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Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. [2]
Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text, where each line of the file typically represents one data record. Each record consists of the same number of fields, and these are separated by commas in the ...
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 ...
^ The RFC CSV specification only deals with delimiters, newlines, and quote characters; it does not directly deal with serializing programming data structures. ^ The netstrings specification only deals with nested byte strings ; anything else is outside the scope of the specification.
The most expansive form using tag pairs results in a much larger (in character count) representation than JSON, but if data is stored in attributes and 'short tag' form where the closing tag is replaced with />, the representation is often about the same size as JSON or just a little larger. However, an XML attribute can only have a single ...
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
JSON-RPC works by sending a request to a server implementing this protocol. The client in that case is typically software intending to call a single method of a remote system. Multiple input parameters can be passed to the remote method as an array or object, whereas the method itself can return multiple output data as well.
Data-driven programming is similar to event-driven programming, in that both are structured as pattern matching and resulting processing, and are usually implemented by a main loop, though they are typically applied to different domains.