How to change dataframe cells values with "coordinate-like" indexes stored in two lists/vectors/series?

Question

Apologize if this has been asked before, somehow I am not able to find the answer to this.

Let's say I have two lists of values:

rows = [0,1,2]
cols = [0,2,3]

that represents indexes of rows and columns respectively. The two lists combined signified sort of coordinates in the matrix, i.e (0,0), (1,2), (2,3).

I would like to use those coordinates to change specific cells of the dataframe without using a loop.

In numpy, this is trivial:

data = np.ones((4,4))
data[rows, cols] = np.nan

array([[nan,  1.,  1.,  1.],
      [ 1.,  1., nan,  1.],
      [ 1.,  1.,  1., nan],
      [ 1.,  1.,  1.,  1.]])

But in pandas, it seems I am stuck with a loop:

df = pd.DataFrame(np.ones((4,4)))
for _r, _c in zip(rows, cols): 
    df.iat[_r, _c] = np.nan

Is there a way to use to vectors that lists coordinate-like index to directly modify cells in pandas?

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Please note that the answer is not to use iloc instead, this selects the intersection of entire rows and columns.

Answer 1

Very simple! Exploit the fact that pandas is built on top of numpy and use DataFrame.values

df.values[rows, cols] = np.nan

Output:

     0    1    2    3
0  NaN  1.0  1.0  1.0
1  1.0  1.0  NaN  1.0
2  1.0  1.0  1.0  NaN
3  1.0  1.0  1.0  1.0