Numpy维度知识总结

import numpy as np
a = 12
print("a : ", a)
print("shape of a : ", np.shape(a))   #标量，0维
print("type of a : ",type(a))
print("_____________________________________")
b = np.array(12)
print("b : ", b)
print("shape of b : ", np.shape(b))   #标量，0维
print("type of b : ",type(b))
print("_____________________________________")
c = [12]
print("c : ", c)
print("shape of c : ", np.shape(c))   #一维向量
print("type of c : ",type(c))
print("_____________________________________")
d = np.array([12])
print("d : ", d)
print("shape of d : ", np.shape(d))   #一维向量
print("type of d : ",type(d))
print("_____________________________________")
e = np.array([12,13])
print("e : ", e)
print("shape of e : ", np.shape(e))   #一维向量
print("type of e : ",type(e))
print("_____________________________________")
f = np.arange(0, 20)
print("f : ", f)
print("shape of f : ", np.shape(f))   #一维向量
print("type of f : ",type(f))
print("_____________________________________")
g = np.array([[11],[12]])
print("g : ", g)
print("shape of g : ", np.shape(g))   #二维向量
print("type of g: ",type(g))

import numpy as np
import pandas
# 1、np.array()初始化，传入一个list即可。
a1 = np.array(12)                             #0维，()
a2 = np.array([12])                           #1维，(1,)
a3 = np.array([12,13])                        #1维，(2,)
a4 = np.array([[12,13], [14,15]])             #2维，(2,2)
# 此函数在pandas.read_csv()导入数据集后得用
# df = pandas.read_csv('temperature_dataset.csv')
# data = np.array(df)
# 2、np.arange()初始化，只能初始化1维。传入start、stop、步长
b1 = np.arange(3, 7, 0.5)                      #1维
# 3、np.linspace()初始化，也是只能初始化1维
c1 = np.linspace(3, 7, num=5)                  #1维
# 4、np.zeros()初始化，传入shape。创建一个全是0的数组
d1 = np.zeros(5)                               #1维
print(d1)  
d2 = np.zeros((5,))                            #1维，和上面等价
print(d2)
d4 = np.zeros(2,3)                             #报错！！！
print(d4)
d3 = np.zeros((2,3))                           #2维
print(d3)