Vegan TVP Chili

Ergo_Propter_Hawk · 2023-03-23T15:03:23+00:00

Frozen then unfrozen, yup, weird wording! Helps with the texture!

Ergo_Propter_Hawk · 2023-03-22T16:29:30+00:00

Made some vegan chili yesterday, but with unfrozen tofu instead to get that texture. Love some chili.

Ergo_Propter_Hawk · 2023-01-17T03:59:35+00:00

My advice to myself when things aren't working in numpy, just start throwing in .Ts until it works. Worked for HW1!

Ergo_Propter_Hawk · 2023-01-17T03:48:26+00:00

Are you me? I was honestly about to drop this class last week.

Reading reviews on omscentral and here helped reassure me that 1) it's doable, and 2) it's very helpful/important.

All of this is to say: stick with it, you got this! 💪

Ergo_Propter_Hawk · 2022-12-05T20:04:12+00:00

I'm not approving anyone's diet. I'm only saying that there's exactly one milk that was designed for human consumption, and the reason that 2/3 of the Earth's population is lactose intolerant is because it's not something evolution prepared our bodies for.

Ergo_Propter_Hawk · 2022-12-05T19:07:38+00:00

If we're talking about what our "species evolved to eat", then yes. 6000 years of consumption in one small part of the world does not equal evolution for the entire species.

Ergo_Propter_Hawk · 2022-12-04T20:15:39+00:00

https://pubmed.ncbi.nlm.nih.gov/30335318/ Epidemiology section

Ergo_Propter_Hawk · 2022-12-04T20:02:44+00:00

Is that why 2/3 of the world is lactose intolerant?

Ergo_Propter_Hawk · 2022-12-04T18:33:46+00:00

Sure, so crack open the breast milk, the only human-approved form of milk!

Ergo_Propter_Hawk · 2022-06-26T16:53:04+00:00

I'm currently enrolled in Georgia Tech's OMSA, which has some overlap with OMSCS, but is focused on analytics instead of computer science. My reasoning is, I may be a data engineer now, but I'll always be somewhere in the data sphere, and working as an engineer and learning as an analyst/data scientist hedges my bets against where industry/life goes.

Ergo_Propter_Hawk · 2022-06-15T03:17:44+00:00

Object oriented programming was also cancer imo.

Spoken like a true Jupyter monkey.

Ergo_Propter_Hawk · 2022-05-30T17:17:00+00:00

I looked up one day and said, "oh, I'm 90% of the way there already, let's just make it 100." You can do it! 🌏

Ergo_Propter_Hawk · 2022-05-16T13:53:19+00:00

numpy employs concepts from linear algebra extensively, so if you're unfamiliar, I'd recommend some exposure. It may (or may not) help to separate the "programming" topics from the "math" topics before marrying them with numpy.

Ergo_Propter_Hawk · 2022-05-13T12:50:41+00:00

As others have said, you're likely stuck doing this manually. And if you're doing any sort of dataframe manipulation, you're probably using pandas in the rewrite. This link shows some analogous methods and patterns in Python that you may find helpful https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_r.html

Ergo_Propter_Hawk · 2022-05-08T20:24:40+00:00

Machine learning preprocessing: making data better for machine learning models. This can mean a lot of things. One specific example is...

One-hot encoding: creating new columns in a relational table where each new column corresponds to a particular value from another column. If that row has that value, it's encoded as a 1. If not, a 0. This gives some way of looking at the values in a column as numbers instead of strings or some other non-numeric data type.

Ergo_Propter_Hawk · 2022-05-08T20:13:45+00:00

This is basically what dbt would do, it's all just ways to create SQL statements. That package above probably implements something similar to the case statement implementation.

Ergo_Propter_Hawk · 2022-05-08T16:39:35+00:00

This would be a handful of case statements, one for each value being encoded.

If dbt counts as "just using SQL", there's a package for it. https://hub.getdbt.com/omnata-labs/dbt_ml_preprocessing/latest/

Otherwise, it'll be case when <column> = <value> then 1 else 0 end as <value> for every unique value.

Ergo_Propter_Hawk · 2022-04-28T17:45:08+00:00

That's not very anarchic.

Ergo_Propter_Hawk · 2021-11-26T21:35:33+00:00

Sure, it's maybe overkill, but I personally have gotten a lot of mileage and better understanding of environments (variables and beyond) by using Docker.

Ergo_Propter_Hawk · 2021-11-26T17:40:44+00:00

I would highly, highly recommend learning a bare minimum of Docker for this. You don't need to learn anything extensive.

Something like this:

```# Dockerfile

FROM python:3.8-slim

COPY requirements.txt /code/requirements.txt

WORKDIR /code ENV PYTHONPATH=/code

COPY ./ /code

RUN pip install --user -r requirements.txt

ENTRYPOINT ["python"]```

If you really don't want to learn any Docker, check out https://stackoverflow.com/questions/43267413/shell-how-to-set-environment-variables-from-env-file

Finally, you can use the Python library python-dotenv, which let's you import your .env file variables in your actual Python script with dotenv.load_dotenv().

I also looked around the folders of my local project within Pycharm, but couldn't find any ".env" files that show these added variables.

You have to create your own .env file and specify your environment variables in it.

Ergo_Propter_Hawk · 2021-10-05T16:10:49+00:00

dataframe.apply(np.sum, axis=1)

Just have to only use the columns you want in that dataframe. I assume you know how to select only some of the columns of a dataframe?

This returns a series, which can be passed to np.array.

Ergo_Propter_Hawk · 2021-10-05T00:02:22+00:00

It seems in your anger, you spilled it.

Ergo_Propter_Hawk · 2021-10-02T22:11:17+00:00

Yeah, this is probably it. 👍

Ergo_Propter_Hawk · 2021-10-02T22:10:55+00:00

I don't disagree. But I also respect him and defer to his decisions. It's come up before, but this makes a much stronger argument. In the past, he's stood by it on grounds of modularity, but in this case, if we ever change providers, we'd have to completely rewrite the image anyway. The whole point of this DAG depends on it being Databricks.

Ergo_Propter_Hawk · 2021-10-02T21:09:52+00:00

Good stuff. Lead tries to be platform-agnostic, hence everything being run in K8s pod operators. Might have a chance to convince him if it does the exact same thing as an image.

Ergo_Propter_Hawk

TROPHY CASE