Key | Value |
---|---|
FileName | ./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DatetimeIndex.is_month_start.html |
FileSize | 10080 |
MD5 | 0FD20E120F31083FC340F32B70F337C8 |
SHA-1 | 0026164E7226D745775A57F8FE39127197D407C9 |
SHA-256 | F87A376A1B23EC5DEBD2B94E87B340A1C5DF1FC82043BE5525B174E438522AC2 |
SSDEEP | 192:z4hiDb/oWfn3WPeeVSmUEv5nx0H00ivnZyh1LVIZnTWPz:TDtfn3iVSmUzbhCZnTa |
TLSH | T17C22009254F25537053791CB81AA1B61FAE6402FED821A0172FC97AC4FCAF457C0F9AE |
hashlookup:parent-total | 2 |
hashlookup:trust | 60 |
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 |
---|---|
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 |