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In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs.
Dataframe may refer to: A tabular data structure common to many data processing libraries: pandas (software) § DataFrames; The Dataframe API in Apache Spark;
In mathematical analysis, the Dirac delta function (or δ distribution), also known as the unit impulse, [1] is a generalized function on the real numbers, whose value is zero everywhere except at zero, and whose integral over the entire real line is equal to one. [2] [3] [4] Thus it can be represented heuristically as
Delta encoding is a way of storing or transmitting data in the form of differences (deltas) between sequential data rather than complete files; more generally this is known as data differencing. Delta encoding is sometimes called delta compression, particularly where archival histories of changes are required (e.g., in revision control software).
The delta potential is the potential = (), where δ(x) is the Dirac delta function. It is called a delta potential well if λ is negative, and a delta potential barrier if λ is positive. The delta has been defined to occur at the origin for simplicity; a shift in the delta function's argument does not change any of the following results.
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
The Flajolet–Martin algorithm is an algorithm for approximating the number of distinct elements in a stream with a single pass and space-consumption logarithmic in the maximal number of possible distinct elements in the stream (the count-distinct problem).
The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations. The "pandas" python package provides a "pivot" method which provides for a narrow to wide transformation.