Python_Note14

Python Numpy

NumPy v1.10 Manual

http://docs.scipy.org/doc/numpy-1.10.1/index.html

Search page

http://docs.scipy.org/doc/numpy/search.html

Numpy for Matlab users

http://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html#numpy-for-matlab-users
相信有許多人是想利用Python Numpy 來取代Matlab的一些功能，譬如科學計算、畫圖等，上面是官方釋出給從Matlab轉過來使用Numpy的使用者手冊。所以有使用過Matlab的人可以先看這篇，相信可以更快上手Numpy。

numpy.ndarray.shape

範例：

>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged

numpy.unique(ar, return_index=False, return_inverse=False,return_counts=False)

http://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html#numpy-unique
Find the unique elements of an array.找出陣列中所有不同的元素。
範例：
In[128]: ar = [1,1,2,3,4,2,3,4,6,6,7,2,1,3,1,1,2,3,5,5,3]
In[129]: np.unique(a)
Out[129]: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

numpy.meshgrid(*xi, **kwargs)

http://docs.scipy.org/doc/numpy/reference/generated/numpy.meshgrid.html#numpy-meshgrid
Return coordinate matrices from coordinate vectors.

範例：

>>> X, Y = np.meshgrid([1,2,3], [4,5,6,7])
>>> X
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])
>>> Y
array([[4, 4, 4],
       [5, 5, 5],
       [6, 6, 6],
       [7, 7, 7]])

numpy.ravel(a, order='C')

http://docs.scipy.org/doc/numpy/reference/generated/numpy.ravel.html#numpy-ravel

Return a contiguous flattened array.

>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> print(np.ravel(x))
[1 2 3 4 5 6]

等同於 reshape(-1,order = order)

It is equivalent to reshape(-1, order=order).

>>> print(x.reshape(-1))
[1 2 3 4 5 6]

numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None)

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

Estimate a covariance matrix, given data and weights.

Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, , then the covariance matrix element  is the covariance of  and . The element  is the variance of .

Linear algebra (numpy.linalg)

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

• Matrix and vector products
• Decompositions
• Matrix eigenvalues

linalg.eig(a)

Compute the eigenvalues and right eigenvectors of a square array.

• Norms and other numbers
• Solving equations and inverting matrices
• Exceptions