Key | Value |
---|---|
FileName | ./usr/share/doc/python-pandas-doc/html/_images/reshaping_unstack.png |
FileSize | 53895 |
MD5 | 9381154FE2257867F80A6D399836FF7C |
SHA-1 | 003214770D338D5213CFCC77E1D4960125C7FF3D |
SHA-256 | 7E79E5A4752FA164F873556F38D77F948E0CAF9638EE9335996087B0333DC08D |
SSDEEP | 1536:LyQP7BNY5iRLrcBO/3sxGTeneALdVcllWemY0qZGlzU:LvP7B7RLrcKALTveEqizU |
TLSH | T1DD338C37CBD62EDB6A591626A78736E1C57B016B22B95E0F1F90E9704C9F094E3F0C60 |
hashlookup:parent-total | 5 |
hashlookup:trust | 75 |
The searched file hash is included in 5 parent files which include package known and seen by metalookup. A sample is included below:
Key | Value |
---|---|
FileSize | 7600084 |
MD5 | 4AD1E1372BD88BF470F7EE252277458C |
PackageDescription | documentation and examples for pandas This package contains documentation and example scripts for python-pandas. |
PackageMaintainer | Debian Science Team <debian-science-maintainers@lists.alioth.debian.org> |
PackageName | python-pandas-doc |
PackageSection | doc |
PackageVersion | 0.23.3+dfsg-3 |
SHA-1 | 26D541AE231CC177576B8702F8E2064AABA1A96E |
SHA-256 | E588ECEC41FA007FA76483D04DBF604F374DB68679877EB7DAAEDD54A275152A |
Key | Value |
---|---|
FileSize | 8196480 |
MD5 | 2F510352466D48A3D3714EF384A1840D |
PackageDescription | data structures for "relational" or "labeled" data - documentation 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 documentation. |
PackageMaintainer | Debian Science Team <debian-science-maintainers@lists.alioth.debian.org> |
PackageName | python-pandas-doc |
PackageSection | doc |
PackageVersion | 1.1.5+dfsg-2 |
SHA-1 | 7E258BB2E5AC3037B382983E1D08559847A8C2B0 |
SHA-256 | D5B984E6CE72C19E23B8703CC7ABAA231F33BDA0213023379DFE310C7F8241D7 |
Key | Value |
---|---|
FileSize | 8184904 |
MD5 | 75A46F7D98F9CB59B2DB3D54A74392F3 |
PackageDescription | data structures for "relational" or "labeled" data - documentation 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 documentation. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python-pandas-doc |
PackageSection | doc |
PackageVersion | 1.1.5+dfsg-2 |
SHA-1 | 05B41ABEF07C7FFB96EB3B1EAC95F1CBF6D2AABB |
SHA-256 | 64D781FB37323CD39C44100BFA0AB071606DFFFB76269F70C2141E3710362387 |
Key | Value |
---|---|
FileSize | 7655112 |
MD5 | 5CA98AF9A11039AB63B4646F6F4E9C85 |
PackageDescription | data structures for "relational" or "labeled" data - documentation 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 documentation. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python-pandas-doc |
PackageSection | doc |
PackageVersion | 1.0.5+dfsg-3 |
SHA-1 | 859E82C01366B6B895DA269F19E4440449F95E9F |
SHA-256 | 7D9973698376C6639C26EF9089283514F2D346AE70D8C0194BE20AA47B394D32 |
Key | Value |
---|---|
FileSize | 6939488 |
MD5 | E0650D66C28112477451D41E38787305 |
PackageDescription | data structures for "relational" or "labeled" data - documentation 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 documentation. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python-pandas-doc |
PackageSection | doc |
PackageVersion | 0.25.3+dfsg-7 |
SHA-1 | 4607CEFF250A7EC5819AD72549EB2EC7730C932B |
SHA-256 | 528FF7B697466AF9606E3BB4C1DF5033C8169E9CE8B6FB765D54A8C16B957318 |