Stats

Basic seaborn repo stats
5
8,358
8.3
11 days ago

mwaskom/seaborn is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.

Seaborn Alternatives

Similar projects and alternatives to seaborn

  • GitHub repo interesting-reads

    This repo contains worthhwile essays, articles and blogposts

  • GitHub repo plotnine

    A grammar of graphics for Python

  • GitHub repo Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

  • GitHub repo Altair

    Declarative statistical visualization library for Python

  • GitHub repo cheatsheets

    Official Matplotlib cheat sheets (by matplotlib)

  • GitHub repo ggplot

    ggplot port for python

  • GitHub repo PandasGUI

    A GUI for Pandas DataFrames

  • GitHub repo JimmyChill

    Analysis of Jim Cramer's stock buy recommendations from "Mad Money"

NOTE: The number of mentions on this list indicates mentions on common posts. Hence, a higher number means a better seaborn alternative or higher similarity.

Posts

Posts where seaborn has been mentioned. We have used some of these posts to build our list of alternatives and similar projects - the last one was on 2021-04-14.
  • Looking for an online site that generates code for LaTeX graphs up to the complexity of pareto chart
    reddit.com/r/LaTeX | 2021-04-14
    You can make pretty much any plot you want in Python using the matplotlib and seaborn packages (for color and styling) with a much higher-degree of control than you'll get in either LaTeX or Excel.
  • US presidents according to the word count of their wikipedia page [OC]
    Data from wikipedia scrapping, (date 2021-04-08) Make with python 3.6, with wikipedia, pandas and seaborn libraries
  • Plotting in R's ggplot2 vs Python's Matplotlib: Is it just me or is ggplot2 WAY smoother of an experience than Matplotlib?
    I'd agree in that it's a well-specified language for defining graphics; it's not very good with rendering performance. There are packages which try to achieve similar goals in Python as well (ggplot / ggpy) and packages like Seaborn. Though, like you, I use R for lots of EDA. Hard to beat data.table and R graphics for speed and expressiveness. I prefer base graphics though; ggplot2 tends to render too slowly for any data sets I work with.
  • Jim Cramer: Professional Stock Picker or Professional Hack? A Statistical Analysis:
  • Which color scale to use when visualizing data
    news.ycombinator.com | 2021-03-17