How do I use np.where with multiple columns at once?

np-where-multiple-columns.py

# how to use np.where with multiple columns at once, even whole data frames?

import numpy as np
import pandas as pd

np.random.seed(42)

df1 = pd.DataFrame(np.random.randint(0, 100, size=(100, 4)), columns=list("ABCD"))
df2 = pd.DataFrame(np.random.randint(0, 100, size=(100, 4)), columns=list("ABCD"))
condition = pd.Series(np.random.choice(a=[False, True], size=100, p=[.1, .9]))

np.where(condition, df1["A"], df2["B"])  # works, lost index + column name
pd.Series(np.where(condition, df1["A"], df2["A"]), index=df1.index, name="A")  # works
np.where(condition, df1, df2)  # ValueError: operands could not be broadcast together
np.where(condition, df1.T, df2.T)  # aha! works - just is transposed
np.where(condition, df1.T, df2.T).T  # works, now just needs index + column names

# perfect:
pd.DataFrame(np.where(condition, df1.T, df2.T).T, columns=df1.columns, index=df1.index)