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In Python, the pandas library offers the Series.clip [1] and DataFrame.clip [2] methods. The NumPy library offers the clip [3] function. In the Wolfram Language, it is implemented as Clip [x, {minimum, maximum}]. [4] In OpenGL, the glClearColor function takes four GLfloat values which are then 'clamped' to the range [,]. [5]
The values of can be found with the quantile function where = for the first quartile, = for the second quartile, and = for the third quartile. The quantile function is the inverse of the cumulative distribution function if the cumulative distribution function is monotonically increasing because the one-to-one correspondence between the input ...
However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a ...
The sample maximum and minimum are the least robust statistics: they are maximally sensitive to outliers.. This can either be an advantage or a drawback: if extreme values are real (not measurement errors), and of real consequence, as in applications of extreme value theory such as building dikes or financial loss, then outliers (as reflected in sample extrema) are important.
The golden-section search is a technique for finding an extremum (minimum or maximum) of a function inside a specified interval. For a strictly unimodal function with an extremum inside the interval, it will find that extremum, while for an interval containing multiple extrema (possibly including the interval boundaries), it will converge to one of them.
In mathematical analysis, the maximum and minimum [a] of a function are, respectively, the greatest and least value taken by the function. Known generically as extremum , [ b ] they may be defined either within a given range (the local or relative extrema) or on the entire domain (the global or absolute extrema) of a function.
Assume we are looking for a maximum of () and that we know the maximum lies somewhere between and . For the algorithm to be applicable, there must be some value x {\displaystyle x} such that for all a , b {\displaystyle a,b} with A ≤ a < b ≤ x {\displaystyle A\leq a<b\leq x} , we have f ( a ) < f ( b ) {\displaystyle f(a)<f(b)} , and
The mid-range is closely related to the range, a measure of statistical dispersion defined as the difference between maximum and minimum values. The two measures are complementary in sense that if one knows the mid-range and the range, one can find the sample maximum and minimum values.