NumPy
NumPy - Numerical Python
A Package for Handling Numerical Arrays
, NumPy
• NumPy provides an efficient interface to store
and operate on dense data buffers.
• In some ways, NumPy arrays are like Python’s
built-in list type, but NumPy arrays provide
much more efficient storage and data
operations as the arrays grow larger in size.
• NumPy arrays form the core of nearly the
entire ecosystem of data science tools in
Python
, NumPy Cont..
• NumPy revolutionized the way we handle
numerical data in Python.
• It is created to address the limitations of
traditional Python lists when it comes to
numerical computing.
• It is developed by Travis Olliphant in 2005.
, NumPy Cont..
• NumPy is a general-purpose array-processing Python
library which provides handy methods/functions for
working n-dimensional arrays.
• NumPy is a short form for “Numerical Python“.
• It provides various computing tools such as
comprehensive mathematical functions and linear
algebra routines.
– NumPy provides both the flexibility of Python and the
speed of well-optimized compiled C code.
– Its easy-to-use syntax makes it highly accessible and
productive for programmers from any background.
NumPy - Numerical Python
A Package for Handling Numerical Arrays
, NumPy
• NumPy provides an efficient interface to store
and operate on dense data buffers.
• In some ways, NumPy arrays are like Python’s
built-in list type, but NumPy arrays provide
much more efficient storage and data
operations as the arrays grow larger in size.
• NumPy arrays form the core of nearly the
entire ecosystem of data science tools in
Python
, NumPy Cont..
• NumPy revolutionized the way we handle
numerical data in Python.
• It is created to address the limitations of
traditional Python lists when it comes to
numerical computing.
• It is developed by Travis Olliphant in 2005.
, NumPy Cont..
• NumPy is a general-purpose array-processing Python
library which provides handy methods/functions for
working n-dimensional arrays.
• NumPy is a short form for “Numerical Python“.
• It provides various computing tools such as
comprehensive mathematical functions and linear
algebra routines.
– NumPy provides both the flexibility of Python and the
speed of well-optimized compiled C code.
– Its easy-to-use syntax makes it highly accessible and
productive for programmers from any background.
