jupyter
seaborn
jupyter | seaborn | |
---|---|---|
13 | 77 | |
14,735 | 11,969 | |
0.2% | - | |
7.2 | 8.4 | |
7 days ago | 12 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
jupyter
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Mastering Data Science: Top 10 GitHub Repos You Need to Know
6. Jupyter Jupyter is a collection of tools and applications designed for interactive computing and data visualization. At the heart of the Jupyter ecosystem is the Jupyter Notebook, an interactive web-based platform that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It’s an excellent tool for exploratory data analysis, model prototyping, and creating reproducible data science workflows.
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You can run Rust code in a Jupyter notebook
How cool. This motivated a quick search - this could be fun:
How to write your own kernel
https://jupyter-client.readthedocs.io/en/stable/kernels.html
All the language kernels (a lot of abandoned ones - the mariaDB one ('binder') will take a while to load but SQL in Jupyter!)
https://github.com/jupyter/jupyter/wiki/Jupyter-kernels
- Resource for interesting data science project notebooks
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Mathics: A free, open-source alternative to Mathematica
There are Jupyter kernels for Python, Mathics, Wolfram, R, Octave, Matlab, xeus-cling, allthekernels (the polyglot kernel). https://github.com/jupyter/jupyter/wiki/Jupyter-kernels
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How does 3[a] gives the element at index 3 in an array?
Not only there is. But it is only a simple Google search away... But to make it simpler... There are 3 😁 https://github.com/jupyter/jupyter/wiki/Jupyter-kernels
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How to use Jupyter notebooks in a conda environment?
As it seems, this is not quite straight forward and manyusers have similar troubles.
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Semi-Weekly Discussion Thread - February 21, 2022
Community maintained kernels : https://github.com/jupyter/jupyter/wiki/Jupyter-kernels
- Node.js Notebooks
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Python Tutorials using Jupyter Notebook
Derek Banas on YouTube is doing a "Python for Finance" course at ghe moment using Jupyter, and is making the files available. I believe he's done others too.Failing that, there's this Git repo: A gallery of interesting jupyter notebooks
- Github Discussion: What is your favorite Data Science Repo?
seaborn
- "No" is not an actionable error message
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Apache Superset
If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.
I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.
I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.
For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.
Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).
The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.
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Seaborn bug responsible for finding of declining disruptiveness in science
It's referring to the seaborn library (https://seaborn.pydata.org/), a Python library for data visualization (built on top of matplotlib).
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Why Pandas feels clunky when coming from R
While it’s not perfect and it’s not ggplot2, Seaborn is definitely a big improvement over bare matplotlib. You can still use matplotlib to modify the plots it spits out if you want to but the defaults are pretty good most of the time.
https://seaborn.pydata.org/
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
Seaborn: A statistical data visualization library based on Matplotlib, enhancing the aesthetics and visual appeal of statistical graphics.
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Seven Python Projects to Elevate Your Coding Skills
Matplotlib Seaborn Example data sets
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
Seaborn - Statistical data visualization using Matplotlib.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/mwaskom/seaborn
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Best Portfolio Projects for Data Science
Seaborn Documentation
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[OC] Nationwide Public Transit Ridership is down 30% from pre-lockdown levels; San Francisco's BART ridership is down almost 70%
You've done a great job presenting this. Maybe you already know, but seaborne is an extension of matplotlib that makes it pretty easy to "beautify" matplotlib charts
What are some alternatives?
nteract - 📘 The interactive computing suite for you! ✨
bokeh - Interactive Data Visualization in the browser, from Python
cookiecutter-data-science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Altair - Declarative statistical visualization library for Python
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
vscode-python - Python extension for Visual Studio Code
ggplot - ggplot port for python
quokka - Repository for Quokka.js questions and issues
plotnine - A Grammar of Graphics for Python
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
matplotlib - matplotlib: plotting with Python