If you have money, you can use Python, but if you don’t have money, PHP is the best programming language.

If you have money Python, if you don’t have money PHP, programming languages ​​​​are too poor to love the rich...

Recently, in the Stack Overflow survey report on the distribution of global mobile developers, it was found that compared with high Android is more widely used in low-income countries.

So what does this really mean? It makes us think deeply about how national programming technology differs in different economic backgrounds, and how it affects our view of the global software development industry. This article will take you through these differences and explain the need to divide the software development industry into high-income countries and the rest of the world.

Survey report analysis is based on the 250 most used programming language tags in 2017 (January-August). To reduce margin of error, we only analyzed the 64 countries with 5 million or more visits to various programming languages ​​during this period.

Per capita GDP related technologies

In a recent article "Where in the World is Mobile Development?", we used the "Stack Overflow Trends tool" to analyze Android Traffic proportion is often negatively correlated with a country's per capita GDP. This prompted us to find out if the same was true for other programming languages.

When digging into the major programming languages ​​and platforms, apart from Android, some of the other major languages ​​are PHP, Python, and R.

As can be seen from the above figure, the number of Android and PHP usage is negatively related to a country’s income However, Python and R languages ​​are just the opposite, showing that the more developed countries are, the higher the frequency of use. Under this trend, we have also found some exceptions. For example, South Korea uses Android more frequently than expected, while China uses Python more. However, generally speaking, the correlation between the traffic of this language and the country is relatively strong. of.

It is also necessary to make a special statement here: Based on the above statistics, it does not mean that the choice of programming language will affect the average income of a country, and the GDP of a country will not affect development. There is no causal relationship between the two when it comes to the choice of programming language.

Why should the world’s software development industry be divided into two types?

When studying trends, it is found that it is more useful to divide the software development industry into two groups of countries (high-income and non-high-income) than to consider a range of other related factors. As a valuable classification we can use the World Bank income classification based on GNL (Gross National Income) per capita.

There are 78 high-income economies in total, mainly composed of the United States and Canada, Western Europe, the Middle East and East Asia Parts of the region and Australia/New Zealand. We have done some analysis of the underlying drivers of differences between countries (e.g. principal components analysis) and think this is a reasonable division and one that makes more sense than other splits, such as Eastern Hemisphere vs. Western Hemisphere. (For example, Australia is more similar to China or Indonesia in terms of access to and use of programming technology).

What are the technological differences between high-income countries?

Now the world's software development is divided into two sections: high-income countries and non-high-income countries. So what are the technical differences between the two?

This article extracts several interesting insights:

• Differences in Data Science Technology: As mentioned above, Python and R languages ​​are related to a country’s income. In high-income countries, Python is accessed twice as often as in the rest of the world, and R approximately three times as often. In addition, we may also notice some small tags, most of which are Python and R language packages in science and technology, such as pandas, numpy, matplotlib and ggplot2. This suggests that part of the difference in earnings between the two languages ​​may be due to their use in science and academic research. In richer, industrialized countries, these technologies are used more frequently, scientific research makes up a larger part of the economy, and programmers can earn higher degrees.

• C/C++: C/C++ are two other languages ​​that are commonly visited in developed countries. I guess one of the main reasons is related to the country's education: because in a previous article, we learned that C and C++ have become one of the most visited languages ​​disproportionately in American universities. Another reason may be related to the geographical distribution of electronics and manufacturing.

• PHP and Android: In addition to Android development technology being more widespread in low-income countries, PHP also ranks in the top 5. Interestingly, CodeIgniter is a PHP open source framework, and this framework is often visited separately in low-income countries. Further investigation revealed that CodeIgniter has a lot of traffic in South/Southeast Asia (especially India, Indonesia, Pakistan, and the Philippines), while there is very little traffic in the United States and Europe. It follows that CodeIgniter may be a popular choice for outsourcing companies building websites.

Conclusion: Why is it so important?

These conclusions are an interesting fact for the programming language ecosystem. Through this article, we distinguish two software development industry problems that are "blended" together. Often, we're interested in which technologies are used more and drive more traffic, such as Flash, which disappears over time. If we were to make a ranking of the most popular programming technologies, it would differ between high- and low-income countries:

For example, so far, Python ranks second among frequently visited tags in high-income countries, but only 8th in the rest of the world; R language ranks 15th in high-income countries , but it doesn’t even rank in the top 50 in other countries.

Therefore, for corporate recruitment in different countries, recruiters and talents need a different set of reference values.

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