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
FileSize	342296
MD5	2FCE94F4BB77C9C0546F1E82B6979272
PackageDescription	hierarchical database for Python3 based on HDF5 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 is the Python 3 version of the package.
PackageMaintainer	Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageName	python3-tables
PackageSection	python
PackageVersion	3.6.1-5
SHA-1	40E7996FE4B444345174A99EDD476D771194DD8A
SHA-256	2A5252E6DDBEE11192957E64E21EA156A0D6CDC2AE988F33330F4A25F8E3D0A9

Key	Value
FileSize	330688
MD5	F1AEAB15AE05388F87E871501C21216C
PackageDescription	hierarchical database for Python3 based on HDF5 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 is the Python 3 version of the package.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python3-tables
PackageSection	python
PackageVersion	3.4.2-4
SHA-1	BE4E69D800DF429D8F7223B5C4A7B92FFEFAC748
SHA-256	5F3F0B2E2F8B392FCAFC9449C80A8AD6369AB31DF164B797D2C00BCDC52DAFB3

Key	Value
FileSize	333208
MD5	6E75946C4169859023EB2DD7A92129B7
PackageDescription	hierarchical database for Python3 based on HDF5 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 is the Python 3 version of the package.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python3-tables
PackageSection	python
PackageVersion	3.6.1-3
SHA-1	A28E0AB1E9B95390BB606132A467E737A4050D5D
SHA-256	6397C2420D916F0679420F4606BBFC32F9859506F0A7912A5019380255D79B8F

Key	Value
FileSize	327092
MD5	1AC7D32D4045486BCFEA36E9D9F3BA85
PackageDescription	hierarchical database for Python based on HDF5 PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - 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, Numeric and numarray objects. . This is the Python 2 version of the package.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python-tables
PackageSection	python
PackageVersion	3.1.1-0ubuntu1
SHA-1	711957E0A382054386B987955291C1219296A1B8
SHA-256	7FB7AC522D32BAD56140A3A9D0DF28196CE95206E1D3797211A16F09C8E042E5

Key	Value
FileSize	341310
MD5	EBDCD62CF323B3107E5475F0DE8DF1EA
PackageDescription	hierarchical database for Python based on HDF5 PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - 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, Numeric and numarray objects. . This is the Python 2 version of the package.
PackageMaintainer	Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageName	python-tables
PackageSection	python
PackageVersion	3.3.0-5
SHA-1	77CF76FCF930C9BB6ABA6CCE377EC2418AE37181
SHA-256	FD0DB23E473B8DEA620EB82E55BD64D7B70D51FC39A48787789F7556FB1FBAEB

Key	Value
FileSize	333056
MD5	2B2226589988FD60B5B2E686C067086F
PackageDescription	hierarchical database for Python3 based on HDF5 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 is the Python 3 version of the package.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python3-tables
PackageSection	python
PackageVersion	3.6.1-2build1
SHA-1	0D3518EA19BB9AD4BBCF1CD496729422749D08ED
SHA-256	046787334FDEFAD19E5D857A7501023981480C1449F8E780A2EED97AB017C7F2

Key	Value
FileSize	342180
MD5	146BEAE504C21365382A82BC07C371B6
PackageDescription	hierarchical database for Python3 based on HDF5 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 is the Python 3 version of the package.
PackageMaintainer	Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageName	python3-tables
PackageSection	python
PackageVersion	3.6.1-3
SHA-1	468337C9CC136E51DB0B1C40235FF489441D325D
SHA-256	C582F06BEC3317B014C40D407B45CD24CDB17C6247EE38C05AEE35DC23E8F674

Key	Value
FileSize	333060
MD5	889E28061C30DABBD05BEA250DB059A1
PackageDescription	hierarchical database for Python3 based on HDF5 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 is the Python 3 version of the package.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python3-tables
PackageSection	python
PackageVersion	3.6.1-2build2
SHA-1	C488DB7160667C5FD095F7B3F66009EAA35060E9
SHA-256	A57DDFA86E002A59E7A9769048F63E02436901392D308193C83A69DBB437FAA6

Key	Value
FileSize	336886
MD5	F4A6D1CA0E57024C3435FE54578A1B76
PackageDescription	hierarchical database for Python based on HDF5 PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - 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, Numeric and numarray objects. . This is the Python 2 version of the package.
PackageMaintainer	Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageName	python-tables
PackageSection	python
PackageVersion	3.1.1-3
SHA-1	50C94198267ACBF73099D73C5256337B91D374F5
SHA-256	880152A49D2E21714DE5D38C16193747C810D7454146E09CB068E88602A7A8C0

Key	Value
FileSize	335424
MD5	D0F9C0B5DC484C03047713E367CB7DF2
PackageDescription	hierarchical database for Python based on HDF5 PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - 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, Numeric and numarray objects. . This is the Python 2 version of the package.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python-tables
PackageSection	python
PackageVersion	3.2.2-2
SHA-1	2442A202BB903B85D9CA5EC77B33222D77DDEDD7
SHA-256	09AA58A3C2B4E916C0B3F5800F342EF70C51DD125D296C1B4E6F7D335818B39F

Key	Value
FileSize	342076
MD5	3A31C4654BABB131613304C443D828D4
PackageDescription	hierarchical database for Python3 based on HDF5 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 is the Python 3 version of the package.
PackageMaintainer	Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageName	python3-tables
PackageSection	python
PackageVersion	3.4.4-2
SHA-1	9070C5B233FA1F038A37ACDD2D983E5BDDED6512
SHA-256	B3C0F63F004170608D366B5A64C0D996E2E7CD32B4DF9C28349A95718013704D