numpy

building

np.zeros((10,5))
np.ones((10,5))
arr[1:3,3:5].copy()

Attributes of arrays

x3 = np.random.randint(10, size=(3, 4, 5))
print("x3 ndim: ", x3.ndim)
print("x3 shape:", x3.shape)
print("x3 size: ", x3.size)
print("dtype:", x3.dtype)
print("itemsize:", x3.itemsize, "bytes")
print("nbytes:", x3.nbytes, "bytes")

Indexing of arrays

x3[2][4]

Slicing of arrays

x2[::-1, ::-1]
x2_sub_copy = x2[:2, :2].copy()

Reshaping of arrays

grid = np.arange(1, 10).reshape((3, 3))

Joining and splitting of arrays

x = np.array([1, 2, 3])
y = np.array([3, 2, 1])
np.concatenate([x, y])   #将0级元素合并
#array([1, 2, 3, 3, 2, 1])
grid = np.array([[1, 2, 3],[4, 5, 6]])
np.concatenate([grid, grid], axis=1) #将1级元素合并（去掉1级括号，并concatenate所有1级括号内的元素，组合成新的一级括号）
#array([[1, 2, 3, 1, 2, 3],[4, 5, 6, 4, 5, 6]])
np.vstack([x, grid])  #垂直append
np.hstack([grid, y])  #水平append

x = [1, 2, 3, 99, 99, 3, 2, 1]
x1, x2, x3 = np.split(x, [3, 5])
np.vsplit(grid, [2])     #[0:2]元素分为一组，[2:]分为一组
np.hsplit(grid,[1])

汇总统计

np.sum((inches > 0.5) & (inches < 1))
&     np.bitwise_and
|      np.bitwise_or
^     np.bitwise_xor
~     np.bitwise_not
x[x < 5]
np.sum()
np.mean()   #平均数
np.std()   #标准差
np.var()   #方差
np.min()  np.max()
np.argmin() np.argmax()
np.cumsum()  #累积和
np.cumprod() #累计积

np.random

np.random.randn(100)

bool

arr.any()
arr.all()

sort

arr.sort(1)  #按第一维排序
np.unique(arr)  #不重复
np.intersect1d(x,y)   #公共元素
np.union1d(x,y)  #并集
np.in1d(x,y)  #x元素是否包含于y
np.setdiff1d(x,y)  #元素在x中不在y中
np.setxor1d(x,y)  #xor

输入输出

np.save('example',arrbig)
np.load('example.npy')  #.npy

存取txt

arr=np.loadtxt('array.txt',delimiter=',')
np.savetxt()

numpy.linalg func

np.dot(x,y)
from numpy.linalg import inv,qr 
inv(arrbig)   #矩阵的逆
q,r=qr(mat) #QR分解
diag  dot  trace  det  eig  inv  qr  svd  solve  lstsq
np.identity(3)  #3阶I
x,y=np.meshgrid(x,y)