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91.
by closed 6y ago
I agree there are ways to organize this that would benefit outside readers, but am guessing the simple, consistent structure probably helped the author get to 5 years of TILs! It's interesting they chose to alphabetize and start many e
92.
by closed 6y ago
I think everyone feels like they're being hit with too much information at some point. But wanting to hide, and knowing it won't be over sounds really frustrating. Have you thought about running it past a therapist? They can help
93.
by closed 6y ago
I'm a huge fan of pydeps! Recently, I have been using it to discuss with developers the structure of their code. Being able to quickly show them how interconnected their imports are, and ask which of the "lines" they might re
94.
by closed 7y ago
> Yes, but later the article shows that "mean difference" is really mean difference divided by variance. I would say later the article shows examples of standardized effect size, so divides by variance. Whether that means effec
95.
by closed 7y ago
Note that there is a handy PeriodIndex version of pd.date_range: pd.period_range(date_from, date_to, freq = "D") AFAICT, a PeriodIndex and DateTimeIndex function mostly the same, and have many of the same methods, except...
96.
by closed 7y ago
> Which example? First paragraph: "Examples of effect sizes include the correlation between two variables,[2] the regression coefficient in a regression, the mean difference, or the ..." > Who would consider the same expecte
97.
by closed 7y ago
> Expected difference is not an interesting statistical property, just as the mean isn't (by itself). Difference in means can meet the definition of effect size, and are listed as an example right away in the wikipedia article on it
98.
Single dispatch for democratizing data science tools(mchow.com)
2 points by closed 7y ago|0 comments
99.
by closed 7y ago
A lot of comments are focusing on note taking processes, but I would step back and ask this: how will you know if your note taking strategy is working? One thing that has helped me is keeping a high-level study journal. In essence, I have a
100.
by closed 7y ago
Python including functools.singledispatch I think is a strong indicator that function overloading IS pythonic (or at least pythonic enough to core python developers)
101.
by closed 7y ago
One common use for function overloading is when you need the function to work on classes defined across several different packages. It's used a lot in R for this reason. You might want to have a function that operates on different mode
102.
by closed 7y ago
It feels pythonic to me. One thing that I think brings perspective here is the PEP on singledispatch, which is essentially on function overloading, and is implemented in functools! https://www.python.org/dev/peps/p
103.
by closed 7y ago
The simple explanation makes sense, but I'm realizing there's a subtle point here that I should been more clear on. They might not be filtering out the bad at the expense of the good. But filtering out some of the good in the name
104.
by closed 7y ago
Something that strikes me in reading articles like this, is the distopian part often seems to be thinking about this: p(job_capable | not_interview_capable) That is, it's crazy that an interview could miss so many people quali
105.
by closed 7y ago
I had--and still do have--similar concerns as you've raised. But there's been one really nice, overriding factor for me: Twitter pals. I feel like I've gained a few internet ride-or-dies, even if it's mostly us liking &#
106.
by closed 7y ago
I just finished a lengthy analysis of why pandas groupby operations ends up harder to use than R's dplyr or data.table. For example, a grouped filter is very cumbersome in pandas. Interested to hear if you think it gets at the heart of
107.
What would it take to recreate R's tidyverse in Python?(mchow.com)
4 points by closed 7y ago|0 comments
108.
by closed 7y ago
I think that thing X superceding thing Y is equivalent to thing Y subceding to thing X. In this case, if black becomes the standard for defining appropriate style then it is superceding pep8 as the standard for style (many styles consistent
109.
by closed 7y ago
> The reason a formatter exists is to make all codebases look consistent I'm not how this is related to not having any knobs to tune. Aren't there many ways for code to be consistent with pep8? Isn't advocating for only on
110.
by closed 7y ago
A single file seems like a forcing function for including the right amount of context you'll need later. I use nvalt, which is basically a small collection of text files. It seems like whenever I let myself create many files, I just fe
111.
by closed 7y ago
Well, I'm a big fan of plotnine, and plydata was part of the inspiration for siuba! I think at its core, the groupby issue is a really big problem, and am devoting most of this year to working on it. So if you ever want to pair to work
112.
by closed 7y ago
Agreed RE GroupBy being challenging, especially compared to dplyr. As I've worked on a port of dplyr to python over the past year, though, I've realized the dtype issue (like you said), indexes, and GroupBy being difficult are lik
113.
by closed 7y ago
Ah, same! I'm also relieved to hopefully never again have to say: "whoops I converted nan into a very large integer".
114.
by closed 7y ago
I've had to dive into the pandas code over the last year for a project [0], and my attitude has shifted dramatically from... * old attitude: why does pandas have to make things so hard * new attitude: pandas has a crazy difficult
115.
by closed 7y ago
Interesting study! I'm curious what shadowing 15 R data scientists would look like, since it seems to resolve some of the pain points around caching results, debugging, and scaling. This is a very minor question (and I am not concerned
116.
by closed 7y ago
Ah, thanks for pointing out the lack of explanation. I'll add one to the readme. It's a transliteration of the cantonese word for minibus, 小巴 :). edit: siu (小) means little!
117.
by closed 7y ago
One thing that really surprises me: NONE of these methods work with grouped DataFrames. But grouping data is extremely common in data analysis. Basically, the strategy with grouped data, is taking the loc approach, and sprinkling in a bunch
118.
by closed 7y ago
I've been working on a library over the past year that does exactly that, including generating dbplyr style SQL queries! Would love your feedback :) https://github.com/machow/siuba
119.
by closed 7y ago
Interestingly, people have run wm training studies with these control groups! (Historical caveats about trusting the results of a single training study apply) http://www.academia.edu/download/36902540/2015_ActaPsyc
120.
by closed 7y ago
> The problem mostly with CSV... is that ultimately the structure is inband with the data I wonder if for 90% of users this seems like a critical UI feature!

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