How to Install and Uninstall python2-seaborn Package on openSUSE Leap

Last updated: July 05,2024

Deprecated! Installation of this package may no longer be supported.

1. Install "python2-seaborn" package

In this section, we are going to explain the necessary steps to install python2-seaborn on openSUSE Leap


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                python2-seaborn
            
                    
    
    Copied

2. Uninstall "python2-seaborn" package

This is a short guide on how to uninstall python2-seaborn on openSUSE Leap:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                python2-seaborn
            
                    
    
    Copied

3. Information about the python2-seaborn package on openSUSE Leap

Information for package python2-seaborn:
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Repository : Main Repository
Name : python2-seaborn
Version : 0.8.1-bp153.1.22
Arch : noarch
Vendor : openSUSE
Installed Size : 1,6 MiB
Installed : No
Status : not installed
Source package : python-seaborn-0.8.1-bp153.1.22.src
Summary : Statistical data visualization for python
Description :
Seaborn is a library for making attractive and informative
statistical graphics in Python. It is built on top of
matplotlib and tightly integrated with the PyData stack,
including support for numpy and pandas data structures and
statistical routines from scipy and statsmodels.
Some of the features that seaborn offers are:
- Several built-in themes that improve on the default matplotlib
aesthetics
- Tools for choosing color palettes to make beautiful plots that
reveal patterns in your data
- Functions for visualizing univariate and bivariate distributions
or for comparing them between subsets of data
- Tools that fit and visualize linear regression models for different
kinds of independent and dependent variables
- Functions that visualize matrices of data and use clustering
algorithms to discover structure in those matrices
- A function to plot statistical timeseries data with flexible
estimation and representation of uncertainty around the estimate
- High-level abstractions for structuring grids of plots that let you
easily build complex visualizations