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Based on our record, Seaborn should be more popular than ggplot2. It has been mentiond 37 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
For random, quick and dirty, ad-hoc plotting tasks my default is GNUPlot[1]. Otherwise I tend to use either Python with matplotlib, or R with ggplot2. I keep saying I'm going to invest the time to properly learn D3[4] or something similar for doing web-based plotting, but somehow never quite seem to find time to do it. sigh [1]: http://www.gnuplot.info/ [2]: https://matplotlib.org/ [3]:... - Source: Hacker News / almost 2 years ago
I got the list of five-letter words from the words package in R, created the QWERTY keyboard grid with base R and tibble, and visualized the data with geom_tile in the ggplot2 package. Source: almost 2 years ago
Thanks, it's an interesting idea! I definitely could implement this with scale_fill_gradientn) in ggplot2. Source: almost 2 years ago
I used the ggplot2 package in R to create these figures. Source: almost 2 years ago
This might not be at the top of your list, but science fiction often presents advanced data analysis and visualization technologies. Open source data analysis tools such as Python's Pandas and R's ggplot2 have revolutionized the field, making complex data manipulation and visualization accessible to all. In the science fiction novel The Martian, astronaut Mark Watney uses a variety of data analysis and... - Source: dev.to / almost 2 years ago
Below are the key insights. If you want to see the Python code I used to do this analysis and generate the charts using Seaborn, you can find my full analysis Jupyter notebook on my Github repo here: Tip Analysis.ipynb. - Source: dev.to / 26 days ago
Additionally, Seaborn (https://seaborn.pydata.org/) is a great mention for people that want to use Matplotlib with better default aesthetics, amongst other conveniences: "Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.". - Source: Hacker News / 8 months ago
Seaborn: built on top of matplotlib, adds a number of functions to make common statistical visualizations easier to generate. - Source: dev.to / 8 months ago
Pandas - The standard data analysis and manipulation tool Numpy - scientific computing library Seaborn - statistical data visualization Sklearn - basic machine learning and predictive analysis CausalML - a suite of uplift modeling and causal inference methods PyTorch - professional deep learning framework PivotTablejs - Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook LazyPredict - build... - Source: dev.to / 9 months ago
How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / 11 months ago
Matplotlib - matplotlib is a python 2D plotting library which produces publication quality figures in a variety...
Plotly - Low-Code Data Apps
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
GnuPlot - Gnuplot is a portable command-line driven interactive data and function plotting utility.
Quantopian - Your algorithmic investing platform
Bokeh - Bokeh visualization library, documentation site.