如何从NumPy数组中逐行选择元素

Question

我有一个像这样的数组numpy数组

dd= [[foo 0.567 0.611]
     [bar 0.469 0.479]
     [noo 0.220 0.269]
     [tar 0.480 0.508]
     [boo 0.324 0.324]]

如何循环数组选择foo并获得0.567 0.611作为单个浮点数。然后选择bar，得到0.469 0.479作为单个浮点数.....

我可以使用

将vector的第一个元素作为列表

dv=  dd[:,1]

'foo'和'bar'元素不是未知变量，它们可以改变。

如果元素位于[1]位置，我该如何更改?

[[0.567 foo2 0.611]
  [0.469 bar2 0.479]
  [0.220 noo2 0.269]
  [0.480 tar2 0.508]
  [0.324 boo2 0.324]]

Answer 1

你已经把NumPy标签放在你的问题上，所以我假设你想要NumPy语法，我之前的答案不使用。

如果实际上你希望使用NumPy，那么你可能不希望数组中的字符串，否则你还必须将浮点数表示为字符串。

您要查找的是 NumPy语法，以按行访问2D数组的元素(并排除第一列)。

语法是:

M[row_index,1:]        # selects all but 1st col from row given by 'row_index'

W/r/t你的问题中的第二个场景-选择非相邻列:

M[row_index,[0,2]]     # selects 1st & 3rd cols from row given by 'row_index'

你的问题中的小复杂性只是你想使用一个字符串的row_index，所以有必要删除字符串(所以你可以创建一个2D NumPy数组的浮点数)，用数字行索引替换它们，然后创建一个查找表映射字符串与数字行索引:

>>> import numpy as NP
>>> # create a look-up table so you can remove the strings from your python nested list,
>>> # which will allow you to represent your data as a 2D NumPy array with dtype=float
>>> keys
      ['foo', 'bar', 'noo', 'tar', 'boo']
>>> values    # 1D index array comprised of one float value for each unique string in 'keys'
      array([0., 1., 2., 3., 4.])
>>> LuT = dict(zip(keys, values))
>>> # add an index to data by inserting 'values' array as first column of the data matrix
>>> A = NP.hstack((vals, A))
>>> A
        NP.array([  [ 0., .567, .611],
                    [ 1., .469, .479],
                    [ 2., .22, .269],
                    [ 3., .48, .508],
                    [ 4., .324, .324] ])
>>> # so now to look up an item, by 'key':
>>> # write a small function to perform the look-ups:
>>> def select_row(key):
        return A[LuT[key],1:]
>>> select_row('foo')
      array([ 0.567,  0.611])
>>> select_row('noo')
      array([ 0.22 ,  0.269])

问题中的第二个场景:如果索引列改变了怎么办?

>>> # e.g., move index to column 1 (as in your Q)
>>> A = NP.roll(A, 1, axis=1)
>>> A
      array([[ 0.611,  1.   ,  0.567],
             [ 0.479,  2.   ,  0.469],
             [ 0.269,  3.   ,  0.22 ],
             [ 0.508,  4.   ,  0.48 ],
             [ 0.324,  5.   ,  0.324]])
>>> # the original function is changed slightly, to select non-adjacent columns:
>>> def select_row2(key):
        return A[LuT[key],[0,2]]
>>> select_row2('foo')
        array([ 0.611,  0.567])

Answer 2

首先，第一个元素的向量为

dv = dd[:,0]

(python为0索引)

其次，要遍历数组(例如存储在字典中)，可以这样写:

dc = {}
ind = 0 # this corresponds to the column with the names
for row in dd:
    dc[row[ind]] = row[1:]