In [1]:
import numpy as np
In [2]:
# array
data = [[1,2,3,4],[5,6,7,8]]
type(data)
Out[2]:
list
In [3]:
data = np.array(data)
type(data)
Out[3]:
numpy.ndarray
In [4]:
data.ndim
Out[4]:
2
In [5]:
data.shape
Out[5]:
(2, 4)
In [6]:
# arange
range(15)
Out[6]:
range(0, 15)
In [7]:
list(range(15))
Out[7]:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
In [8]:
for i in range(15):
print(i)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
In [9]:
range(1,16,3)
Out[9]:
range(1, 16, 3)
In [10]:
list(range(1,16,3))
Out[10]:
[1, 4, 7, 10, 13]
In [11]:
data2 = np.arange(15).reshape(5,3)
In [12]:
data2
Out[12]:
array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]])
In [13]:
# zeros
data3 = np.zeros(10) # size
data3
Out[13]:
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
In [14]:
data3.reshape(2,5)
Out[14]:
array([[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]])
In [15]:
np.zeros((2,3,4))
Out[15]:
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.]]])
In [16]:
np.zeros((2,3,4)).ndim
Out[16]:
3
In [17]:
# ones
np.ones((2,5))
Out[17]:
array([[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.]])
In [19]:
# empty
np.empty((2,2,3))
Out[19]:
array([[[1.26670832e-311, 2.47032823e-322, 0.00000000e+000], [0.00000000e+000, 1.33511969e-306, 5.64233733e-067]], [[5.64281696e-091, 2.32002798e-056, 1.29133695e+160], [1.85423562e-051, 3.99910963e+252, 9.34608432e-307]]])
In [20]:
# 识别数组类型
data4 = np.array([1,2,3,4,5])
In [21]:
data4
Out[21]:
array([1, 2, 3, 4, 5])
In [22]:
data4.dtype
Out[22]:
dtype('int32')
In [23]:
data4[1] = 9.99
data4 # 不会自动转换数据类型
Out[23]:
array([1, 9, 3, 4, 5])
In [28]:
# 创建ndarry时显示指定
data5 = np.array([1,2,3,4,5],dtype=np.float32)
data5.dtype
Out[28]:
dtype('float32')
In [29]:
data5
Out[29]:
array([1., 2., 3., 4., 5.], dtype=float32)
In [30]:
data5[1]=9.99
In [31]:
data5
Out[31]:
array([1. , 9.99, 3. , 4. , 5. ], dtype=float32)
In [32]:
# 对ndarray进行类型转换:astype
data4.astype(np.float64)
Out[32]:
array([1., 9., 3., 4., 5.])
In [ ]: