# Learning NumPy - An Introduction

## We'll cover the following

### Why NumPy

Data comes in all shapes and sizes. We can have image data, audio data, text data, numerical data, etc. We have all these heterogeneous sources of data but computers understand only 0’s and 1’s — At its core, data can be thought of as arrays of numbers. In fact, the prerequisite for performing any data analysis is to convert the data into numerical form. This means it is important to be able to store and manipulate arrays efficiently, and this is where Python’s NumPy package comes into picture.

Now, you might be questioning, *“When can I use Python’s built-in lists and to do all sorts of computations and manipulations through list comprehensions, for-loops, etc., why should I bother with NumPy arrays?”* You are right in thinking so because, in some aspects, NumPy arrays are like Python’s lists. Their advantage is that they provide **more efficient storage and data operations** as the arrays grow larger in size. This is the reason NumPy arrays are at the core of nearly all data science tools in Python. This, in turn, implies that it is essential to **know NumPy well!**

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