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Nowadays, it is a quite popular to store semi-structured information using JSON format. Indeed, JSON files have quite simple structure and can be easily read by human beings. JSON syntax allows one to represent complex dependencies in data and avoid data duplication. Moreover, all modern programming languages have libraries that facilitate JSON parsing and storing data into this format. Not surprisingly, JSON is extensively used to return data in Application Programming Interfaces (APIs) .
At the same time, data analysts prefer to deal with structured data represented in the form of series and dataframes. Unfortunately, transforming JSON data into structured format is not that straightforward. Previously, I preferred to develop code to parse manually complex JSON files and create a pandas dataframe from the parsed data. However, recently I have discovered a pandas function called
json_normalize that saved me some time in my projects. In this article, I explain how you can start using it in your projects.
Until recently, I used tmux occasionally, only if I had to run some experiments on a remote server and later see the results of the execution. Basically, I used it only as a mean to execute commands in the background. If I needed to run several commands on a remote server parallelly, I used to open several terminals, connect each of them to the remote host and then switch between them.
Recently, I started working with a remote server through
ssh more often and the routine, I used to, became very operation consuming. So, to improve my effectiveness, I spend several hours reading articles, watching videos and trainings how to use tmux. This article combines the knowledge I have acquired. It is also a crib for me if I forget something in the future.
Several weeks ago during a compilation process, I noticed that my laptop became very hot under my palms. At first, I did not pay any attention to this, however, when it became uncomfortable to work I started to worry. My first thought was that the laptop got dusted and cannot remove the heat effectively. But then I noticed that I did not hear the fan noise when the load on the CPU increases, and I decided that my cooler is either broken or blocked. I was almost about to start disassembling my laptop, but luckily I decided to check the temperature using Linux utilities. There I found out that, despite I feel the laptop being hot, the sensor [
temp1] showed that the CPU temperature was normal (showing all the time the temperature of 45°C). This looked suspicious, and I checked other sensors measurements and found out that the [
coretemp-isa-0000] sensors showed more correct temperature values, which in addition reacted on load increase. In this article, I want to describe, how I forced my system to react also on the values from these additional sensors and cooled down my laptop.