Fantastic pandas
It’s never too late to learn: take it and learn. And all that I have dug up from everywhere during the times of doubt is that in the modern IT industry there is no time for sentimentality now. I often read that there are fewer vacancies than applicants, that the labor market is more and more inclined towards the employer, the competition among junior specialists is going through the roof. Passions boil in the comments on articles about finding a job after the courses – the whole spectrum of emotions: from “no one needs these hordes of“ Whites ”” to something like “if you are brave, hard-working and fanatical, then the IT jungle is calling you.”
By the way, is it because the FAQ of the portals on the question of employment after training answers me bluntly, vaguely, shifting the responsibility onto the student himself, but so that this student does not immediately run away. And on the main page there are so many songs about the salary for the first, fifth, tenth year of work.
Hello Panda!
We study in order to work. And then we work in order to have the means to retrain, if there is enough time and energy. It happens. At my age and with my career track, it seems that it’s too late and you don’t need to torture yourself with teaching (teach with torture). Pulling a turnip is not easy – how many people have suffered with it. But it’s interesting! Pushes something new and exciting. Maybe I reviewed science fiction as a child or read the stories about Iyon the Quiet too seriously? What if something from the long tail of the distribution happens? Pull-pull this tail…
Basic Python. Maybe the exploration of dataframes doesn’t (yet) sound like the fantastic technology dreams of Lem’s books. However, I’ve had a genuine love for spreadsheets since my days with Excel, and this love only intensified when I got to know python and pandas.
From “Hello World!” one print, which is what the “pythonists” are so proud of, through lists and
dictionaries to the well-documented pandas, with which everything is the same
the most that has just been done by loops and conditions is done in one line
code. I love logical indexing! df[df['bear'] == “Панда”]I don’t know why I have tender feelings for her. Probably because of the feeling of simplicity and intuitiveness of understanding. Thanks Python and Pandas! You guys, like Chip and Dale, brightened up my dull everyday life by getting rid of the routine. Does not leave the feeling that the python will always have some import do_everything_for_meto do well. And now the first sprouts of fantasy have already appeared!
I’m studying. The system that accepts answers encourages, jokes, praises. I hardly fall for her compliments. Although, no. When I see a green tick after checking my code, I feel a little like Pavlov’s dog, reflexively there is pleasure, even for the most insignificant task.
The power of the exclamation mark
Experts say that a data scientist must constantly learn and keep his nose to the wind, like, probably, all IT people. So I started playing with mathematics. Let it be “two plus two” for now. Fortunately, there is a free course, among others, testing basic mathematics. The combinatorics went especially well.
There is a separate pleasure in the process of counting the number of combinations, placements, options for everything, anything. Factorial is a synonym for a man on edge: always in a raised voice, constantly with an exclamation point, and not the largest number swells like that, just manage to count orders by moving your finger across the screen. And all these incredible possibilities to combine and arrange (with and without repetitions, taking into account the order and without) seem so strange and even unnecessary: just know, multiply heaps of numbers on the calculator. So what? But they threaten that without this you can’t even build a route around the city – you need to know all the possible options. In general, I took my word for it.
Of course, sometimes it is difficult to fit in your head so many opportunities to take out balloons, order a drink, go around cities, etc. at once. But with practice, you can bring it to automatism – and click on training tasks. Maybe it will also come in handy, for example, at some kind of interview?
Mathematics is a strange thing: it seems that without it there is nowhere, and at the same time it is often difficult to understand how it can be applied in the real world. Well, why should I know that 10 books can be put on a shelf 3 628 800 ways? I’ll scatter them somehow, as I can. In everyday life, questions are solved simply and routinely, but at high levels, with large numbers and large data, everything can be counterintuitive. So, thanks to combinatorics for a lot of options.
What will you sow…
Learned about the GIGO principle applied in the field of data processing and analysis. It sounds rude: “garbage in – garbage out”. If you already collected garbage in the dataset, then process it, model it, clean it, and the output will be … it’s clear what. Of course, you can tweak something, remember about distributions and statistics, put a marathon on the data. I don’t know if this helps. Is there a magic python import here too, so that a non-clumsy answer comes out of clumsy data? This is interesting. Here is fantasy.
So you study – brains boil. And as I look ahead, how much more is there – alternately it takes my breath away, then my hands drop. Not only pandas and pythons will dream here! In general, so far – only exclamation marks.
