Result for 10A36411F9DD721A6B2001D4D562A218020A45E4

Query result

Key Value
FileName./usr/lib/python3.10/site-packages/seaborn/__pycache__/_core.cpython-310.pyc
FileSize31250
MD5F7A1599DCCEDFAA41E7FE04579701FE5
SHA-110A36411F9DD721A6B2001D4D562A218020A45E4
SHA-256992FD638283461D86EB3D773E931857EA97753D97B9C6AD7957E1F68FC098FD2
SSDEEP768:sk6+Bb04go3sP1xhAr1B3diuaqqVL8ekvNXv:sGB32rha1BYuaqqVgekvNf
TLSHT1BBE21A86BD656F3BFED1F1BA809C1140D724A29B674C7182384D922E2F0691C7A7D3ED
hashlookup:parent-total3
hashlookup:trust65

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Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD58C0E9C24728389B0D0E7D97EFA8D32C6
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
PackageNamepython310-seaborn
PackageRelease33.1
PackageVersion0.11.2
SHA-15A25E1F9614CDD6489D8E355CFC535120BF173C4
SHA-2560CC2DA4A133F1FDF364ADAE04D702C04A141287FEDD94BE103C2EF5223646EDA
Key Value
MD56523EA65AA08E14082E6106DCDBD551C
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
PackageNamepython310-seaborn
PackageRelease33.1
PackageVersion0.11.2
SHA-16E3E23A673F255682DA6B6DCF518679E624DB829
SHA-2567E956DD6106C2AA43C9628E72FB69CCAB1FE4F69AC09E1BFE7E71840C2A29D40
Key Value
MD5A0B9D0D4E3E26497C60372D820528C7A
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
PackageNamepython310-seaborn
PackageRelease33.1
PackageVersion0.11.2
SHA-131A1CC3990A233566AC968A9672951243BF7C248
SHA-256F978A313AA70BF578EF7303541CFE4D9FC1F7A8E7F203F4FEBD1E39417E9A3CB