek\n",
    "\n",
    "1. Porównaj wyniki z sekcją \"Output\" (patrz poniżej)\n",
    "\n",
    "Output:\n",
    "```python\n",
    "result: List[dict]\n",
    "# [{'Sepal length': 5.8,\n",
    "#   'Sepal width': 2.7,\n",
    "#   'Petal length': 5.1,\n",
    "#   'Petal width': 1.9,\n",
    "#   'Species': 'virginica'},\n",
    "#  {'Sepal length': 5.1,\n",
    "#   'Sepal width': 3.5,\n",
    "#   'Petal length': 1.4,\n",
    "#   'Petal width': 0.2,\n",
    "#   'Species': 'setosa'},\n",
    "#  ...]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = [\n",
    "    ('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),\n",
    "    (5.8, 2.7, 5.1, 1.9, 'virginica'),\n",
    "    (5.1, 3.5, 1.4, 0.2, 'setosa'),\n",
    "    (5.7, 2.8, 4.1, 1.3, 'versicolor'),\n",
    "    (6.3, 2.9, 5.6, 1.8, 'virginica'),\n",
    "    (6.4, 3.2, 4.5, 1.5, 'versicolor'),\n",
    "    (4.7, 3.2, 1.3, 0.2, 'setosa'),\n",
    "    (7.0, 3.2, 4.7, 1.4, 'versicolor'),\n",
    "    (7.6, 3.0, 6.6, 2.1, 'virginica'),\n",
    "    (4.9, 3.0, 1.4, 0.2, 'setosa'),\n",
    "    (4.9, 2.5, 4.5, 1.7, 'virginica'),\n",
    "    (7.1, 3.0, 5.9, 2.1, 'virginica'),\n",
    "    (4.6, 3.4, 1.4, 0.3, 'setosa'),\n",
    "    (5.4, 3.9, 1.7, 0.4, 'setosa'),\n",
    "    (5.7, 2.8, 4.5, 1.3, 'versicolor'),\n",
    "    (5.0, 3.6, 1.4, 0.3, 'setosa'),\n",
    "    (5.5, 2.3, 4.0, 1.3, 'versicolor'),\n",
    "    (6.5, 3.0, 5.8, 2.2, 'virginica'),\n",
    "    (6.5, 2.8, 4.6, 1.5, 'versicolor'),\n",
    "    (6.3, 3.3, 6.0, 2.5, 'virginica'),\n",
    "    (6.9, 3.1, 4.9, 1.5, 'versicolor'),\n",
    "    (4.6, 3.1, 1.5, 0.2, 'setosa'),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop Dict Label Encoder\n",
    "\n",
    "1. Użyj danych z sekcji \"Input\" (patrz poniżej)\n",
    "1. Zdefiniuj:\n",
    "\n",
    "    * ``features: List[tuple]`` - pomiary\n",
    "    * ``labels: List[int]`` - gatunki\n",
    "    * ``label_encoder: Dict[int, str]`` - słownik zakodowanych (jako cyfry) nazw gatunków\n",
    "\n",
    "1. Odseparuj nagłówek od danych\n",
    "1. Aby móc zakodować i odkodować ``labels`` (gatunki) potrzebny jest ``label_encoder: Dict[int, str]``:\n",
    "\n",
    "    * key - identyfikator (kolejna liczba rzeczywista)\n",
    "    * value - nazwa gatunku\n",
    "\n",
    "1. ``label_encoder`` musi być wygenerowany z ``DATA``\n",
    "1. Dla każdego wiersza dodawaj odpowiednie dane do ``feature``, ``labels`` i ``label_encoder``\n",
    "1. Wypisz ``feature``, ``labels`` i ``label_encoder``\n",
    "1. Porównaj wyniki z sekcją \"Output\" (patrz poniżej)\n",
    "\n",
    "Output:\n",
    "```python\n",
    "features: List[tuple]\n",
    "# [(5.8, 2.7, 5.1, 1.9),\n",
    "#  (5.1, 3.5, 1.4, 0.2),\n",
    "#  (5.7, 2.8, 4.1, 1.3),\n",
    "#  (6.3, 2.9, 5.6, 1.8),\n",
    "#  (6.4, 3.2, 4.5, 1.5),\n",
    "#  (4.7, 3.2, 1.3, 0.2), ...]\n",
    "\n",
    "labels: List[int]\n",
    "# [0, 1, 2, 0, 2, 1, 2, 0, 1, 0, 0, 1, 1, 2, 1, 2, 0, 2, 0, 2, 1]\n",
    "\n",
    "label_encoder: Dict[int, str]\n",
    "# {0: 'virginica',\n",
    "#  1: 'setosa',\n",
    "#  2: 'versicolor'}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = [\n",
    "    ('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),\n",
    "    (5.8, 2.7, 5.1, 1.9, 'virginica'),\n",
    "    (5.1, 3.5, 1.4, 0.2, 'setosa'),\n",
    "    (5.7, 2.8, 4.1, 1.3, 'versicolor'),\n",
    "    (6.3, 2.9, 5.6, 1.8, 'virginica'),\n",
    "    (6.4, 3.2, 4.5, 1.5, 'versicolor'),\n",
    "    (4.7, 3.2, 1.3, 0.2, 'setosa'),\n",
    "    (7.0, 3.2, 4.7, 1.4, 'versicolor'),\n",
    "    (7.6, 3.0, 6.6, 2.1, 'virginica'),\n",
    "    (4.9, 3.0, 1.4, 0.2, 'setosa'),\n",
    "    (4.9, 2.5, 4.5, 1.7, 'virginica'),\n",
    "    (7.1, 3.0, 5.9, 2.1, 'virginica'),\n",
    "    (4.6, 3.4, 1.4, 0.3, 'setosa'),\n",
    "    (5.4, 3.9, 1.7, 0.4, 'setosa'),\n",
    "    (5.7, 2.8, 4.5, 1.3, 'versicolor'),\n",
    "    (5.0, 3.6, 1.4, 0.3, 'setosa'),\n",
    "    (5.5, 2.3, 4.0, 1.3, 'versicolor'),\n",
    "    (6.5, 3.0, 5.8, 2.2, 'virginica'),\n",
    "    (6.5, 2.8, 4.6, 1.5, 'versicolor'),\n",
    "    (6.3, 3.3, 6.0, 2.5, 'virginica'),\n",
    "    (6.9, 3.1, 4.9, 1.5, 'versicolor'),\n",
    "    (4.6, 3.1, 1.5, 0.2, 'setosa'),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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