Numpy使用案例

"""
一维 列表
二维
三维
。。。
"""
#
# l1 = [1, 3, 5]
# l2 = [
# 	[1, 3, 5],
# 	[2, 4, 6]
# ]
# l3 = [
# 	[
# 		[1, 2],
# 		[3, 4]
# 	],
# 	[
# 		[5, 6],
# 		[7, 8]
# 	]
# ]
#
# print(l3[1][1][1])

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# a1 = np.array([
# 	[
# 		[1, 3],
# 		[2, 4],
# 	],
# 	[
# 		[5, 6],
# 		[7, 8]
# 	]
# ], dtype="float32")

# a1 = np.arange(1, 10, 1)
# a1 = np.linspace(1, 100, 10)
# a1 = np.zeros((2, 2, 2, 2))
# a1 = np.ones((2, 2), dtype="int32")

# a1 = np.arange(1, 9, 1)
# a1 = np.reshape(a1, (2, 4))
# a1 = np.reshape(a1, (4, 2))
# a1 = np.reshape(a1, (2, 2, 2))
# a1 = np.reshape(a1, (8, ))

# print(a1)
# # 维度
# print("ndim", a1.ndim)
# # 形状
# print("shape", a1.shape)
# # 个数
# print("size", a1.size)
# # 元素类型 以及内存大小
# print(a1.dtype, a1.itemsize)

# a1 = np.arange(9)
# a1 = np.reshape(a1, (3, 3))
# print(a1)
# print(a1[2: 5])

# np.savetxt("data.txt", a1, fmt="%d", delimiter=",")

# a1 = np.loadtxt(fname="data.txt", dtype="int32", delimiter=",")
# print(a1)
# print(a1.ndim, a1.shape)

# a1 = np.arange(0, 2*np.pi, 0.01)
# print(a1)
# r = np.sin(a1)
# print(r)


a1 = np.array([1, 5, 3, 6, 2, 8])
# print(np.amax(a1), np.min(a1))
a1 = np.reshape(a1, (3,2))
print(a1)
a1 = np.transpose(a1)
print(a1)