[Python] Replacing values in columns with values from another columns according to mapping

I have this kind of dataframe:

df = pd.DataFrame({
"A1": [1, 11, 111],
"A2": [2, 22, 222],
"A3": [3, 33, 333],
"A4": [4, 44, 444],
"A5": [5, 55, 555]
})

A1 A2 A3 A4 A5
0 1 2 3 4 5
1 11 22 33 44 55
2 111 222 333 444 555


and this kind of mapping:

mapping = {
"A1": ["A2", "A3"],
"A4": ["A5"]
}


which means that I want all columns in list to have values from key column so: A2 and A3 should be populated with values from A1, and A5 should be populated with values from A4. Resulting dataframe should look like this:

A1 A2 A3 A4 A5
0 1 1 1 4 4
1 11 11 11 44 44
2 111 111 111 444 444


I managed to do it pretty simply like this:

for k, v in mapping.items():
for col in v:
df[col] = df[k]


but I was wondering if there is vectorized way of doing it (more pandactic way)?

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