I have a statistical background, though never in my life, I’ve worked with statistics. I got rusty; I mean, short fragme

Remember my NaN issues? We can use this Imputer to fill missing values, which can be NaN or any other different value we specify, to the values we want. The default strategy is mean, but we can use the median, most_frequent, and constant.

Investigating it visually (there are some light blue lines), we see that it looks like the normal distribution, but it’s skewed to the left. There is a solution to it! Let’s use np.log1p on column and see if it solves the issue:

We have to check on numbers row by row, and it gets out of hand fast. Maybe we can plot this and have a more visually pleasing approach? As with Python — we have libraries for everything. This is not an exception. I knew about matplotlib before, but as I recalled, it required a bit of tinkering; I was 99% sure that there is a library for easier use. In this case, it was Seaborn. Easy to use and graphically pleasing. And for correlation best thing is a heatmap.

If we see huge differences in percentile values, we might have issues to solve. In the image above, we can see that our column amount_current_loan has a max value of 99999999, which is ~200 times bigger than 75%. However, the column doesn’t have missing values. I naturally implied that a particular constant is a result of treating missing values. Already one issue solved by one quick glimpse at the summary table.

I work as a data developer/engineer by day and father of two 24/7. Whenever I have free time from these activities, I try to do something interesting to me. I had a data science field for some time in my backlog, but I couldn’t find a good use case to deep dive into it. The Focus area for me (is now) and was stream processing, Apache Spark, and other Big Data processing related things.

Keep in mind that this is the first legit time I tried to make some predictions. I’m not doing anything production-grade, and this post is most likely to be useful for new kids on the Data Science block (explorers or just starting). Learn from my mistakes as well!

Bloc is probably the oldest solution on this list, and is still great. Recently, BLoC has added Cubit into the mix, which makes BLoC more or less obsolete as Cubit lowers the boilerplate needed — the good thing is they’re both in the same package, which means migration is super easy. BLoC excels in two different domains, in my opinion: 1. Working With Teams: BLoC does a really good job making things not flexible. For a lot of people, that’s a bad thing: they want to be able to quickly change their application without having to write too much code, or change too much code. For teams, this isn’t the case: by having things be a little inflexible, you can guarantee that everything is working as the original developer intended — if a state in a BLoC is only intended to have the values 1, 2, and 3, you can code your BLoC so that the events only change the state to those values, so other programmers can’t accidentally move it the value to 4. For this reason, it’s also really easy and great to test.

If you like to rigidly define your events and states, then BLoC is for you. If you need flexibility / development speed, then BLoC may not be the way to go. (I personally prefer BLoC and cubit in my apps.)

Where InheretedWidget falls apart is that there’s a lot of boilerplate. The Widget system requires a lot of code to be repeated over and over, and thus it gets a bit repetitive if you’re doing a lot of higher scope state management.

So from the heatmap, we can see that only two columns have a high correlation (by high — I mean 0.7 ). It’s credit_problems with bankruptcies, which is obvious if you think about it. What is great (not sure if I can categorize it like this), that I have NaN values in the bankruptcies column and no missing values in the credit_score. I can use one to infer other missing values. Keep an eye on this in Part 2.

so for playing around until you get steps sorted out — it’s straightforward. Though working on some ML things (the challenge which got me hooked on DS) and mostly working in Jupyter Notebooks, It’s easy to get lost. Also, it’s hard to track all the changes (and current data values). This won’t work in production pipelines; even slight changes might mess the pipeline!

Let’s say we want a bit more intelligent imputation. We can use (still experimental, though) — IterativeImputer. Which comes way close than it should [3, 9], [5,25] was expected. Compared to simple mean/median, which wouldn’t be sufficient here.

2. Event Driven State: BLoC is based on events, so it’s no wonder that it does event driven states really, really well. What I mean by this is you have to define your events. Performing an API call might trigger an event, that pushes out a state CallingAPIState . Then, when the API call gets completed, It’ll push out a HaveAPIResultsState .

Though originally, I didn’t try to look at this method until my colleague shared its value. Describe provides the most generic information, from which we can see our data skewness, outline potential outliers in some columns.

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to the jokes. He stays within the same theme, not as serious. Jokes about Trump, Chris Christie, tough jokes, the haphazard way that the Pandemic has been handled, but alas, in the same theme. If I’m just starting out as a comedian, this is the half I’m studying. This is the set-up/punchline comedy that people associate with comedians. Chappelle can do this because like most seasoned comedians, he’s mastered it.