Pandas fill missing values in dataframe from another dataframe

Question

I cannot find a pandas function (which I had seen before) to substitute the NaN\'s in a dataframe with values from another dataframe (assuming a common index which can be sp

Answer 1

As I just learned, there is a DataFrame.combine_first() method, which does precisely this, with the additional property that if your updating data frame d2 is bigger than your original df, the additional rows and columns are added, as well.

df = df.combine_first(d2)