{"id":1682,"date":"2019-01-30T18:08:40","date_gmt":"2019-01-30T18:08:40","guid":{"rendered":"https:\/\/www.codeastar.com\/?p=1682"},"modified":"2019-01-31T02:08:28","modified_gmt":"2019-01-31T02:08:28","slug":"2019-top-programming-languages","status":"publish","type":"post","link":"https:\/\/www.codeastar.com\/2019-top-programming-languages\/","title":{"rendered":"2019 Top Programming Languages to code"},"content":{"rendered":"\n<p>Like <a href=\"https:\/\/www.codeastar.com\/top-programming-languages-2018\/\">last time in 2018<\/a>, we do the Top Programming Languages (TPL) to code again, this time, in 2019! After reviewing the past TPL posts, we simplify the TPL criteria into 2 categories:<\/p>\n\n\n\n<!--more-->\n\n\n\n<ul class=\"wp-block-list\"><li>Popularity &#8211; how popular the language is<\/li><li>Career Value &#8211; how does the language help you develop your career<\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Language Popularity <\/h3>\n\n\n\n<p>Popularity as our ranking criteria is based on &#8220;a higher popularity means a bigger community&#8221;. So we can find resources like tutorials, code samples, tips and guides easier. We use <a rel=\"noreferrer noopener\" href=\"http:\/\/pypl.github.io\/PYPL.html\" target=\"_blank\">PopularitY of Programming Language Index (PYPL<\/a>) as&nbsp;our reference to create following table: <\/p>\n\n\n\n<table id=\"tablepress-8\" class=\"tablepress tablepress-id-8\">\n<thead>\n<tr class=\"row-1\">\n\t<th class=\"column-1\">Rank (2018 Rank)<\/th><th class=\"column-2\">Language<\/th><th class=\"column-3\">Share (2018 Share)<\/th><th class=\"column-4\">Change<\/th>\n<\/tr>\n<\/thead>\n<tbody class=\"row-striping row-hover\">\n<tr class=\"row-2\">\n\t<td class=\"column-1\">1 (2)<\/td><td class=\"column-2\">Python<\/td><td class=\"column-3\">26.0% (19.3%)<\/td><td class=\"column-4\">+6.7%<\/td>\n<\/tr>\n<tr class=\"row-3\">\n\t<td class=\"column-1\">2 (1)<\/td><td class=\"column-2\">Java<\/td><td class=\"column-3\">21.4% (21.2%)<\/td><td class=\"column-4\">+0.2%<\/td>\n<\/tr>\n<tr class=\"row-4\">\n\t<td class=\"column-1\">3 (4)<\/td><td class=\"column-2\">Javascript<\/td><td class=\"column-3\">8.3% (7.9%)<\/td><td class=\"column-4\">+0.4%<\/td>\n<\/tr>\n<tr class=\"row-5\">\n\t<td class=\"column-1\">4 (5)<\/td><td class=\"column-2\">C#<\/td><td class=\"column-3\">7.6% (7.5%)<\/td><td class=\"column-4\">+0.1%<\/td>\n<\/tr>\n<tr class=\"row-6\">\n\t<td class=\"column-1\">5 (3)<\/td><td class=\"column-2\">PHP<\/td><td class=\"column-3\">7.4% (8.0%)<\/td><td class=\"column-4\">-0.6%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<!-- #tablepress-8 from cache -->\n\n\n<p>And we can visualize the top 5 languages using <a rel=\"noreferrer noopener\" aria-label=\"Plot.ly (opens in a new tab)\" href=\"https:\/\/plot.ly\" target=\"_blank\">Plot.ly<\/a>, likes the way we analyzed sales data in <a href=\"https:\/\/www.codeastar.com\/revenue-prediction-google-store\/\">Google Store revenue prediction<\/a>.<\/p>\n\n\n\n<pre lang=\"python\" line=\"1\">top5_lang_df = pd.DataFrame(columns=['year', 'language', 'share'])\nyears = [2017, 2018, 2019]\nlanguages = ['Java', 'Python', 'PHP', 'C#', 'JavaScript']\nshares = [[22.7, 21.2, 21.4], [15.7, 19.3, 26.0], [9.3, 8.0, 7.4], [8.3, 7.5, 7.6], [7.9, 7.9, 8.3]]\n\nlang_index = 0\nyear_index = 0 \nfor year in years: \n    for language in languages:  \n      share = shares[lang_index][year_index]\n      top5_lang_df = top5_lang_df.append({'year': year, 'language': language, 'share': share}, ignore_index=True)\n      lang_index += 1   \n    year_index += 1\n    lang_index = 0\n\ndata = []\nfor language in languages:\n    y_values = top5_lang_df.loc[top5_lang_df['language'] == language]['share'].values\n    y_labels = [\"{}%\".format(member) for member in y_values]\n    trace = go.Bar(\n            x = top5_lang_df.loc[top5_lang_df['language'] == language]['year'].values,\n            y = y_values,\n            text=y_labels,\n            textposition = 'auto',\n            name=language\n        )\n    data.append(trace)\nlayout = go.Layout(\n                title = \"Top 5 Programming Languages\",\n                xaxis=dict(\n                  title='Year'\n                ),\n                yaxis=dict(\n                  title=\"Share (%)\"\n                )               \n             )\nfig = go.Figure(data=data, layout=layout)\npy.iplot(fig)  \n<\/pre>\n\n\n\n<figure class=\"wp-block-image\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"700\" height=\"450\" data-attachment-id=\"1700\" data-permalink=\"https:\/\/www.codeastar.com\/2019-top-programming-languages\/newplot-2\/\" data-orig-file=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/newplot-2.png?fit=700%2C450&amp;ssl=1\" data-orig-size=\"700,450\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"newplot (2)\" data-image-description=\"\" data-image-caption=\"\" data-medium-file=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/newplot-2.png?fit=300%2C193&amp;ssl=1\" data-large-file=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/newplot-2.png?fit=700%2C450&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/newplot-2.png?resize=700%2C450&#038;ssl=1\" alt=\"Top 5 Programming Languages 2017-2019\" class=\"wp-image-1700\" srcset=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/newplot-2.png?w=700&amp;ssl=1 700w, https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/newplot-2.png?resize=300%2C193&amp;ssl=1 300w\" sizes=\"auto, (max-width: 700px) 100vw, 700px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Google Trends in 2018<\/h3>\n\n\n\n<p>Besides the top 5 languages, we can use <a href=\"https:\/\/www.codeastar.com\/word-cloud-easy-python-job-seekers\/\">word cloud<\/a> and <a rel=\"noreferrer noopener\" aria-label=\"Google Trends (opens in a new tab)\" href=\"https:\/\/trends.google.com\/trends\" target=\"_blank\">Google Trends<\/a> to display other languages&#8217; popularity in 2018. But first of all, let&#8217;s install <a rel=\"noreferrer noopener\" aria-label=\"pytrends (opens in a new tab)\" href=\"https:\/\/github.com\/GeneralMills\/pytrends\" target=\"_blank\">pytrends<\/a> package before running following codes:<\/p>\n\n\n\n<pre lang=\"python\" line=\"1\">from pytrends.request import TrendReq\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud\nlanguage_list = [\"Python\", \"C\", \"Rust\", \"Java\", \"JavaScript\", \"C#\", \"PHP\", \"R\", \"Go\", \"Assembly\", \n                \"Objective C\", \"Swift\", \"VBA\", \"Visual Basic\", \"Perl\", \"Ruby\", \"HTML\", \"SQL\", \"CSS\", \"MatLab\", \"Kotlin\", \"Scala\", \"react\", \"angular\", \"vue js\",\n                \".NET\", \"node js\", \"Delphi\", \"COBOL\"]\n\npytrends = TrendReq(hl='en-US', tz=0)     #use UTC +0000 timezone\nlang_dict = {}\nfor language in language_list: \n    general_search_term = \"programming language\"\n    language_search_term = \"{} tutorial\".format(language)\n    keyword_list = [language_search_term, general_search_term]\n    pytrends.build_payload(keyword_list, cat=0, timeframe='2018-01-01 2018-12-31', geo='', gprop='')\n    int_o_t = pytrends.interest_over_time()\n    int_o_t['p_ratio'] = int_o_t[language_search_term]\/int_o_t[general_search_term]\n    lang_dict.update({language: int_o_t['p_ratio'].mean()})\n\nw = WordCloud(width=800,height=600,mode='RGBA',background_color='white').fit_words(lang_dict)\nplt.figure(figsize=(12,9))\nplt.title(\"Programming Languages in Google Trends 2018\")\nplt.imshow(w)\nplt.axis(\"off\")\nplt.show()\n<\/pre>\n\n\n\n<p>Here we go:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"702\" height=\"536\" data-attachment-id=\"1711\" data-permalink=\"https:\/\/www.codeastar.com\/code_cloud\/\" data-orig-file=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/code_cloud.png?fit=702%2C536&amp;ssl=1\" data-orig-size=\"702,536\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"code_cloud\" data-image-description=\"\" data-image-caption=\"\" data-medium-file=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/code_cloud.png?fit=300%2C229&amp;ssl=1\" data-large-file=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/code_cloud.png?fit=702%2C536&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/code_cloud.png?resize=702%2C536&#038;ssl=1\" alt=\"programming language word cloud in Google Trends 2018\" class=\"wp-image-1711\" srcset=\"https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/code_cloud.png?w=702&amp;ssl=1 702w, https:\/\/i0.wp.com\/www.codeastar.com\/wp-content\/uploads\/2019\/01\/code_cloud.png?resize=300%2C229&amp;ssl=1 300w\" sizes=\"auto, (max-width: 702px) 100vw, 702px\" \/><\/figure>\n\n\n\n<p>Please note that other than the top 5 languages we mentioned earlier, SQL is another hot language as well. Although we never consider SQL as a programming language. It is inevitable to use when we work on an application or a system with a database. <\/p>\n\n\n\n<p>Now we go back to the top 5 languages in 2019. They are no much different than the top 5 in 2018. Just our favorite language, Python, got a larger change (+6.7%) then other four. And Python is the most popular language from Google Trend 2018. Then why Python is so fast-growing? We come up with following 3 reasons:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li><strong>Ease of Use<\/strong> &#8211; a long time reader of this blog should know <a href=\"https:\/\/www.codeastar.com\/easy-python-weather-forecast-tool\/\">how easy&nbsp;Python&nbsp;is<\/a> to perform different tasks. Python  reads like English and it is straight forward, which help <a href=\"https:\/\/www.codeastar.com\/make-a-tool-within-10-lines-of-code\/\">beginners adopt the language easily<\/a>.<\/li><li><strong>Coverage of Machine Learning and AI<\/strong> &#8211; there are a lot of Machine Learning libraries available for Python, and once again, our readers should know, we use <a href=\"https:\/\/www.codeastar.com\/beginner-data-science-tutorial\/\">Python to solve all the machine learning challenges<\/a> here :]]<\/li><li><strong>Large community<\/strong> &#8211; we can take a look at the <a rel=\"noreferrer noopener\" aria-label=\"Python Package Index (opens in a new tab)\" href=\"https:\/\/pypi.org\/\" target=\"_blank\">Python Package Index<\/a> (PyPI, please don&#8217;t confuse the term with PYPL, it is a capital letter &#8220;I&#8221; at this end), there are 165K+ Python projects available already in a wide range of categories. No matter you are working on a game project, financial project, web project or other, you can always find related packages and discussion groups for help.<\/li><\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Language Career Value<\/h3>\n\n\n\n<p>Other than the language popularity, we should consider on which language can help us develop our career most. Since this kind of research result should vary according to job market location. We use the US&#8217;s job market, where many giant technology companies are running, as our research location. Then we use major job search engines (<a rel=\"noreferrer noopener\" aria-label=\"Indeed (opens in a new tab)\" href=\"https:\/\/www.indeed.com\/\" target=\"_blank\">Indeed<\/a>, <a href=\"https:\/\/www.glassdoor.com\">Glassdoor<\/a> and <a rel=\"noreferrer noopener\" aria-label=\"Monster (opens in a new tab)\" href=\"https:\/\/www.monster.com\" target=\"_blank\">Monster<\/a>) in the US to search developer jobs. Thus we can record the job counts and salaries for our finding.<\/p>\n\n\n\n<p>We used <a href=\"https:\/\/www.codeastar.com\/word-cloud-easy-python-job-seekers\/\">BeautifulSoup to scrap information from Indeed<\/a> last time, so we can reuse the technique again. This time, we apply it on 3 job search engines. Let&#8217; start from Indeed:<\/p>\n\n\n\n<pre lang=\"python\" line=\"1\">search_keyword_arr =[\"Java\", \"Python\", \"HTML\", \"SQL\", \"JavaScript\", \"C#\", \"PHP\", \"CSS\", \".NET\", \"angular\", \"react\"] \nlocation = \"\"\nurl_prefix = \"https:\/\/www.indeed.com\"\npre_fix_text = \"Page 1 of \"\nindeed_developer_jobs = []\nfor search_keyword in search_keyword_arr: \n    params = {\n        'q':search_keyword+' developer',\n        'l':location\n    } \n    url = url_prefix + \"\/jobs?\"+urllib.parse.urlencode(params)\n    html = urllib.request.urlopen(url)\n    soup = BeautifulSoup(html, 'lxml')        \n    search_count = soup.find('div', {'id':'searchCount'})    \n    salary_title_txt = soup.find('div', {'id':'univsrch-salary-title'}).get_text()\n    salary_txt = soup.find('p', {'id':'univsrch-salary-currentsalary'}).get_text() \n    salary_value = salary_txt[:salary_txt.find(' per year')].replace('$', '').replace(',', '')\n    search_count_txt = search_count.get_text().strip()\n    if search_count_txt.startswith(pre_fix_text):\n       job_count_txt = search_count_txt[len(pre_fix_text):search_count_txt.find('jobs')]\n       job_count_value = int(re.sub(',','', job_count_txt))        \n    indeed_developer_job = {\"search engine\":\"Indeed\", \"language\":search_keyword, \"job count\":job_count_value, \"salary\": salary_value}\n    indeed_developer_jobs.append(indeed_developer_job)\n    print(\"Keyword:{}\\tJob count:{}\\t{}:{}\".format(search_keyword, job_count_txt, salary_title_txt, salary_txt))<\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>Keyword:Java\tJob count:27,939 \tJava Developer salaries in United States:$102,430 per year\nKeyword:Python\tJob count:25,846 \tPython Developer salaries in United States:$122,654 per year\nKeyword:HTML\tJob count:15,383 \tWeb Developer salaries in United States:$76,253 per year\nKeyword:SQL\tJob count:29,565 \tSQL Developer salaries in United States:$84,555 per year\nKeyword:JavaScript\tJob count:27,197 \tJavascript Developer salaries in United States:$111,243 per year\nKeyword:C#\tJob count:20,880 \t.NET Developer salaries in United States:$91,681 per year\nKeyword:PHP\tJob count:9,280 \tPHP Developer salaries in United States:$89,089 per year\nKeyword:CSS\tJob count:17,491 \tDeveloper salaries in United States:$97,116 per year\nKeyword:.NET\tJob count:13,997 \t.NET Developer salaries in United States:$91,681 per year\nKeyword:angular\tJob count:11,079 \tFront End Developer salaries in United States:$108,408 per year\nKeyword:react\tJob count:11,415 \tDeveloper salaries in United States:$97,116 per year<\/code><\/pre>\n\n\n\n<p>Now we go for Glassdoor:<\/p>\n\n\n\n<pre lang=\"python\" line=\"1\">url_prefix = \"https:\/\/www.glassdoor.com\"\nglassdoor_developer_jobs = []\nfor search_keyword in search_keyword_arr: \n    url = url_prefix + \"\/Job\/us-{}-developer-jobs-SRCH_IL.0,2_IN1_KO3,{}.htm\".format(re.sub('#','', search_keyword) , len(search_keyword+' developer')+3)\n    req = urllib.request.Request(url, headers={'User-Agent' : \"Magic Browser\"}) \n    html = urllib.request.urlopen( req )\n    soup = BeautifulSoup(html, 'lxml')   \n    search_count = soup.find('p', {'class':'jobsCount'})  \n    search_count_txt = search_count.get_text().strip()\n    search_count_value = int(search_count_txt[:search_count_txt.find('Jobs')].replace(',', ''))\n    url = url_prefix + \"\/Salaries\/us-{}-developer-salary-SRCH_IL.0,2_IN1_KO3,{}.htm\".format(re.sub('#','', search_keyword) , len(search_keyword+' developer')+3)\n    req = urllib.request.Request(url, headers={'User-Agent' : \"Magic Browser\"}) \n    html = urllib.request.urlopen( req )\n    soup = BeautifulSoup(html, 'lxml')   \n    salary_detail = soup.find('div', {'class':'OccMedianBasePayStyle__payDetails'}) \n    if (salary_detail == None):\n        salary_detail = soup.find('div', {'id': 'MeanPay_N'}) \n    if (salary_detail == None):    \n        salary_detail_txt = \"N\/A\"\n        salary_value = 0 \n    else:\n        salary_detail_txt = salary_detail.get_text().strip()\n        salary_value = int(salary_detail_txt.split(\"$\",1)[1].replace(\",\",\"\").replace(\"\/yr\", \"\").replace(\"*\",\"\"))\n    glassdoor_developer_job = {\"search engine\":\"Glassdoor\", \"language\":search_keyword, \"job count\":search_count_value, \"salary\": salary_value}\n    glassdoor_developer_jobs.append(glassdoor_developer_job)\n    print(\"Keyword:{}\\tJob count:{}\\t{}\".format(search_keyword, search_count_txt, salary_detail_txt))\n<\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>Keyword:Java\tJob count:62,454 Jobs\tAverage Base Pay$88,116\/yr\nKeyword:Python\tJob count:57,045 Jobs\tAverage Base Pay$92,000\/yr\nKeyword:HTML\tJob count:11,002 Jobs\t$66,912*\nKeyword:SQL\tJob count:22,496 Jobs\tAverage Base Pay$84,779\/yr\nKeyword:JavaScript\tJob count:23,989 Jobs\tAverage Base Pay$72,500\/yr\nKeyword:C#\tJob count:6,877 Jobs\tAverage Base Pay$95,052\/yr\nKeyword:PHP\tJob count:8,471 Jobs\tAverage Base Pay$93,987\/yr\nKeyword:CSS\tJob count:9,223 Jobs\tN\/A\nKeyword:.NET\tJob count:4,546 Jobs\tAverage Base Pay$95,052\/yr\nKeyword:angular\tJob count:9,751 Jobs\tN\/A\nKeyword:react\tJob count:7,707 Jobs\tN\/A<\/code><\/pre>\n\n\n\n<p>And finally, we have Monster: <\/p>\n\n\n\n<pre lang=\"python\" line=\"1\">url_prefix = \"https:\/\/www.monster.com\"\nmonster_developer_jobs = []\nfor search_keyword in search_keyword_arr: \n    params = {\n        'q':search_keyword+' developer'\n    } \n    url = url_prefix + \"\/jobs\/search\/?\"+urllib.parse.urlencode(params)\n    html = urllib.request.urlopen(url)\n    soup = BeautifulSoup(html, 'lxml')   \n    search_count = soup.find('h2', {'class':'figure'})  \n    search_count_txt = search_count.get_text().strip()\n    search_count_txt = (re.search( \"[^\\(](.+?)(?=Jobs)\", search_count_txt)).group()\n    monster_developer_job = {\"search engine\":\"Monster\", \"language\":search_keyword, \"job count\":int(search_count_txt), \"salary\": 0}\n    monster_developer_jobs.append(monster_developer_job)    \n    print(\"Keyword:{}\\tJob count:{}\".format(search_keyword, search_count_txt))\n<\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>Keyword:Java\tJob count:82576 \nKeyword:Python\tJob count:40367 \nKeyword:HTML\tJob count:19999 \nKeyword:SQL\tJob count:37206 \nKeyword:JavaScript\tJob count:32062 \nKeyword:C#\tJob count:23025 \nKeyword:PHP\tJob count:3353 \nKeyword:CSS\tJob count:21743 \nKeyword:.NET\tJob count:16587 \nKeyword:angular\tJob count:11362 \nKeyword:react\tJob count:10006 <\/code><\/pre>\n\n\n\n<p>Please note that there is no salary information from Monster.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Language EDA on Job Market<\/h3>\n\n\n\n<p>Now we have data from 3 job search engines, we can use it to plot a Job Market chart. First, let&#8217;s transform the data into a data frame.<\/p>\n\n\n\n<pre lang=\"python\" line=\"1\">developer_jobs = indeed_developer_jobs+glassdoor_developer_jobs+monster_developer_jobs\ndeveloper_jobs_df = pd.DataFrame(developer_jobs)\n<\/pre>\n\n\n\n<p>Then we calculate the mean of job count and salary for different languages.<\/p>\n\n\n\n<pre lang=\"python\" line=\"1\">language_jobcount_df = (developer_jobs_df.groupby([\"language\"])[\"job count\"].mean().reset_index()).sort_values(\"job count\", ascending=False\ndeveloper_jobs_wo_zero_df = developer_jobs_df.loc[developer_jobs_df['salary'] !=0]\nlanguage_salary_df = developer_jobs_wo_zero_df.groupby([\"language\"])[\"salary\"].mean().reset_index()\nlanguage_jobcount_salary_df = language_jobcount_df.merge(language_salary_df, on=[\"language\"], how='left')\n<\/pre>\n\n\n\n<p>And we plot a chart using plotly. <\/p>\n\n\n\n<pre lang=\"python\" line=\"1\">trace = go.Bar(\n        x = language_jobcount_salary_df[\"language\"],\n        y = language_jobcount_salary_df[\"job count\"],\n        name=\"Job Count\",\n        marker=dict(color = \"royalblue\")\n    )    \ntrace2 = go.Scatter(\n        x = language_jobcount_salary_df[\"language\"],\n        y = language_jobcount_salary_df[\"salary\"],\n        yaxis='y2',\n        name=\"Salary\",\n        marker=dict(color = \"orangered\")\n    )  \nlayout = go.Layout(\n            title = \"Programming Lanaguage on Job Market\",\n            xaxis=dict(\n              title=\"Language\",\n              tickangle=45,\n            ),\n            yaxis=dict(\n              title=\"Job Count\"\n            ), \n            yaxis2=dict(\n                title=\"Salary per year\",\n                titlefont=dict(\n                    color=\"orangered\"\n                ),\n                tickfont=dict(\n                    color=\"orangered\"\n                ),\n                overlaying='y',\n                side='right'\n            ),\n            legend=dict(orientation=\"h\"),\n            width = 800, \n            height = 500\n         )\ndata = [trace, trace2]\nfig = go.Figure(data=data, layout=layout)\npy.iplot(fig, config={'showLink': True})\n<\/pre>\n\n\n\n<p>Here it comes:<\/p>\n\n\n\n<iframe loading=\"lazy\" width=\"800\" height=\"500\" frameborder=\"0\" scrolling=\"no\" src=\"\/\/plot.ly\/~codeastar\/26.embed\"><\/iframe>\n\n\n\n<h3 class=\"wp-block-heading\">Conclusion for TPL 2019<\/h3>\n\n\n\n<p>Well, we can see Java is still the most wanted job on the job market. But Python is the runner-up in both job count and salary per year. Furthermore, Python is the most popular language in term of Google Trend and PYPI. So we declare <strong>Python as our language to code in 2019<\/strong>. That is why we keep prompting people to explore in Python. (And I have been saying this kind of thing <a href=\"https:\/\/www.codeastar.com\/our-code-to-code-in-2017\/\">since 2017<\/a> :]] ) Besides our beloved language, Python, you may notice the Angular developer is the highest paid job among others. Frontend languages, React and CSS, also have higher salary. We may awake the rise of frontend languages nowadays. Those frontend languages can now handle many operations that only backend modules handled in the past.<\/p>\n\n\n\n<p>For career development in 2019, I would suggest to sharpen up and specialize in either Python or Frontend languages.  Being a full stack developer is fun, like being a lovable all-rounded bard in a fantasy role-playing game. But there is always the great fighter or mage killing a dragon to save the day. So be focus on one skillset, in order to make good your development career in 2019!<\/p>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">What have we learnt in this post?<\/h3>\n\n\n\n<ol class=\"wp-block-list\"><li>Python is our language to code in 2019<\/li><li>Frontend languages are more powerful and demanding nowadays<\/li><li>We use what we have learnt in past (Word Cloud, Plotly and BeautifulSoup) to analyze our findings<\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Like last time in 2018, we do the Top Programming Languages (TPL) to code again, this time, in 2019! After reviewing the past TPL posts, we simplify the TPL criteria into 2 categories:<\/p>\n","protected":false},"author":1,"featured_media":1727,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"Top Programming Languages to code in 2019","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[2],"tags":[129,5,74,92],"class_list":["post-1682","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-we-code-therefore-we-are","tag-129","tag-code","tag-eda","tag-plotly"],"jetpack_publicize_connections":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>2019 Top Programming Languages to code &#8902; 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