Does Numpy provide built-in capabilities to print the indices/coordinates of a 2-d Numpy array at its borders?
What I mean is the following: Given, for example, the array
a = np.arange(6).reshape(2, 3).astype(float)
I would like to have a printout as follows:
0 1 2
0 0.0 1.0 2.0
1 3.0 4.0 5.0
It needn't be exactly like that, but the corresponding information should be conveyed. That is, I want to have a "header column" that shows the row indices (here: 0, 1) and a "header row" that shows the column indices (here: 0, 1, 2).
At present, I simply use Pandas for this purpose (i.e. I convert the Numpy array to a Pandas DataFrame and then print the DataFrame). This is also suggested, for example, in this answer to a related question. However, this would always require Pandas, even if I don't use it for anything else in a given project.
So my question, again, is: can I achieve the same printout only with Numpy (thus without Pandas)? Or, if not, is there a lightweight solution (in the sense of a few lines of code without an additional library) to achieve a similar output?
You can try:
def pretty_print(arr, precision=2, sep=1):
rows = [[f"{v:.{precision}f}" for v in row] for row in arr]
maxl, maxw = len(str(arr.shape[0])), max(len(v) for row in rows for v in row)
print(" " * maxl, *(f"{i:>{maxw}}" for i in range(0, arr.shape[1])), sep=" " * sep)
for i, row in enumerate(rows):
print(f"{i:>{maxl}}", *(f"{v:>{maxw}}" for v in row), sep=" " * sep)
a = np.arange(12).reshape(3, 4).astype(float)
a[0, 0] = np.inf
a[1, 1] = -np.inf
a[2, 3] *= -1
pretty_print(a, precision=2, sep=1)
Prints:
0 1 2 3
0 inf 1.00 2.00 3.00
1 4.00 -inf 6.00 7.00
2 8.00 9.00 10.00 -11.00