How to add column to numpy array
Question:
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)
Answers:
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)
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 = 0hstack assumes axis = 1 unless inputs are 1d, then axis = 0vstack assumes axis = 0 after adding an axis if inputs are 1dappend 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')
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?
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.
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)
'''
np.append or np.hstack expects the appended column to be the proper shape, that is N x 1. We can use np.zeros to create this zeros column (or np.ones to create a ones column) and append it to our original matrix (2D array).
def append_zeros(x):
zeros = np.zeros((len(x), 1)) # zeros column as 2D array
return np.hstack((x, zeros)) # append column
Similar to some of the other answers suggesting using numpy.hstack, but more readable:
import numpy as np
# declare 10 rows x 3 cols integer array of all 1s
arr = np.ones((10, 3), dtype=np.int64)
# get the number of rows in the original array (as if we didn't know it was 10 or it could be different in other cases)
numRows = arr.shape[0]
# declare the new array which will be the new column, integer array of all 0s so it's visually distinct from the original array
additionalColumn = np.zeros((numRows, 1), dtype=np.int64)
# use hstack to tack on the additionl column
result = np.hstack((arr, additionalColumn))
print(result)
result:
$ python3 scratchpad.py
[[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]]
Here’s a shorter one-liner:
import numpy as np
data = np.random.rand(210, 8)
data = np.c_[data, np.zeros(len(data))]
Something that I use often to convert points to homogenous coordinates with np.ones instead.
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)
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:
concatenateassumesaxis = 0hstackassumesaxis = 1unless inputs are 1d, thenaxis = 0vstackassumesaxis = 0after adding an axis if inputs are 1dappendflattens 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')
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?
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.
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)
'''
np.append or np.hstack expects the appended column to be the proper shape, that is N x 1. We can use np.zeros to create this zeros column (or np.ones to create a ones column) and append it to our original matrix (2D array).
def append_zeros(x):
zeros = np.zeros((len(x), 1)) # zeros column as 2D array
return np.hstack((x, zeros)) # append column
Similar to some of the other answers suggesting using numpy.hstack, but more readable:
import numpy as np
# declare 10 rows x 3 cols integer array of all 1s
arr = np.ones((10, 3), dtype=np.int64)
# get the number of rows in the original array (as if we didn't know it was 10 or it could be different in other cases)
numRows = arr.shape[0]
# declare the new array which will be the new column, integer array of all 0s so it's visually distinct from the original array
additionalColumn = np.zeros((numRows, 1), dtype=np.int64)
# use hstack to tack on the additionl column
result = np.hstack((arr, additionalColumn))
print(result)
result:
$ python3 scratchpad.py
[[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]
[1 1 1 0]]
Here’s a shorter one-liner:
import numpy as np
data = np.random.rand(210, 8)
data = np.c_[data, np.zeros(len(data))]
Something that I use often to convert points to homogenous coordinates with np.ones instead.