Result for 06144118BD9968220C2C47C344E51413CD142660

Query result

Key Value
FileName./usr/share/doc/python-tables-doc/examples/Single_Table-vs-EArray_Table.ipynb
FileSize60850
MD54424674A0C3B62EB3E03A9D57E6448D4
SHA-106144118BD9968220C2C47C344E51413CD142660
SHA-256E6790FE9B0D6C0A8D6945F20EEC637500DC25236D939AC57D07145BFC8D5C02A
SSDEEP1536:GrKmC02QL1P502QL1BE02QL1ui02QL1d02QL1z02QL1ycJR2bHvthJacXREK0LIu:GK02QL1h02QL1y02QL1D02QL1d02QL1K
TLSHT149532921E4602C36418BF434519AAA86E232928B4E042D2F7B5DD69CDF9D71E43F1F9E
hashlookup:parent-total11
hashlookup:trust100

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Parents (Total: 11)

The searched file hash is included in 11 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize4394384
MD599C97B5DBFC207BB711A054C1B82FE46
PackageDescriptionhierarchical database for Python based on HDF5 - documentation PyTables is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data. . It is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code (generated using Cython), makes it a fast, yet extremely easy to use tool for interactively save and retrieve very large amounts of data. One important feature of PyTables is that it optimizes memory and disk resources so that they take much less space (between a factor 3 to 5, and more if the data is compressible) than other solutions, like for example, relational or object oriented databases. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy objects. . This package includes the manual in PDF and HTML formats.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-tables-doc
PackageSectiondoc
PackageVersion3.6.1-2build1
SHA-13574003E2ED1AD838773A1F903492143273C7168
SHA-256B2E4265583EB29FC16B66AA85132D306ADE4AB18C55D5CBD86B2894EC43E8F28
Key Value
MD5C97FBB903815312D5DAA6F97A161B7CA
PackageArchnoarch
PackageDescriptionThe python-tables-doc package contains the documentation for python-tables.
PackageMaintainerFedora Project
PackageNamepython-tables-doc
PackageRelease2.fc34
PackageVersion3.6.1
SHA-147AF363C4E0633369D528850AF7DF04C58DD860F
SHA-2567D2D9C43E0719E5B4CAB9B81DD0347E496B6FAC8C110E548095F957A9D75C2C1
Key Value
FileSize4405072
MD513A317B841FED3A50573608A112CEA6A
PackageDescriptionhierarchical database for Python based on HDF5 - documentation PyTables is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data. . It is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code (generated using Cython), makes it a fast, yet extremely easy to use tool for interactively save and retrieve very large amounts of data. One important feature of PyTables is that it optimizes memory and disk resources so that they take much less space (between a factor 3 to 5, and more if the data is compressible) than other solutions, like for example, relational or object oriented databases. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy objects. . This package includes the manual in PDF and HTML formats.
PackageMaintainerDebian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamepython-tables-doc
PackageSectiondoc
PackageVersion3.6.1-3
SHA-128E783FA4D5BC9F199BDCAF1B0ACE4C16FBFA212
SHA-2561E1BC921A26917E47C93533BAEFF8201FD9DAB87F9CA8B07B02A56D6F0BD3BC6
Key Value
MD5A5B6C2D919E84E61867D77BD269E7FBB
PackageArchnoarch
PackageDescriptionThe python-tables-doc package contains the documentation for python-tables.
PackageMaintainerFedora Project
PackageNamepython-tables-doc
PackageRelease7.fc32
PackageVersion3.5.2
SHA-14B64DADCD6F4BF41BE55EA4E80227F07EDE50FC3
SHA-256A103AC939658C74E09A4063CC27D3DC0B99639A1DA557B5B48DA1095235DF92F
Key Value
MD51D7C80CE254EFC19E98ADE58B26EB703
PackageArchnoarch
PackageDescriptionThe python-tables-doc package contains the documentation for python-tables.
PackageMaintainerCBS <cbs@centos.org>
PackageNamepython-tables-doc
PackageRelease4.el7
PackageVersion3.3.0
SHA-1E9E8A889AB76AEDA735AC50EE918E3BFA606CFB3
SHA-2561B3F49C63A7ABBF6E64D97C89D762705BC3A6CEE3ADF0E3208E72719232AA26F
Key Value
MD534968B6BE8925C78521775B2E8365F98
PackageArchnoarch
PackageDescriptionThe python-tables-doc package contains the documentation for python-tables.
PackageMaintainerCBS <cbs@centos.org>
PackageNamepython-tables-doc
PackageRelease6.el8
PackageVersion3.5.2
SHA-14CD9396FD22A713CF78C36C8048E7D88E0C4E97D
SHA-2566A6C6AE55DA5C5EDA6E37B63DA1A5B24A61B010A9078176A5E033BD6E32AE73E
Key Value
MD57995F9AB12484F60D0D9183C9954AA22
PackageArchnoarch
PackageDescriptionThe python-tables-doc package contains the documentation for python-tables.
PackageMaintainerFedora Project
PackageNamepython-tables-doc
PackageRelease6.el8
PackageVersion3.5.2
SHA-1E9DF6500DC5A7221F6CAADB222B3365DB950ADE3
SHA-256A1FB89373FF1D2DEB0CD14F2AD0411DD2A4B82D3B5B29D532F36127C11F5CF69
Key Value
FileSize4395264
MD5D744C820D480B832281A88F6140EA1C8
PackageDescriptionhierarchical database for Python based on HDF5 - documentation PyTables is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data. . It is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code (generated using Cython), makes it a fast, yet extremely easy to use tool for interactively save and retrieve very large amounts of data. One important feature of PyTables is that it optimizes memory and disk resources so that they take much less space (between a factor 3 to 5, and more if the data is compressible) than other solutions, like for example, relational or object oriented databases. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy objects. . This package includes the manual in PDF and HTML formats.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-tables-doc
PackageSectiondoc
PackageVersion3.6.1-2build2
SHA-1F332464C91A81DC996B44745E697952E36881C8C
SHA-256F9F8980CD8B5CCEE348E770D48B98864B979DA4FC73CE03DB1F4207AF214599A
Key Value
MD5253DE96CA2672B344833259D32B61676
PackageArchnoarch
PackageDescriptionThe python-tables-doc package contains the documentation for python-tables.
PackageMaintainerFedora Project
PackageNamepython-tables-doc
PackageRelease10.fc33
PackageVersion3.5.2
SHA-1B872E9137C855E3B274DA5579BFE1190A002E479
SHA-256270D49DC48454E47A6A42D8A3739E45EF472EED07EE6A0DBD3D368B6A5B1AC4D
Key Value
FileSize4513560
MD55300659EB0F4B66AE20854F9B65889DE
PackageDescriptionhierarchical database for Python based on HDF5 - documentation PyTables is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data. . It is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code (generated using Cython), makes it a fast, yet extremely easy to use tool for interactively save and retrieve very large amounts of data. One important feature of PyTables is that it optimizes memory and disk resources so that they take much less space (between a factor 3 to 5, and more if the data is compressible) than other solutions, like for example, relational or object oriented databases. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy objects. . This package includes the manual in PDF and HTML formats.
PackageMaintainerDebian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamepython-tables-doc
PackageSectiondoc
PackageVersion3.6.1-5
SHA-1EBABA7625B7819DAED3202CC5C50DBD872A9623E
SHA-256E3670CB3B72F07CD46A1AA7C7AE9710833CFAB3C17A91C7343FC5D2B400E2419
Key Value
FileSize4397216
MD50D5776646A46DBF6470D9D214DF90A0C
PackageDescriptionhierarchical database for Python based on HDF5 - documentation PyTables is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data. . It is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code (generated using Cython), makes it a fast, yet extremely easy to use tool for interactively save and retrieve very large amounts of data. One important feature of PyTables is that it optimizes memory and disk resources so that they take much less space (between a factor 3 to 5, and more if the data is compressible) than other solutions, like for example, relational or object oriented databases. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy objects. . This package includes the manual in PDF and HTML formats.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-tables-doc
PackageSectiondoc
PackageVersion3.6.1-3
SHA-1855C3A84B62282A2225F6025594F60985613BD36
SHA-25686D5ECAF1D816064F694363B3927154BEF4D8DFEBAE8A97742572B682FFDD6A4