{"id":192060,"date":"2025-01-10T06:30:00","date_gmt":"2025-01-10T05:30:00","guid":{"rendered":"https:\/\/liora.io\/en\/?p=192060"},"modified":"2026-02-06T07:50:47","modified_gmt":"2026-02-06T06:50:47","slug":"golang-vs-python","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/golang-vs-python","title":{"rendered":"Golang vs Python"},"content":{"rendered":"<b>When it comes to programming for data science projects, two languages often come to mind: Go (or Golang) and Python. These are popular choices among developers and data scientists, yet they have very distinct characteristics.<\/b>\n\n<style><br \/>\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style>\n<h3>Python: The Benchmark for Data Science and AI<\/h3>\n<a href=\"https:\/\/liora.io\/en\/python-the-most-popular-programming-language\">Python<\/a> is an object-oriented programming language created by Guido van Rossum. It is a <b>dynamically typed<\/b> and <b>open-source<\/b> language. Python is especially favored by <b>data scientists<\/b> and development teams focused on <b>data science and machine learning projects<\/b>. Being a general-purpose language, Python enables rapid prototyping of models and algorithms due to its extensive libraries, such as <a href=\"https:\/\/liora.io\/en\/pandas-the-python-library\">Pandas<\/a>, <a href=\"https:\/\/liora.io\/en\/numpy-the-python-library-in-data-science\">NumPy<\/a>, <a href=\"https:\/\/liora.io\/en\/scikit-learn-discover-the-python-library-dedicated-to-machine-learning\">Scikit-learn<\/a>, and <a href=\"https:\/\/liora.io\/en\/tensorflow-course-where-to-learn-how-to-use-the-framework\">TensorFlow<\/a>.\n\nThe <b>flexibility<\/b> of Python is one of the reasons it is considered an excellent choice for data science. Python allows for handling diverse data, constructing machine learning models, managing web server applications, and carrying out complex data analyses.\n\nHowever, it does have limitations, especially concerning performance. Being an interpreted language, it is relatively slow compared to other compiled languages. Fortunately, to mitigate this drawback, integration of C or other optimizations is possible, although this requires additional skills.\n\nBelow is a simple example that demonstrates how to read a CSV file and perform basic calculations:\n<pre data-line=\"\">\t\t\t\t<code readonly=\"true\">\n\t\t\t\t\t<xmp>\t\t\t\t\timport pandas as pd\n# Reading the CSV file\ndf = pd.read_csv('data.csv')\n# Calculating the average of a column\naverage = df['colonne_1'].mean()\nprint(f'Average of the column: {average}')\n# Filtering rows based on a condition\nfiltered_df = df[df['colonne_1'] > 50]\nprint(filtered_df)\n<\/xmp>\n\t\t\t\t<\/code>\n<\/pre>\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-scientist\">Learn all about Python<\/a><\/div><\/div>\n\n<h3>Go: The High-Performance Competitor<\/h3>\nGolang, commonly referred to as Go, is a programming language developed by Google. Go is statically typed and compiled, which gives it a significant advantage in <b>performance<\/b> compared to Python. Go is particularly suitable for projects involving <b>concurrent programs and high-scale web services<\/b>.\n\nThe <b><i>goroutines<\/i><\/b> and <b><i>channels<\/i><\/b> in Go are potent tools for managing concurrency. Unlike traditional threads, goroutines are lightweight and allow developers to write highly concurrent programs without dealing with the complexities introduced by threads.\n\nHowever, Go&#8217;s ecosystem for data science is less developed than Python&#8217;s. The number of libraries and resources is simply not comparable to those available in Python.\n\nThe following example demonstrates how to read a CSV file and perform simple calculations in Go. The code is more verbose than Python but benefits from very fast execution, which is ideal for tasks where performance is crucial.\n<pre data-line=\"\">\t\t\t\t<code readonly=\"true\">\n\t\t\t\t\t<xmp>\t\t\t\t\tpackage main\nimport (\n\t\"encoding\/csv\"\n\t\"fmt\"\n\t\"os\"\n\t\"strconv\"\n)\n\/\/ Entry point of the Go program\nfunc main() {\n\t\/\/ Opening the CSV file\n\tfile, err := os.Open(\"data.csv\")\n\tif err != nil {\n\t\tfmt.Println(\"Error opening the file:\", err)\n\t\treturn\n\t}\n\tdefer file.Close()\n\treader := csv.NewReader(file)\n\trecords, err := reader.ReadAll()\n\tif err != nil {\n\t\tfmt.Println(\"Error reading the file:\", err)\n\t\treturn\n\t}\n\tsum := 0.0\n\tcount := 0\n\/\/ Iterating through the file\n\tfor i, record := range records {\n\t\tif i == 0 {\n\t\t\t\/\/ Skip the header\n\t\t\tcontinue\n\t\t}\n\t\tvalue, err := strconv.ParseFloat(record[0], 64)\n\t\tif err != nil {\n\t\t\tfmt.Println(\"Conversion error:\", err)\n\t\t\tcontinue\n\t\t}\n\t\tsum += value\n\t\tcount++\n\t}\n\/\/ Calculating average\n\taverage := sum \/ float64(count)\n\tfmt.Printf(\"Average of the column: %.2fn\", average)\n}<\/xmp>\n\t\t\t\t<\/code>\n<\/pre>\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-scientist\">Learn more programming languages<\/a><\/div><\/div>\n\n<h3>When to Choose Go or Python?<\/h3>\nThe decision between Go and Python depends on the project&#8217;s needs and characteristics. Below are some situations where each language is more appropriate:\n<ul>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data Science and Machine Learning<\/b>: Python remains the preferred option. For data modeling, developing machine learning models, and conducting exploratory analyses, Python is a powerful tool widely embraced by the scientific community.<\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>High-Performance Web Services<\/b>: Go is a better choice if you require a high-performance web server capable of handling a large number of simultaneous requests.<\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Infrastructure and System Tools<\/b>: Go excels in developing tools tailored for system administrators and in creating programs that require fast execution with minimal overhead.<\/li>\n<\/ul>\n<h3>Python vs Go: Evolution and Popularity<\/h3>\nPython has long been considered <a href=\"https:\/\/liora.io\/en\/online-data-scientist-training\">the language of choice for data scientists<\/a>, thanks to its clear syntax and numerous tools for data processing and machine learning.\n\nGo, on the other hand, continues to gain traction. It is often chosen by development teams seeking performance and parallelism without sacrificing code clarity. Go&#8217;s ability to handle concurrent events and quickly compile efficient programs makes it an essential tool for modern applications, particularly for companies operating in cloud or distributed environments.\n<h3>Comparison Table<\/h3>\nThe table below displays a comparison of these two languages according to various criteria:\n\n<style type=\"text\/css\">\n.tg  {border-collapse:collapse;border-spacing:0;}<br \/>\n.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Rubik, sans-serif;font-size:14px;<br \/>\n  overflow:hidden;padding:1px 1px;word-break:normal;height:5px;}<br \/>\n.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Rubik, sans-serif;font-size:14px;<br \/>\n  font-weight:normal;overflow:hidden;padding:1px 1px;word-break:normal;height:5px;}<br \/>\n.tg .tg-38vg{background-color:#efefef;border-color:inherit;font-family:Rubik, Helvetica, sans-serif !important;font-size:17px;<br \/>\n  text-align:left;vertical-align:middle}<br \/>\n.tg .tg-k4mi{background-color:#c0c0c0;border-color:inherit;font-family:Rubik, Helvetica, sans-serif !important;font-size:17px;<br \/>\n  text-align:center;vertical-align:middle}<br \/>\n.tg .tg-18oh{background-color:#c0c0c0;border-color:inherit;font-family:Rubik, Helvetica, sans-serif !important;font-size:17px;<br \/>\n  font-weight:bold;text-align:left;vertical-align:middle}<br \/>\n.tg .tg-88qu{background-color:#efefef;border-color:inherit;font-family:Rubik, Helvetica, sans-serif !important;font-size:17px;<br \/>\n  text-align:center;vertical-align:middle}<br \/>\n.tg .tg-gmyv{background-color:#c0c0c0;border-color:inherit;font-family:Rubik, Helvetica, sans-serif !important;font-size:17px;<br \/>\n  font-weight:bold;text-align:left;vertical-align:middle}<br \/>\n<\/style>\n<table style=\"undefined;table-layout: fixed; width: 750px\">\n<colgroup>\n<col style=\"width: 100px\">\n<col style=\"width: 150px\">\n<col style=\"width: 250px\">\n<col style=\"width: 250px\">\n<\/colgroup>\n<thead>\n<tr>\n<th><\/th>\n<th>Criterion<\/th>\n<th><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/11\/image4.webp\" alt=\"Image\" width=\"5\" height=\"5\"><\/th>\n<th><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/11\/image9.webp\" alt=\"Image\" width=\"5\" height=\"5\"><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/11\/image1-1.webp\" alt=\"Image\" width=\"100\" height=\"100\"><\/td>\n<td>Type<\/td>\n<td>Static, strong typing<\/td>\n<td>Dynamic, weak typing<\/td>\n<\/tr>\n<tr>\n<td><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/11\/image7.webp\" alt=\"Image\" width=\"100\" height=\"100\"><\/td>\n<td>Performance<\/td>\n<td>Compiled<\/td>\n<td>Interpreted<\/td>\n<\/tr>\n<tr>\n<td><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/11\/image8.webp\" alt=\"Image\" width=\"100\" height=\"100\"><\/td>\n<td>Concurrency<\/td>\n<td>Goroutines and channels, excellent for concurrency<\/td>\n<td>Less suited for concurrency<\/td>\n<\/tr>\n<tr>\n<td><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/11\/image2.webp\" alt=\"Image\" width=\"100\" height=\"100\"><\/td>\n<td>Learning Curve<\/td>\n<td>Easy, concise syntax but less permissive<\/td>\n<td>Easy, simple syntax<\/td>\n<\/tr>\n<tr>\n<td><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/11\/image5.webp\" alt=\"Image\" width=\"100\" height=\"100\"><\/td>\n<td>Ecosystem<\/td>\n<td>More limited<\/td>\n<td>Very rich<\/td>\n<\/tr>\n<tr>\n<td><img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2024\/11\/image3.webp\" alt=\"Image\" width=\"100\" height=\"100\"><\/td>\n<td>Use Cases<\/td>\n<td>Performant web services, system tools<\/td>\n<td>Data Science, Machine Learning, prototyping<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>Conclusion: Go or Python?<\/h3>\nThe decision between these two languages depends on the project&#8217;s nature: for data science, machine learning, or data analysis projects, <b>Python is a proven choice<\/b>. If your project emphasizes performance, concurrency management, or web service development, Go might be the most suitable option.\n\n<a href=\"\/en\/courses\/data-ai\/data-scientist\">\nFollow a course in Data Science\n<\/a>","protected":false},"excerpt":{"rendered":"<p>When it comes to programming for data science projects, two languages often come to mind: Go (or Golang) and Python. These are popular choices among developers and data scientists, yet they have very distinct characteristics. Python: The Benchmark for Data Science and AI Python is an object-oriented programming language created by Guido van Rossum. It [&hellip;]<\/p>\n","protected":false},"author":74,"featured_media":192062,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-192060","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/192060","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/74"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=192060"}],"version-history":[{"count":5,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/192060\/revisions"}],"predecessor-version":[{"id":205619,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/192060\/revisions\/205619"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/192062"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=192060"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=192060"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}