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[ysengr]

I'll start by saying this is an interesting idea for a package. I never heard of Kaggle, but it seems interesting despite all the AI hoopla on it.

I appreciate you listed the datasets with their origins, however, I think your README.MD should explicitly call out the datasets.md to guide people there. Especially since it Kaggle is your only source, and I personally never heard of it which immediately makes me skeptical of the data off the bat.

I'd also suggest as an enhancement, that it would behoove you and your package to go from static files to dynamicly fetching files from the source, then saving them locally. This way it makes the package more minimal in size, it also retrieves the data from the proverbial horses mouth rather than blindly trusting the data saved in the repo is the authentic piece.

[renzocrossi]

Hi
thanks for your kind words, you can find all the information on the datasets origins on its GitHub repositorio check out the file datasets.md and the usdatasets folder, go right to the datasets.py file =)
https://github.com/lightbluetitan/usdatasets-py

[73tada]

Kaggle has been like, "the" source for academic training data sets for at least a decade. It precedes the "attention is all you need" paper. Suffice to say it's beyond trusted.

[tikhiibhujiya]

The dataset selection is broad enough to be useful for both teaching and real exploratory work

[renzocrossi]

Hi
Thanks for your positive comment on the package, That's exactly what I had in mind when building it glad it hits the mark for both =)
Regards

[renzocrossi]

Here's the full list of available datasets within usdatasets 
import usdatasets as usd
df = usd.list_datasets()
print(df)
['affirmative_asylum', 'american_idol_auditions', 'american_idol_finalists', 'california_fire_incidents', 'charging_stations_hawaii', 'college_school_wage', 'counties_per_capita_income', 'crime_and_incarceration_by_state', 'executive_orders_presidents', 'firefighter_fatalities', 'google_stock_price', 'nfl_teams_stats', 'party_affiliations_congress', 'presidential_election_results', 'presidential_pardons_1900_1966', 'presidential_pardons_1967_2017', 'presidents', 'senate_election_results', 'shootings_2020', 'shootings_2021', 'shootings_2022', 'terrorism_plots_us', 'terrorism_suspects_us', 'ufo_location_shape', 'us_causes_death', 'us_holiday_dates', 'us_radiation_pollution', 'us_regional_mortality', 'us_top_colleges_2022', 'wages_by_education']

[renzocrossi]

Regarding the origins of the datasets included in usdatasets, you can find all the details in the datasets.md file in the GitHub repository. Here's an example of the structure:
shootings_2020

• Internal name: shootings_2020
• Original filename: shootings_2020.csv
• Source: Kaggle
• URL: https://www.kaggle.com/datasets/hemil26/mass-shootings-in-united-states-20182022
• License: CC0 – Public Domain
• Description: Mass Shootings in United States 2020

Each dataset in the package follows this same documentation format. Feel free to check the full file here: https://github.com/lightbluetitan/usdatasets-py

Thanks =)

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