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Dask's high-level collections are the natural entry point for users who are interested in scaling up their pandas, NumPy or scikit-learn workload. Dask’s DataFrame, Array and Dask-ML are alternatives to Pandas DataFrame, Numpy Array and scikit-learn respectively with slight variations to the original interfaces.
A GROUP BY statement in SQL specifies that a SQL SELECT statement partitions result rows into groups, based on their values in one or several columns. Typically, grouping is used to apply some sort of aggregate function for each group.
GIS data for global datasets; Name Description; Natural Earth: Public domain vector and raster dataset. Supported by the NACIS. [1]Global Map: Provides consistent coverage of all the Earth's land cover area.
A data structure known as a hash table.. In computer science, a data structure is a data organization and storage format that is usually chosen for efficient access to data. [1] [2] [3] More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data, [4] i.e., it is an algebraic structure about data.
Data flow diagram with data storage, data flows, function and interface. A data-flow diagram is a way of representing a flow of data through a process or a system (usually an information system).
In computing, a data segment (often denoted .data) is a portion of an object file or the corresponding address space of a program that contains initialized static variables, that is, global variables and static local variables.
In telecommunications, frame synchronization or framing is the process by which, while receiving a stream of fixed-length frames, the receiver identifies the frame boundaries, permitting the data bits within the frame to be extracted for decoding or retransmission.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]