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Top 23 Jupyter Notebook Panda Projects
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CodeRabbit
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machine_learning_complete
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
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SaaSHub
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100-pandas-puzzles
100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)
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mito
Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet
3. Tables that translate as Pandas dataframes. We support at most one table per sheet, at the tables must be contigious. If the formulas in a column are consistent, then we will try and translate this as a single pandas statement.
We do not support: pivot tables or complex formulas. When we fail to translate these, we generate TODO statements. We also don’t support graphs or macros - and you won’t see these reflected in the output at all currently.
*Why we built this:*
We did YCS20 and built an open source tool called [Mito](https://trymito.io). It’s been a good journey since then - we’ve scaled revenue and to over [2k Github stars](https://github.com/mito-ds/mito). But fundamentally, Mito is a tool that’s useful for Excel users who wanted to start writing Python code more effectively.
We wanted to take another stab at the Excel -> Python pain point that was more developer focused - that helped developers that have to translate Excel files into Python do this much more quickly. Hence, Pyoneer!
I’ll be in the comments today if you’ve got feedback, criticism, questions, or comments.
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hamilton
Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
Project mention: Show HN: I built an open-source data pipeline tool in Go | news.ycombinator.com | 2024-12-17I always thought Hamilton [1] does a good job of giving enough visual hooks that draw you in.
I also noticed this pattern where library authors sometimes do a bit extra in terms of discussing and even promoting their competitors, and it makes me trust them more. A “heres why ours is better and everyone else sucks …” section always comes across as the infomercial character who is having quite a hard time peeling an apple to the point you wonder if this the first time they’ve used hands.
One thing wish for is a tool that’s essentially just Celery that doesn’t require a message broker (and can just use a database), and which is supported on Windows. There’s always a handful of edge cases where we’re pulling data from an old 32-bit system on Windows. And basically every system has some not-quite-ergonomic workaround that’s as much work as if you’d just built it yourself.
It seems like it’s just sending a JSON message over a queue or HTTP API and the worker receives it and runs the task. Maybe it’s way harder than I’m envisioning (but I don’t think so because I’ve already written most of it).
I guess that’s one thing I’m not clear on with Bruin, can I run workers if different physical locations and have them carry out the tasks in the right order? Or is this more of a centralized thing (meaning even if its K8s or Dask or Ray, those are all run in a cluster which happens to be distributed, but they’re all machines sitting in the same subnet, which isn’t the definition of a “distributed task” I’m going for.
[1] https://github.com/DAGWorks-Inc/hamilton
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fecon235
Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics
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project-walkthroughs
Data science, machine learning, and web development project code for https://www.youtube.com/c/Dataquestio .
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code
Compilation of R and Python programming codes on the Data Professor YouTube channel. (by dataprofessor)
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kglab
Graph Data Science: an abstraction layer in Python for building knowledge graphs, integrated with popular graph libraries – atop Pandas, NetworkX, RAPIDS, RDFlib, pySHACL, PyVis, morph-kgc, pslpython, pyarrow, etc.
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Python-Roadmap
Python Roadmap. Learn Python programming as your first programming language. Python for Absolute Beginners, Non-Tech Professionals, 15+ Projects, 30 Topics, 500+ Practice Questions, with Data Structures & Algorithms
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tempo
API for manipulating time series on top of Apache Spark: lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, downsampling, and interpolation (by databrickslabs)
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SaaSHub
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Jupyter Notebook Pandas discussion
Jupyter Notebook Pandas related posts
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Show HN: Create Data Visualization with Data Formulator from Microsoft Research
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Welcome to 14 days of Data Science!
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Financial Economics: Financial Economics Models. Extended Research - star count:1033.0
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Performance Analysis: Performance analysis of predictive (alpha) stock factors. Factor and Risk Analysis - star count:2892.0
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Performance Analysis: Performance analysis of predictive (alpha) stock factors. Factor and Risk Analysis - star count:2892.0
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Performance Analysis: Performance analysis of predictive (alpha) stock factors. Factor and Risk Analysis - star count:2892.0
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Performance Analysis: Performance analysis of predictive (alpha) stock factors. Factor and Risk Analysis - star count:2892.0
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Index
What are some of the best open-source Panda projects in Jupyter Notebook? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | PythonDataScienceHandbook | 44,078 |
| 2 | Data-Science-For-Beginners | 29,021 |
| 3 | pandas_exercises | 11,142 |
| 4 | py | 6,988 |
| 5 | machine_learning_complete | 4,671 |
| 6 | ta | 4,541 |
| 7 | alphalens | 3,518 |
| 8 | Andrew-NG-Notes | 2,851 |
| 9 | jetson-containers | 2,798 |
| 10 | 100-pandas-puzzles | 2,639 |
| 11 | mito | 2,356 |
| 12 | hamilton | 2,053 |
| 13 | fecon235 | 1,148 |
| 14 | project-walkthroughs | 1,001 |
| 15 | code | 959 |
| 16 | pdpipe | 717 |
| 17 | 100-days-of-code-python | 773 |
| 18 | kglab | 614 |
| 19 | ydata-quality | 434 |
| 20 | Python-Roadmap | 383 |
| 21 | awesome-data-centric-ai | 331 |
| 22 | tempo | 320 |
| 23 | feature-engineering-tutorials | 283 |
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