ickle
RasgoQL
ickle | RasgoQL | |
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1 | 11 | |
13 | 267 | |
- | 0.4% | |
6.3 | 0.0 | |
3 months ago | almost 2 years ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU Affero General Public License v3.0 |
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ickle
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My experience with PyPi and NPM publishing process
I recently published Ickle (Data Analysis Library) on PyPi.
RasgoQL
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Dbt Vs python scripts
I built an open source package to bridge the gap between python and dbt, would love your feedback if you have a chance to check it out: https://github.com/rasgointelligence/RasgoQL
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How to balance multiple time series data?
I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.
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RasgoQL - Open source data transformations in Python, without having to write SQL.
I created RasgoQL to give anyone a pandas-like syntax that you can use to quickly generate hundreds of lines of SQL that will run directly in your Snowflake or BigQuery data warehouse (with more data warehouse support coming soon). The best part? In one line of code, you can export this SQL to your dbt project so that it can run in production alongside other data pipelines.
- RasgoQL - Transform tables directly with Python, without writing SQL
- RasgoQL - Open data transformations in Python, no SQL required
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[P] Open data transformations in Python, no SQL required
You can check it out here: https://github.com/rasgointelligence/RasgoQL
- [Project] Open data transformations in Python, no SQL required
- Open data transformations in Python, no SQL required
What are some alternatives?
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bootcampalura_datascienceaplicada - Desafio do módulo 1 do bootcamp de Data Science aplicada da Alura.
Data-Science-For-Beginners - 10 Weeks, 20 Lessons, Data Science for All!
code - Compilation of R and Python programming codes on the Data Professor YouTube channel.
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warehouse - The Python Package Index
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100-pandas-puzzles - 100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️