python库--Numpy and pandas

• list转为矩阵

array = numpy.array([[1,2,3]
[4,5,6]]
)

矩阵的维度属性：array.ndim # 注意这是最小的那个

array.shape # 几行几列
array.size #总元素的个数

>>> import numpy as np
>>> a = np.array([1,2,3,4,5],np.int)
>>> a
array([1, 2, 3, 4, 5])
>>> print(a)
[1 2 3 4 5]

>>> a= np.arange(1,9)
>>> print(a)
[1 2 3 4 5 6 7 8]
>>> a.reshape((2,4))
array([[1, 2, 3, 4],
       [5, 6, 7, 8]])

>>> a =np.linspace(1,10,4)
>>> print(a)
[ 1.  4.  7. 10.]

>>> import numpy as np
>>> a =np.array(range(9))
>>> print(a)
[0 1 2 3 4 5 6 7 8]
>>> b=a**2
>>> print(b)
[ 0  1  4  9 16 25 36 49 64]
>>> c =np.sin(b)
>>> print(c)
[ 0.          0.84147098 -0.7568025   0.41211849 -0.28790332 -0.13235175
 -0.99177885 -0.95375265  0.92002604]
>>> print(b)
[ 0  1  4  9 16 25 36 49 64]
>>> print(b>9)
[False False False False  True  True  True  True  True]

>>> a=np.arange(4).reshape((2,2))
>>> b=np.arange(1,5).reshape((2,2))
>>> print(a)
[[0 1]
 [2 3]]
>>> print(b)
[[1 2]
 [3 4]]
>>> c= a*b
>>> print(c)
[[ 0  2]
 [ 6 12]]
>>> d = np.dot(a,b)
>>> print(d)
[[ 3  4]
 [11 16]]
>>> print(np.max(d))
16
>>> print(np.max(d,axis=0))
[11 16]
>>> print(np.max(d,axis=1))#每一列的最大值
[ 4 16]

 b =a.reshape((3,4))
>>> print(b)
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
>>> print(np.argmin(b))
0
>>> print(np.argmax(b))#求最大值的引索
11
>>> print(b.mean())
5.5
>>> print(np.average(b))
5.5
>>> print(np.median(b))
5.5

print(np.nonzero(b))#返回不是0的元素的位置
(array([0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], dtype=int32), array([1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], dtype=int32))

>>> print(b)
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
>>> print(b.T)
[[ 0  4  8]
 [ 1  5  9]
 [ 2  6 10]
 [ 3  7 11]]

>>> print(np.clip(b,4,9))
[[4 4 4 4]
 [4 5 6 7]
 [8 9 9 9]]
>>> print(np.mean(b,axis=1))
[1.5 5.5 9.5]

>>> for row in a:
	print(row)

	
[0 1 2 3]
[4 5 6 7]
[ 8  9 10 11]

合并

>>> a =np.array([1,2,3])
>>> b=np.array([4,5,6])
>>> print(np.vstack(a,b))
>>> print(np.vstack((a,b)))
[[1 2 3]
 [4 5 6]]

>>> print(np.hstack((a,b)))
[1 2 3 4 5 6]

a=np.arange(4)
>>> print(a)
[0 1 2 3]
>>> print(a[:,np.newaxis])
[[0]
 [1]
 [2]
 [3]]

>>> b=np.arange(6).reshape((2,3))
>>> print(a)
[[1 2 3]
 [3 4 5]]
>>> print(b)
[[0 1 2]
 [3 4 5]]
>>> c = np.concatenate((a,b,a))
>>> print(c)
[[1 2 3]
 [3 4 5]

分割

>>> print(a)
[[1 2 3]
 [3 4 5]]
>>> print(np.split(a,2,axis=0))#分割成2行
[array([[1, 2, 3]]), array([[3, 4, 5]])]
print(np.split(a,3,axis=1))#分割成3列

[array([[1],
       [3]]), array([[2],
       [4]]), array([[3],
      [5]])]