如何将列添加到numpy数组

user2130951:

我正在尝试向从创建的数组中添加一列recfromcsv在这种情况下,它是一个数组:([210,8]行,列)。

我想添加第九列。空或零都无所谓。

from numpy import genfromtxt
from numpy import recfromcsv
import numpy as np
import time

if __name__ == '__main__':
 print("testing")
 my_data = recfromcsv('LIAB.ST.csv', delimiter='\t')
 array_size = my_data.size
 #my_data = np.append(my_data[:array_size],my_data[9:],0)

 new_col = np.sum(x,1).reshape((x.shape[0],1))
 np.append(x,new_col,1)
askewchan:

我认为您的问题是您希望np.append就地添加该列,但是由于存储的numpy数据的原因,它的作用是创建连接数组的副本

Returns
-------
append : ndarray
    A copy of `arr` with `values` appended to `axis`.  Note that `append`
    does not occur in-place: a new array is allocated and filled.  If
    `axis` is None, `out` is a flattened array.

所以你需要保存输出all_data = np.append(...)

my_data = np.random.random((210,8)) #recfromcsv('LIAB.ST.csv', delimiter='\t')
new_col = my_data.sum(1)[...,None] # None keeps (n, 1) shape
new_col.shape
#(210,1)
all_data = np.append(my_data, new_col, 1)
all_data.shape
#(210,9)

替代方式:

all_data = np.hstack((my_data, new_col))
#or
all_data = np.concatenate((my_data, new_col), 1)

我相信这三个函数(以及np.vstack之间的唯一区别axis是未指定when的默认行为

  • concatenate 假设 axis = 0
  • hstack假设axis = 1除非输入为1d,否则axis = 0
  • vstackaxis = 0如果输入为1d,则假设在添加轴后
  • append 展平数组

根据您的评论,并更加仔细地查看示例代码,我现在认为您可能想做的是记录数组中添加一个字段您都导入了返回结构化数组返回略有不同的记录数组)的方法现在使用的实际上是a ,这意味着最有可能是因为recarrays是记录的1d数组,其中每个记录都是具有给定dtype的元组。genfromtxtrecfromcsvrecarrayrecfromcsvmy_datarecarraymy_data.shape = (210,)

因此,您可以尝试以下操作:

import numpy as np
from numpy.lib.recfunctions import append_fields
x = np.random.random(10)
y = np.random.random(10)
z = np.random.random(10)
data = np.array( list(zip(x,y,z)), dtype=[('x',float),('y',float),('z',float)])
data = np.recarray(data.shape, data.dtype, buf=data)
data.shape
#(10,)
tot = data['x'] + data['y'] + data['z'] # sum(axis=1) won't work on recarray
tot.shape
#(10,)
all_data = append_fields(data, 'total', tot, usemask=False)
all_data
#array([(0.4374783740738456 , 0.04307289878861764, 0.021176067323686598, 0.5017273401861498),
#       (0.07622262416466963, 0.3962146058689695 , 0.27912715826653534 , 0.7515643883001745),
#       (0.30878532523061153, 0.8553768789387086 , 0.9577415585116588  , 2.121903762680979 ),
#       (0.5288343561208022 , 0.17048864443625933, 0.07915689716226904 , 0.7784798977193306),
#       (0.8804269791375121 , 0.45517504750917714, 0.1601389248542675  , 1.4957409515009568),
#       (0.9556552723429782 , 0.8884504475901043 , 0.6412854758843308  , 2.4853911958174133),
#       (0.0227638618687922 , 0.9295332854783015 , 0.3234597575660103  , 1.275756904913104 ),
#       (0.684075052174589  , 0.6654774682866273 , 0.5246593820025259  , 1.8742119024637423),
#       (0.9841793718333871 , 0.5813955915551511 , 0.39577520705133684 , 1.961350170439875 ),
#       (0.9889343795296571 , 0.22830104497714432, 0.20011292764078448 , 1.4173483521475858)], 
#      dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8'), ('total', '<f8')])
all_data.shape
#(10,)
all_data.dtype.names
#('x', 'y', 'z', 'total')

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