Suppose I have np.array like below
dat = array([[ 0, 1, 0],
[ 1, 0, 0],
[0, 0, 1]]
)
What I want to do is that adding the (index of row + 1) as a new column to this array, which is like
newdat = array([[ 0, 1, 0, 1],
[ 1, 0, 0, 2],
[0, 0, 1, 3]]
)
How should I achieve this.
You can also use np.append(). You can also get more info about [...,None] here
import numpy as np
dat = np.array([
[0, 1, 0],
[1, 0, 0],
[0, 0, 1]
])
a = np.array(range(1,4))[...,None] #None keeps (n, 1) shape
dat = np.append(dat, a, 1)
print (dat)
The output of this will be:
[[0 1 0 1]
[1 0 0 2]
[0 0 1 3]]
Or you can use hstack()
a = np.array(range(1,4))[...,None] #None keeps (n, 1) shape
dat = np.hstack((dat, a))
And as hpaulj mentioned, np.concatenate is the way to go. You can read more about concatenate documentation. Also, see additional examples of concatenate on stackoverflow
dat = np.concatenate([dat, a], 1)
np.append is misused so often, I prefer to see dat = np.concatenate([dat, a], 1) used instead (or of course the hstack).Use numpy.column_stack:
newdat = np.column_stack([dat, range(1,dat.shape[0] + 1)])
print(newdat)
#[[0 1 0 1]
# [1 0 0 2]
# [0 0 1 3]]
Try something like this using numpy.insert():
import numpy as np
dat = np.array([
[0, 1, 0],
[1, 0, 0],
[0, 0, 1]
])
dat = np.insert(dat, 3, values=[range(1, 4)], axis=1)
print(dat)
Output:
[[0 1 0 1]
[1 0 0 2]
[0 0 1 3]]
More generally, you can make use of numpy.ndarray.shape for the appropriate sizing:
dat = np.insert(dat, dat.shape[1], values=[range(1, dat.shape[0] + 1)], axis=1)
concatenateis your friend here. Read its docsnp.hstack([dat, np.linspace(1, dat.shape[0], dat.shape[0]).reshape((-1, 1))])np.c_[dat,1:len(dat)+1]