deshpand

8 ms·

1.
by deshpand 2y ago
Growing up in a somewhat remote part of India, I would tune to BBC, Radio Australia to listen to test cricket commentary, on short wave. I have fond memories and owe a lot of my personal growth to SW.
2.
by deshpand 2y ago
Thanks for the clarification on this particular point. I was not aware of it.
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by deshpand 2y ago
I don't know all the history, but Magnus comes off as above the game. “I am playing at least one more day here in New York and, if I do well, another day after that,” Really? And I saw another quote that said "f*k you" Whate
4.
by deshpand 3y ago
Of late, I have been googling a lot of SAS and may have contributed to its rise in ranking! Not coding in SAS but moving SAS to Python. Speaking of enterprise Java, there's a ton of enterprise SAS and it's moving to Python.
5.
by deshpand 3y ago
Not sure why you were downvoted. Fitness, equipment (racket and strings) are the reason if not the height. For height, above a certain level, say 6 ft 2 inch, it starts becoming a liability in movement. Tennis used to have a lot of varie
6.
by deshpand 3y ago
I used to wear cotton long pants (thermals) all year, under the suit pant. Not letting the suit pant touch the skin made it easier to wear it for a very long time (multiple months) before dry-cleaning. Don't miss those days of mandato
7.
by deshpand 4y ago
I didn't mean to exaggerate. I think part of the improvement was the luxury of refactoring which should generally reduce the bloat. And, as someone else said, part of the issue is C++/Java, not static typing. When I move from J
8.
by deshpand 4y ago
A ~100k LOC project in a statically typed system with type hierarchies, interfaces, contracts and boiler plate will boil down to ~10k LOC in a dynamically typed language like Python. A 10k LOC project will be more readable than a 100k LOC
9.
by deshpand 4y ago
Another advice is to have a thick skin and ignore the jerks, like for instance someone downvoting your note that was written with a good intention, sharing something learnt the hard way.
10.
by deshpand 4y ago
For longevity, I will provide one advice to younger people.. get away from the mouse as much as you can and use the keyboard. My working style is editing code in vim, dark screen, 2 buffers when necessary, go to the colon prompt to run the
11.
by deshpand 4y ago
Can you please elaborate why? I worked in Manhattan for many years, lived a few as well, and moved out in 2009.
12.
by deshpand 4y ago
I think it's helpful to keep in mind that Python is general purpose and used in many domains in addition to data analysis (no matter which side of the walrus operator you are on)
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by deshpand 4y ago
A little over a year ago, there was an incident with a lot of sites being down and Cloudflare/Lumen being involved (per Hacker News chatter, I only ready about the incident here). I bought a few shares of each, at around 39 and 11. W
14.
by deshpand 5y ago
In the end, it's not about an OPEN SOURCE tool being perfect but whether it is helping you solving a problem. If it did not help you and YOU don't consider it production ready, then that's fine. But you seem to argue that D
15.
by deshpand 5y ago
Do you have any citation on why "Dask doesn't consider their distributed version" to be ready? If it is your own view, then that's ok. I think dask is in heavy usage in real production systems. Let me cite one such usa
16.
by deshpand 5y ago
We use dask heavily, along with rest of the pydata ecosystem. I guess we are in the 'sweet spot' where data doesn't fit memory, to begin with, but once we perform any filtering and aggregations, switch over to pandas. That&
17.
by deshpand 5y ago
Not easy, unless you have an ability to identify opportunities that others don't have (such as applying your domain knowledge on a new trend, as I mentioned). You may do better than the market (market == passive index fund == averages
18.
by deshpand 5y ago
Technically, beating the market should be like being in the top 50 percentile of your class. The reason most big funds fail to do this is because they need to overcome the fees they charge, to match the market. If you are investing yoursel
19.
by deshpand 5y ago
I believe compression is still a work in progress in duckdb. My current use is with parquet files which compress well but don't lend themselves to updates like a db.
20.
by deshpand 5y ago
I work heavily with pandas and dask (when you want to use multiple cores), using parquet files for storage. We see a lot of benefits in selectively bringing in duckdb into the mix. For instance, the joins are extremely slow with both pand
21.
by deshpand 5y ago
Many enterprises are coming up with patterns where they replicate the data from the database (say Redshift) into parquet files (data lake?) and directing more traffic including analytical workloads onto the parquet files. duckdb will be ver
22.
by deshpand 5y ago
If you can completely stay away from Python/pandas, get all your work done with typed languages like Scala/Java, that's good. A lot of scientists and non-CS folks are using Python/R. They need to avoid mish mash of bri
23.
by deshpand 5y ago
Spark may be a mature solution for truly big data, in a SQL like fashion, 1TB and more. But I constantly see it being misused, even with datasets as small as 5GB. Maybe the valuation of the company reflects this 'growth' and &#x
24.
by deshpand 5y ago
R language is an open source implementation of the original S language from IBM. Its name was a play on S and also based on the fact that both the authors of the R language have first names starting with R, Ross and Robert. I suppose R2 m
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by deshpand 5y ago
A little python training in the python stack (pandas/numpy/matplotlib or other visualization libraries) can go a long way to simplifying tech stack and get rid of these mind numbing BI tools. And companies are trying. Ex: https:&
26.
by deshpand 5y ago
Banks also have a huge cost of supporting legacy systems in Cobol, SAS.. even some 'modern' systems in Java/Python are a huge mess. https://www.efinancialcareers.com/news/2021/02/jpmorgan-stil..
27.
by deshpand 5y ago
< I hate finding CSVs that other data scientists Ideally you should be using the parquet format which will use the binary format, preserve column types and indexes [df.to_parquet(<file>); df = pd.read_parquet(<file>)] You can
28.
by deshpand 5y ago
I'd be interested and will check this out. I have used plotly dash and love to hear a compare and contrast
29.
by deshpand 5y ago
Mobil + Chevron => Moron
30.
by deshpand 7y ago
< numba and numpy do not interact well at all Can you please elaborate on this? Not challenging you but trying to understand the nuances. Numba tutorial says otherwise: https://numba.pydata.org/numba-doc/dev/us

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