I agree completely. Also definitions matter. Currently in the US one of the leading causes of death, if not the leasing cause, is opiods which the CDC classifies as “accidental overdose”. The healthiest people in the US are probably abusing opiods at lower rates and therefore less likely to die from the leading cause of “accidental death”. The study is from sweden but just pointing out that the assumption they make is flawed - “we should not expect a relationship between fitness and accidental death”.
I thought your anecdote and commentary were relevant and extremely thought-provoking.
The person you're responding to here was clearly emotionally triggered by your anecdote. I wouldn't spend too much time trying "convince" them that what you wrote is true.
Those responses have puzzled me the most. Like, OK, there was an odd little girl somewhere in the world back in the '80s. So what?
We know there are kids who earn graduate degrees in their early teens. Why is it implausible that the occasional 3yo could have thought about picking 1, but then suddenly had a flash of, "No wait! That's exactly what he will expect! He'll never expect me to pick 3 again!" and remember it?
it's pretty simple but on various consulting jobs I've had to build SQL databases sometimes with lot's of tables with lot's of columns. Sometimes we switch from on prem to cloud, or vice versa or switch from postgres to sql server, etc. I have this toolkit that automates a lot of the tedious stuff. it allows me to take pandas dataframes and do the following:
- auto detect and convert column types
- save as a parquet file in a folder
- then autogenerate a sqlalchemy table/metadata file in python for all tables with sensible defaults for column types (e.g. 2x the longest string in a column for varchar)
- build the db and all tables
- load data from the files into the tables
this makes it really easy to bootstrap the entire db from a folder of parquet files for testing with sqlite and then makes it easy to move to prod on postgres/sqlserver etc. Before I go to prod i still have to add constraints and keys and indexes but that doesn't take too long. and for dev/testing the data's not too big so performance doesn't really suffer from lack of keys/constraints then we can use something like alembic on the big sqlalchemy tables definition file to do db migrations.
This looks awesome. I'd be super interested in testing this out and providing feedback on it. I'm going to make an account and is there an email/link to submit feedback as I use it?
>As far as I’m concerned Bayer and Monsanto are like evil incarnate
Especially so when they were a subsidiary of IG Farben.
>The company had ties in the 1920s to the liberal German People's Party and was accused by the Nazis of being an "international capitalist Jewish company".[8] A decade later, it was a Nazi Party donor and, after the Nazi takeover of Germany in 1933, a major government contractor, providing significant material for the German war effort. Throughout that decade it purged itself of its Jewish employees; the remainder left in 1938.[9] Described as "the most notorious German industrial concern during the Third Reich"[10] in the 1940s the company relied on slave labour from concentration camps, including 30,000 from Auschwitz,[11] and was involved in medical experiments on inmates at both Auschwitz and the Mauthausen concentration camp.[12][13] One of its subsidiaries supplied the poison gas, Zyklon B, that killed over one million people in gas chambers during the Holocaust.[b][15]
The Allies seized the company at the end of the war in 1945[a] and the US authorities put its directors on trial. Held from 1947 to 1948 as one of the subsequent Nuremberg trials, the IG Farben trial saw 23 IG Farben directors tried for war crimes and 13 convicted.[16]
Not drag and drop but for a 100% python solution h2o wave is pretty cool. It has a focus on data-based dashboards but can be used to pretty easily create very good looking frontends for all sorts of applications. And you never have to leave python (whether that's a pro or a con is up for debate :))
He has been saying exactly this for many years and has lead many efforts to improve both the implementation and API via projects like arrow and pyarrow.
Also, pandas was purpose built for a pretty specific domain of financial timeseries and cross-sectional data analysis at a time when the python ecosystem was much younger and very different from today.
It's not his fault it became so wildly successful! (actually it was - it's a great piece of sofware :))
Just want to say that I respect the fact that you built this library, are offering it open source, for free, and want the telemetry on. You are up front and open about it.
Sometimes I think people can get a little entitled with all the work someone else puts in to a project they want to use (not accusing GP of this, speaking generally). As you said, under the license anyone is more than welcome to fork, modify and open source. Yay open source indeed :)
