sin7

Here is one for R:

https://github.com/tidyverse/magrittr

Light use is prime market for lemons. No one is going to sell a good mouse.

pandas is inconsistent and verbose. Isn't SQL a better alternative? Or even dplyr? Heck, I would learn one of those array languages such as J or APL rather than use pandas.

Couldn't I sell a fake Banksy with the real authentication?

And they should be.

7z the csv. Get a million rows from your table. Compare the csv to the 7z. That will give you a good idea on what kind of compression you will see.

I have very redundant data. I often see 10:1 compression. It's a lot less when I have hashes, addresses and timestamps.

Take the data in your database, 7zip it. If your data is very redundant, you'll see great compression. If it's not you won't.

The $70 dollar fine in Seattle will make anyone very patient.

Ties. It's probably because of rounding.

It used to be part by the month. I took the data science nano degree and I finished a lot of it in a month and a half or so. The course wasn't complete and I felt like I couldn't do the reinforcement learning section based on the class so I dropped it.

It was a great deal then.

Hustler is used a lot in rap. It's always been there. A hustler hustles. They put in work. They are in the grind. It's all about turning a nickel into a dime. In other words, they sell a lot of crack. Nothing deep.

https://www.youtube.com/watch?v=xZbwTCrnktY - Cassidy - I'm a hustler

https://www.youtube.com/watch?v=sv6CbP4kVno - Lil Wayne - Hustler Muzic

I use PostgreSQL, which is a real database. I use DBeaver as the database manager. It allows me to enter data by typing it in. For reports, I query the data. It has autocomplete. No forms.

For presentation, I toss the data on Excel. Bold the headers. Change the number type (pct, $, etc) to the correct one.

I would say that it has made my life a lot easier. There is one repository where the data is held. If I mess up, I go back to the master tables. In Excel, I would usually start over. Joins are easy. Excel can do some things better. When it can, then I use it. It's not a one or the other. It's ok to use both.

You can copy the stable data to another table, then insert the updated data. The database has excellent compression and copying from one table to another is super fast.

You can run it on a cluster or a single server. It's pretty easy to setup either way.

No updates. Fast inserts.

You can only join two tables at a time, but the joins can be chained to deal with this limitation.

I tried Monet. It wasn't very stable for me. I didn't stick with it long enough to judge it. ClickHouse has backing of Yandex. I think that makes a huge difference.

I have used Clickhouse for the past year. Thrown 3000 column by 120 million row tables on it. It worked where PostgreSQL came to a halt. Different use cases really.

I fits my use case perfectly. Large amounts of data with no updates and tons of aggregations. It's lighting fast.

Why is it a shame to use R?

R is not inferior to Python. Perhaps for general programming, but for stats and visualization, R is the gold standard.

All the ones that can't make it will just drop out. And if any random can make it, then your school is not that special.

This is how you quit.

What can pandas do that SQL can't?

Horseshoe theory in action.

They were doping, but it wasn't systematic. Nor was it a team bonding excessive.

Physicians already have good reasons to overstate the patient's severity. It's part of the payment formula used by CMS. But there are plenty of incentives not to lie such as jail time.

This must be the reason that you can't say Legos online without being corrected.

I have worked all kinds of jobs. Office work is cake.

I assume you are saying that Blacks and Hispanics will not meet the educational standards. Asians will and they are under represented.

The level of the problem doesn't matter. Working at Google is an elite job. We could go a level lower to Stanford. Asians are under represented at Stanford. We could go down a level to an elite California High School. Asians are still under represented. Blacks and Hispanics are really under represented at this level. It only gets worse for them the lower you go at elite institutions.

I use the general population because that is where the workers are coming from. If the pool of qualified applicants does not match the general population, then somewhere the qualification process failed.

> Odds of getting hired under those criteria: 0.25% Asians, 0.02% Hispanics, 0.01% Black, 0.13% White

> about 2% of employees would be black, 5% Hispanic, 30% white and 63% Asian.

These two comments say the same thing. Mine is standardized.

Your take away from all this is a little strange. The Google numbers say that Asians are 25 times more likely to be hired than blacks. That has happened in real life. No one complains about that. I have read that Google is too Asian though.

People harp about less-talented people not deserving to be there, but that is already the case. From a statistical point of view, Whites are over represented at the expense of Hispanics and Asians with a higher IQ. Real life is messy, but you get the idea.

This battle has been fought already and enough people at Google were convinced that there was a bias in their hiring practices. So, now they are in the process of correcting.

Ok. California is 39% Hispanic, 38.8% White, 5.8% Black and 13% Asian. California has 40 Million people. Let's say half have not graduated college yet and are not in the employee pool. That means 8 Million Hispanics, 8 Million whites, 1.16 Million Blacks and 2.6 Million Asians. Taking this new information into account, Asians should be around 37% of the employees, Blacks less than 1%, Hispanics around 9% and Whites 54%. Google's numbers say 61% White, 30% Asian, 2% Black and 3% Hispanic. So, it's both Hispanics and Asians who are under represented.

If you were to accept the current estimates on IQ, where blacks have an average IQ of 85, Hispanics an average IQ of 90, whites an average IQ of 100 and Asians an average IQ of 105 with a standard deviation of 15 by design, and that Youtube only hires people two standard deviations from the mean, then about 2% of employees would be black, 5% Hispanic, 30% white and 63% Asian. I am quite sure that whites are over represented at the expense of Asians.

Us poor people paid taxes to get those lines up and running we are definitely poorer.

Nested for loop. Count the cycles.

Neither is Python, but people still use it.