详解Python中where()函数的用法

>>>b=np.arange(10)
>>>b
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>>np.where(b>5)
 (array([6, 7, 8, 9], dtype=int64),)

>>>a=np.reshape(np.arange(20),(4,5))
>>>a 
array([[ 0, 1, 2, 3, 4],
    [ 5, 6, 7, 8, 9],
    [10, 11, 12, 13, 14],
    [15, 16, 17, 18, 19]])
>>>np.where(a>10)
(array([2, 2, 2, 2, 3, 3, 3, 3, 3], dtype=int64),
 array([1, 2, 3, 4, 0, 1, 2, 3, 4], dtype=int64))

numpy.where(condition[, x, y])

>>> np.where([[True, False], [True, True]],
...     [[1, 2], [3, 4]],
...     [[9, 8], [7, 6]])
array([[1, 8],
    [3, 4]])
>>>
>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
>>>
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )]        # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1)        # Note: broadcasting.
array([[ 0., 1., 2.],
    [ 3., 4., -1.],
    [-1., -1., -1.]])
Find the indices of elements of x that are in goodvalues.

>>>
>>> goodvalues = [3, 4, 7]
>>> ix = np.in1d(x.ravel(), goodvalues).reshape(x.shape)
>>> ix
array([[False, False, False],
    [ True, True, False],
    [False, True, False]], dtype=bool)
>>> np.where(ix)
(array([1, 1, 2]), array([0, 1, 1]))

import numpy as np
'''
x = np.random.randn(4,4)
print(np.where(x>0,2,-2))
#试试效果
xarr = np.array([1.1,1.2,1.3,1.4,1.5])
yarr = np.array([2.1,2.2,2.3,2.4,2.5])
zarr = np.array([True,False,True,True,False])
result = [(x if c else y)
     for x,y,c in zip(xarr,yarr,zarr)]
print(result)

#where()函数处理就相当于上面那种方案

result = np.where(zarr,xarr,yarr)
print(result)

'''
#发现个有趣的东西
# #处理2组数组
# #True and True = 0
# #True and False = 1
# #False and True = 2
# #False and False = 3

cond2 = np.array([True,False,True,False])
cond1 = np.array([True,True,False,False])
#第一种处理 太长太丑
result = []
for i in range(4):
  if (cond1[i] & cond2[i]):  result.append(0);
  elif (cond1[i]):  result.append(1);
  elif (cond2[i]):  result.append(2);
  else : result.append(3);
print(result)
#第二种 直接where() 很快很方便
result = np.where(cond1 & cond2,0,np.where(cond1,1,np.where(cond2,2,3)))
print(result)
#第三种 更简便（好像这跟where()函数半毛钱的关系都没有
result = 1*(cond1 & -cond2)+2*(cond2 & -cond1)+3*(-(cond1 | cond2)) （没想到还可以这么表达吧）
print(result)

x = np.arange(16)
print(x[np.where(x>5)])
#输出：(array([ 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=int64),)

x = np.arange(16).reshape(-1,4)
print(np.where(x>5))

#(array([1, 1, 2, 2, 2, 2, 3, 3, 3, 3], dtype=int64), array([2, 3, 0, 1, 2, 3, 0, 1, 2, 3], dtype=int64))
#注意这里是坐标是前面的一维的坐标，后面是二维的坐标

ix = np.array([[False, False, False],
    [ True, True, False],
    [False, True, False]], dtype=bool)
print(np.where(ix))
#输出：(array([1, 1, 2], dtype=int64), array([0, 1, 1], dtype=int64))