Key | Value |
---|---|
FileName | snap-hashlookup-import/usr/lib/python3/dist-packages/pandas/tests/indexes/datetimes/test_date_range.py |
FileSize | 25566 |
MD5 | 71F6A63C86D7A26433CD30D7BE8F7907 |
SHA-1 | 03E35889111A3F8A4A500AA7CA0B371275350831 |
SHA-256 | C2DBCAA672862DD204FB9463BDFC3DB52E586495A7D4C8215084FC462848EF42 |
SHA-512 | 427E499C6E955A3B6D9599A7E4E36AEA736A6726D0C851AC56CA15B902DF266B712AD5298D04E96834FFA2681941C9EA9035DB11584E87D21E3B8A2680F337BA |
SSDEEP | 384:OD6B646nK0W8XpmQL9m9yF/+rLO2M022mi+irQ:WPmQJ9FUQ |
TLSH | T12DB2436341774A05F783B13EC4EA9B479B05EA8789856BB03BAC01803F9C53DDB5F265 |
insert-timestamp | 1721663976.374184 |
mimetype | text/plain |
source | snap:ZSh2k4zUkOPaBYfSVD4Xv37rwJENkbHU_7 |
hashlookup:parent-total | 6 |
hashlookup:trust | 80 |
The searched file hash is included in 6 parent files which include package known and seen by metalookup. A sample is included below:
Key | Value |
---|---|
SHA-1 | A5566FF1C1FD8E8266BC1A50B3D4F0228AF394C0 |
snap-authority | canonical |
snap-filename | qlekdqRb9ScClFkFW13cMQrTnfuSU59E_10.snap |
snap-id | qlekdqRb9ScClFkFW13cMQrTnfuSU59E_10 |
snap-name | python-ai-toolkit |
snap-publisher-id | TTSsCAwEIBlVcQVMlOn24DAVMnu5A8Ii |
snap-signkey | BWDEoaqyr25nF5SNCvEv2v7QnM9QsfCc0PBMYD_i2NGSQ32EF2d4D0hqUel3m8ul |
snap-timestamp | 2021-09-08T10:22:45.634752Z |
source-url | https://api.snapcraft.io/api/v1/snaps/download/qlekdqRb9ScClFkFW13cMQrTnfuSU59E_10.snap |
Key | Value |
---|---|
FileSize | 2763848 |
MD5 | E625F6F403D9C4950113D5F9DDD786EB |
PackageDescription | data 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. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python-pandas |
PackageSection | python |
PackageVersion | 0.22.0-4ubuntu1 |
SHA-1 | 909E814A66D0896748DC1FBD3AF3B009F511B41B |
SHA-256 | 4282027B5D7A9AB20C02D579CC0C69F3C6FE709E969EF93A0028C9E28E5AABBA |
Key | Value |
---|---|
SHA-1 | D24A0E10CD2BF20541138E08B4EA4AD7C64EC039 |
snap-authority | canonical |
snap-filename | ZSh2k4zUkOPaBYfSVD4Xv37rwJENkbHU_7.snap |
snap-id | ZSh2k4zUkOPaBYfSVD4Xv37rwJENkbHU_7 |
snap-name | opencv-demo-webapp |
snap-publisher-id | TTSsCAwEIBlVcQVMlOn24DAVMnu5A8Ii |
snap-signkey | BWDEoaqyr25nF5SNCvEv2v7QnM9QsfCc0PBMYD_i2NGSQ32EF2d4D0hqUel3m8ul |
snap-timestamp | 2021-01-02T22:45:02.460847Z |
source-url | https://api.snapcraft.io/api/v1/snaps/download/ZSh2k4zUkOPaBYfSVD4Xv37rwJENkbHU_7.snap |
Key | Value |
---|---|
FileSize | 2763672 |
MD5 | E6C8F40261C0E90609D8FC9B882B1813 |
PackageDescription | data 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. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python3-pandas |
PackageSection | python |
PackageVersion | 0.22.0-4 |
SHA-1 | FACA5C37C673D243EAA585C72602D1B3160F87CE |
SHA-256 | EC621613E9BCC87F97B634D21E1CECF3B866005711520D791DB06E2C70D1AF59 |
Key | Value |
---|---|
FileSize | 2764812 |
MD5 | 98AC8B5C1A6DD9AA7943DC0BD4B0183A |
PackageDescription | data 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. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python3-pandas |
PackageSection | python |
PackageVersion | 0.22.0-4ubuntu1 |
SHA-1 | B3B3A20255251A115D967A047A3CBF417B5B5756 |
SHA-256 | 4C16C3AC39905460A964712DB59AA2D0702EB22D2EFD1B64B3B085C95B3EA7BD |
Key | Value |
---|---|
FileSize | 2763744 |
MD5 | 28831EB80B14D3DD1C0ED62AB8B7ED33 |
PackageDescription | data 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. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python-pandas |
PackageSection | python |
PackageVersion | 0.22.0-4 |
SHA-1 | 070746205EE88382CAAEB9C921A7252AE134FB25 |
SHA-256 | CAA099B08A65116197A137D398AFA2237CF02875CD5B366E5C8F23E745027F21 |