How do I find: Is the first non-NaN value in each column the maximum for that column in a DataFrame?

Question

For example:

      0     1
0  87.0   NaN
1   NaN  99.0
2   NaN   NaN
3   NaN   NaN
4   NaN  66.0
5   NaN   NaN
6   NaN  77.0
7   NaN   NaN
8   NaN   NaN
9  88.0   NaN

My expected output is: [False, True] since 87 is the first !NaN value but not the maximum in column 0. 99 however is the first !NaN value and is indeed the max in that column.

Answer 1

Option a): Just do groupby with first

(May not be 100% reliable )

df.groupby([1]*len(df)).first()==df.max()
Out[89]: 
       0     1
1  False  True

Option b): bfill

Or using bfill(Fill any NaN value by the backward value in the column , then the first row after bfill is the first not NaN value )

df.bfill().iloc[0]==df.max()
Out[94]: 
0    False
1     True
dtype: bool

Option c): stack

df.stack().reset_index(level=1).drop_duplicates('level_1').set_index('level_1')[0]==df.max()
Out[102]: 
level_1
0    False
1     True
dtype: bool

Option d): idxmax with first_valid_index

df.idxmax()==df.apply(pd.Series.first_valid_index)
Out[105]: 
0    False
1     True
dtype: bool

Option e)(From Pir): idxmax with isna

df.notna().idxmax() == df.idxmax()     
Out[107]: 
0    False
1     True
dtype: bool