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# -*- coding: utf-8 -*-
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"""
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Created on Wed Dec 22 13:11:41 2021
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@author: hzh
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"""
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import numpy as np # 导入numpy库
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import pandas as pd
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from pandas import DataFrame
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# 创建ndarray数组
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# 一维数组
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data1 = [6, 7.5, 8, 0, 1] # 列表
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arr1 = np.array(data1)
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print(arr1.ndim)
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arr2 = np.array((5.6, 6.7, 7.8, 8.9, 9.)) # 元组
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print(arr2)
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# pandas series
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print('pandas series')
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df1 = pd.Series(data1)
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print(df1)
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print(df1.values)
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print((df1.values).ndim)
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df2 = df1.values.reshape(1, -1)
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print(df2)
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print(df2.ndim)
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print(df2[0])
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print(df2[0, 0])
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# print(df1.values.reshape(1,-1))
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# 二维numpy数组
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import numpy as np
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data1 = [[6, 7.5, 8, 0, 1], [3.2, 3, 7, 52, 23.4]]
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arr1 = np.array(data1)
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print(arr1)
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print(arr1.shape, arr1.ndim, arr1.dtype, arr1.size)
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# pandas DataFrame
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print('pandas DataFrame')
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arr2 = pd.DataFrame(data1)
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print(arr2.values)
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print(arr2.shape, arr2.ndim, arr2.size)
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print(arr2[0]) # 显示0列数据
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print(arr2.loc[0]) # 显示0行数据
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print(arr2.iloc[0, 0]) # 显示0行0列数据
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# reshape(),astype()
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import numpy as np
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arr1 = np.array([1, 2, 3, 4, 5, 6]) # 6个元素的一维数组
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arr1 = arr1.reshape(2, 3) # 改变为：2行3列的二维数组
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print(arr1)
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arr2 = arr1.astype(str) # 转换成字符型
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print(arr2)
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# 自动创建数组1
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print(np.ones(6))
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print(np.zeros(6).reshape(2, 3))
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print(np.arange(2, 10, 2))
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print(np.linspace(2, 13, 12))
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# 创建随机数组
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np.random.seed(100)
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# 生成2行3列的随机数数组，值在[0,1)之间
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n1 = np.random.rand(2, 3)
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print(n1)
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# 生成2行3列的随机数数组，值为正态分布的随机样本数
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n2 = np.random.randn(2, 3)
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print(n2)
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# 生成包含10个元素的值在[0,100) 的数组
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n3 = np.random.randint(0, 100, 10)
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print(n3)
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# 生成2*3维的值在[0,100) 的数组
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n4 = np.random.randint(0, 100, (2, 3))
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print(n4)
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# 通过文件读取保存数组
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import numpy as np
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arr1 = np.arange(10)
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np.savetxt('2.txt', arr1, fmt='%0.4f', delimiter=',')
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arr2 = np.loadtxt('2.txt')
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print(arr2)
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# 访问数组元素
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# 用下标访问元素1
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x = np.array([1, 2, 3, 4, 5, 6])
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print(x[4]) # 一维数组，访问第4个元素（从0开始计数）
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x = np.array([[1, 2, 3], [4, 5, 6]])
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print(x[1]) # 二维数组，访问第1行数据（从0开始计数）
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# 列表切片2
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arr = np.arange(10)
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print(arr)
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print(arr[5: 8])
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arr[5: 8] = 12 # 值的广播
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print(arr)
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# 数组的切片不是一个新的数组，是数组的一个视图
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arr = np.arange(10)
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arr_slice = arr[5:8] # 获得一个视图而不是新的数组
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arr_slice[1] = 32
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print(arr)
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# 通过切片获得新数组的方法：copy()
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arr = np.arange(10)
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arr_slice = arr[5:8].copy()
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arr_slice[1] = 32
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print(arr)
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# 访问高维数组元素, 切片不改变维度
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arr1d = np.array([[1, 2, 3], [4, 5, 6]])
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print(arr1d)
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print(arr1d[1, 2]) # 等价于print(arr1d[1][2])
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print(arr1d[1]) # 数组子集（也是视图）
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print()
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arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
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print(arr2d)
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print(arr2d[:2]) # 在轴0上切片
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print(arr2d[:2, 1:]) # 在轴0和轴1上切片
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print()
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arr3d = np.array([[1, 2, 3], [4, 5, 6]])
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print(arr3d[1, :2]) # 轴0选第一行，然后在轴1上切片
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print(arr3d[:, 1]) # 轴0全选，然后选取第1列
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# print(arr3d)
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# 列表索引（花式索引）获得的是新数组，不是视图
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import numpy as np
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arr2d = np.linspace(1, 100, 12).reshape(4, 3)
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print(arr2d)
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print(arr2d[[2, 0, 1]]) # 依次选取轴0的2,0,1行
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print(arr2d[:, [2, 0]]) # 依次选取轴1的2,0列
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# print(arr2d)
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# 布尔索引
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arr2d = np.array([[1, -2, 3], [-4, 5, 6]])
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print(arr2d)
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print(arr2d < 0) # arr2d<0 是个布尔型数组
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print(arr2d[arr2d < 0]) # 用布尔型数组来索引
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arr2d[arr2d < 0] = 0 # 赋值
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print(arr2d)
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# numpy的基本运算：标量，向量和矩阵
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# 广播运算
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data1 = [[6, 7.5, 8, 0, 1], [3.2, 3, 7, 52, 23.4]]
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arr1 = np.array(data1)
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arr1 = arr1 * 2 # 这是广播运算
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print(arr1)
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# print(1/arr1)
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# print(arr1**0.5)
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# 标量运算
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print(np.sum([1, 2, 3, 4]))
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print(np.sqrt(100))
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print(np.abs(-10))
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# 向量运算
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lst = [[6, 7.5, 8, 0, 1], [3.2, 3, 7, 52, 23.4]]
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arr2d = np.array(lst)
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print(arr2d)
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print(np.sum(arr2d))
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print(np.min(arr2d, axis=1)) # 求每一行中的最小值，向量
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print('*' * 20)
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np.mean(arr2d, axis=0) # 求每一列的平均值，向量
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# 矩阵运算函数diag(),dot(),trace(),det()....
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import numpy as np
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A = np.array([[m + n for m in np.arange(4)] for n in np.arange(4)]) # 4 x 4 2D arrays
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B = np.array([[m - n for m in np.arange(3)] for n in np.arange(4)]) # 4 x 3 2D arrays
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C = np.arange(4) # 1D vector with a size of 4
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# A, B, C
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print(A)
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print(B)
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print(C)
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print(A.shape)
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print(B.shape)
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print(C.shape)
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# 计算矩阵相乘
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print(np.dot(A, B), '\n')
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print(np.dot(A, C))
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print(np.dot(C, A)) # 数学上的矩阵相乘
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""" 练一练：
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1、用arange()生成一个0-20以内，5行4列的A数组
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2、将A数组元素保存在arr1.txt文件中，保存格式为%4d,以逗号作为分隔符
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3、访问A数组，输出数组的0行最后一个元素
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4、访问A数组，分别输出数组的第2，3行的数据，第3，4列的数据
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5、访问A数组，分别输出数组的轴0方向全选，轴1方向输出第2，3列元素
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6、访问A数组，输出行选取第一行和最后一行，列选取0，3，2列显示
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7、将A数组中所有的奇数变成其平方数输出
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8、求出所有数据、每一行和每一列的平均值并输出
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"""
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