Data Manipulation Library
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A Data Manipulation Library is a software library that can be used by a data manipulation system (to perform modifications, transformations, and analyses of data within software applications).
- AKA: Data Transformation Library.
- Context:
- It can (typically) enable developers to perform complex Data Transformations and Data Cleaning tasks efficiently.
- It can (often) be used in Data Science and Data Analysis to prepare data for Data Modeling and Statistical Analysis.
- It can range from supporting Basic Data Operations like sorting and filtering to executing Advanced Analytical Functions like machine learning data preparation.
- It can support various Data Formats and be compatible with multiple Programming Languages.
- It can be an integral part of Data Pipelines in both small-scale and Large-Scale Data Processing environments.
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- Example(s):
- a Python Data Manipulation Library, such as Pandas library, Pyarrow, ...
- dplyr, which provides a grammar for data manipulation, focusing on Data Frame operations.
- a Graph Manipulation Library.
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- Counter-Example(s):
- Data Visualization Libraries like Matplotlib or Seaborn, which are specialized for graphical representation of data rather than data manipulation.
- Statistical Software like SPSS or Stata, which, although they manipulate data for analysis, are comprehensive suites with a broader focus beyond just data manipulation.
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- See: Pandas, R (Programming Language), Data Processing System, Python Libraries, Data Analysis Techniques