Numpy库基础___三

ndarray一个强大的N维数组对象Array

?ndarray的操作

• 索引

a = np.arange(24).reshape((2,3,4))
print(a)
#[[[ 0  1  2  3]
#   [ 4  5  6  7]
#   [ 8  9 10 11]]
# 
#  [[12 13 14 15]
#   [16 17 18 19]
#   [20 21 22 23]]]
print(a[1,2,3])
#23
print(a[0,1,2])
#6
print(a[-1,-2,-3])
#17

 

• 切片

a = np.arange(24).reshape((2,3,4))
print(a)
#[[[ 0 1 2 3]
# [ 4 5 6 7]
# [ 8 9 10 11]]
# 
# [[12 13 14 15]
# [16 17 18 19]
# [20 21 22 23]]]

print(a[:,1,-3])
#[5,17]

print(a[:,1:3,:])#第二个维度内切片 和list类似
#[[[4,5,6,7]
#[8,9,10,11]]
#
#[[16,17,18,19]
#[20,21,22,23]]]

print(a[:,:,::2])#和list类似，步长
#[[[0,2][[[ 0  2]
#  [ 4  6]
#  [ 8 10]]
#
# [[12 14]
#  [16 18]
3  [20 22]]]

 

?ndarray的运算

 数组与标量之间的运算作用于数组的所有元素

x = np.arange(24).reshape((2,3,4))
print(a)
#[[1 1 1 1]
#  [1 1 1 1]
#  [1 1 1 1]]
print(a/4)
# [[0.25 0.25 0.25 0.25]
#  [0.25 0.25 0.25 0.25]
#  [0.25 0.25 0.25 0.25]]

 

 

 

Numpy库基础___三

原文：https://www.cnblogs.com/empolder-minoz/p/14358781.html