tile and repeat are handy functions when you want to repeat an array in various ways:
In [233]: np.tile(np.array([4,6,6,1]),(3,1))
Out[233]:
array([[4, 6, 6, 1],
[4, 6, 6, 1],
[4, 6, 6, 1]])
On the failure, note the docs for fill:
a.fill(value)
Fill the array with a scalar value.
np.array([4,6,6,1]) is not a scalar value. a was initialized as a 3 element float array.
It is possible to assign values to elements of an array, provided the shapes are right:
In [241]: a=np.empty(3)
In [242]: a[:]=np.array([1,2,3]) # 3 numbers into 3 slots
In [243]: a
Out[243]: array([ 1., 2., 3.])
In [244]: a=np.empty((3,4))
In [245]: a[:]=np.array([1,2,3,4]) # 4 numbers into 4 columns
In [246]: a
Out[246]:
array([[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.]])
This fill works with an object type array, but the result is quite different, and should be used with considerable caution:
In [247]: a=np.empty(3, object)
In [248]: a
Out[248]: array([None, None, None], dtype=object)
In [249]: a.fill(np.array([1,2,3,4]))
In [250]: a
Out[250]: array([array([1, 2, 3, 4]), array([1, 2, 3, 4]), array([1, 2, 3, 4])], dtype=object)
This (3,) array is not the same as the (3,4) array produced by other methods. Each element of the object array is a pointer to the same thing. Changing a value in one element of a changes that value in all the elements (because they are the same object).
In [251]: a[0][3]=5
In [252]: a
Out[252]: array([array([1, 2, 3, 5]), array([1, 2, 3, 5]), array([1, 2, 3, 5])], dtype=object)
tile and repeat are handy functions when you want to repeat an array in various ways:
In [233]: np.tile(np.array([4,6,6,1]),(3,1))
Out[233]:
array([[4, 6, 6, 1],
[4, 6, 6, 1],
[4, 6, 6, 1]])
On the failure, note the docs for fill:
a.fill(value)
Fill the array with a scalar value.
np.array([4,6,6,1]) is not a scalar value. a was initialized as a 3 element float array.
It is possible to assign values to elements of an array, provided the shapes are right:
In [241]: a=np.empty(3)
In [242]: a[:]=np.array([1,2,3]) # 3 numbers into 3 slots
In [243]: a
Out[243]: array([ 1., 2., 3.])
In [244]: a=np.empty((3,4))
In [245]: a[:]=np.array([1,2,3,4]) # 4 numbers into 4 columns
In [246]: a
Out[246]:
array([[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.]])
This fill works with an object type array, but the result is quite different, and should be used with considerable caution:
In [247]: a=np.empty(3, object)
In [248]: a
Out[248]: array([None, None, None], dtype=object)
In [249]: a.fill(np.array([1,2,3,4]))
In [250]: a
Out[250]: array([array([1, 2, 3, 4]), array([1, 2, 3, 4]), array([1, 2, 3, 4])], dtype=object)
This (3,) array is not the same as the (3,4) array produced by other methods. Each element of the object array is a pointer to the same thing. Changing a value in one element of a changes that value in all the elements (because they are the same object).
In [251]: a[0][3]=5
In [252]: a
Out[252]: array([array([1, 2, 3, 5]), array([1, 2, 3, 5]), array([1, 2, 3, 5])], dtype=object)
Use broadcasting
vstack, tile, and repeat are all great and whatnot, but broadcasting can be several orders of magnitude faster...
import numpy as np
from time import time
t = time()
for _ in xrange(10000):
a = np.array([4,6,6,1])
b = np.vstack((a,)*100)
print time()-t
t = time()
for _ in xrange(10000):
a = np.array([4,6,6,1])
b = np.tile(a,(3,1))
print time()-t
t = time()
for _ in xrange(10000):
a = np.array([4,6,6,1])
b = np.empty([100,a.shape[0]])
b[:] = a
print time()-t
prints:
2.76399993896
0.140000104904
0.0490000247955
python - How to declare and fill an array in NumPy? - Stack Overflow
Trying to fill an empty numpy array with data
python - Best way to initialize and fill an numpy array? - Stack Overflow
python - Numpy array, fill empty values for a single column - Stack Overflow
You could do:
data1 = np.zeros((56,4))
to get a 56 by 4 array. If you don't like to start the array with 0, you could use np.ones or np.empty or np.ones((56, 4)) * np.nan
Then, in most cases it is best not to python-loop if not needed for performance reasons. So as an example this would do your loop:
data[:, 3] = 32
data1 = np.array([ra,dec,[32]*len(ra)])
Gives a single-line solution to your problem; but for efficiency, allocating an empty array first and then copying in the relevant parts would be preferable, so you avoid the construction of the dummy list.
Hi,
My goal is to take the data read from a file and enter it into an empty numpy array in form of two-tuple pairs. I can print out the data easily, but I can't figure out how to print it out as an array.
(I've tried starting off with an empty array and filling it up, but I can't get that to work.)
Any help is appreciated.
(The type of data being read:
1234, 5432
5765, 195
262, 7356
2345, 872
Desired result: [(1234, 5432) (5765, 195) ..etc] )
You could also try:
In [79]: np.full(3, np.nan)
Out[79]: array([ nan, nan, nan])
The pertinent doc:
Definition: np.full(shape, fill_value, dtype=None, order='C')
Docstring:
Return a new array of given shape and type, filled with `fill_value`.
Although I think this might be only available in numpy 1.8+
np.fill modifies the array in-place, and returns None. Therefor, if you're assigning the result to a name, it gets a value of None.
An alternative is to use an expression which returns nan, e.g.:
a = np.empty(3) * np.nan