Python scientific computing learns a NumPy fast processing data

1 Creating an array

(1) Array (boject, Dtype=none, Copy=true, Order=none, Subok=false, ndmin=0)

A = Array ([1, 2, 3, 4])

b = Array ([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

A.dtype-Dtype (' int32 ')

A.shape---(4,)

B.shape---(3, 4)

a.shape=2, 1 # (-1 when automatically calculated, equivalent to 2, 6)

c = A.reshape ((2,2)) #c和a公用一个空间

(2) Arange ([Start,] stop [, step], Dtype=none)

A = Arange (5)-->array ([0, 1, 2, 3, 4])

A[2:4]-->array ([2,3])

A[:-1]-->array ([0, 1, 2, 3]) #下标为负数, indicating the number of forwards from the back

A[2:4] = 4,-->array ([0, 1,]) #可以通过下标修改元素

x = Arange (5, 0,-1)-->array ([5, 4, 3, 2, 1])

X[array ([True, False, True, false])]

-->array ([5, 3]) #只获取布尔数组中True所在的下标 0 2 length not counted false

X[array ([True, False, False, True, false])) = -5, 2 #用布尔数组修改True所在下标的元素

x-->array ([-5, 4, 3,-2, 1])

(3) Linspace (Start, Stop, num=50, Endpoint=true, Retstep=false) #等差数列的一维数组

Logspane (Start, Stop, num=50, Endpoint=true, base=10) #等比数列的一维数组

(4) Frombuffer

FromFile

FromString (String, Dtype=float, Count=-1, sep= ")

FromString (' abcdefgh ', int8)

-->array ([98, 101, 102, 103, 104], dtype=int8) #一个字符占1个字节 (Byte) = 8 bit (bit),

FromString (' abcdefgh ', In16)

-->array ([25185, 25699, 26213, 26727], dtype=int16) #25185 =98*256 + 97

(5) FromFunction (funtion, Shape, **kwargs)

def func (I, J):

Return (i+1) * (j+1)

A = FromFunction (func, (9, 9))--generates a 99 multiplication Formula two-dimensional array a[i, j] = func (i, J)

The above is equivalent to Arange (1,10). Reashape ( -1,1) * Arange (1,10)

Python scientific computing learns a NumPy fast processing data