NumPy_2021.07.05

# 避免隐式拷贝，采用就地操作。
# ndarray 解决了多维数组问题
# ufunc 解决对数据进行处理的函数
print('--------------------1--------结构数组----------------------')
import numpy as np
a = np.array([1,2,3])b = np.array([[1,2,3],[4,5,6],[7,8,9]])

print(a.shape)print(b.shape)print(a.dtype,b.dtype)print(b)
b[1,1]=10print(b)
print('--------------------2--------结构数组----------------------')# 2、结构数组
persontype = np.dtype({    'names':['name','age','chinese','math','english'],    'formats':['S32','i','i','i','f']})
people = np.array([    ('Zhangfei',32,75,100,90),('guanyu',24,45,57,56),('zhaoyun',28,84,54,85.4),('HuangZhong',29,65,87,100)],dtype=persontype)
ages = people[:]['age']print(ages)
chineses = people[:]['chinese']
print(np.mean(chineses))  #语文平均值
print('--------------------3--------ufunc运算----------------------')
x1 = np.arange(1,11,2)  # 初始值、终值、步长print(x1)
x2 = np.linspace(1,9,5) # 初始值、终值、元素个数print(x2)
print('--------------------4 ： 算术运算----------------------')
print(np.add(x1,x2)) # 加print(np.subtract(x1,x2)) #减print(np.multiply(x1,x2)) # 乘print(np.divide(x1,x2)) #除print(np.power(x1,x2)) # n次方print(np.remainder(x1,x2)) # 取余print(np.mod(x1,x2)) #取余
print('--------------------5：统计函数----------------------')
a = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(np.amin(a))print(np.amin(a,0))  #  ?print(np.amin(a,1)) #  ?
print(np.amax(a))print(np.amax(a,0))  #  ？print(np.amax(a,1))  #  ？

print('--------------------6：统计最大值最小值之差ptp()----------------------')
print(a)

print(np.ptp(a)) #
print(np.ptp(a,0))
print(np.ptp(a,1))
print('--------------------7：统计数组的百分位数 percentile()----------------------')
a = np.array([[1,2,3],[4,5,6],[7,8,9]])print(np.percentile(a,50))
print(np.percentile(a,50,axis=0))print(np.percentile(a,50,axis=1))
print('--------------------8：统计数组的中位数median（）、平均数mean（）----------------------')a = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(np.median(a))  # 中位数print(np.median(a,axis=0))print(np.median(a,axis=1))
print(np.mean(a)) # 平均数
print('--------------------9：统计数组中的加权平均值 average()----------------------')
a = np.array([1,2,3,4])wst = np.array([1,2,3,4])
print(np.average(a))print(np.average(a,weights=wst)) # 权重
print('--------------------10：统计数组中的标准差 std(),方差 var()----------------------')
# 方差的计算是指每个数值与平均值之差的平方和的平均值# 标准差是方差的算术平方根a = np.array([1,2,3,4])print(np.std(a))  # 标准差print(np.var(a))  # 方差
print('--------------------11：NumPy 排序----------------------')
a = np.array([[4,3,2],[2,4,1]])print(a)print(np.sort(a))print(np.sort(a,axis=None))print(np.sort(a,axis=0)) # 凌乱print(np.sort(a,axis=1))