[英]How to select elements row-wise from a NumPy array?
我有一个像这个numpy数组的数组
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]]
如何循环通过数组选择foo并获得0.567 0.611作为浮点数作为单例。 然后选择酒吧并获得0.469 0.479作为浮动单身......
我可以通过使用得到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。
如果元素处于位置[1],我将如何改变?
[[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]]
你已经在你的问题上放了NumPy标签,所以我假设你需要NumPy语法,我之前的答案是不使用的。
如果实际上你想使用NumPy,那么你可能不希望数组中的字符串,否则你还必须将你的浮点数表示为字符串。
您正在寻找的是NumPy语法,用于按行访问2D数组的元素(并排除第一列) 。
该语法是:
M[row_index,1:] # selects all but 1st col from row given by 'row_index'
W / r / t问题中的第二个场景 - 选择不相邻的列 :
M[row_index,[0,2]] # selects 1st & 3rd cols from row given by 'row_index'
您的问题中的小复杂性只是您想要为row_index使用字符串,因此有必要删除字符串(这样您就可以创建一个2D NumPy浮点数组),用数字行索引替换它们然后创建一个外观 - up table用数字行索引映射字符串 :
>>> 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])
您的问题中的第二个场景:如果索引列发生了变化,该怎么办?
>>> # 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])
首先,第一个元素的向量是
dv = dd[:,0]
(python是0索引的)
其次,要遍历数组(例如存储在dict中),您可以编写:
dc = {}
ind = 0 # this corresponds to the column with the names
for row in dd:
dc[row[ind]] = row[1:]
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