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NumPy is a basic level external library in Python used for complex mathematical operations. NumPy overcomes slower executions with the use of multi-dimensional array objects. It has built-in functions for manipulating arrays. We can convert different algorithms to can into functions for applying on arrays. NumPy has applications that are not only limited to itself. It is a very diverse library and has a wide range of applications in other sectors. Numpy can be put to use along with Data Science, Data Analysis and Machine Learning. It is also a base for other python libraries. These libraries use the functionalities in NumPy to increase their capabilities.
This course introduce with all majority of concept of NumPy – numerical python library.
You will learn following topics :
1) Creating Arrays
2) Accessing Arrays
3) Finding Dimension of the Array
4) Negative Indexing on Arrays
5) Slicing an Array
6) Checking Datatype of an Array
7) Copying an Array
8) Iterating through arrays
9) Shape of Arrays
10) Reshaping Arrays
11) Joining Arrays
12) Splitting Array
13) Sorting an Array
14) Searching in Array
15) Filtering an Array
16) Generating a Random Array
Arrays in Numpy are equivalent to lists in python. Like lists in python, the Numpy arrays are homogenous sets of elements. The most important feature of NumPy arrays is they are homogenous in nature. This differentiates them from python arrays. It maintains uniformity for mathematical operations that would not be possible with heterogeneous elements. Another benefit of using NumPy arrays is there are a large number of functions that are applicable to these arrays. These functions could not be performed when applied to python arrays due to their heterogeneous nature.
Surendra Varma Pericherla
Who this course is for:
- Engineering Students, Software Developers And Aspiring Data Scientists