import numpy as np
import pandas as pd
df1 = pd.DataFrame([], columns=['a', 'b', 'c'])
df2 = pd.DataFrame(np.random.randn(3, 3), columns=['x', 'y', 'z'])
# OK
pd.merge(df1, df2, how='right', left_index=True, right_index=True)
# a b c x y z
#0 NaN NaN NaN 0.665359 0.087728 -0.608138
#1 NaN NaN NaN -0.730847 0.882151 0.175648
#2 NaN NaN NaN 2.370834 -1.347337 -0.478547
pd.merge(df1, df2, how='outer', left_index=True, right_index=True)
# a b c x y z
#0 NaN NaN NaN 0.665359 0.087728 -0.608138
#1 NaN NaN NaN -0.730847 0.882151 0.175648
#2 NaN NaN NaN 2.370834 -1.347337 -0.478547
# NG, this should be the same as above
pd.merge(df1, df2, how='right', left_on='a', right_index=True)
# IndexError: cannot do a non-empty take from an empty axes.
pd.merge(df1, df2, how='outer', left_on='a', right_index=True)
# IndexError: cannot do a non-empty take from an empty axes.
This works if both DataFrame are empty.
df1 = pd.DataFrame([], columns=['a', 'b', 'c'])
df2 = pd.DataFrame([], columns=['x', 'y', 'z'])
pd.merge(df1, df2, how='right', left_on='a', right_index=True)
# Empty DataFrame
# Columns: [a, b, c, x, y, z]
# Index: []
pd.merge(df1, df2, how='outer', left_on='a', right_index=True)
# Empty DataFrame
# Columns: [a, b, c, x, y, z]
# Index: []
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