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[4]: 114 A DataFrame is a 2-dimensional data structure of rows and columns, similar to a spreadsheet, and analogous to a Python dictionary mapping column names (keys) to Series (values), with each Series sharing an index. [4]: 115 DataFrames can be concatenated together or "merged" on columns or indices in a manner similar to joins in SQL.
Data orientation is the representation of tabular data in a linear memory model such as in-disk or in-memory. The two most common representations are column-oriented (columnar format) and row-oriented (row format). [1] [2] The choice of data orientation is a trade-off and an architectural decision in databases, query engines, and numerical ...
LMDB treats the computer's memory as a single address space, shared across multiple processes or threads using shared memory with copy-on-write semantics (known historically as a single-level store). Most former modern computing architectures had a 32-bit memory address space, imposing a hard limit of 4 GB on the size of any database that ...
Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory. [1] It is a popular algorithm for parameter estimation in machine learning.
Each segment was placed at a specific location in memory by the software being executed and all instructions that operated on the data within those segments were performed relative to the start of that segment. This allowed a 16-bit address register, which would normally be able to access 64 KB of memory space, to access 1 MB of memory space.
In SDRAM chips, the memory in each chip is divided into banks which are refreshed in parallel, saving further time. So the number of refresh cycles needed is the number of rows in a single bank, given in the specifications, which in the 2012 generation of chips has been frozen at 8,192. [needs update]
A database table can be thought of as consisting of rows and columns. [1] Each row in a table represents a set of related data, and every row in the table has the same structure. For example, in a table that represents companies, each row might represent a single company. Columns might represent things like company name, address, etc.
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]