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Ploomber: Maintainable and Collaborative Pipelines in Jupyter

Oct 27, 2021

ICYMI: We published a guest post on the Jupyter blog! 🤩

blog.jupyter.org

Ploomber is an open-source framework that allows teams to develop maintainable pipelines in Jupyter.

Evaluation reports are an important skill for data scientists. With #sklearn_evaluation you'll be able to create them in a snap! 🤌 Learn how to create your own reports: 👉 buff.ly/3jdRpfH #DataScience #OpenSource #Ploomber

We work on awesome tools for awesome #DataScientists. Be part of the future: 👉 buff.ly/3wwygcc

Feb 3

A ROC curve is used to evaluate and compare the performance of a binary classifier system. #sklearn_evaluation contains tools to generate a ROC curve easily! Generate tour ROC curves and evaluate your models easily: 👉 buff.ly/3wx9vwo #DataScience #ML #Ploomber

Export your #SQL data into a #df! 🐼 Being able to export your SQL data into a #Pandas DataFrame is an essential skill for data scientists. Learn how to do it using #JupySQL in just a few steps! 👉 buff.ly/3JgIXqT #DataScience #OpenSource #Ploomber

Become a pro with #SQL. Use #JupySQL: 👉 buff.ly/3DcWjAs

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Feb 1

Try out ploomber-engine today! 🎩 As a #DataScientist, reproducibility is an important part of your work. With #ploomber_engine, you can re-run your notebooks and ensure that their outputs still match. Get started with this guide: 👉 buff.ly/3DfE9hs #OpenSource

Your #notebooks with interactive widgets! 📊 #Voilà turns #Jupyter notebooks into standalone web apps, which opens the door for using any notebook as an interactive web-based platform. Try it now: 👉 buff.ly/3R5FpJV #DataScience #OpenSource

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Jan 30

Rank the #features in your #models! 📊 Feature ranking allows to evaluate single features or pairs of features using a variety of metrics that score the features on the scale [-1, 1] or [0, 1] allowing them to be ranked. Learn to rank your features: 👉 buff.ly/3WH6gwO

If you're experiencing issues with new Ploomber installations, it's most likely due to a breaking change in jupyter_client; you can fix it by downgrading the package: pip install 'jupyter_client<8'

Jan 27

Don't miss our collaboration: #JupySQL x

@hydradatabase

! 🐘 Hydra is an open-source data warehouse built on Postgres. Hydra leverages columnar storage, vectorized execution, and query parallelization. Read the guide we prepared for you: 👉 buff.ly/3WC9tOr

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Jan 26

Welcome to part II of #CausalInference and experimentation with #RDDs in #Python! 🐍 In this post, we illustrate how to build #Bayesian regression discontinuity designs (RDDs) with PyMC. Get ready for this awesome content! 👉 buff.ly/3j7aWi0 #DataScience #Ploomber

Jan 26

Have you tried #ploomber_engine's CLI? ⚙️ With ploomber-engine you can profile #Jupyter notebook's memory usage. This is one of our newest features: try our CLI for running notebooks and profiling them! ⚡ Get started: buff.ly/3R9tt9L #DataScience #Ploomber

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Jan 26

I have a #Jupyter notebook joke, but I forgot what the order is.

Jan 25

Once you start using #JupySQL, you can't stop. Try it by yourself: buff.ly/3WyszoE

Jan 25

What tool do you use to run SQL on Jupyter? If you use another tool, please comment!

%%sql magic
50%
pandas.read_sql
50%

8 votesFinal results

Jan 25

Don't miss our collaboration: #JupySQL x #MindsDB! 🐻

@MindsDB

enables you to use your data and make forecasts. It speeds up the ML development process by bringing #MachineLearning into the database. Read the guide we prepared for you: 👉 buff.ly/3XVkX0p #DataScience

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Jan 24

Meet the tool of the week: #ipydatagrid, an intuitive and highly performant datagrid widget for #Jupyter Notebook and #JupyterLab. 💠 The widget supports all common grid interactions, such as sorting and filtering of tabular data. Try it out! 👉 buff.ly/3R2RZcI

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Jan 24

Profile your notebooks with #ploomber_engine! 🔍 We developed a tool that allows you to find memory leaks and runtime bottlenecks so you can optimize your code. Read our guide on how to profile the memory usage across cell runs in #Jupyter: 👉 buff.ly/3H1xCbf

Do you know what #CumulativeGains and #LiftCharts are used for? Both consist of a curve and a baseline for measuring #model performance. The greater the area between the curve and the baseline, the better the model. Learn about them in this post: 👉 buff.ly/3XOC0kS

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Jan 23

We bring a modern #SQL experience to #Jupyter! 🎩 Run SQL in Jupyter via magic commands. With #JupySQL, you can run query compositions and plot large-scale datasets in a SIMPLE way. Check out this #guide with code samples that we built just for you! 👉 buff.ly/3XBfD2v

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#sklearn_evaluation: The easiest way to evaluate your #ML models. Don't believe us? Try it yourself. 👉 buff.ly/3XnLLqo

Learn about #CausalInference! 🧠 Regression Discontinuity Designs (#RDDs) are a special natural experiment framework for analyzing causal relationships. In this post, we will discuss causal inference in #DataScience via RDDS. Check it out: 👉 buff.ly/3WoQ1og

Software Engineer at Ploomber

To our friends from Google and Microsoft. Our sympathy is with you! We have a few spots for Software Engineers and Data Scientists. Submit an application here: buff.ly/3kqjZuB Feel free to retweet for visibility! #microsoft #google #datascientists #layoffs

workatastartup.com

## About us Ploomber (YC W22) is the Heroku for Data Science. We help data scientists build faster and deploy anywhere with our cloud platform. Want to build new products and improve data scientists'...

All about classification! 👽🖖 We prepared a quickstart guide on #classification using #sklearn_evaluation, which includes: data loading, data cleaning, model fitting, features evaluation, and model comparison. It includes code samples! Check it out! 👉 buff.ly/3XAD8IG

An essential skill every #DataScientist should master is how to EFFECTIVELY evaluate #ML models. One way is by using plots, which allow you to better understand your models. 🔝 Learn 5 evaluation plots to take your model performance to the next level: buff.ly/3Wm5xBd

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Jan 19

Check out this quick tip to plot the K-S statistic the EASY way! ⚡️ The Kolmogorov–Smirnov statistic quantifies the distance between the empirical distribution functions of two samples. Learn to plot it easily with #sklearn_evaluation: 👉 buff.ly/3klfEZz #DataScience

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Jan 18

#JupySQL is a powerful tool to work with #SQL in #Jupyter, allowing you to run queries and plot large datasets EASILY. 🦾 Get started: buff.ly/3iK1dOv #DataScience #Ploomber #OpenSource

Jan 18

Follow this guide to use JupySQL in our notebooks! ⚡️ #DataScience involves running #SQL queries and accessing connections to data from different sources. With #JupySQL you can write your own queries in #Jupyter over existing #dataframes! 👉 buff.ly/3IWGiSQ

As a #DataScientist, correct documentation in notebooks and files is important! 📝 The #spellchecker is a #JupyterLab extension highlighting misspelled words in markdown cells within notebooks and in the text files. Check it out: 👉 buff.ly/3IVQlrp #OpenSource

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Jan 17

Good #DataScientists know how to measure classifiers! A lift curve determines how effective a predictive #model is. The greater the area between the lift curve and the baseline, the better the model. Plot a lift curve easily with #sklearn_evaluation: 👉 buff.ly/3XAeTKx

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Jan 16

Can I trust my model’s probabilities? 🤔 A #calibration_curve is a graphical representation of a model’s calibration. It allows us to benchmark our model against a target: a perfectly calibrated model. Evaluate your calibration with #sklearn_evaluation: buff.ly/3kjciGz

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Jan 16

We've prepared a #quickstart guide just for you! #JupySQL allows you to run #SQL and plot large datasets in Jupyter via a %sql, %%sql, and %sqlplot magics. Check out this video with

@rodo_ferro

and learn to use JupySQL! 👉 buff.ly/3QPLf1W #DataScience #OpenSource

CPU and memory go brr buff.ly/3Zsikoh

Replying to

Jedis use PyPRQL and

... (which uses JupySQL under the hood 🙏) pypi.org/project/pyprql/

Jan 13

Cools kids use JupySQL. buff.ly/3Zqqhuk

Jan 13

Plot large datasets with #JupySQL! 📊 The plotting module in JupySQL runs computations in the #SQL engine, delegating memory management to ensure that intermediate computations do eat up the memory, allowing you to efficiently plot MASSIVE datasets. 👉 buff.ly/3QBY1Rt

With #ploomber_engine you can profile #Jupyter notebook’s memory usage. Unlike papermill, which isn’t capable of doing it. 🔥 We're glad to present one of the newest features: The #CLI for running notebooks, profiling them, and more! Check it out: buff.ly/3X4gzMw

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