Numpy Array Vs. List: What's the Difference

Here are the differences between List and NumPy Array. Both store data, but technically these are not the same. You'll find here where they differ from each other.

Python Lists

Here is all about Python lists:

• Lists can have data of different data types. For instance, data = [3, 3.2, 4.6, 6, 6.8, 9, “hello”, ‘a’]
• Operations such as subtraction, multiplying, and division allow doing through loops
• Storage space required is more, as each element is considered an object in Python
• Execution time is high for large datasets
• Lists are inbuilt data types

• How to create array types in Python

NumPy Arrays

Here is all about NumPy Arrays:

• Numpy arrays are containers for storing only homogeneous data types. For example: data= [3.2, 4.6, 6.8]; data=[3, 6, 9]; data=[‘hello’, ‘a’]
• Numpy is designed to do all mathematical operations in parallel and is also simpler than Python
• Numpy storage space is very much less compared to the list due to the practice of homogeneous data type
• Execution time is very less for large datasets
• Numpy is a third-party library that needs to be installed by Conda or PIP

• The easy way how to split text in Python

References

• Hands-on data science with Anaconda
• How to Install NumPy