Result for 473ADEC6D78D191B1206ABECC63B829F845D5CB8

Query result

Key Value
FileName./usr/lib/python3/dist-packages/seaborn/miscplot.py
FileSize1498
MD5C3C51E91C4A17759FE1C4FE3C7C2C549
SHA-1473ADEC6D78D191B1206ABECC63B829F845D5CB8
SHA-256EDBC2502F0F35CC130E0D2BD5381BF0B4518EFFD1F203CC37CF0BF89071DEB4D
SSDEEP24:1Rige6l7cm4pyxqw7vZ0shyI28KFZay8C7LXzuRD0UQCvdv5wucv0:Pz7h4wxq0mshkngxQCvNa3v0
TLSHT19F31568FCB4353639753C96E19A240269332386729571DEC79BC23A01FDB53989B4B3C
hashlookup:parent-total4
hashlookup:trust70

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Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize117996
MD5B27A9F3A4B372E7A7F4E14AB14670DA9
PackageDescriptionstatistical visualization library 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 - 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 . This is the Python 2 version of the package.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-seaborn
PackageSectionpython
PackageVersion0.6.0-1
SHA-1300146FFFC2042FA457997A8BDED85FD9CD1FB30
SHA-256890B5D26F612A61976834963AF309E3FC9672D3A0858A2A27B0C4B4FF797DB5A
Key Value
FileSize75186
MD57B6830A3D1439FDD611EDB6ED6EDC14C
PackageDescriptionstatistical visualization library 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 - 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 . This is the Python 2 version of the package.
PackageMaintainerNeuroDebian Team <team@neuro.debian.net>
PackageNamepython-seaborn
PackageSectionpython
PackageVersion0.4.0-3
SHA-11A57AF2AF6075B1F949D1DE5B558801B2CA9E365
SHA-2561313126D78A29E06B2DC1B48C275CA25344D76EEDCC0131137D6F4A2667C4BF8
Key Value
FileSize118066
MD5D0E96D1EA661B234F74F3E859692D9E1
PackageDescriptionstatistical visualization library 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 - 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 . This is the Python 3 version of the package.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython3-seaborn
PackageSectionpython
PackageVersion0.6.0-1
SHA-1A05FE5285E571A33079AD03D595890CB3BF7B5E0
SHA-256BA9F7E24E4C3228AA96BF0201F03BBF16911E79A7149E31CB561ABF424680856
Key Value
FileSize75258
MD557B995B560724CD100104BC0CD78B3C1
PackageDescriptionstatistical visualization library 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 - 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 . This is the Python 3 version of the package.
PackageMaintainerNeuroDebian Team <team@neuro.debian.net>
PackageNamepython3-seaborn
PackageSectionpython
PackageVersion0.4.0-3
SHA-11976FCD03BB705F3A80A9CAB64CC86EB4A24BA82
SHA-256DDF0B82808EB93FC3FD99AABEE1EA809F544F949EE396FE151F73940C3DCEED4