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seaborn | learnxinyminutes-docs | |
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76 | 226 | |
11,836 | 11,103 | |
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8.5 | 9.1 | |
10 days ago | 4 days ago | |
Python | JavaScript | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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seaborn
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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).
The seaborn bug linked in the paper: “Treat binwidth as approximate to avoid dropping outermost datapoints” (https://github.com/mwaskom/seaborn/pull/3489)
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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.
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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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Introducing seaborn-polars, a package allowing to use Polars DataFrames and LazyFrames with Seaborn
I'm sure that your package is great, but seaborn will soon support the interchange protocol and will work relatively seamlessly with polars. https://github.com/mwaskom/seaborn/pull/3340
learnxinyminutes-docs
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Scripts should be written using the project main language
> Learning a new language shouldn't be difficult. Programmers are expected to familiarize themselves with new tech.
I wish any large company agreed with this. I've worked for a company that on boarded every single new engineer to a very niche language (F#) in a few days. Also, everybody I worked with there was amazing. Probably because of that kind of mindset.
Meanwhile google tiptoes around teams adopting kotlin because "oh no, what if other teams touching the code might not be able to read it". Google is supposed to be hiring the brightest but internally is worried the brightest can't review slightly-different-java.
It's shocking how everybody acts like senior engineers might need months to learn a new language. Sure, maybe for some esoteric edge cases, but 5 mins on https://learnxinyminutes.com/ should get you 80% of the way there, and an afternoon looking at big projects or guidelines/examples should you another 18% of the way.
> Sure, maybe for some esoteric edge cases, but 5 mins on https://learnxinyminutes.com/ should get you 80% of the way there, and an afternoon looking at big projects or guidelines/examples should you another 18% of the way.
Not for C++, and even for other languages, it's not the language that's hard, it's the idioms.
Python written by experts can be well-nigh incomprehensible (you can save typing out exactly one line if you use list-comprehensions everywhere!).
Someone who knows Javascript well still needs to know all the nooks and crannies of the popular frameworks.
Java with the most popular frameworks (Spring/Boot/etc) can be impossible for a non-Java programmer to reason about (where's all this fucking magic coming from? Where is it documented? What are the other magic words I can put into comments?)
C# is turning into a C++ wannabe as far as comprehension complexity goes.
Right now, the quickest onboarding I've seen by far are Go codebases.
The knowledge tree required to contribute to a codebase can exists on a Deep axis and a Wide axis. C++ goes Deep and Wide. Go and C are the only projects I've seen that goes neither deep nor wide.
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100+ FREE Resources Every Web Developer Must Try
Learn x in y minutes: Concise tutorials to learn various programming languages and tools quickly.
- SQL for Data Scientists in 100 Queries
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New GitHub Copilot Research Finds 'Downward Pressure on Code Quality'
StackOverflow's making their own competing LLM for all this stuff.
IMO, one of the biggest problems with the way people use LLMs right now, is that they're being treated as a single oracle: to know Java, it must be trained on examples of Java.
It would be much better if their language comprehension abilities were kept separated from their knowledge (and there are development efforts in this direction), so in this example it would be trained to be able to be able to read a Java tutorial rather than by actually reading a Java tutorial, so when the overall system is asked to write something in Java, the language model within the system decides to do this by opening https://learnxinyminutes.com and combining the user query with the webpage.
I think this will help make the models more compact, which is a benefit all by itself, but it would also mean that knowledge can be updated much more easily.
Someone would have to actually do this in order to see if those benefits are worth the extra cost of having to load a potentially huge a tutorial into the context window, and likewise the extent to which a more compact training set makes the language comprehension worse.
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Ask HN: Programming Courses for Experienced Coders?
I'm still partial to LearnXinYMinutes[0]. It's how I learned enough MatLab/Octave in a couple hours to test out of an intro CS course.
Here's their article on Elixir[1]
The project was created and is maintained by Adam Bard, but is open sourced with over 1.7k contributors since 2013
- Lean 4.0.0, first official lean4 release
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Anyone got good resources for experienced devs that don't know front end?
Very light compared to the other resources people have linked for you, but I love https://learnxinyminutes.com/
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Any advice on how to learn from programming tutorials, or are there any better ways to learn a new language?
https://learnxinyminutes.com is good when you know how to program but just need a quick look at the syntax and idioms of a new language.
What are some alternatives?
bokeh - Interactive Data Visualization in the browser, from Python
Altair - Declarative statistical visualization library for Python
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
ggplot - ggplot port for python
plotnine - A Grammar of Graphics for Python
matplotlib - matplotlib: plotting with Python
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
folium - Python Data. Leaflet.js Maps.
pygal - PYthon svg GrAph plotting Library
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
learn-x-by-doing-y - 🛠️ Learn a technology X by doing a project - Search engine of project-based learning
bqplot - Plotting library for IPython/Jupyter notebooks