In the area of data science with Python , you have certainly come across the business phone list library. This powerful Python library is used extensively by other super popular libraries such as Pandas. Scikit-learn, matplotlib, and largely data science oriented libraries . But do you know exactly what she does? In this article we will understand a little about the NumPy Python package. Its data structures, what are its advantages and follow a small tutorial of the first steps to use this library.
What is NumPy?
Created in 2005 by Travis Oliphant. The NumPy project was based on the Numeric and Numarray projects with the aim of bringing the community together around a single array processing framework. Therefore, the package NumPy. Named that way due to the abbreviation of Numerical Python (Numerical Python), is an open source library destined to perform operations on multidimensional arrays, amicably named as ndarray in this library. Due to its construction and functionality being based on the ndarray data structure. The library offers quick operations for data treatment and cleaning, generation of subsets and filtering, descriptive statistics, manipulation of relational data, manipulation of data in groups, among other types of processing.
What are arrays?
Well, so far you can see that the DV Leads Python package is based on a very important core structure. But what are these arrays anyway? An array is a multidimensional structure that allows us to store data in our computer’s memory, so that each item located in that structure can be found through an indexing scheme. NumPy Python calls this structure ndarray, short for N-dimensional array. The ndarray always stores the elements with the same format, that’s why it is known as a homogeneous data structure, in the dimensions defined by the application or by the developer. Dimensions in the NumPy library are known as axes.