Python Numpy for Data science Course

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 into functions for applying to arrays. NumPy has applications that are not 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 introduces the majority of concepts of NumPy, a numerical Python library.

You will learn the following topics :

• Creating Arrays using Numpy in Python

• Accessing Arrays using Numpy in Python

• Finding the Dimension of the Array using Numpy in Python

• Negative Indexing on Arrays using Numpy in Python

• Slicing an Array using Numpy in Python

• Checking the Datatype of an Array using Numpy in Python

• Copying an Array using Numpy in Python

• Iterating through arrays using Numpy in Python

• Shape of Arrays using Numpy in Python

• Reshaping Arrays using Numpy in Python

• Joining Arrays using Numpy in Python

• Splitting an Array using Numpy in Python

• Sorting an Array using Numpy in Python

• Searching in an Array using Numpy in Python

• Filtering an Array using Numpy in Python

• Generating a Random Array using Numpy in Python

Arrays in Numpy are equivalent to lists in Python. Like lists in Python, the Numpy arrays are homogeneous sets of elements. The most important feature of NumPy arrays is that they are homogeneous 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 that 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.

Happy learning!!!