You should use numpy.zeros. If that isn't an option, you want the first version. In the second version, if you change one value, it will be changed elsewhere in the list -- e.g.:
>>> a = [[0]*3]*3
>>> a
[[0, 0, 0], [0, 0, 0], [0, 0, 0]]
>>> a[0][0] = 1
>>> a
[[1, 0, 0], [1, 0, 0], [1, 0, 0]]
This is because (as you read the expression from the inside out), you create a list of 10 zeros. You then create a list of 10 references to that initial list of 10 zeros.
Note that this will also work and it avoids the nested list comprehension:
zeros = [ [0]*M for _ in range(N) ]
If numpy isn't on the table, this is the form I would use.
(Use xrange if you're still stuck in the python2.x dark ages :).)
You should use numpy.zeros. If that isn't an option, you want the first version. In the second version, if you change one value, it will be changed elsewhere in the list -- e.g.:
>>> a = [[0]*3]*3
>>> a
[[0, 0, 0], [0, 0, 0], [0, 0, 0]]
>>> a[0][0] = 1
>>> a
[[1, 0, 0], [1, 0, 0], [1, 0, 0]]
This is because (as you read the expression from the inside out), you create a list of 10 zeros. You then create a list of 10 references to that initial list of 10 zeros.
Note that this will also work and it avoids the nested list comprehension:
zeros = [ [0]*M for _ in range(N) ]
If numpy isn't on the table, this is the form I would use.
(Use xrange if you're still stuck in the python2.x dark ages :).)
for Python 3 (no more xrange), the preferred answer
zeros = [ [0] * N for _ in range(M)]
for M x N array of zeros
python - Create arbitrary multidimensional zeros array - Stack Overflow
python - How to define a two-dimensional array? - Stack Overflow
Populate numpy matrix with zeros and ones
How to mix strings and floats in a NumPy array?
I'd consider a pandas DataFrame instead - the actual data is stored as a numpy array and so supports everything it needs to, and the headers are separate so you don't need to do awkward slicing when working on the whole data set. You can also then index rows and columns by their index. And it supports going to and from CSVs easily.
As an aside, the way to get arrays to accept mixed types is
np.array([1, 'a'], dtype=object)
but obviously you lose practically all of the benefits of using numpy in the first place.
More on reddit.comYou can multiply a tuple (n,) by the number of dimensions you want. e.g.:
Use with AI>>> import numpy as np
>>> N=2
>>> np.zeros((N,)*1)
array([ 0., 0.])
>>> np.zeros((N,)*2)
array([[ 0., 0.],
[ 0., 0.]])
>>> np.zeros((N,)*3)
array([[[ 0., 0.],
[ 0., 0.]],
[[ 0., 0.],
[ 0., 0.]]])
Use with AI>>> sh = (10, 10, 10, 10)
>>> z1 = zeros(10000).reshape(*sh)
>>> z1.shape
(10, 10, 10, 10)
While above is not wrong, it's just excessive.
You're technically trying to index an uninitialized array. You have to first initialize the outer list with lists before adding items; Python calls this "list comprehension".
# Creates a list containing 5 lists, each of 8 items, all set to 0
w, h = 8, 5
Matrix = [[0 for x in range(w)] for y in range(h)]
#You can now add items to the list:
Matrix[0][0] = 1
Matrix[6][0] = 3 # error! range...
Matrix[0][6] = 3 # valid
Note that the matrix is "y" address major, in other words, the "y index" comes before the "x index".
print Matrix[0][0] # prints 1
x, y = 0, 6
print Matrix[x][y] # prints 3; be careful with indexing!
Although you can name them as you wish, I look at it this way to avoid some confusion that could arise with the indexing, if you use "x" for both the inner and outer lists, and want a non-square Matrix.
If you really want a matrix, you might be better off using numpy. Matrix operations in numpy most often use an array type with two dimensions. There are many ways to create a new array; one of the most useful is the zeros function, which takes a shape parameter and returns an array of the given shape, with the values initialized to zero:
>>> import numpy
>>> numpy.zeros((5, 5))
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
Here are some other ways to create 2-d arrays and matrices (with output removed for compactness):
numpy.arange(25).reshape((5, 5)) # create a 1-d range and reshape
numpy.array(range(25)).reshape((5, 5)) # pass a Python range and reshape
numpy.array([5] * 25).reshape((5, 5)) # pass a Python list and reshape
numpy.empty((5, 5)) # allocate, but don't initialize
numpy.ones((5, 5)) # initialize with ones
numpy provides a matrix type as well, but it is no longer recommended for any use, and may be removed from numpy in the future.