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)
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
vstack
axis = 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在记录数组中添加一个字段。您都导入了返回结构化数组和返回略有不同的记录数组()的方法。您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。genfromtxt
recfromcsv
recarray
recfromcsv
my_data
recarray
my_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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