Quick and easy packages list

I created a new project and knew at some point I would have to look into how requirements.txt files are created. I'm still in the early stages of the project though, so I didn't really look into it. As luck would have it, I accidentally came across it while looking up something else. It's so easy! All I have to do is run a command while my virtual environment is active.

pip freeze > requirements.txt

Running the command pip freeze alone would list out all the third party packages that have been installed along with their version numbers. Using the command above puts it all into a file named requirements.txt. Technically I could name the file anything I want but it appears to be convention to name it thus. This way, anyone who clones your project can easily install the packages and run the project.

Reference: https://towardsdatascience.com/virtual-environments-104c62d48c54

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Certified CSS Surgeon

I've just been certified as a top notch CSS Surgeon 😹😹😹. Codepip has just officially launched and they're offering CSS Surgeon for free until the 1st of September. I've already finished Flexbox Froggy(Free) and Grid Garden(Also free). I really do enjoy the CSS games on this website. It has helped me better understand CSS. There is still so much to learn. I've been trying to create a simple language learning website and it has been a struggle. I'm using the most simple website layout and yet it's just kicking my butt(I'm looking at you Nav bar). I hope to add some Javascript to make it more interactive in the future. At one point I really wanted to throw in the towel because it felt like I would never get this. It's hard not to give up when there are so many hurdles to cross. Can I really be confident at coding after just a 6 month bootcamp?

Deviants in a normal world

It's definitely been a bit since I've seen this graphy. Anyone who has learnt about standard deviation knows this graph. Standard Deviation Standard deviation shows us how spread out all the values in a set are from the mean. The higher the standard deviation, the more spread out the values are over a wider range and the flatter this curve. In a normal distribution, most values are within 1 standard deviation from the mean(the green part of the graph). Apparently NumPy can calculate standard deviation too! import numpy numSet = [ *lots of numbers* ] numSetStdDev = numpy.std(numSet) Variance The variance also indicates how spread out the values in a set are. It measures the average degree to which each value differs from the mean. variance = standard deviation ^2 import numpy numSet = [ *lots of numbers * ] numSetVar = numpy.var(numSet) Source: https://www.w3schools.com/python/python_ml_standard_deviation.asp

Portfolio on GitHub

As part of the bootcamp, I've been building a portfolio website based on a template provided by the school. Honestly, I hate the template. It is hideous. But I guess it's a great starting point for someone who is unsure about what to do. It was also an opportunity for me to practice publishing a website online. Unfortunately, I had to pay for a webhosting service. After submitting my third update to my portfolio website, one of the instructors suggested that I could use GitHub Pages instead. *mindblown* This is amaziiiiing. And it's so simple. When I finally get down to creating my own portfolio from scratch, I am definitely going to put it up on GitHub instead. Also, speaking of GitHub, I'm slowly understanding how GitHub functions. It is taking me awhile. Especially because I don't use many of the functions that are available. I look forward to learning more and becoming a GitHub extraordinaire :D JK

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