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 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 using Numpy in Python

2) Accessing Arrays using Numpy in Python

3) Finding Dimension of the Array using Numpy in Python

4) Negative Indexing on Arrays using Numpy in Python

5) Slicing an Array using Numpy in Python

6) Checking Datatype of an Array using Numpy in Python

7) Copying an Array using Numpy in Python

8) Iterating through arrays using Numpy in Python

9) Shape of Arrays using Numpy in Python

10) Reshaping Arrays using Numpy in Python

11) Joining Arrays using Numpy in Python

12) Splitting Array using Numpy in Python

13) Sorting an Array using Numpy in Python

14) Searching in Array using Numpy in Python

15) Filtering an Array using Numpy in Python

16) 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 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.

Happy learning

Surendra Varma Pericherla