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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]
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 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.
By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.
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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.
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