How to add column to numpy array

Solution 1:

I think that your problem is that you are expecting np.append to add the column in-place, but what it does, because of how numpy data is stored, is create a copy of the joined arrays

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.

so you need to save the output 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)

Alternative ways:

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

I believe that the only difference between these three functions (as well as np.vstack) are their default behaviors for when axis is unspecified:

• concatenate assumes axis = 0
• hstack assumes axis = 1 unless inputs are 1d, then axis = 0
• vstack assumes axis = 0 after adding an axis if inputs are 1d
• append flattens array

Based on your comment, and looking more closely at your example code, I now believe that what you are probably looking to do is add a field to a record array. You imported both genfromtxt which returns a structured array and recfromcsv which returns the subtly different record array (recarray). You used the recfromcsv so right now my_data is actually a recarray, which means that most likely my_data.shape = (210,) since recarrays are 1d arrays of records, where each record is a tuple with the given dtype.

So you could try this:

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')

Solution 2:

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)

Solution 3:

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)
'''