{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Matematikai Algoritmusok és Felfedezések I.\n", "\n", "## 9. Előadás: Matplotlib\n", "### 2021 április 12." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Adatvizualizáció \n", "\n", "Fontos, hogy az adatokat informatív módon tudjuk prezentálni mások (és persze magunk) számára. \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " $\"Drawing\"$ " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![mis](misleading1.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![mis](misleading2.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![mis](nomasksgraph.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![mis](nomasksgraphfunny.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![mis](Deathsclutter.png)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "import numpy as np\n", "import random as rn\n", "import matplotlib.pyplot as plt\n", "import csv # csv fájlból fogunk beolvasni adatokat. \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Matplotlib\n", "- A legismertebb grafikon és ábra készítő Pythonban. \n", "- Példákon keresztül nézzük meg a működését." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# New Yorki mókusok\n", "Az adatok egy csv (comma separated values) fájlban vannak, a honlapon megtalálható. Minden sor egy mókus észlelés adatait írja le a Central Parkban. Az első sorból látszik, hogy milyen adatok vannak megadva észlelésenként: \n", "\n", "long,lat,unique_squirrel_id,hectare,shift,date,hectare_squirrel_number,age,primary_fur_color,highlight_fur_color,\n", "combination_of_primary_and_highlight_color,color_notes,location,above_ground_sighter_measurement,specific_location,running,\n", "chasing,climbing,eating,foraging,other_activities,kuks,quaas,moans,tail_flags,tail_twitches,approaches,indifferent,runs_from,\n", "other_interactions,lat_long,zip_codes,community_districts,borough_boundaries,city_council_districts,police_precincts\n", "\n", "Először készítünk egy függvényt, ami beolvas egy oszlopot:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def getcolumn(fajlnev, ind): # egy oszlop beolvasása egy cvs fájlból, de legfeljebb 3000 elem. \n", " ans=[]\n", " c=0\n", " with open(fajlnev, newline='') as csvfile:\n", " sqreader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n", " ans=[next(sqreader)[ind] for _ in range(3000)]\n", " del ans[0]\n", " return ans\n", " return ans " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "['NA',\n", " 'Adult',\n", " 'Adult',\n", " 'Juvenile',\n", " 'NA',\n", " 'Juvenile',\n", " 'Adult',\n", " 'NA',\n", " 'Adult',\n", " 'Adult']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getcolumn('nyc_squirrels.csv',7)[0:10] # első 10 mókus kora" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "a=getcolumn('nyc_squirrels.csv',7)\n", "num=np.array(a)\n", "unique, counts = np.unique(num, return_counts=True) # A tömbben előforduló egyedei objektumok és hogy hányszor \n", " # fordulnak elő" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['?' 'Adult' 'Juvenile' 'NA']\n", "[ 4 2550 327 118]\n" ] } ], "source": [ "print(unique)\n", "print(counts)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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