2021-03-17 20:43:37 +01:00
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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"source": [
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"### AITech — Uczenie maszynowe — laboratoria\n",
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2021-03-18 11:50:38 +01:00
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"# 1b. Wczytywanie i prezentowanie danych"
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2021-03-17 20:43:37 +01:00
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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2021-03-18 11:50:38 +01:00
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"## Formaty CSV i TSV"
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2021-03-17 20:43:37 +01:00
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Przechowywanie danych w plikach w formatach CSV i TSV"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**CSV** (*Comma-Separated Values*) i **TSV** (*Tab-Separated Values*) to proste formaty przechowywania danych tabelarycznych w postaci tekstowej.\n",
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"\n",
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"Każdy rekord zapisywany jest w osobnym wierszu, a poszczególne pola oddzielone są przecinkami (CSV) lub znakami tabulacji (TSV)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Przykład\n",
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"\n",
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"Następujące dane tabelaryczne:\n",
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"\n",
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"imię | nazwisko | rok ur. | adres\n",
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":----|:----------|:--------|:----------------------\n",
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"Jan | Nowak | 1999 | Poznańska 3, Warszawa\n",
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"Anna | Kowalska | 2001 | Warszawska 9, Poznań\n",
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"Ewa | Kaczmarek | 1990 | Wrocławska 123, Gdańsk\n",
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"\n",
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"możemy zapisać w formacie CSV jako:\n",
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"\n",
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" imię,nazwisko,rok ur.,adres\n",
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" Jan,Nowak,1999,\"Poznańska 3, Warszawa\"\n",
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" Anna,Kowalska,2001,\"Warszawska 9, Poznań\"\n",
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" Ewa,Kaczmarek,1990,\"Wrocławska 123, Gdańsk\"\n",
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"\n",
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"lub w formacie TSV jako:\n",
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"\n",
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" imię nazwisko rok ur. adres\n",
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" Jan Nowak 1999 Poznańska 3, Warszawa\n",
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" Anna Kowalska 2001 Warszawska 9, Poznań\n",
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" Ewa Kaczmarek 1990 Wrocławska 123, Gdańsk\n",
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" \n",
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"W formacie TSV poszczególne pola oddzielone są znakami tabulacji, a nie spacjami.\n",
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"\n",
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"Znak tabulacji to ten biały (tj. niedrukowalny) znak, który pojawia się po naciśnięciu klawisza **Tab** na klawiaturze."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Formaty CSV i TSV mają tę zaletę, że są proste, łatwe do przetorzenia przez komputer oraz czytelne dla człowieka."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Wczytywanie danych z plików CSV i TSV"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Biblioteka `csv`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['price', 'isNew', 'rooms', 'floor', 'location', 'sqrMetres']\n",
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"['476118.0', 'False', '3', '1', 'Centrum', '78']\n",
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"['459531.0', 'False', '3', '2', 'Sołacz', '62']\n",
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"['411557.0', 'False', '3', '0', 'Sołacz', '15']\n",
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"['496416.0', 'False', '4', '0', 'Sołacz', '14']\n",
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"['406032.0', 'False', '3', '0', 'Sołacz', '15']\n",
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"['450026.0', 'False', '3', '1', 'Naramowice', '80']\n",
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"['571229.15', 'False', '2', '4', 'Wilda', '39']\n",
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"['325000.0', 'False', '3', '1', 'Grunwald', '54']\n",
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"['268229.0', 'False', '2', '1', 'Grunwald', '90']\n"
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]
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}
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],
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"source": [
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"import csv\n",
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"\n",
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"with open('data1.csv') as data_file:\n",
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" reader = csv.reader(data_file)\n",
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" for row in reader:\n",
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" print(row)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'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': 'Sołacz', 'sqrMetres': '62'}\n",
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"{'price': '411557.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '15'}\n",
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"{'price': '496416.0', 'isNew': 'False', 'rooms': '4', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '14'}\n",
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"{'price': '406032.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '15'}\n",
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"{'price': '450026.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Naramowice', 'sqrMetres': '80'}\n",
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"{'price': '571229.15', 'isNew': 'False', 'rooms': '2', 'floor': '4', 'location': 'Wilda', 'sqrMetres': '39'}\n",
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"{'price': '325000.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Grunwald', 'sqrMetres': '54'}\n",
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"{'price': '268229.0', 'isNew': 'False', 'rooms': '2', 'floor': '1', 'location': 'Grunwald', 'sqrMetres': '90'}\n"
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]
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}
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],
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"source": [
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"import csv\n",
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"\n",
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"with open('data1.csv') as data_file:\n",
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" reader = csv.DictReader(data_file)\n",
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" for row in reader:\n",
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" print(dict(row))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['price', 'isNew', 'rooms', 'floor', 'location', 'sqrMetres']\n",
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"['476118.0', 'False', '3', '1', 'Centrum', '78']\n",
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"['459531.0', 'False', '3', '2', 'Sołacz', '62']\n",
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"['411557.0', 'False', '3', '0', 'Sołacz', '15']\n",
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"['496416.0', 'False', '4', '0', 'Sołacz', '14']\n",
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"['406032.0', 'False', '3', '0', 'Sołacz', '15']\n",
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"['450026.0', 'False', '3', '1', 'Naramowice', '80']\n",
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"['571229.15', 'False', '2', '4', 'Wilda', '39']\n",
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"['325000.0', 'False', '3', '1', 'Grunwald', '54']\n",
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"['268229.0', 'False', '2', '1', 'Grunwald', '90']\n"
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]
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}
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],
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"source": [
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"import csv\n",
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"\n",
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"with open('data1.tsv') as data_file:\n",
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" reader = csv.reader(data_file, delimiter='\\t')\n",
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" for row in reader:\n",
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" print(row)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'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': 'Sołacz', 'sqrMetres': '62'}\n",
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"{'price': '411557.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '15'}\n",
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"{'price': '496416.0', 'isNew': 'False', 'rooms': '4', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '14'}\n",
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"{'price': '406032.0', 'isNew': 'False', 'rooms': '3', 'floor': '0', 'location': 'Sołacz', 'sqrMetres': '15'}\n",
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"{'price': '450026.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Naramowice', 'sqrMetres': '80'}\n",
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"{'price': '571229.15', 'isNew': 'False', 'rooms': '2', 'floor': '4', 'location': 'Wilda', 'sqrMetres': '39'}\n",
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"{'price': '325000.0', 'isNew': 'False', 'rooms': '3', 'floor': '1', 'location': 'Grunwald', 'sqrMetres': '54'}\n",
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"{'price': '268229.0', 'isNew': 'False', 'rooms': '2', 'floor': '1', 'location': 'Grunwald', 'sqrMetres': '90'}\n"
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]
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}
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],
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"source": [
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"import csv\n",
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"\n",
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"with open('data1.tsv') as data_file:\n",
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" reader = csv.DictReader(data_file, delimiter='\\t')\n",
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" for row in reader:\n",
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" print(dict(row))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Biblioteka `pandas`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" price isNew rooms floor location sqrMetres\n",
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"0 476118.00 False 3 1 Centrum 78\n",
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"1 459531.00 False 3 2 Sołacz 62\n",
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"2 411557.00 False 3 0 Sołacz 15\n",
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"3 496416.00 False 4 0 Sołacz 14\n",
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"4 406032.00 False 3 0 Sołacz 15\n",
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"5 450026.00 False 3 1 Naramowice 80\n",
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"6 571229.15 False 2 4 Wilda 39\n",
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"7 325000.00 False 3 1 Grunwald 54\n",
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"8 268229.00 False 2 1 Grunwald 90\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"data = pd.read_csv('data1.csv')\n",
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"print(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
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|
|
"output_type": "stream",
|
|
|
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|
|
"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",
|
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|
|
|
|
"\n",
|
|
|
|
|
|
"data = pd.read_csv('data1.tsv', sep='\\t')\n",
|
|
|
|
|
|
"print(data)"
|
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|
|
]
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|
|
},
|
2022-03-12 10:10:59 +01:00
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{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
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|
|
"Dane z Pandas DataFrame można przekonwertować na tablicę NumPy:\n",
|
|
|
|
|
|
"* za pomocą właściwości `values`\n",
|
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|
|
|
"* za pomocą metody `to_numpy()` (od wersji Pandas 1.1.0)"
|
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|
]
|
|
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|
},
|
2021-03-17 20:43:37 +01:00
|
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{
|
|
|
|
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|
"cell_type": "code",
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"execution_count": 9,
|
2021-03-17 20:43:37 +01:00
|
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|
|
"metadata": {},
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|
|
|
|
|
"outputs": [
|
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|
|
|
|
{
|
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|
|
|
|
"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": [
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"print(data.to_numpy()) # Pandas w wersji >=1.1.0\n",
|
|
|
|
|
|
"# lub:\n",
|
|
|
|
|
|
"# print(data.values) # dla starszych wersji biblioteki Pandas"
|
2021-03-17 20:43:37 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
2021-03-18 11:50:38 +01:00
|
|
|
|
"## Wykresy – biblioteka `matplotlib`"
|
2021-03-17 20:43:37 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"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": [
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"# Trochę jupyterowej magii, żeby wykresy wyświetlały się bezpośrednio pod kodem, który go wywołuje\n",
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"%matplotlib inline"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"execution_count": 10,
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"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') – 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"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"execution_count": 11,
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
|
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|
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"text/plain": [
|
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|
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"<Figure size 432x288 with 1 Axes>"
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|
]
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|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
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|
},
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"output_type": "display_data"
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|
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}
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|
|
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|
],
|
|
|
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|
|
"source": [
|
|
|
|
|
|
"# Wykres danych wczytanych z pliku\n",
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|
"\n",
|
|
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|
|
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"import matplotlib.pyplot as plt\n",
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|
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|
|
|
"import pandas as pd\n",
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|
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|
"\n",
|
|
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|
|
"# Wczytanie danych\n",
|
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|
|
"data = pd.read_csv('data1.tsv', sep='\\t')\n",
|
|
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|
|
"data_array = data.to_numpy()\n",
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"\n",
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|
|
"# Wybór kolumn do przedstawienia na wykresie\n",
|
|
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"x = data_array[:, 0]\n",
|
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"y = data_array[:, 5]\n",
|
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|
"\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"
|
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|
|
]
|
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|
|
|
|
},
|
|
|
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|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"execution_count": 12,
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"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",
|
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|
|
|
"\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",
|
|
|
|
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|
"\n",
|
|
|
|
|
|
"x = np.arange(0.0, 10.0, 0.01) # x przebiega zakres od 0 do 10 co 0.01\n",
|
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|
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|
"\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",
|
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|
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|
|
"\n",
|
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|
|
"plt.show() # pokaż wykres"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"### Style wykresów"
|
|
|
|
|
|
]
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|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 12,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-03-12 10:10:59 +01:00
|
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|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
|
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|
"text/plain": [
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|
"<Figure size 720x576 with 3 Axes>"
|
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|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
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|
|
},
|
|
|
|
|
|
"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(x, y3, color='#002d69', lw=2) # 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(x, y3, color='#ff0000', lw=2,linestyle=\"-.\") # 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=.4)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"plt.show() # pokaż wykres"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"### Wykresy trójwymiarowe – linie"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 13,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
|
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|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
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|
},
|
|
|
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"output_type": "display_data"
|
|
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}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"import matplotlib as mpl\n",
|
|
|
|
|
|
"from mpl_toolkits.mplot3d import Axes3D\n",
|
|
|
|
|
|
"import numpy as np\n",
|
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
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|
|
|
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"\n",
|
|
|
|
|
|
"fig = plt.figure(figsize=(10,8))\n",
|
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|
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"ax = fig.add_subplot(111, projection='3d')\n",
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"\n",
|
|
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|
"theta = np.linspace(-4 * np.pi, 4 * np.pi, 50)\n",
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"z = np.linspace(-2, 2, 50)\n",
|
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|
"r = z**2 + 1\n",
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"x = r * np.sin(theta)\n",
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|
"y = r * np.cos(theta)\n",
|
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"\n",
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|
|
|
"ax.plot(x, y, z, label='krzywa parametryczna')\n",
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"ax.legend() # pokaż legendę wykresu\n",
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"\n",
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"plt.show() # pokaż wykres"
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|
]
|
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|
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|
|
},
|
|
|
|
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{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"### Wykresy trójwymiarowe – powierzchnie"
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|
]
|
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|
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|
},
|
|
|
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|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 14,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-03-12 10:10:59 +01:00
|
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|
|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"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",
|
|
|
|
|
|
"\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(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",
|
|
|
|
|
|
"execution_count": 15,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"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",
|
|
|
|
|
|
"execution_count": 16,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
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"text/plain": [
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"<Figure size 1080x504 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"fig = plt.figure(figsize=(15,7))\n",
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"\n",
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"# left panel\n",
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"N = 1000\n",
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"ax1 = fig.add_subplot(121)\n",
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"\n",
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"x = np.random.randn(N)\n",
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"y = np.random.randn(N)\n",
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"ax1.scatter(x, y, color='blue', s=10, edgecolor='none')\n",
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"# make axes square\n",
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"ax1.set_aspect(1. / ax1.get_data_ratio()) \n",
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"\n",
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"# right panel\n",
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"ax2 = fig.add_subplot(122)\n",
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"props = dict(alpha=0.5, edgecolors='none')\n",
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"\n",
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"M = 200\n",
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"colors = ['blue', 'green', 'magenta', 'cyan']\n",
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"handles = []\n",
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"\n",
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"for color in colors:\n",
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" x = np.random.randn(M)\n",
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" y = np.random.randn(M)\n",
|
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" size = np.random.randint(25,200)\n",
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" handles.append(ax2.scatter(x, y, c=color, s=size, **props))\n",
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"\n",
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"#ax2.set_ylim([-5,10])\n",
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"#ax2.set_xlim([-5,10])\n",
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"\n",
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"ax2.legend(handles, colors)\n",
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"ax2.grid(True)\n",
|
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"ax2.set_aspect(1./ax2.get_data_ratio())\n",
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"\n",
|
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|
"plt.show()"
|
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]
|
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|
|
|
|
},
|
|
|
|
|
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{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
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|
|
"source": [
|
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|
|
|
|
"### Histogramy"
|
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|
|
|
|
]
|
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|
|
},
|
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|
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{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 17,
|
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|
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|
"metadata": {},
|
|
|
|
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"outputs": [
|
|
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|
{
|
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|
|
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|
"data": {
|
2022-03-12 10:10:59 +01:00
|
|
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|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"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",
|
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|
|
|
|
"num_bins = 50\n",
|
|
|
|
|
|
"ax.hist(x, num_bins, color='green', alpha=0.8)\n",
|
|
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|
"plt.show()"
|
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|
]
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},
|
|
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{
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|
"cell_type": "markdown",
|
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|
|
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|
"metadata": {},
|
|
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|
|
"source": [
|
2021-03-18 11:50:38 +01:00
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|
|
|
"## Wizualizacja danych - biblioteka `seaborn`"
|
2021-03-17 20:43:37 +01:00
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]
|
|
|
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|
|
},
|
|
|
|
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|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"Biblioteka `seaborn` (https://seaborn.pydata.org) to oparta o `matplotlib` biblioteka do wizualizacji danych."
|
|
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|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
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|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"### Przykład 1\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"Wizualizacja zbioru danych *Iris* poprzez rzutowanie go na różne osie:"
|
|
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|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
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|
{
|
|
|
|
|
|
"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": {
|
2022-03-12 10:10:59 +01:00
|
|
|
|
"image/png": "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
|
2021-03-17 20:43:37 +01:00
|
|
|
|
"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', height=1.5, aspect=1.75)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"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": {
|
2022-03-12 10:10:59 +01:00
|
|
|
|
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2021-03-17 20:43:37 +01:00
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"text/plain": [
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"<Figure size 554.875x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import seaborn\n",
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"\n",
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"data = pd.read_csv('../wyk/mieszkania4.tsv', sep='\\t')\n",
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"\n",
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"data = data[\n",
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" (data['Powierzchnia w m2'] < 10000)\n",
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" & (data['cena'] < 10000000)\n",
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" ]\n",
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"\n",
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"seaborn.relplot(data=data, x='Rok budowy', y='Powierzchnia w m2', hue='Typ zabudowy')"
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]
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}
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],
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"metadata": {
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"celltoolbar": "Slideshow",
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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2022-03-12 10:10:59 +01:00
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"version": "3.7.6"
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2021-03-17 20:43:37 +01:00
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},
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"livereveal": {
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"start_slideshow_at": "selected",
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"theme": "amu"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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