numpy学习

import numpy as np

# a = np.array([1, 2, 3])
# print(a[0])
# b = np.array([[1, 2, 3], [4, 5, 6]])
# print(b[0])
# print(b.shape)

# 创造numpy的方式
# np.array(), np.zeros(), np.ones(),
# np.empty(), np.arange(), np.linspace(), dtype
print(np.zeros(4))
print(np.ones(4, dtype=np.int16))
print(np.empty(3))  # 随机赋值
print(np.arange(4))
print(np.arange(4, 12, 4))
print(np.linspace(0, 10, num=6))  # 生成指定范围内的均匀等差数列

# 增添、移除和排序元素
# np.sort(), np.concatenate()
# aixs用来指定轴，第0轴为纵向，第1轴为横向，None为平展排序
print(np.sort(np.array([[2, 4, 3], [3, 7, 1]]), axis=0))
dtype = [('name', 'S10'), ('height', float), ('age', int)]
values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38), ('Galahad', 1.7, 38)]
people = np.array(values, dtype=dtype)       # create a structured array
print(np.sort(people, order=['name', 'age']))
x = np.array([[1, 2], [3, 4]])
y = np.array([[5, 6]])
print(np.concatenate((x, y), axis=0))

# 获取array的shape和size
# ndarray.ndim, ndarray.size, ndarray.shape
# 分别是维数、总元素、shape（各个维数的元素个数）

# reshape array
# arr.reshape() ?arr和ndarray的区别
print(np.arange(6))
# order C按行输出 F按列输出 A按内存顺序输出
print(np.arange(6).reshape((2, 3), order='A'))
print(np.reshape(np.arange(6), (2, 3), order='F'))

# 把1D array转化为2D array
# np.newaxis, np.expand_dims
print(np.arange(1, 7)[np.newaxis, :, np.newaxis].shape)
print(np.expand_dims(np.arange(1, 7), axis=1).shape)

# 索引和切片
# 直接索引[index]
# 切片索引1[start:end]（不包括end）
# 切片索引2[start:]
# 若start为正数，从前往后索引；负数，从后往前
# 条件索引
test = np.array([[1, 2, 3, 4], [4, 6, 7, 8], [9, 10, 11, 12]])
print(test[test < 5])
five_up = (test > 5)
print(test[five_up])
print(five_up)
divisible_by_2 = test[test % 2 == 0]
print(divisible_by_2)
# 返回满足条件的数字的索引，返回值数据类型为tuple
print(np.nonzero(test < 5))
print(test[np.nonzero(test < 5)])  

# 怎么从已存在的数据创建array