Numpy Introduction

Why to use Numpy in Python?

Because, in Python, Lists are used in order to serve the purpose of the array but lists are very slow to process. Hence we use Numpy in Python because it provides an array object that is up to 50x faster than traditional Python lists. And Python has other modules too, which makes data analysis and presentation very easy. So Numpy library is used with Python along with other Python libraries like Matplotlib, Scikit Learn, etc for AI/ML and Data analysis purposes.

  • In NumPy, the array object is commonly known as ndarray. Numpy provides a lot of supporting functions for performing operations on its array object and with these functions, working with ndarray becomes very easy.
  • Also, the NumPy arrays are more compact than Python Lists in terms of the size.
  • NumPy uses much less memory in order to store data and it provides an easy mechanism of specifying the data types. Thus code can be optimized easily.

Now you must be thinking, that how NumPy works faster than lists. Don’t worry, we have an answer for your question.

NumPy arrays are mainly stored at one continuous place in memory contrary to lists. Thus you can access and manipulate them very efficiently and this behavior is commonly known as locality of reference. Due to this reason, Numpy is faster than lists. Numpy is optimized to work with latest CPU architecture.

Like we mentioned above, NumPy is also used along with packages like SciPy (Scientific Python) and Matplotlib (plotting library in python).

This combination is mainly a replacement for MatLab(which is a popular platform for technical computing). Also, Python is an alternative to MatLab and is now seen as a modern and complete programming language

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