DS: Wrangling library (programming library or tool collection for data processing and conversion) explanation, meaning, detailed guide to common operations
Table of contents
Explanation, significance, common operations of Wrangling library (programming library or tool collection for data processing and transformation)
| Introduction |
"Wrangling library" is a rather vague term without an exact definition or official standard. Typically, it can refer to a programming library or collection of tools for data processing and transformation. |
| significance |
Data manipulation and transformation is a very important part of data science because real-world data is rarely clean, consistent, or easily analyzable. Therefore, data scientists need to use various programming libraries and tools to clean, transform and preprocess data to make it suitable for analysis and modeling. |
| common operation |
Common data processing and transformation libraries include Pandas, NumPy, Dplyr, Tidyverse, etc. These libraries provide various functions and tools for manipulating and transforming datasets such as filtering, sorting, aggregating, reshaping, and more. In data science, these libraries are often referred to as "wrangling libraries" because they are used to "tame" (wrangling) data, making it more suitable for analysis and modeling. |