Pandas Dataframe object types fillna exception over different datatypes

Question

I have a Pandas Dataframe with different dtypes for the different columns. E.g. df.dtypes returns the following.

Date                    datetime64[ns]
FundID                           int64
FundName                        object
CumPos                           int64
MTMPrice                       float64
PricingMechanism                object

Various of cheese columns have missing values in them. Doing a group operations on it with NaN values in place cause problems. To get rid of them with the .fillna() method is the obvious choice. Problem is the obvious clouse for strings are .fillna("") while .fillna(0) is the correct choice for ints and floats. Using either method on DataFrame throws exception. Any elegant solutions besides doing them individually (have about 30 columns)? I have a lot of code depending on the DataFrame and would prefer not to retype the columns as it is likely to break some other logic. Can do:

df.FundID.fillna(0)
df.FundName.fillna("")
etc

Answer 1

similar to @Guddi: A bit verbose, but still more concise then @Ryan's answer and keeping all columns:

df[df.select_dtypes("object").columns] = df.select_dtypes("object").fillna("")