Result for 28B82F8B894E4BE8FF0B175496C6557DE76CE7B4

Query result

Key Value
FileName./usr/lib/python2.7/site-packages/seaborn/tests/test_utils.pyc
FileSize14473
MD5571DA94B9D9BB3B15E7C9896AFFE6461
SHA-128B82F8B894E4BE8FF0B175496C6557DE76CE7B4
SHA-2566BAB4D66CD3FFF276F6ED9E08E7E9A86F286B49FC3134D37E53BEDF3494DB518
SSDEEP384:Z4xh5jVKIHfNg41nJXldIr4XIhRICSIWTIHIVUQAtxkbstxp+0ATW/AMn+:Sxh5jVK6fNg4dJXldIr4XIhRICSI4IHO
TLSHT15D521F81E3F6498FC6B06574E2F00217DDA9F1B39A01A75526BCE4393AD8799C42F3C9
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
MD51F879E345A7410FFEBC8E98D9F5A20AB
PackageArchnoarch
PackageDescriptionSeaborn 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
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython2-seaborn
PackageReleaselp151.2.3
PackageVersion0.8.1
SHA-162B02A4A78326A1BC7F6B04B26258E586FA56DE9
SHA-256A4C7FCCAB05806D05115F046D73A3EB706A25801E8B62746DA8A4AADC61E5187
Key Value
MD5BCCA4174061CA0070703BF60F0CD058C
PackageArchnoarch
PackageDescriptionSeaborn 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
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython2-seaborn
PackageReleaselp150.1.4
PackageVersion0.8.1
SHA-1F8E341900ABF873C5D42A7145A89167D9849880B
SHA-25680A7CCCD656D74CA4A2F041E6EDFC513F9BBC8F4F76C1106EB90B78384017534
Key Value
MD5891CE12DB4796650C5D3112A988AC580
PackageArchnoarch
PackageDescriptionSeaborn 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
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython2-seaborn
PackageReleasebp153.1.22
PackageVersion0.8.1
SHA-1C42DE8D34B089AF541967F1F4604E648506230FB
SHA-256D56324C8C42143BA3FF6E8CC836589551F6371FF643293F256D2946C6C1036AC
Key Value
MD5F54E328CF5CBDA244A2BAD4FD4D2EA46
PackageArchnoarch
PackageDescriptionSeaborn 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
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython2-seaborn
PackageReleaselp152.3.5
PackageVersion0.8.1
SHA-1EAB5C23FCE3C6985F9937D5966199C7A2CB00BBE
SHA-2567C0FFA5F3749848A1B825E9BD52D0054C108D7A541528FD79F2CB730C9709FCC