Figuring out Postgres Part 1(Setting it up)

I've been meaning how to use Postgres for a while now and I've finally decided to dive into it. First step, installing Postgres from their website.

I kept all the default settings which meant it installed PostgreSQL Server, pgAdmin4, Stack Builder, and Command Line Tools. It later prompted me to set up Stack Builder, but I took a look at this tutorial and determined that I don't really need to do that right now. It also helped me figure out how to verify the installation using SQL Shell(psql). Everything looks good so far.

I followed another tutorial on Linkedin learning to create a database.

Next on the tutorial, create a virtual environment and install Psycopg2-binary in it. Apparently it's a Postgres database adapter.

And because I'm an idiot, I forgot where I saved the database. I opened up Postgres shell and used the command SHOW data_directory;

But it turns out I didn't need it anyway 😁 I created a new Python file and added the following lines of code to create a table in the db.

Ran the file. No errors. Opened up SQL shell(psql) to take a look at the table using the command \dt .

And a query to check that the columns have been built right.

So it looks like my table was created correctly and all is right with the world.

Comments

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

I gotta feeling...

I've been helping a colleague with his portfolio site. He's making it retro video game themed at my suggestion. He found an interesting pixelated font called arcade classic and used it for the headings on his page. Unfortunately, some of the letters almost overlapped, making it not quite readable. Before letter spacing I looked into typography ages ago and learnt about letter and word spacing and wondered if that was something that I could fiddle with using CSS. Turns out it is a property you can customise. I opened up Chrome Dev tools and added 3 pixels of letter spacing and it looked so much better. And there's letter spacing too, so that's pretty neat. After letter spacing Can't say CSS is my favourite thing ever but it's always nice to learn something new in unexpected ways.

Snakes and ladders

I've started on my Python course. So far, the code has been familiar because the first few basic codes are similar to Javascript. And then modules happened. Confusion and despair! What is the world is 'if __name__ == "__main__": ' and why must I reach this section of my course on a public holiday when none of the instructors are in :( Stack overflow to the rescue, providing me a lifeline while I was drowning in a pit of serpents. I feel eternally indebted to a particular Mr Fooz. Picture from here From my understanding, when the Python interpreter reads a source file, it first sets the variable __name__ and then it executes all the code in the file. If that particular file that you are running(i.e. your module) is the main program, the interpreter will assign '__name__ = "__main__" '. Thereafter, any code in the aforementioned 'if' statement is run. If you have, instead, imported a module, the interpreter assigns '__name__ ...

I do it for my cat

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