NaN-10.py

In [62]:
# Usually .fillna() method is used to replace NaNs
new_df['c0'].fillna(mean_c0, inplace=True)

# We can do the same by .replace() method
new_df['c1'].replace(to_replace= np.nan, value= median_c1, inplace=True)

# Also by vectorised indexing
null_indexes_in_c3 = new_df.index[new_df['c3'].isnull()]
new_df.loc[null_indexes_in_c3, 'c3'] = mode_c3[0]

new_df
Out[62]:
	      c0 	  c1 	  c2 	  c3
index 				
i0 	    1.0 	2.0 	NaN 	3.0
i1 	    5.5 	7.0 	NaN 	10.0
i2 	    4.0 	5.0 	NaN 	10.0
i3 	    6.0 	7.0 	NaN 	 8.0
i4 	    5.5 	9.0 	NaN 	10.0
i5 11.0 12.0 NaN 10.0