{"id":16401,"date":"2018-05-03T10:07:08","date_gmt":"2018-05-03T08:07:08","guid":{"rendered":"http:\/\/www.highcharts.com\/blog\/?p=16401"},"modified":"2026-01-12T09:51:03","modified_gmt":"2026-01-12T09:51:03","slug":"a-fun-interactive-comparison-of-programming-language-verbosity","status":"publish","type":"post","link":"https:\/\/www.highcharts.com\/blog\/tutorials\/a-fun-interactive-comparison-of-programming-language-verbosity\/","title":{"rendered":"A fun, interactive comparison of programming language verbosity"},"content":{"rendered":"

 <\/p>\n

 
\nIf you want to start a flame war among developers, all you need to do is start a discussion about what programming language is \u201cbest\u201d. <\/p>\n

I wanted to do a twist on that, starting with something less subjective. I\u2019m simply curious about which languages are most or least verbose? <\/p>\n

A starting point could be to assess their \u201cconciseness\u201d in performing various tasks (and not make any judgment as to any language\u2019s efficiency or effectiveness).<\/p>\n

This leads me to Rosettacode.org<\/a>, which is an awesome source of information for any programming enthusiast. It offers various tasks (more than 870) and code snippets to solve them, in many programming languages (more than 680). <\/p>\n

I always wanted to explore Rosettacode.org and compare the different programming languages. It can help discover new ways of addressing the problems you face in your \u201cnative\u201d language, and identify alternative ways of thinking. To that end, I wrote a little app to facilitate this process, which I will share that with you here. <\/p>\n

The app, is very basic. It simply compares the length of code snippets from Rosettacode.org for different tasks and languages, and displays the result on a bar chart using my favorite Javascript library, Highcharts<\/a>.<\/p>\n

Python, Flask, Highcharts, and Azure (my playground) Environment<\/h3>\n

Azure is Microsoft\u2019s cloud (disclaimer, I work for Microsoft). It is a great infrastructure to host your data<\/a>, create bots<\/a> and automate tasks<\/a>, deploy Python apps<\/a> or leverage AI<\/a>.
\nFor this demo, I installed a python 3.4 environment on Azure and a few libraries (Beautiful Soup<\/a> and Flask<\/a>)<\/p>\n

Steps 1: Scraping<\/h3>\n

The first step is to scrape RosettaCode to get all available tasks. I wrote a very short python script to do the scraping. I pre-selected a subset of programming languages (see languages_dict<\/code> below) in order to avoid scraping too much irrelevant data.<\/p>\n

# Selected language names and corresponding spelling\/encoding as in HTML page \r\nlanguages_dict={\"Java\":\"Java\",\"JavaScript\":\"JavaScript\",\"C\":\"C\",\"C.2B.2B\":\"C++\",\"C.23\":\"C#\",\"COBOL\":\"Cobol\",\"Haskell\":\"Haskell\",\"Python\":\"Python\",\"R\":\"R\",\"Julia\":\"Julia\",\"MATLAB_.2F_Octave\":\"Matlab\",\"Pascal\":\"Pascal\",\"Fortran\":\"Fortran\",\"BASIC\":\"BASIC\",\"Go\":\"Go\",\"Ruby\":\"Ruby\",\"SAS\":\"SAS\",\"Stata\":\"Stata\",\"Swift\":\"Swift\",\"Processing\":\"Processing\",\"UNIX_Shell\":\"UNIX Shell\",\"VBA\":\"VBA\",\"PowerShell\":\"PowerShell\"}\r\n\r\n#store language names in an array\r\nlanguage_name=[]\r\n\r\n#populate the array from the dict\r\nfor item in languages_dict:language_name.append(languages_dict[item])\r\n\r\n#get all tasks from Rosettacode.org\r\nurl_task=\"http:\/\/www.rosettacode.org\/wiki\/Category:Programming_Tasks\"\r\nr = requests.get(url_task)\r\nsoup = BeautifulSoup(r.text, 'html.parser')\r\ntable=soup.find(\"div\", {\"class\": \"mw-category\"})\r\n\r\n#Create an empty dictionary to be filled with the tasks as they appear in the HTML source and name (as header)\r\nurl_dict={}\r\n\r\n#get all links (a tags) \r\ntags=table('a')\r\n\r\n#iterate over tag list and fill the task\/url dictionary\r\nfor tag in tags:\r\n    url_dict[tag.get('title',None)]=tag.get('href',None)[6:]\r\n\r\n#store task names in an array\r\ntask_name=[]\r\nfor item in url_dict: task_name.append(item)\r\narray_language=[]\r\ncount=[]\r\ntask=\"\"\r\n\r\n#flask method to get the task the user wants to compare\r\nif request.method == \"POST\":\r\n    # get url from task the user requested\r\n    task = request.form['task']\r\n    url=\"https:\/\/www.rosettacode.org\/wiki\/\"+url_dict[task]\r\n    r = requests.get(url)\r\n    soup = BeautifulSoup(r.text, 'html.parser')\r\n    dict_count={}\r\n    for language in languages_dict:\r\n        try: \r\n            header=soup.find(\"span\", {\"id\": language})\r\n            snippet=BeautifulSoup(header.find_next(\"pre\").text, 'html.parser')\r\n            dict_count[languages_dict[language]] = len(snippet.text)\r\n        except: \r\n            continue\r\n\r\n    #sort dictionary\r\n    for lang in sorted(dict_count, key=dict_count.get, reverse=True):\r\n        array_language.append(lang)\r\n        count.append(dict_count[lang])\r\n<\/pre>\n
When the user selects the task to compare, the script above looks for the pre-selected languages and evaluates the length of the corresponding snippets (within the \u201cpre\u201d tag after the corresponding header within the HTML). The script stores the results in an array which will be sent to the frontend via Flask to my Highcharts.<\/p>\n
Here are the resulting arrays for the snippets corresponding to a \u201cFor Loop<\/a>\u201d:<\/p>\n
# array_language\r\n['C', 'Swift', 'Pascal', 'Go', 'BASIC', 'Haskell', 'UNIX Shell', 'Matlab', 'Processing', 'PowerShell', 'Fortran', 'Cobol', 'VBA', 'C++', 'Java', 'Julia', 'Ruby', 'JavaScript', 'Python', 'R', 'SAS', 'Stata', 'C#']\r\n\r\n#count (length of the snippets)\r\n[90, 73, 144, 167, 70, 115, 400, 103, 103, 116, 1211, 533, 193, 104, 119, 71, 55, 114, 92, 85, 293, 79, 261]<\/pre>\n
These arrays will then feed the bar chart.<\/p>\n
Step 2: Highcharts<\/h3>\n
Python and Flask send the arrays of data to Highcharts using {{ array_language | safe}}<\/code> and {{count}}<\/code>. During the HTML page rendering, the server injects the arrays constructed with python. Notice that I use the | safe<\/code> option to avoid any encoding surprise.<\/p>\n
Highcharts.chart('container', {\r\n    chart: {\r\n        type: 'bar'\r\n    },\r\n    title: {\r\n        text: 'Programming language comparison: {{task | safe}} '\r\n    },\r\n    subtitle: {\r\n        text: 'Source: rosettacode.org<\/a>'\r\n    },\r\n    xAxis: {\r\n        categories: {{ array_language   | safe}},\r\n        title: {\r\n            text: null\r\n        }\r\n    },\r\n    yAxis: {\r\n        min: 0,\r\n        title: {\r\n            text: 'Task size (char)',\r\n            align: 'high'\r\n        },\r\n        labels: {\r\n            overflow: 'justify'\r\n        }\r\n    },\r\n    tooltip: {\r\n        valueSuffix: 'chars'\r\n    },\r\n    plotOptions: {\r\n        bar: {\r\n            dataLabels: {\r\n                enabled: true\r\n            }\r\n        }\r\n    },\r\n\r\n    series: [{\r\n        name: '{{task}}',\r\n        data: {{count}}\r\n    }]\r\n});<\/pre>\n
Results<\/h3>\n