uczenie-maszynowe/lab/02_Wczytywanie_i_prezentowanie_danych.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### AITech — Uczenie maszynowe — laboratoria\n",
    "# 2. Wczytywanie i prezentowanie danych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1. Formaty CSV i TSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przechowywanie danych w plikach w formatach CSV i TSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CSV** (*Comma-Separated Values*) i **TSV** (*Tab-Separated Values*) to proste formaty przechowywania danych tabelarycznych w postaci tekstowej.\n",
    "\n",
    "Każdy rekord zapisywany jest w osobnym wierszu, a poszczególne pola oddzielone są przecinkami (CSV) lub znakami tabulacji (TSV)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Przykład\n",
    "\n",
    "Następujące dane tabelaryczne:\n",
    "\n",
    "imię | nazwisko  | rok ur. | adres\n",
    ":----|:----------|:--------|:----------------------\n",
    "Jan  | Nowak     | 1999    | Poznańska 3, Warszawa\n",
    "Anna | Kowalska  | 2001    | Warszawska 9, Poznań\n",
    "Ewa  | Kaczmarek | 1990    | Wrocławska 123, Gdańsk\n",
    "\n",
    "możemy zapisać w formacie CSV jako:\n",
    "\n",
    "    imię,nazwisko,rok ur.,adres\n",
    "    Jan,Nowak,1999,\"Poznańska 3, Warszawa\"\n",
    "    Anna,Kowalska,2001,\"Warszawska 9, Poznań\"\n",
    "    Ewa,Kaczmarek,1990,\"Wrocławska 123, Gdańsk\"\n",
    "\n",
    "lub w formacie TSV jako:\n",
    "\n",
    "    imię    nazwisko    rok ur. adres\n",
    "    Jan     Nowak       1999    Poznańska 3, Warszawa\n",
    "    Anna    Kowalska    2001    Warszawska 9, Poznań\n",
    "    Ewa     Kaczmarek   1990    Wrocławska 123, Gdańsk\n",
    "    \n",
    "W formacie TSV poszczególne pola oddzielone są znakami tabulacji, a nie spacjami.\n",
    "\n",
    "Znak tabulacji to ten biały (tj. niedrukowalny) znak, który pojawia się po naciśnięciu klawisza **Tab** na klawiaturze."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formaty CSV i TSV mają tę zaletę, że są proste, łatwe do przetorzenia przez komputer oraz czytelne dla człowieka."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wczytywanie danych z plików CSV i TSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Biblioteka `csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['price', 'isNew', 'rooms', 'floor', 'location', 'sqrMetres']\n",
      "['476118.0', 'False', '3', '1', 'Centrum', '78']\n",
      "['459531.0', 'False', '3', '2', 'Sołacz', '62']\n",
      "['411557.0', 'False', '3', '0', 'Sołacz', '15']\n",
      "['496416.0', 'False', '4', '0', 'Sołacz', '14']\n",
      "['406032.0', 'False', '3', '0', 'Sołacz', '15']\n",
      "['450026.0', 'False', '3', '1', 'Naramowice', '80']\n",
      "['571229.15', 'False', '2', '4', 'Wilda', '39']\n",
      "['325000.0', 'False', '3', '1', 'Grunwald', '54']\n",
      "['268229.0', 'False', '2', '1', 'Grunwald', '90']\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open(\"data1.csv\", encoding=\"utf-8\") as data_file:\n",
    "    reader = csv.reader(data_file)\n",
    "    for row in reader:\n",
    "        print(row)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'price': '476118.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Centrum', 'sqrMetres': '78'}\n",
      "{'price': '459531.0', 'isNew': 'False', 'rooms': '3', 'floor': '2', 'location': 'Sołacz', 'sqrMetres': '62'}\n",
      "{'price': '411557.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '15'}\n",
      "{'price': '496416.0', 'isNew': 'False', 'rooms': '4', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '14'}\n",
      "{'price': '406032.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '15'}\n",
      "{'price': '450026.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Naramowice', 'sqrMetres': '80'}\n",
      "{'price': '571229.15', 'isNew': 'False', 'rooms': '2', 'floor': '4', 'location': 'Wilda', 'sqrMetres': '39'}\n",
      "{'price': '325000.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Grunwald', 'sqrMetres': '54'}\n",
      "{'price': '268229.0', 'isNew': 'False', 'rooms': '2', 'floor': '1', 'location': 'Grunwald', 'sqrMetres': '90'}\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open(\"data1.csv\", encoding=\"utf-8\") as data_file:\n",
    "    reader = csv.DictReader(data_file)\n",
    "    for row in reader:\n",
    "        print(dict(row))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['price', 'isNew', 'rooms', 'floor', 'location', 'sqrMetres']\n",
      "['476118.0', 'False', '3', '1', 'Centrum', '78']\n",
      "['459531.0', 'False', '3', '2', 'Sołacz', '62']\n",
      "['411557.0', 'False', '3', '0', 'Sołacz', '15']\n",
      "['496416.0', 'False', '4', '0', 'Sołacz', '14']\n",
      "['406032.0', 'False', '3', '0', 'Sołacz', '15']\n",
      "['450026.0', 'False', '3', '1', 'Naramowice', '80']\n",
      "['571229.15', 'False', '2', '4', 'Wilda', '39']\n",
      "['325000.0', 'False', '3', '1', 'Grunwald', '54']\n",
      "['268229.0', 'False', '2', '1', 'Grunwald', '90']\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open(\"data1.tsv\", encoding=\"utf-8\") as data_file:\n",
    "    reader = csv.reader(data_file, delimiter=\"\\t\")\n",
    "    for row in reader:\n",
    "        print(row)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'price': '476118.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Centrum', 'sqrMetres': '78'}\n",
      "{'price': '459531.0', 'isNew': 'False', 'rooms': '3', 'floor': '2', 'location': 'Sołacz', 'sqrMetres': '62'}\n",
      "{'price': '411557.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '15'}\n",
      "{'price': '496416.0', 'isNew': 'False', 'rooms': '4', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '14'}\n",
      "{'price': '406032.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '15'}\n",
      "{'price': '450026.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Naramowice', 'sqrMetres': '80'}\n",
      "{'price': '571229.15', 'isNew': 'False', 'rooms': '2', 'floor': '4', 'location': 'Wilda', 'sqrMetres': '39'}\n",
      "{'price': '325000.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Grunwald', 'sqrMetres': '54'}\n",
      "{'price': '268229.0', 'isNew': 'False', 'rooms': '2', 'floor': '1', 'location': 'Grunwald', 'sqrMetres': '90'}\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open(\"data1.tsv\", encoding=\"utf-8\") as data_file:\n",
    "    reader = csv.DictReader(data_file, delimiter=\"\\t\")\n",
    "    for row in reader:\n",
    "        print(dict(row))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Biblioteka `pandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       price  isNew  rooms  floor    location  sqrMetres\n",
      "0  476118.00  False      3      1     Centrum         78\n",
      "1  459531.00  False      3      2      Sołacz         62\n",
      "2  411557.00  False      3      0      Sołacz         15\n",
      "3  496416.00  False      4      0      Sołacz         14\n",
      "4  406032.00  False      3      0      Sołacz         15\n",
      "5  450026.00  False      3      1  Naramowice         80\n",
      "6  571229.15  False      2      4       Wilda         39\n",
      "7  325000.00  False      3      1    Grunwald         54\n",
      "8  268229.00  False      2      1    Grunwald         90\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"data1.csv\")\n",
    "print(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       price  isNew  rooms  floor    location  sqrMetres\n",
      "0  476118.00  False      3      1     Centrum         78\n",
      "1  459531.00  False      3      2      Sołacz         62\n",
      "2  411557.00  False      3      0      Sołacz         15\n",
      "3  496416.00  False      4      0      Sołacz         14\n",
      "4  406032.00  False      3      0      Sołacz         15\n",
      "5  450026.00  False      3      1  Naramowice         80\n",
      "6  571229.15  False      2      4       Wilda         39\n",
      "7  325000.00  False      3      1    Grunwald         54\n",
      "8  268229.00  False      2      1    Grunwald         90\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"data1.tsv\", sep=\"\\t\")\n",
    "print(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[476118.0 False 3 1 'Centrum' 78]\n",
      " [459531.0 False 3 2 'Sołacz' 62]\n",
      " [411557.0 False 3 0 'Sołacz' 15]\n",
      " [496416.0 False 4 0 'Sołacz' 14]\n",
      " [406032.0 False 3 0 'Sołacz' 15]\n",
      " [450026.0 False 3 1 'Naramowice' 80]\n",
      " [571229.15 False 2 4 'Wilda' 39]\n",
      " [325000.0 False 3 1 'Grunwald' 54]\n",
      " [268229.0 False 2 1 'Grunwald' 90]]\n"
     ]
    }
   ],
   "source": [
    "print(data.to_numpy())  # dane z Pandas DataFrame można przekonwertować na tablicę NumPy\n",
    "# data.values  # dla starszych wersji biblioteki Pandas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. Wykresy – biblioteka `matplotlib`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dokumentacja modułu `pyplot` biblioteki `matplotlib`: https://matplotlib.org/api/pyplot_api.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline  # Jupyterowa \"magia\", żeby wykresy wyświetlały się inline, a nie w osobnym oknie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Prosty wykres punktowy\n",
    "# Litery 'ro' oznaczają, że dane zostaną przedstawione za pomocą czerwonych kółek:\n",
    "#    'r' ('red') – czerwone\n",
    "#    'o' – kółko\n",
    "# Zobacz też: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot\n",
    "\n",
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], \"ro\")\n",
    "plt.axis([0, 5, 0, 20])  # opcjonalnie ustawiamy zakres osi wykresu\n",
    "\n",
    "plt.show()  # pokaż wykres\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Wykres danych wczytanych z pliku\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Wczytanie danych\n",
    "data = pd.read_csv(\"data1.tsv\", sep=\"\\t\")\n",
    "data_array = data.to_numpy()\n",
    "\n",
    "# Wybór kolumn do przedstawienia na wykresie\n",
    "x = data_array[:, 0]\n",
    "y = data_array[:, 5]\n",
    "\n",
    "plt.plot(x, y, \"gx\")  # \"gx\" - zielone (Green) krzyżyki (x)\n",
    "plt.axis([0, 600000, 0, 100])  # opcjonalnie ustawiamy zakres osi wykresu\n",
    "\n",
    "plt.show()  # pokaż wykres\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# inicjalizacja\n",
    "fig = plt.figure()\n",
    "# dodanie \"podwykresu\" (zobacz: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplot )\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.set_xlabel(\"x\")  # opis osi x\n",
    "ax.set_ylabel(\"y\")  # opis osi y\n",
    "ax.set_title(\"Wykres funkcji\")  # tytuł wykresu\n",
    "\n",
    "x = np.arange(0.0, 10.0, 0.01)  # x przebiega zakres od 0 do 10 co 0.01\n",
    "\n",
    "# zdefiniowanie wartości y w zależności od x\n",
    "y = np.sin(2 * np.pi * x)\n",
    "# spróbuj też inne funkcje:\n",
    "# y = 2 * x**2 + 5 * x - 10\n",
    "# y = np.sqrt(x)\n",
    "\n",
    "ax.plot(x, y, color=\"blue\", lw=2)  # \"lw\" oznacza grubość linii (Line Width)\n",
    "\n",
    "plt.show()  # pokaż wykres\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Style wykresów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Umieszczanie kilku układów współrzędnych i kilku wykresów na jednym rysunku\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# inicjalizacja zestawu wykresów\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "\n",
    "# wygenerowanie danych\n",
    "x = np.arange(0.0, 1.0, 0.025)\n",
    "y1 = np.sin(2 * np.pi * x) + 5.0\n",
    "y2 = np.sin(2 * np.pi * x) + 2.5\n",
    "y3 = np.sin(2 * np.pi * x)\n",
    "\n",
    "# pierwszy wykres\n",
    "ax1 = fig.add_subplot(3, 1, 1)\n",
    "ax1.set_ylabel(\"y\")\n",
    "ax1.set_title(\"pierwszy wykres\")\n",
    "\n",
    "ax1.plot(x, y1, color=\"red\", lw=2)  # pierwsza krzywa - czerwona\n",
    "ax1.plot(x, y2, color=\"green\", lw=2)  # druga krzywa - zielona\n",
    "ax1.plot(\n",
    "    x, y3, color=\"#002d69\", lw=2\n",
    ")  # trzecia krzywa - kolor zdefiniowany szesnastkowo\n",
    "\n",
    "# drugi wykres\n",
    "ax2 = fig.add_subplot(3, 1, 2)\n",
    "ax2.set_ylabel(\"y\")\n",
    "ax2.set_title(\"drugi wykres\")\n",
    "\n",
    "ax2.plot(x, y1, color=\"black\", lw=2, linestyle=\"--\")  # pierwsza krzywa - kreskowana\n",
    "ax2.plot(x, y2, color=\"blue\", lw=2, linestyle=\":\")  # druga krzywa - kropkowana\n",
    "ax2.plot(\n",
    "    x, y3, color=\"#ff0000\", lw=2, linestyle=\"-.\"\n",
    ")  # trzecia krzywa - kropkowano-kreskowana\n",
    "\n",
    "# trzeci wykres\n",
    "ax3 = fig.add_subplot(3, 1, 3)\n",
    "ax3.set_ylabel(\"y\")\n",
    "ax3.set_title(\"trzeci wykres\")\n",
    "\n",
    "ax3.plot(x, y1, color=\"brown\", marker=\"x\")  # pierwsza krzywa - krzyżyki\n",
    "ax3.plot(x, y2, color=\"purple\", marker=\"o\")  # druga krzywa - kółka\n",
    "ax3.plot(x, y3, color=\"orange\", marker=\"^\")  # trzecia krzywa - trójkąty\n",
    "\n",
    "# dostosowanie odstępów pomiędzy wykresami\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.4)\n",
    "\n",
    "plt.show()  # pokaż wykres\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wykresy trójwymiarowe – linie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "\n",
    "theta = np.linspace(-4 * np.pi, 4 * np.pi, 50)\n",
    "z = np.linspace(-2, 2, 50)\n",
    "r = z**2 + 1\n",
    "x = r * np.sin(theta)\n",
    "y = r * np.cos(theta)\n",
    "\n",
    "ax.plot(x, y, z, label=\"krzywa parametryczna\")\n",
    "ax.legend()  # pokaż legendę wykresu\n",
    "\n",
    "plt.show()  # pokaż wykres\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wykresy trójwymiarowe – powierzchnie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import (\n",
    "    Axes3D,\n",
    ")  # niezbędne do rysowania powierzchni w 3 wymiarach\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "\n",
    "X = np.arange(-5, 5, 0.25)\n",
    "Y = np.arange(-5, 5, 0.25)\n",
    "X, Y = np.meshgrid(\n",
    "    X, Y\n",
    ")  # wygenerowanie tablicy danych wejściowych dla wykresu trójwymiarowego\n",
    "\n",
    "# obliczenie wartości rzędnych\n",
    "Z = np.sqrt(X**2 + Y**2)\n",
    "# Spróbuj też innych funkcji\n",
    "# Z = np.sin(np.sqrt(X**2 + Y**2))\n",
    "# Z = np.cos(X + Y)\n",
    "# Z = X**2 * Y\n",
    "\n",
    "surf = ax.plot_surface(\n",
    "    X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0, antialiased=True\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wykresy kolumnowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# data: scores in five groups for men and women\n",
    "N = 5\n",
    "menMeans = [18, 35, 30, 35, 27]\n",
    "menStd = [2, 3, 4, 1, 2]\n",
    "womenMeans = [25, 32, 34, 20, 25]\n",
    "womenStd = [3, 5, 2, 3, 3]\n",
    "\n",
    "# start creating figure\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# necessary variables\n",
    "LOCATIONS = np.arange(N)  # the x locations for the groups\n",
    "WIDTH = 0.35  # the width of the bars\n",
    "\n",
    "# the bars\n",
    "rects_m = ax.bar(\n",
    "    LOCATIONS,\n",
    "    menMeans,\n",
    "    WIDTH,\n",
    "    color=\"blue\",\n",
    "    yerr=menStd,\n",
    "    error_kw=dict(elinewidth=2, ecolor=\"red\"),\n",
    ")\n",
    "\n",
    "rects_f = ax.bar(\n",
    "    LOCATIONS + WIDTH,\n",
    "    womenMeans,\n",
    "    WIDTH,\n",
    "    color=\"red\",\n",
    "    yerr=womenStd,\n",
    "    error_kw=dict(elinewidth=2, ecolor=\"blue\"),\n",
    ")\n",
    "\n",
    "# axes and labels\n",
    "ax.set_xlim(-WIDTH, len(LOCATIONS))\n",
    "ax.set_ylabel(\"Scores\")\n",
    "ax.set_title(\"Scores by group and gender\")\n",
    "xTickMarks = [\"Group {}\".format(i) for i in range(1, 6)]\n",
    "ax.set_xticks(LOCATIONS + WIDTH)\n",
    "xtickNames = ax.set_xticklabels(xTickMarks)\n",
    "plt.setp(xtickNames, rotation=45, fontsize=10)\n",
    "\n",
    "# add a legend\n",
    "ax.legend((rects_m[0], rects_f[0]), (\"Men\", \"Women\"))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wykresy punktowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "\n",
    "# left panel\n",
    "N = 1000\n",
    "ax1 = fig.add_subplot(121)\n",
    "\n",
    "x = np.random.randn(N)\n",
    "y = np.random.randn(N)\n",
    "ax1.scatter(x, y, color=\"blue\", s=10, edgecolor=\"none\")\n",
    "# make axes square\n",
    "ax1.set_aspect(1.0 / ax1.get_data_ratio())\n",
    "\n",
    "# right panel\n",
    "ax2 = fig.add_subplot(122)\n",
    "props = dict(alpha=0.5, edgecolors=\"none\")\n",
    "\n",
    "M = 200\n",
    "colors = [\"blue\", \"green\", \"magenta\", \"cyan\"]\n",
    "handles = []\n",
    "\n",
    "for color in colors:\n",
    "    x = np.random.randn(M)\n",
    "    y = np.random.randn(M)\n",
    "    size = np.random.randint(25, 200)\n",
    "    handles.append(ax2.scatter(x, y, c=color, s=size, **props))\n",
    "\n",
    "# ax2.set_ylim([-5,10])\n",
    "# ax2.set_xlim([-5,10])\n",
    "\n",
    "ax2.legend(handles, colors)\n",
    "ax2.grid(True)\n",
    "ax2.set_aspect(1.0 / ax2.get_data_ratio())\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogramy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "x = np.random.normal(0, 1, 1000)\n",
    "num_bins = 50\n",
    "ax.hist(x, num_bins, color=\"green\", alpha=0.8)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3. Wizualizacja danych - biblioteka `seaborn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biblioteka `seaborn` (https://seaborn.pydata.org) to oparta o `matplotlib` biblioteka do wizualizacji danych."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przykład 1\n",
    "\n",
    "Wizualizacja zbioru danych *Iris* poprzez rzutowanie go na różne osie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x20279e6c0d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 859.25x432 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn\n",
    "\n",
    "data_iris = pd.read_csv(\"../wyk/iris.csv\")\n",
    "\n",
    "seaborn.pairplot(\n",
    "    data_iris,\n",
    "    vars=[c for c in data_iris.columns if c != \"Gatunek\"],\n",
    "    hue=\"Gatunek\",\n",
    "    height=1.5,\n",
    "    aspect=1.75,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przykład 2\n",
    "\n",
    "Rozkład różnych typów zabudowy w zbiorze danych *Mieszkania4* pod względem roku budowy i powierzchni lokalu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x202002cc580>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 554.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn\n",
    "\n",
    "data = pd.read_csv(\"../wyk/mieszkania4.tsv\", sep=\"\\t\")\n",
    "\n",
    "data = data[(data[\"Powierzchnia w m2\"] < 10000) & (data[\"cena\"] < 10000000)]\n",
    "\n",
    "seaborn.relplot(data=data, x=\"Rok budowy\", y=\"Powierzchnia w m2\", hue=\"Typ zabudowy\")\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "livereveal": {
   "start_slideshow_at": "selected",
   "theme": "amu"
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}