Unexpected behavior in assigning 2d numpy array to pandas DataFrame

Question

I have the following code:

x = pd.DataFrame(np.zeros((4, 1)), columns=['A'])
y = np.random.randn(4, 2)
x['A'] = y

I expect it to throw an exception because of shape mismatch. But pandas silently accepted the assignment: y's first column is assigned to x.

Is this an intentional design? If yes, what is the rationale behind?

I tried both pandas 0.21 and 0.23.

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Thanks for those who tried to help. However, nobody gives a satisfactory answer although the bounty is going to expire.

Let me emphasis what is expected as an answer:

1. whether this design is intentional? Is it a bug ? Is it a false design?
2. what is the rationale to design it in this way?

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Since the bounty is going to expiry, I accepted the most voted answer. But it does not provide a answer to the above questions.

Answer 1

The values in y are un-indexed matrix. The case x['A'] = y works here as it take the first item from the matrix and assign it to the 'A'.

Similarly,

x = pd.DataFrame(np.zeros((4, 2)), columns=['A', 'B'])
y = np.random.randn(4, 2)
x[['A', 'B']] = y

will also work because the extra data is being discarded by pandas. If you're trying to pass less columns, say:

x = pd.DataFrame(np.zeros((4, 2)), columns=['A', 'B'])
y = np.random.randn(4, 1)
x[['A', 'B']] = y

That will also work as it will assign the same values to both the columns. This case is similar to x['A'] = 0 which will replace all the data in column A with zeros.