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Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforeseeable using a fixed design.
Comma-separated values is a data format that predates personal computers by more than a decade: the IBM Fortran (level H extended) compiler under OS/360 supported CSV in 1972. [14] List-directed ("free form") input/output was defined in FORTRAN 77 , approved in 1978.
A standardized surrogate high date (e.g. 9999-12-31) may instead be used as an end date, so that the field can be included in an index, and so that null-value substitution is not required when querying. In some database software, using an artificial high date value could cause performance issues, that using a null value would prevent.
Stack Overflow published on their blog that approximately 184 public network users were affected by this breach, which "could have returned IP address, names, or emails". [ 23 ] [ 24 ] 2023 controversy over AI-generated content and moderation strike
In the phone book example with a composite index created on the columns (city, last_name, first_name), if we search by giving exact values for all the three fields, search time is minimal—but if we provide the values for city and first_name only, the search uses only the city field to retrieve all matched
Boolean values still behave as integers, can be stored in integer variables, and used anywhere integers would be valid, including in indexing, arithmetic, parsing, and formatting. This approach (Boolean values are just integers) has been retained in all later versions of C. Note, that this does not mean that any integer value can be stored in a ...
Most lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that "probable" (i.e. frequently encountered) data will produce shorter output than "improbable" data.