Result for 0481A39F7DC4D9532219C7214A8F6188746D4320

Query result

Key Value
FileName./usr/lib/python3/dist-packages/pandas/tests/test_panel.py
FileSize93726
MD531AE6DF60A21469AEC3EC7B4D0B1BB56
SHA-10481A39F7DC4D9532219C7214A8F6188746D4320
SHA-25617E0679B02328483A970036FDEC6D44935C72458715F9DC1807F7404390DC7BC
SSDEEP1536:Soifj25ykXER3sC44pl+L9hwRSARS5ReR4RvZnEw+ClIf0ygi86kdbi:zB50R3sC44pl+L9hwRSARS5ReR4RRn5O
TLSHT19C93314A2D8A89999343E1B958EF9C0B4E0E6D57840E15A47CFC80041F8953FB5BDFF9
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize2615560
MD563C959B6861897EE7516902BFE1597BA
PackageDescriptiondata structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 2 version.
PackageMaintainerNeuroDebian Team <team@neuro.debian.net>
PackageNamepython-pandas
PackageSectionpython
PackageVersion0.19.2-5.1
SHA-1EC6A749B591597AE552564152BDAF96A2FB565A2
SHA-2560D52FEA01643C50C4A8B39B0D8BEE8F142AA8B07A56AE661EFF71522D5BCDCE9
Key Value
FileSize2614778
MD5494881CCCA39595324CEE650A37A493F
PackageDescriptiondata structures for "relational" or "labeled" data - Python 3 pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 3 version.
PackageMaintainerNeuroDebian Team <team@neuro.debian.net>
PackageNamepython3-pandas
PackageSectionpython
PackageVersion0.19.2-5.1
SHA-144EE888F5C3FDE9C4FF13096B0F0A5551F17A735
SHA-25644CCE0E3D7A3BCDE1E13E54F5C85AEF58D3658D5583E8441DEA85A7FD5A47267