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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.
The following list contains syntax examples of how to determine the dimensions (index of the first element, the last element or the size in elements). Some languages index from zero. Some index from one. Some carry no such restriction, or even allow indexing by any enumerated type, not only integers.
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In order to effectively implement variables of such types as array structures (with indexing done by pointer arithmetic), many languages restrict the indices to integer data types [6] [7] (or other types that can be interpreted as integers, such as bytes and enumerated types), and require that all elements have the same data type and storage size.
1 (one-based indexing) The first element of the array is indexed by subscript of 1. n (n-based indexing) The base index of an array can be freely chosen. Usually programming languages allowing n-based indexing also allow negative index values and other scalar data types like enumerations, or characters may be used as an array index.
The indexing expression for a 1-based index would then be a ′ + s × i . {\displaystyle a'+s\times i.} Hence, the efficiency benefit at run time of zero-based indexing is not inherent, but is an artifact of the decision to represent an array with the address of its first element rather than the address of the fictitious zeroth element.
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Even though the row is indicated by the first index and the column by the second index, no grouping order between the dimensions is implied by this. The choice of how to group and order the indices, either by row-major or column-major methods, is thus a matter of convention. The same terminology can be applied to even higher dimensional arrays.