Given your data
d = [[0, 1, 2, 3 ],
[0, np.nan, 0, 1 ],
[0, np.nan, 3, np.nan],
[0, 1, 2, 5 ],
[0, np.nan, 2, np.nan]] ]
df = pd.DataFrame(d, columns=['a', 'b', 'c', 'd'])
d = {'df_b' : [11,22,44], 'df_d': [33,54]}
try pandas.isna()
for key in d:
column_name = key.split('_')[-1]
val = d[key]
for i,v in zip(df[df[column_name].isna()].index, val):
df.loc[i, column_name] = v
output
a b c d
0 1.0 2 3.0
0 11.0 0 1.0
0 22.0 3 33.0
0 1.0 2 5.0
0 44.0 2 54.0
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