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Kaggle
docker-python
Python2017–2026
Kaggle / docker-python

docker-python

Kaggle Python docker image

3Distinct people
8Mentions
0Explicit recommenders
2.7KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Kaggle Python docker image

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 6:01:08 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year8 dated mentions

Observed mentions: 2017-10-30 08:48:27 UTC to 2026-07-29 22:53:20 UTC.

012017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC101
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

1 sources
Pachyderm may be basically what you're looking for. It does data version control with/for language-agnostic pipelines that don't need to always redo the ETL phase. https://www.pachyderm.io Dask-ML works with {scikit-learn, xgboost, tensorflow, TPOT,}. ETL is your responsibility. Loading things into parquet format affords a lot of flexibility in terms of (non-SQL) datastores or just efficiently packed files on disk that need to be paged into/over in RAM. http://ml.dask.org/examples/scale-scikit-learn.html Sklearn.pipeline.Pipeline API: {fit(), transform(), predict(), score(),} https://scikit-learn.org/stable/modules/generated/sklearn.pi... https://docs.featuretools.com can also minimize ad-hoc boilerplate ETL / feature engineering : > Featuretools is a framework to perform automated feature engineering. It excels at transforming temporal a
Read the rest
nd relational datasets into feature matrices for machine learning. The PLoS 10 Simple Rules papers distill a number of best practices: "Ten Simple Rules for Reproducible Computational Research" http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fj... “Ten Simple Rules for Creating a Good Data Management Plan” http://journals.plos.org/ploscompbiol/article?id=10.1371/jou... In terms of the scientific method, a null hypothesis like "there is no significant relation between the [independent and dependent] variables" may be dangerously unprofessional p-hacking and data dredging; and may result in an overfit model that seems to predict or classify the training and test data (when split with e.g. sklearn.model_selection.train_test_split and a given random seed). One of these days (in the happy new year!) I'll get around to updating these notes with the aforementioned tools and docs: https://wrdrd.github.io/docs/consulting/data-science#scienti... IDK what https://kaggle.com/learn has specifically in terms of analysis workflow? Their docker containers have very many tools configured in a reproducible way: https://github.com/Kaggle/docker-python/blob/master/Dockerfi...
In Ask HN: Data analysis workflow?

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