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PYPL PopularitY of Programming Language index

PYPL PopularitY of Programming Language

The PYPL PopularitY of Programming Language Index is created by analyzing how often language tutorials are searched on Google.

The more a language tutorial is searched, the more popular the language is assumed to be. It is a leading indicator. The raw data comes from Google Trends.

If you believe in collective wisdom, the PYPL Popularity of Programming Language index can help you decide which language to study, or which one to use in a new software project.

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This chart uses a logarithmic scale. It can show your favorite languages in a country

Please check our other popularity indices :

Kudos

The PYPL index has been cited in the following trade journals :

FAQ

The PYPL PopularitY of Programming Language Index is created by analyzing how often language tutorials are searched on Google : the more a language tutorial is searched, the more popular the language is assumed to be. It is a leading indicator. The raw data comes from Google Trends.

If you believe in collective wisdom, the PYPL Popularity of Programming Language index can help you decide which language to study, or which one to use in a new software project.

The

TIOBE Index

is a lagging indicator. It counts the number of web pages with the language name.

Objective-c programming

has over 20 million pages on the web,

[s]

while

C programming

has only 11 million.

[s]

This explains why Objective-C has a high TIOBE ranking. But who is reading those Objective-C web pages ? Hardly anyone, according to Google Trends data.

Objective C programming

is searched 30 times less than

C programming

.

[s]

In fact, the use of

programming

by the TIOBE index is misleading (see next question).

The following principles were used:

The index is currently limited to 29 languages. You can still analyze the popularity of your favorite language and compare it to others, using Google Trends. C++ has the same popularity as C on Google trends: to avoid duplication, it is not included in the PYPL index.

We export the data from Google Trends in CSV format,

[s]

and analyze it with python's pandas. We first calculate the interest of each language tutorials relative to java tutorials every month. Normalizing the total to 100% yields the share of interest in each language, i.e. their popularity, which we smooth over 6 months.

That's because Google Trends diagrams show how the total number of Java tutorial searches varies over time. That has been going significantly down for most programming languages (and most keywords, by the way). Instead, PYPL's diagram shows the share of Java tutorial searches in all language tutorial searches. That share has been fairly stable for Java since 2004.

© Pierre Carbonnelle, 2023,

CC BY 3.0

.


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