{"id":18213,"date":"2020-04-17T12:16:44","date_gmt":"2020-04-17T12:16:44","guid":{"rendered":"https:\/\/pythoncursus.nl\/?p=18213"},"modified":"2025-06-18T13:23:52","modified_gmt":"2025-06-18T13:23:52","slug":"data-visualiseren-python","status":"publish","type":"post","link":"https:\/\/datasciencepartners.nl\/data-visualiseren-python\/","title":{"rendered":"Data visualiseren met Python: hoe doe ik dat? [incl. tutorial]"},"content":{"rendered":"<div class=\"vgblk-rw-wrapper limit-wrapper\">\n<pre><code><p><a href=\"https:\/\/datasciencepartners.nl\/wat-is-python\/\"><strong>Python<\/strong><\/a> is een fantastische tool om data mee te visualiseren. De mogelijkheden zijn eindeloos. Zo zijn er verschillende <a href=\"https:\/\/datasciencepartners.nl\/python-package\/\"><strong>packages<\/strong><\/a> waarmee data gevisualiseerd kan worden. In totaal zijn er honderden verschillende visualisaties mogelijk, een stuk meer dus dan in bijvoorbeeld Excel.<\/p><\/code><\/pre>\n<blockquote><p>Data visualiseren is als <a href=\"https:\/\/datasciencepartners.nl\/wat-is-een-data-scientist\/\"><strong>data scientist<\/strong><\/a> van belang omdat je verborgen patronen makkelijker herkent en omdat je inzichten makkelijker over kunt dragen op anderen.<\/p><\/blockquote>\n<p>Het volgende overzicht geeft een indruk van de diversiteit aan mogelijkheden binnen visualisaties.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datasciencepartners.nl\/wp-content\/uploads\/2020\/04\/data-visualiseren-met-python-tutorial.png\" alt=\"data visualiseren met python tutorial\" width=\"666\" height=\"721\" \/><\/p>\n<p>In deze blog leer je:<\/p>\n<ul>\n<li><a href=\"#python-packages-data-visualiseren\"><strong>Welke packages er zijn voor Python om data te visualiseren<\/strong><\/a><\/li>\n<li><a href=\"#matplotlib-data-visualiseren-python\"><strong>Hoe je Matplotlib gebruikt voor visualisaties<\/strong><\/a><\/li>\n<li><a href=\"#pandas-data-visualiseren-python\"><strong>Hoe je Pandas gebruikt voor visualisaties<\/strong><\/a><\/li>\n<\/ul>\n<h2><strong>Welke packages er zijn voor Python om data te visualiseren<\/strong><\/h2>\n<p>Wie met <a href=\"https:\/\/datasciencepartners.nl\/data-science\/\"><strong>data science<\/strong><\/a> bezig is zal vroeg of laat ingewikkelde of zeer specifieke visualisaties willen maken. Er zijn diverse packages beschikbaar waarmee visualisaties gemaakt kunnen worden. De drie bekendste zijn:<\/p>\n<ul>\n<li><strong>Matplotlib<\/strong>: dit package biedt enorm veel mogelijkheden en vrijheid in het opmaken van figuren. Matplotlib is de basis voor visualisatie in Python en daarom een must-know voor data scientists. De standaard lay-out van Matplotlib is wat minder mooi en je zult redelijk wat code moeten schrijven om tot grafieken te komen (vanwege de hoge mate van vrijheid in de visualisaties). Het is goed om Matplotlib te begrijpen omdat andere packages op deze functionaliteit voortborduren.<\/li>\n<li><a href=\"https:\/\/datasciencepartners.nl\/python-pandas\/\"><strong>Pandas<\/strong><\/a>: dit package wordt zeer veel gebruikt. Met Pandas is het mogelijk om data te visualiseren, waarbij het package gebruik maakt van de functionaliteit van Matplotlib. Je kunt intu\u00eftief grafieken maken zonder veel code te hoeven schrijven.<\/li>\n<li><strong>Seaborn<\/strong>: ook dit package maakt onderliggend gebruik van Matplotlib. Het pakket biedt extra mogelijkheden qua visualisaties (vooral op het gebied van statistische visualisaties), heeft een gemakkelijke syntax en bevat mooie standaard-layouts.<\/li>\n<\/ul>\n<p>Zelf data visualiseren met Python? Schrijf je in voor een van onze trainingen.<br \/>\n<a href=\"https:\/\/datasciencepartners.nl\/python-cursus\/\"><button>Python training voor data science<\/button><\/a><br \/>\n<a href=\"https:\/\/datasciencepartners.nl\/python-machine-learning-training\/\"><button>Python machine learning training<\/button><\/a><br \/>\n<a href=\"https:\/\/datasciencepartners.nl\/data-science-opleiding\/\"><button>Data Science opleiding (4 dagen)<\/button><\/a><br \/>\n<a href=\"https:\/\/datasciencepartners.nl\/data-science-bootcamp\/\"><button>Data Science bootcamp (10 dagen)<\/button><\/a><\/p>\n<h2><strong>Hoe je Matplotlib gebruikt voor visualisaties<\/strong><\/h2>\n<p>Laten we eens een grafiek proberen te plotten met Matplotlib, zodat je begrijpt wat daarbij komt kijken. Allereerst is het uiteraard essentieel om een dataset te hebben. Als je mee wilt doen kun je hier de dataset met <a href=\"https:\/\/datasciencepartners.nl\/wp-content\/uploads\/2020\/04\/covid_19_data.csv\"><strong>COVID-19 data<\/strong><\/a> downloaden die in de volgende visualisaties gebruikt zal worden. De bron van de data is Kaggle en de dataset heet '<em>novel Corona Virus 2019 Dataset<\/em>'.<\/p>\n<p>Doorloop de volgende stappen:<\/p>\n<ol>\n<li><a href=\"https:\/\/datasciencepartners.nl\/python-installeren\/\"><strong>Installeer Python<\/strong><\/a> op je computer als je dat nog niet hebt<\/li>\n<li><a href=\"https:\/\/datasciencepartners.nl\/jupyter-notebook\/\"><strong>Installeer Jupyter Notebook<\/strong><\/a> op je computer zodat je Python code kunt runnen in een fijne interface<\/li>\n<li>Download <strong><a href=\"https:\/\/datasciencepartners.nl\/wp-content\/uploads\/2020\/04\/covid_19_data.csv\">jouw dataset<\/a><\/strong><\/li>\n<li>Schoon de dataset op als dat nodig is<\/li>\n<li>Lees de dataset in met Pandas in Jupyter Notebook (zie onderstaande code)<\/li>\n<\/ol>\n<!-- This site is converting visitors into subscribers and customers with OptinMonster - https:\/\/optinmonster.com :: Campaign Title: Cheatsheet inline -->\n<div id=\"om-xzn5asfsy4ouhuahjqhr-holder\"><\/div>\n<script>(function(d,u,ac){var s=d.createElement('script');s.type='text\/javascript';s.src='https:\/\/a.omappapi.com\/app\/js\/api.min.js';s.async=true;s.dataset.user=u;s.dataset.campaign=ac;d.getElementsByTagName('head')[0].appendChild(s);})(document,77079,'xzn5asfsy4ouhuahjqhr');<\/script>\n<!-- \/ OptinMonster -->\n<p>Met onderstaande code importeren we het package Pandas, want die hebben we nodig om de dataset (covid_19_data.csv) in Python in te lezen (<strong>lees ook: <a href=\"https:\/\/datasciencepartners.nl\/open-csv-python\/\">csv bestanden openen in Python<\/a><\/strong>). Let op, het is belangrijk dat je eenmalig Pandas installeert als je dat nog nooit hebt gedaan. Naast het inlezen van de dataset verkrijgen we een indruk van de data door info over de kolommen te tonen.<\/p>\n<p>In\u00a0[76]:\nimport pandas as pd\ncorona_data = pd.read_csv('covid_19_data.csv')\ncorona_data.info()<\/p>\n<p>&lt;class 'pandas.core.frame.DataFrame'&gt;\nRangeIndex: 15769 entries, 0 to 15768\nData columns (total 8 columns):\nSNo                15769 non-null int64\nObservationDate    15769 non-null object\nProvince\/State     7940 non-null object\nCountry\/Region     15769 non-null object\nLast Update        15769 non-null object\nConfirmed          15769 non-null float64\nDeaths             15769 non-null float64\nRecovered          15769 non-null float64\ndtypes: float64(3), int64(1), object(4)\nmemory usage: 985.6+ KB<\/p>\n<p>De dataset bevat gegevens over het aantal corona doden, bevestigde gevallen, en herstelde pati\u00ebnten per land per provincie per datum. De dataset heeft 8 kolommen en 15.769 rijen.<\/p>\n<p>Om data te visualiseren moeten we nu het package Matplotlib importeren. Ook dit package zal eenmalig ge\u00efnstalleerd moeten worden op jouw computer. Naast Matplotlib importeren we ook Numpy, een package dat we later nodig zullen hebben.<\/p>\n<p>In\u00a0[77]:\nimport matplotlib.pyplot as plt\nimport numpy as np<\/p>\n<p>In\u00a0[78]:<\/p>\n<h1>maak een figuur aan en assen om op te plotten<\/h1>\n<p>fig, ax = plt.subplots()<\/p>\n<h1>onderzoek de correlatie tussen het aantal confirmed cases en aantal sterfgevallen<\/h1>\n<p>ax.scatter(corona_data['Confirmed'], corona_data['Deaths'])<\/p>\n<h1>Grafiektitel en as-labels<\/h1>\n<p>ax.set_title('Bevestigde gevallen en doded COVID-19')\nax.set_xlabel('Bevestigde COVID-19 pati\u00ebnten')\nax.set_ylabel('Doden met doodsoorzaak COVID-19')<\/p>\n<p>Out[78]:\nText(0, 0.5, 'Doden met doodsoorzaak COVID-19')<\/p>\n<img decoding=\"async\" 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\" \/>\n<p>In de code kun je zien dat we eerst een leeg figuur aanmaken waarop we kunnen plotten. Vervolgens kiezen we voor een scatterplot om de relatie tussen het aantal bevestigde COVID-19 gevallen en het dodental te onderzoeken. We zien duidelijke lijnen naar voren komen. Voor hetzelfde aantal bevestigde pati\u00ebnten zien we grote verschillen in dodental. Dit zou verklaard kunnen worden doordat bepaalde regio's of landen anders omgaan met het virus of anders over aantallen rapporteren. We geven met de code de grafiek, de x-as en de y-as een naam.<\/p>\n<p>In\u00a0[17]:<\/p>\n<h1>maak een figuur aan en assen om op te plotten<\/h1>\n<p>fig, ax = plt.subplots()<\/p>\n<h1>onderzoek de correlatie tussen het aantal confirmed cases en aantal sterfgevallen en licht Hubei uit in rood<\/h1>\n<p>ax.scatter(corona_data['Confirmed'], corona_data['Deaths'])\nax.scatter(corona_data[corona_data['Province\/State']=='Hubei']['Confirmed'], corona_data[corona_data['Province\/State']=='Hubei']['Deaths'], color='red')<\/p>\n<h1>Grafiektitel en as-labels<\/h1>\n<p>ax.set_title('Bevestigde gevallen en doded COVID-19')\nax.set_xlabel('Bevestigde COVID-19 pati\u00ebnten')\nax.set_ylabel('Doden met doodsoorzaak COVID-19')<\/p>\n<p>Out[17]:\nText(0, 0.5, 'Doden met doodsoorzaak COVID-19')<\/p>\n<img decoding=\"async\" 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\" \/>\n<p>Als we verschillende datapunten in dezelfde grafiek willen plotten dan kan dat makkelijk door een extra lijn code toe te voegen. In dit geval hebben we specifiek de gevallen uit Hubei (de bron van corona uitbraak) in het rood uitgelicht. Hiermee krijg je een indruk van de flexibiliteit die Matplotlib biedt.<\/p>\n<p>Matplotlib kent honderden data-visualisaties die je kunt uitproberen. Wij blijven nu bij de basics en laten zien hoe je met een line-chart kunt onderzoeken hoe je oorspronkelijke uitbraak in Hubei zich verhoudt tot de uitbraak van New York die later volgde.<\/p>\n<p>In\u00a0[79]:<\/p>\n<h1>maak een figuur aan en assen om op te plotten<\/h1>\n<p>fig, ax = plt.subplots()<\/p>\n<h1>check het verloop door de tijd tussen Hubei en New York<\/h1>\n<p>ax.plot(corona_data[corona_data['Province\/State']=='Hubei']['ObservationDate'], corona_data[corona_data['Province\/State']=='Hubei']['Confirmed'], color='orange', label='Hubei')\nax.plot(corona_data[corona_data['Province\/State']=='New York']['ObservationDate'], corona_data[corona_data['Province\/State']=='New York']['Confirmed'], color='blue', label='New York')<\/p>\n<h1>zorg voor nette x-as en titel en legenda<\/h1>\n<p>plt.xticks(np.arange(0,100,10), rotation = 45)\nax.set_title('Bevestigde COVID-19 gevallen Hubei vs New York')\nax.legend()\nplt.show()<\/p>\n<img decoding=\"async\" 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\" \/>\n<p>We kunnen zien dat de Chinezen een stuk effectiever te werk zijn gegaan in het terugdringen van het virus dan de Amerikanen. Tenminste, in de gerapporteerde cijfers...<\/p>\n<p>Naast alle negatieve berichtgeving is het misschien leuk om te zien in welke landen er al veel mensen zijn hersteld van COVID-19. Met een bar chart visualisatie geven we inzicht in de landen die al meer dan 10.000 herstelde burgers kennen.<\/p>\n<p>In\u00a0[80]:\nlanden_met_corona = corona_data['Country\/Region'].unique()\nmaximum_hersteld_per_land = {}\nfor land in landen_met_corona:\nsample_land = corona_data[corona_data['Country\/Region']==land]\nmax_hersteld = sample_land['Recovered'].max()\nif max_hersteld &gt; 10000:\nmaximum_hersteld_per_land[land] = max_hersteld<\/p>\n<h1>maak een figuur aan en assen om op te plotten<\/h1>\n<p>fig, ax = plt.subplots()\nax.bar(list(maximum_hersteld_per_land.keys()), list(maximum_hersteld_per_land.values()))\nplt.xticks(rotation = 45)\nplt.show()<\/p>\n<img decoding=\"async\" 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\" \/>\n<p>Deze basale visualisaties geven je een eerste indruk van de mogelijkheden, maar op <strong><a href=\"https:\/\/matplotlib.org\/3.1.1\/gallery\/index.html\">deze pagina<\/a><\/strong> vind je &gt;100 visualisatie-mogelijkheden binnen Matplotlib. In onze <strong><a href=\"https:\/\/datasciencepartners.nl\/python-cursus\/\">tweedaagse Python cursus voor data science<\/a><\/strong> leer je werken met Python en staan we uitgebreid stil bij visualisaties met Matplotlib. Na de cursus kun je zelfstandig datasets visualiseren in een grafiek-type naar wens.<\/p>\n<p>Zelf data visualiseren met Python? Schrijf je in voor een van onze trainingen.<br \/>\n<a href=\"https:\/\/datasciencepartners.nl\/python-cursus\/\"><button>Python training voor data science<\/button><\/a><br \/>\n<a href=\"https:\/\/datasciencepartners.nl\/python-machine-learning-training\/\"><button>Python machine learning training<\/button><\/a><br \/>\n<a href=\"https:\/\/datasciencepartners.nl\/data-science-opleiding\/\"><button>Data Science opleiding (4 dagen)<\/button><\/a><br \/>\n<a href=\"https:\/\/datasciencepartners.nl\/data-science-bootcamp\/\"><button>Data Science bootcamp (10 dagen)<\/button><\/a><\/p>\n<h2><strong>Hoe je Pandas gebruikt voor visualisaties<\/strong><\/h2>\n<p>Het package Pandas gebruikt zoals gezegd in de introductie Matplotlib functionaliteit. De syntax binnen Pandas is sterk vereenvoudigd waardoor data visualisaties intu\u00eftiever maar ook minder flexibel worden.<\/p>\n<p>We kunnen met zeer weinig code dezelfde scatterplot genereren in Pandas als dat we deden met Matplotlib:<\/p>\n<p>In\u00a0[81]:\ncorona_data.plot.scatter(x='Confirmed', y='Deaths', title='Bevestigde gevallen en doded COVID-19')<\/p>\n<p>Out[81]:\n&lt;matplotlib.axes._subplots.AxesSubplot at 0x119cf2828&gt;<\/p>\n<img decoding=\"async\" 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\" \/>\n<p>Ook een line-chart is gemakkelijk te genereren. Hier bekijken we bijvoorbeeld het verloop van de ziekte in een andere infectie-haard, namelijk Iran.<\/p>\n<p>In\u00a0[82]:\ncorona_data[corona_data['Country\/Region']=='Iran'][['ObservationDate','Confirmed','Deaths','Recovered']].plot.line(x='ObservationDate', rot=45)<\/p>\n<p>Out[82]:\n&lt;matplotlib.axes._subplots.AxesSubplot at 0x119e8c7f0&gt;<\/p>\n<img decoding=\"async\" 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\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\" \/>\n<p>We kunnen dus met zeer weinig code op intu\u00eftieve wijze data visualiseren met Pandas.<\/p>\n<p><strong>Wil je nog veel meer leren over visualisaties met Python? Schrijf je dan in voor onze <a href=\"https:\/\/datasciencepartners.nl\/python-cursus\/\">Python cursus<\/a> voor data science, onze <a href=\"https:\/\/datasciencepartners.nl\/python-machine-learning-training\/\">machine learning cursus<\/a>, of voor onze <a href=\"https:\/\/datasciencepartners.nl\/data-science-opleiding\/\">data science opleiding<\/a>\u00a0en leer met vertrouwen te programmeren en visualiseren in Python. Nadat je een van onze trainingen hebt gevolgd kun je zelfstandig verder aan de slag. Je kunt ook altijd even <a href=\"https:\/\/datasciencepartners.nl\/contact\/\">contact<\/a> opnemen als je een vraag hebt.<\/strong><\/p>\n<p><a href=\"https:\/\/datasciencepartners.nl\/opleidingsbrochures\/\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/datasciencepartners.nl\/wp-content\/uploads\/2020\/07\/opleidingsbrochures-data-science-en-python-trainingen.png\" alt=\"\" width=\"410\" height=\"336\" \/><\/a><\/p>\n<p><strong><a href=\"https:\/\/datasciencepartners.nl\/opleidingsbrochures\/\">Download \u00e9\u00e9n van onze opleidingsbrochures voor meer informatie<\/a><\/strong><\/p>\n<\/div><!-- .vgblk-rw-wrapper -->","protected":false},"excerpt":{"rendered":"<p>Python is een fantastische tool om data mee te visualiseren. De mogelijkheden zijn eindeloos. Zo zijn er verschillende packages waarmee data gevisualiseerd kan worden. In totaal zijn er honderden verschillende visualisaties mogelijk, een stuk meer dus dan in bijvoorbeeld Excel. Data visualiseren is als data scientist van belang omdat je verborgen patronen makkelijker herkent en&#8230;<\/p>\n","protected":false},"author":4,"featured_media":18214,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"footnotes":""},"categories":[111,110,112],"tags":[],"class_list":["post-18213","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science-blogs","category-python","category-tutorial"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.7 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Data visualiseren met Python: hoe doe ik dat? [incl. tutorial]<\/title>\n<meta name=\"description\" content=\"Data visualiseren in Python kan met diverse packages. 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