Do i need to handle missing values before EDA?
If the number of rows includes missing values are very small according to sample size I recommend dismissing it.
But if you decide to keep them according to not lose any information, you can do a bunch of things according to the feature that involves a null value.
You should understand the pattern of the feature column well before deciding the filling method below.

You can change the null value as;

mean of the column
median of the column
same as above or below
just zero
most repeated value along the column
etc.



If there is any categorical feature, you can group by a feature like gender and can do the same thing as above. For example, if there is a NaN height value for a male you can fill it with the mean of the heights of males etc.
Besides all you can decide to discard the whole column with:

Checking the correlation between the column and the dependent variable
Checking the column's representation level of source data with PCA