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Pandas supports hierarchical indices with multiple values per data point. An index with this structure, called a "MultiIndex", allows a single DataFrame to represent multiple dimensions, similar to a pivot table in Microsoft Excel. [4]: 147–148 Each level of a MultiIndex can be given a unique name.
A Dask DataFrame comprises many smaller Pandas DataFrames partitioned along the index. It maintains the familiar Pandas API, making it easy for Pandas users to scale up DataFrame workloads. During a DataFrame operation, Dask creates a task graph and triggers operations on the constituent DataFrames in a manner that reduces memory footprint and ...
In many systems for computational statistics, such as R and Python's pandas, a data frame or data table is a data type supporting the table abstraction. Conceptually, it is a list of records or observations all containing the same fields or columns. The implementation consists of a list of arrays or vectors, each with a name.
The set ret can be saved efficiently by just storing the index i, which is the last character of the longest common substring (of size z) instead of S[(i-z+1)..i]. Thus all the longest common substrings would be, for each i in ret, S[(ret[i]-z)..(ret[i])]. The following tricks can be used to reduce the memory usage of an implementation:
This is a list of well-known data structures. For a wider list of terms, see list of terms relating to algorithms and data structures. For a comparison of running times for a subset of this list see comparison of data structures.
In the description, there are given (only for classical groups), a representative of the isogeny class of the group of the given Tits index. The following complete list of all possible Tits indices over those special fields, which are the finite fields, the local and global fields (in any characteristic) is given (see [2] and [3] (with full ...
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."
The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one. The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations.