What Is the PYPL Popularity of Programming Language Index and How Does It Rank Languages?

Updated

The PYPL (PopularitY of Programming Language) Index ranks programming languages by how often their tutorials are searched on Google, using raw data from Google Trends. It is designed as a leading indicator of language interest: the more people search for a language's tutorial, the more popular that language is assumed to be. If you accept the idea of collective wisdom, PYPL can serve as one input when deciding which language to study or use in a new project. It measures search interest, not lines of code written, job postings, or repository activity.

How the Ranking Is Built

PYPL's method is deliberately simple and narrow in scope:

  • Input: Google Trends data on searches for language tutorials.
  • Assumption: Tutorial searches reflect people learning or adopting a language, so they signal future usage.
  • Output: A ranked list of languages with a share percentage and a 1-year trend for each.

Because the signal is search behavior, PYPL is forward-looking rather than a snapshot of what is currently deployed in production. A language can rank high on PYPL while having a smaller installed base, or rank lower while remaining heavily used in existing systems.

Reading the Share and Trend Columns

The index presents each language with a rank, a share (percentage of total tutorial-search interest), and a 1-year trend showing how that share changed over the past year. The worldwide snapshot below illustrates how to read it:

Rank Language Share 1-year trend
1 Python 53.9% +24.9%
2 Java 12.98% -0.1%
3 C/C++ 7.97% -5.0%
4 R 3.87% -2.4%
5 Objective-C 3.51% +1.6%
6 JavaScript 2.81% -3.2%
7 PHP 1.74% -1.6%
8 Rust 1.56% -1.3%
9 Ada 1.55% -1.1%
10 Swift 1.47% -2.3%

How to interpret the two columns together:

  • Share tells you the relative size of search interest right now. Python at 53.9% means it accounts for more than half of the measured tutorial-search interest worldwide.
  • Trend tells you the direction and speed of change. Python's +24.9% indicates rapidly growing interest; C/C++ at -5.0% indicates declining search interest over the year.
  • A high share with a negative trend (Java, C/C++) suggests a large but cooling base. A smaller share with a positive trend (Objective-C) suggests growing interest from a lower starting point.

The index also reports that, worldwide, Python grew the most over the last 5 years (+25.6%) and JavaScript lost the most (-5.9%).

Why the Trend Chart Uses a Logarithmic Scale

The trend chart is plotted on a logarithmic scale. This matters because language shares span a very wide range — from Python's double-digit share down to fractions of a percent for languages like Cobol or Groovy. On a linear scale, the smaller languages would be compressed into an unreadable band at the bottom. A logarithmic scale gives proportional visibility to both large and small languages, so you can compare their relative growth rates rather than just their absolute sizes.

PYPL vs. TIOBE

Both indices are frequently cited, but they answer different questions:

PYPL TIOBE
Signal Google searches for language tutorials Search-engine queries across multiple engines
Framing Leading indicator of learning/adoption interest Popularity measure based on search volume
Best for Gauging where learner interest is heading A broader popularity snapshot

If your question is "what are people trying to learn right now?", PYPL's tutorial-search signal is the more direct fit. If you want a general popularity measure, TIOBE is the more common reference. Neither measures job demand or code volume directly.

Using PYPL as One Input in a Language Decision

PYPL is a single data point, not a decision rule. A practical way to use it:

  1. Check the share and trend for your candidate languages. A rising trend suggests growing ecosystem momentum and future tooling, libraries, and community support.
  2. Match against your actual constraint. If you need hiring depth, check job markets separately — PYPL does not measure employment demand. If you need a specific domain (data science, mobile, systems), filter by relevance rather than rank.
  3. Look at the country view. PYPL lets you switch between Worldwide, US, India, Germany, United Kingdom, and France, so regional interest can differ from the global picture.
  4. Treat it as directional. Use it to confirm or challenge a hunch, not to override domain requirements.

The index also offers companion rankings for Top IDE, Top Online IDE, and Top Databases, which can help when you are choosing a full toolchain rather than just a language.

Practical Caveats

  • PYPL measures tutorial search interest, so it reflects learners and evaluators more than established production teams.
  • A language with a large existing codebase can rank modestly on PYPL while remaining important in practice.
  • Shares are relative: when one language's share grows sharply, others' shares can fall even if their absolute interest is stable.
  • The index is maintained by Pierre Carbonnelle and has been cited in trade journals including InfoWorld, eWeek, and Techworld.