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I think that your problem is that you are expecting <code>np.append</code> 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 <pre><code>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. </code></pre> so you need to save the output <code>all_data = np.append(...)</code>: <pre><code>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) </code></pre> Alternative ways: <pre><code>all_data = np.hstack((my_data, new_col)) #or all_data = np.concatenate((my_data, new_col), 1) </code></pre> I believe that the only difference between these three functions (as well as <code>np.vstack</code>) are their default behaviors for when <code>axis</code> is unspecified: <ul> <li><code>concatenate</code> assumes <code>axis = 0</code></li> <li><code>hstack</code> assumes <code>axis = 1</code> unless inputs are 1d, then <code>axis = 0</code></li> <li><code>vstack</code> assumes <code>axis = 0</code> after adding an axis if inputs are 1d</li> <li><code>append</code> flattens array</li> </ul> <hr> 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 <a href="http://www.scipy.org/Cookbook/Recarray" rel="noreferrer">record array</a>. You imported both <code>genfromtxt</code> which returns a <a href="http://docs.scipy.org/doc/numpy/user/basics.rec.html" rel="noreferrer">structured array</a> and <code>recfromcsv</code> which returns the subtly different <a href="http://www.scipy.org/Cookbook/Recarray" rel="noreferrer">record array (<code>recarray</code>)</a>. You used the <code>recfromcsv</code> so right now <code>my_data</code> is actually a <code>recarray</code>, which means that most likely <code>my_data.shape = (210,)</code> since recarrays are 1d arrays of records, where each record is a tuple with the given dtype. So you could try this: <pre><code>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') </code></pre>
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