A short summary
NumPy is a Python external library for performing complex mathematical operations at a low level. The use of multi-dimensional array artifacts in NumPy allows it to solve longer execution times. It has array manipulation functions built-in. Different algorithms can be transformed into functions that can be used on arrays. NumPy has a lot of applications that aren’t all about NumPy. It is a very diverse library with many implementations in other fields. Data Science, Data Analysis, and Machine Learning will all benefit from Numpy. It also serves as a foundation for other Python libraries. These libraries make use of NumPy’s functionality to extend their capabilities.
This course contains the bulk of NumPy’s – numerical Python library’s – concepts.
The following subjects will be covered:
Creating Arrays is the first step.
2) Working with Arrays
3) Discovering the Array’s Scale
4) Arrays with Negative Indexing
5) How to Dice an Array
6) Testing an Array’s Datatype
7) Duplicating an Array
8) Iterating through arrays
9) Arrays’ Shape
10) Array Reshaping
11) Assembling Arrays
12) Array Splitting
13) How to Sort an Array
14) Array Searching
15) Array Filtering
In Numpy, arrays are the same as lists in Python. Numpy arrays are homogeneous collections of elements, similar to lists in Python. The fact that NumPy arrays are homogeneous is their most important function. This sets them apart from Python arrays. It keeps mathematical operations uniform, which would be impossible for heterogeneous components. Another advantage of using NumPy arrays is that they can be combined for a huge variety of features. Owing to the heterogeneous existence of python arrays, these tasks could not be performed.
Good luck with your exams.
This course is planned for the following individuals:
Aspiring Data Scientists, Engineering Students, and Software Developers
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