What is the use of Numpy Arrays in Python?

Programming Language
#1

NumPy arrays are faster and more compact than Python lists. An array consumes less memory and is convenient to use. NumPy uses much less memory to store data and it provides a mechanism of specifying the data types. This allows the code to be optimized even further.

Numpy makes many mathematical operations used widely in scientific computing fast and easy to use, such as:

  • Vector-Vector multiplication
  • Matrix-Matrix and Matrix-Vector multiplication
  • Element-wise operations on vectors and matrices (i.e., adding, subtracting, multiplying, and dividing by a number )
  • Element-wise or array-wise comparisons
  • Applying functions element-wise to a vector/matrix ( like pow, log, and exp)
  • A whole lot of Linear Algebra operations can be found in NumPy.linalg
  • Reduction, statistics, and much more