numpy - Python for climatology, oceanograpy and atmospheric science

numpy

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

* a powerful N-dimensional array object

* sophisticated (broadcasting) functions

* tools for integrating C/C++ and Fortran code

* useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

https://www.numpy.org/

Cool points of numpy

Numpy can handle arrays with missing or invalid entries

numpy masked array

Various ways of indexig

numpy indexing

Official documents

Routines grouped by functionality

https://docs.scipy.org/doc/numpy/reference/routines.html

NumPy for Matlab users

https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html

NumPy Reference

https://docs.scipy.org/doc/numpy/reference/

dtype

Data types

https://docs.scipy.org/doc/numpy/user/basics.types.html

Data type objects (dtype)

https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html

numpy.dtype

https://docs.scipy.org/doc/numpy/reference/generated/numpy.dtype.html

[numpy.einsum https://numpy.org/doc/stable/reference/generated/numpy.einsum.html

evaluates the Einstein summation convention on the operands.]

Turorial

NumPy Cheat Sheet: Data Analysis in Python (article) - DataCamp

https://www.datacamp.com/community/blog/python-numpy-cheat-sheet

Numpy FAQ | Xdev

https://ncar.github.io/xdev/posts/numpy-faq/

NumPy をマルチスレッドで計算させる - sgryjp.log

Subpages

numpy datetime and timedelta