In this article, I am going to talk about Numpy, Python’s one of the most important libraries.
What is Numpy
import numpy as np # np label can be used to access the numpy library.
Why Use Numpy
So why do we use Numpy when we have the list data structure in Python? Here are the reasons :
- Numpy is faster than list :
Numpy stores its data in a continuous location in memory, unlike Python list data structures. Therefore Numpy can access its data much more easily and efficiently.
- NumPy uses much less memory to store data :
Numpy arrays take up less memory space than Python list data structures.
- Convenient to use
- Lots of Built-in Functions
If you are interested in machine learning or data science, Numpy is a Python library you will use frequently.
So how do you learn Numpy? As with everything else in life, the best way to learn something is to learn by doing it. Below I have listed the best courses and books for you to learn Numpy. These courses and books will enable you to learn Numpy as efficiently as possible.
Codeacademy is one of the best platforms among e-learning platforms. The course I recommend here is Codeacademy – Learn Statistics with Numpy.
Why Learn NumPy?
Datacamp is an e-learning platform that offers high-quality courses in the field of data science. The course I recommend here is the Datacamp Introduction to Python course. This course starts by teaching the programming language Python and then continues with Numpy.
Udemy - Python Numpy for Absolute Beginners
- Learn How To Use Python Interactively And By Using a Script
- Use Python for Data Science and Machine Learning
- Create Your First Numpy array and Acquaint Yourself With Python Numpy
- Learn to work with powerful tools in the NumPy array, and get started with data exploration.
Best Numpy Books
- IPython and Jupyter: provide computational environments for data scientists using Python
- NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
- Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
- Matplotlib: includes capabilities for a flexible range of data visualizations in Python
- Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
- Use the IPython shell and Jupyter notebook for exploratory computing
- Learn basic and advanced features in NumPy (Numerical Python)
- Get started with data analysis tools in the pandas library
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create informative visualizations with matplotlib
- Apply the pandas groupby facility to slice, dice, and summarize datasets
- Analyze and manipulate regular and irregular time series data
- Learn how to solve real-world data analysis problems with thorough, detailed examples.
- Work with vectors and matrices using NumPy
- Plot and visualize data with Matplotlib
- Perform data analysis tasks with Pandas and SciPy
- Review statistical modeling and machine learning with statsmodels and scikit-learn
- Optimize Python code using Numba and Cython