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## Python String Formatting And String Multiplication Oddity

Tag : python , By : Hugo Hernan Buitrago
Date : March 29 2020, 07:55 AM
In python % has the same precedence as * so they group left to right.
``````print('%d' % 2 * 4)
``````
``````print( ('%d' % 2) * 4)
``````

## optimize multiplication by using for loop supposing multiplication is slower than addition

Tag : optimization , By : nemof
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Have you tried it and timed it?
On nearly every single modern machine today, there is fast hardware support for multiplication. So unless it's simple multiplication by 2, no, it will not be faster.
``````add/sub   1 cycle latency
mul/imul  3 cycle latency
``````

## multiplication of string [ containing integer], output also stored in string, How?

Tag : cpp , By : ArmHead
Date : March 29 2020, 07:55 AM

## Why is this string multiplication throwing an error?

Tag : python , By : Lars
Date : March 29 2020, 07:55 AM

## Why same symbol shows element wise multiplication for array and matrix like multiplication for matrics?

Tag : python , By : avi
Date : March 29 2020, 07:55 AM
may help you . Numpy matrices are strictly 2-dimensional, while numpy arrays (ndarrays) are N-dimensional. Matrix objects are a subclass of ndarray, so they inherit all the attributes and methods of ndarrays. Ndarrays apply almost all operations elemntwise.
The main advantage of numpy matrices is that they provide a convenient notation for matrix multiplication: if a and b are matrices, then a*b is their matrix product (not elementwise). The elementwise operation with np.matrix is obtained with np.multiply(a,b).
``````a= np.arange(2*2).reshape(2,2)
#> a = [[0 1]
#       [2 3]]
b= np.array([[0,0],[1,1]])
#> b = [[0 0]
#       [1 1]]
a@b
#>[[1 1]
#  [3 3]]
a*b
#>[[0 0]
#  [2 3]]

d=np.matrix([[0,1],[2,3]])
e= np.matrix([[0,0],[1,1]])
d*e                 # Equivalent to a@b
#> [[1 1]
#   [3 3]]
np.multiply(d,e)    # Equivalent to a*b
#> [[0 0]
#   [2 3]]
``````