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Why is Python Growing So Quickly We recently showed that, based on Stack Overflow question visits, Python has a claim to being the fastest growing major programming language, and that it has become the most visited tag on Stack Overflow within high income countries. Why is Python growing so fastPython is used in a variety of purposes, ranging from web development to data science to Dev. Ops, and its worth understanding what particular applications of Python have recently become more common. Im a data scientist who uses R, so Im certainly interested in how much of Pythons growth has been within my own field. In this post, Ill take another look at Stack Overflow data to understand what kinds of Python development have been growing, and in what kinds of companies and organizations its most used. These analyses suggest two conclusions. First, the fastest growing use of Python is for data science, machine learning and academic research. This is particularly visible in the growth of the pandas package, which is the fastest growing Python related tag on the site. As for which industries are using Python, we found that it is more visited in a few industries, such as electronics, manufacturing, software, government, and especially universities. However, Pythons growth is spread pretty evenly across industries. In combination this tells a story of data science and machine learning becoming more common in many types of companies, and Python becoming a common choice for that purpose. Just like in the previous post, all of these analyses are constrained to World Bank high income countries. Types of Python Development. Python is a versatile language used for a variety of tasks, such as web development and data science. How could we disentangle Pythons recent growth across these fields For starters, we could examine the growth in traffic to tags representing notable Python packages in each field. We could compare the web frameworks Django and Flask to the data science packages Num. Py, matplotlib, and pandas. You can also use Stack Overflow Trends to compare rates of questions asked, rather than ones visited. In terms of Stack Overflow traffic from high income countries, pandas is clearly the fastest growing Python package it had barely been introduced in 2. Stack Overflow question views. Questions about numpy and matplotlib have also grown in their share of visits over time. Python Serial Buffer Size' title='Python Serial Buffer Size' />If you are doing any serial port communications these days in C and would like your code to be portable, then you are probably using boosts asioserialport. Ethernet. The S30 motherboard implements onboard gigabit Ethernet via one Intel Lewisville 82579 controller. This integrated solution has support for the industry. There is XMODEM module on PyPi. It handles both sending and receiving of data with XModem. Below is sample of its usage import serial try from cStringIO import. In contrast, traffic to Django questions has stayed fairly steady during that time, and while Flask is growing it remains at a smaller share. This suggests that much of Pythons growth may be due to data science, rather than to web development. However, this gives us only part of the picture, since it can measure only widely used Python specific packages. Python is also popular among system administrators and Dev. Ops engineers, who might visit Linux, Bash, and Docker questions alongside Python questions. Similarly, plenty of Python web development is done without Django or Flask, and such developers would likely visit Java. Script, HTML and CSS as supporting tags. We cant simply measure the growth of tags like linux, bash, javascript and assume theyre associated with Python. Thus, wed like to measure the tags visited alongside Python. Well consider only visits in the summer June August of 2. We considered only signed in users who had visited at least 5. Stack Overflow questions during that time. We considered someone a Python user only if a their most visited tag is Python, and b Python makes up at least 2. Which tags were often visited by the same people who tended to visit Python Pandas is by a large margin the tag most visited by Python developers, which isnt surprising after we saw its earlier growth. The second most visited tag by Python visitors is Java. Script, which likely represents the set of Python web developers as does Django a few slots lower. This confirms our suspicion that we should consider what tags are visited alongside Python, and not just the growth of Python related tags in general. Going down the list, we can see other clusters of technologies. We can examine their relationships by considering what pairs of tags tend to be correlated that is, whether pairs of Python users are disproportionately likely to visit both tags. By filtering for pairs of tags with a high Pearson correlation, we can display these relationships in a network diagram see here for more on this kind of visualization. We can see a few large clusters of technologies, which roughly describe categories of problems that are often solved with Python. A6GgTiEdU/Uscs95BoVOI/AAAAAAAABn4/PJD7SEiOXgs/s1600/AnimationStudio.png' alt='Python Serial Buffer Size' title='Python Serial Buffer Size' />All programs need to perform input and output. This chapter covers common idioms for working with different kinds of files, including text and binary files, file. Via via kwam ik op zonnestraal. DSMR P1 poort. Nu Janus de sitebeheerder van bovenstaande site kiest. In the upper center we see a cluster for data science and machine learning it has pandas, Num. Py, and matplotlib at the center, and is closely connected to technologies like R, Keras, and Tensor. Flow. The cluster below describes web development, with tags like Java. Script, HTML, CSS, Django, Flask and JQuery. Two other clusters we can spot are system administrationDev. Ops on the left centered around Linux and Bash, and data engineering on the right Spark, Hadoop, and Scala. Windows Server Build Checklist Template there. Growth by topic. Weve seen how Python related Stack Overflow traffic can generally be divided into a few topics. This lets us examine which of the topics is responsible for most of Pythons growth in Stack Overflow visits. Imagine we were looking at the history of a user, and we see that Python is their most visited tag. How might we guess whether they are a web developer, data scientist, system administrator, or something elseWell, we could consider their second most visited tag, then their third, and work our way down the list of their most visited tags until we saw something recognizable from one of the clusters above. Thus, we propose the following simple approach for classifying a user into a topic, where we find the tag most visited by each user from the nine listed below, and use that to classify them. Data scientist Pandas, Num. Py, or Matplotlib. Web developer Java. Script, Django, HTMLSysadminDev. Ops Linux, Bash, or Windows. None None of the nine tags above made up more than 5 of their traffic. This isnt very sophisticated, but it lets us quickly estimate the influence of each major category on Pythons growth. We also tried the more rigorous approach of latent Dirichlet allocation, and got qualitatively similar results. Which categories of Python developer have become more common over time Note that since were categorizing users rather than question visits, were showing this as a percentage of Stack Overflow registered visitors whether they visited Python or not. We can see that the number of Python visitors who work with web technologies or system administration is growing at a slow or moderate pace in the last three years, out of all visitors to Stack Overflow. But the share of Python developers who are visiting data science technologies is growing very rapidly. This suggests that Pythons popularity in data science and machine learning is probably the main driver of its fast growth. We could also consider growth on the level of individual tags, by calculating the traffic to tags visited by Python developers in 2. For instance, its possible that Javascript traffic is steady overall, but that its shrinking as a percentage of visits from Python developers. Once we have those per tag growth rates, its useful to lay them out in our network to understand what topics are growing and shrinking. Full examples of using py. Serial package. I have not used pyserial but based on the API documentation at http pyserial. Serial it seems like a very nice interface. It might be worth double checking the specification for AT commands of the deviceradiowhatever you are dealing with. Specifically, some require some period of silence before andor after the AT command for it to enter into command mode. I have encountered some which do not like reads of the response without some delay first.