How To Fill Missing Values In A Dataframe Based On Group Value Counts?

I have a pandas DataFrame with 2 columns: Year(int) and Condition(string). In column Condition I have a nan value and I want to replace it based on information from groupby operati

Solution 1:

I did a little extra transformation to get stat as a dictionary mapping the year to its highest frequency name (credit to this answer):

In[0]:
fill_dict = stat.unstack().idxmax(axis=1).to_dict()
fill_dict

Out[0]:
{2015: 'good', 2016: 'good', 2017: 'excellent'}

Then use fillna with map based on this dictionary (credit to this answer):

In[0]:
X['condition'] = X['condition'].fillna(X['year'].map(fill_dict))
X

Out[0]:
   year  condition
0  2015       good
1  2016       good
2  2017  excellent
3  2016       good
4  2016  excellent
5  2017  excellent
6  2015       good
7  2016       good
8  2015  excellent
9  2015       good