I am trying to add one column to the array created from recfromcsv. In this case it's an array: [210,8] (rows, cols).
I want to add a ninth column. Empty or with zeroes doesn't matter.
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)
If you have an array, a of say 210 rows by 8 columns:
a = numpy.empty([210,8])
and want to add a ninth column of zeros you can do this:
b = numpy.append(a,numpy.zeros([len(a),1]),1)
It can be done like this:
import numpy as np
# create a random matrix:
A = np.random.normal(size=(5,2))
# add a column of zeros to it:
print(np.hstack((A,np.zeros((A.shape[0],1)))))
In general, if A is an m*n matrix, and you need to add a column, you have to create an n*1 matrix of zeros, then use "hstack" to add the matrix of zeros to the right of the matrix A.
I add a new column with ones to a matrix array in this way:
Z = append([[1 for _ in range(0,len(Z))]], Z.T,0).T
Maybe it is not that efficient?
The easiest solution is to use numpy.insert().
The Advantage of np.insert() over np.append is that you can insert the new columns into custom indices.
import numpy as np
X = np.arange(20).reshape(10,2)
X = np.insert(X, [0,2], np.random.rand(X.shape[0]*2).reshape(-1,2)*10, axis=1)
'''
Source: Stackoverflow.com