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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Uczenie maszynowe 2019/2020 –
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"### 17 marca 2021\n",
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"# 2. Wczytywanie i prezentowanie danych"
]
},
{
"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": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['price', 'isNew', 'rooms', 'floor', 'location', 'sqrMetres']\n",
"['476118.0', 'False', '3', '1', 'Centrum', '78']\n",
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"['459531.0', 'False', '3', '2', 'Soł
"['411557.0', 'False', '3', '0', 'Soł
"['496416.0', 'False', '4', '0', 'Soł
"['406032.0', 'False', '3', '0', 'Soł
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"['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') as data_file:\n",
" reader = csv.reader(data_file)\n",
" for row in reader:\n",
" print(row)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'price': '476118.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Centrum', 'sqrMetres': '78'}\n",
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"{'price': '459531.0', 'isNew': 'False', 'rooms': '3', 'floor': '2', 'location': 'Soł
"{'price': '411557.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Soł
"{'price': '496416.0', 'isNew': 'False', 'rooms': '4', 'floor': '0', 'location': 'Soł
"{'price': '406032.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Soł
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"{'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') as data_file:\n",
" reader = csv.DictReader(data_file)\n",
" for row in reader:\n",
" print(dict(row))"
]
},
{
"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",
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"['459531.0', 'False', '3', '2', 'Soł
"['411557.0', 'False', '3', '0', 'Soł
"['496416.0', 'False', '4', '0', 'Soł
"['406032.0', 'False', '3', '0', 'Soł
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"['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') as data_file:\n",
" reader = csv.reader(data_file, delimiter='\\t')\n",
" for row in reader:\n",
" print(row)"
]
},
{
"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",
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"{'price': '459531.0', 'isNew': 'False', 'rooms': '3', 'floor': '2', 'location': 'Soł
"{'price': '411557.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Soł
"{'price': '496416.0', 'isNew': 'False', 'rooms': '4', 'floor': '0', 'location': 'Soł
"{'price': '406032.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Soł
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"{'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') as data_file:\n",
" reader = csv.DictReader(data_file, delimiter='\\t')\n",
" for row in reader:\n",
" print(dict(row))"
]
},
{
"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)"
]
},
{
"cell_type": "code",
"execution_count": 6,
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"metadata": {
"scrolled": true
},
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"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)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wykresy –
]
},
{
"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": [
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"# Trochę jupyterowej magii, żeby wykresy wyświetlały się bezpośrednio pod kodem, który go wywołuje\n",
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"%matplotlib inline"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"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') –
"# 'o' –
"# Zobacz też: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot\n",
"\n",
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"plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
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"plt.axis([0, 5, 0, 20]) # opcjonalnie ustawiamy zakres osi wykresu\n",
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"\n",
"plt.show() # pokaż wykres"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
"data": {
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"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",
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"plt.plot(x, y, 'gx') # \"gx\" - zielone (Green) krzyżyki (x)\n",
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"plt.axis([0, 600000, 0, 100]) # opcjonalnie ustawiamy zakres osi wykresu\n",
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"\n",
"plt.show() # pokaż wykres"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
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"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",
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"ax.set_xlabel('x') # opis osi x\n",
"ax.set_ylabel('y') # opis osi y\n",
"ax.set_title('Wykres funkcji') # tytuł wykresu\n",
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"\n",
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"x = np.arange(0.0, 10.0, 0.01) # x przebiega zakres od 0 do 10 co 0.01\n",
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"\n",
"# zdefiniowanie wartości y w zależności od x\n",
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"y = np.sin(2*np.pi*x)\n",
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"# spróbuj też inne funkcje:\n",
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"# y = 2 * x**2 + 5 * x - 10\n",
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"# y = np.sqrt(x)\n",
"\n",
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"ax.plot(x, y, color='blue', lw=2) # \"lw\" oznacza grubość linii (Line Width)\n",
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"\n",
"plt.show() # pokaż wykres"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Style wykresów"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 720x576 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"# Umieszczanie kilku układów współrzędnych i kilku wykresów na jednym rysunku\n",
"\n",
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"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",
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"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",
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"\n",
"# pierwszy wykres\n",
"ax1 = fig.add_subplot(3,1,1)\n",
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"ax1.set_ylabel('y')\n",
"ax1.set_title('pierwszy wykres')\n",
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"\n",
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"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(x, y3, color='#002d69', lw=2) # trzecia krzywa - kolor zdefiniowany szesnastkowo\n",
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"\n",
"# drugi wykres\n",
"ax2 = fig.add_subplot(3,1,2)\n",
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"ax2.set_ylabel('y')\n",
"ax2.set_title('drugi wykres')\n",
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"\n",
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"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(x, y3, color='#ff0000', lw=2,linestyle=\"-.\") # trzecia krzywa - kropkowano-kreskowana\n",
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"\n",
"# trzeci wykres\n",
"ax3 = fig.add_subplot(3,1,3)\n",
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"ax3.set_ylabel('y')\n",
"ax3.set_title('trzeci wykres')\n",
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"\n",
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"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",
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"\n",
"# dostosowanie odstępów pomiędzy wykresami\n",
"plt.subplots_adjust(wspace=0.2,hspace=.4)\n",
"\n",
"plt.show() # pokaż wykres"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wykresy trójwymiarowe –
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [
{
"data": {
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"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",
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"ax.plot(x, y, z, label='krzywa parametryczna')\n",
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"ax.legend() # pokaż legendę wykresu\n",
"\n",
"plt.show() # pokaż wykres"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wykresy trójwymiarowe –
]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.mplot3d import Axes3D # 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(X, Y) # wygenerowanie tablicy danych wejściowych dla wykresu trójwymiarowego\n",
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"\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",
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"\n",
"surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,\n",
" linewidth=0, antialiased=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wykresy kolumnowe"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [
{
"data": {
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"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(LOCATIONS, menMeans, WIDTH,\n",
" color='blue',\n",
" yerr=menStd,\n",
" error_kw=dict(elinewidth=2,ecolor='red'))\n",
"\n",
"rects_f = ax.bar(LOCATIONS+WIDTH, womenMeans, WIDTH,\n",
" color='red',\n",
" yerr=womenStd,\n",
" error_kw=dict(elinewidth=2,ecolor='blue'))\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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wykresy punktowe"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
"outputs": [
{
"data": {
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"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. / 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./ax2.get_data_ratio())\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Histogramy"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
"outputs": [
{
"data": {
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"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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Zadania"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zadania podstawowe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zadanie 2.1 (2 punkty)\n",
"\n",
"Wybierz dwie dowolne kolumny (z wyjątkiem pierwszej) z pliku *data2.csv*. Przedstaw zależność między tymi danymi na wykresie punktowym (wartości z jednej kolumny powinny znaleźć się na jednej osi, a wartości z drugiej kolumny –
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"### Zadanie 2.2A (2 punkty)\n",
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"\n",
"Wygeneruj wykres funkcji $y = f(x) = (a - 4) \\, x^2 + (b - 5) \\, x + (c - 6)$, gdzie $a, b, c$ to trzy ostatnie cyfry Twojego numeru indeksu. Pamiętaj o opisaniu osi wykresu ($x$ oraz $y$)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zadania zaawansowane"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"### Zadanie 2.2B (1 punkt)\n",
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"\n",
"Na wykresie z zadania 2.2 przedstaw dodatkowo wykres funkcji $y = g(x) = \\frac{e^x}{e^x + 1}$. Linie przestawiające funkcje $y = f(x)$ i $y = g(x)$ powinny znaleźć się na tym samym wykresie, ale powinny różnić się kolorami. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"### Zadanie 2.3 (2 punkty)\n",
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"\n",
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"Stwórz trójwymiarowy (powierzchniowy) wykres funkcji $f(x,y) = -(x^2 + y^3)$.\n",
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"\n",
"Wykres powinien przedstawiać powierzchnię $z = -(x^2 + y^3)$."
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"livereveal": {
"start_slideshow_at": "selected",
"theme": "amu"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
