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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 .
In a database, a table is a collection of related data organized in table format; consisting of columns and rows.. In relational databases, and flat file databases, a table is a set of data elements (values) using a model of vertical columns (identifiable by name) and horizontal rows, the cell being the unit where a row and column intersect. [1]
dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language. [1]
If an intersection (in the United States) is represented in data by the zip code (5-digit number) and two street names (strings of text), bugs may appear when a city where streets intersect multiple times is encountered. While this example may be oversimplified, restructuring of data is a fairly common problem in software engineering, either to ...
Comma separated files are used for the interchange of database information between machines of two different architectures. The plain-text character of CSV files largely avoids incompatibilities such as byte-order and word size. The files are largely human-readable, so it is easier to deal with them in the absence of perfect documentation or ...
In statistics, the Freedman–Diaconis rule can be used to select the width of the bins to be used in a histogram. [1] It is named after David A. Freedman and Persi Diaconis .
In object-oriented programming, the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled.
In two dimensions the difference between random sampling, Latin hypercube sampling, and orthogonal sampling can be explained as follows: In random sampling new sample points are generated without taking into account the previously generated sample points. One does not necessarily need to know beforehand how many sample points are needed.