I have a dataframe like this
>>df1 = pd.DataFrame({'A': ['1', '2', '3', '4','5'],
'B': ['1', '1', '1', '1','1'],
'C': ['c', 'A1', None, 'c3',None],
'D': ['d0', 'B1', 'B2', None,'B4'],
'E': ['A', None, 'S', None,'S'],
'F': ['3', '4', '5', '6','7'],
'G': ['2', '2', None, '2','2']})
>>df1
A B C D E F G
0 1 1 c d0 A 3 2
1 2 1 A1 B1 None 4 2
2 3 1 None B2 S 5 None
3 4 1 c3 None None 6 2
4 5 1 None B4 S 7 2
and I drop the rows which contain nan valuesdf2 = df1.dropna()
A B C D E F G
1 2 1 A1 B1 None 4 2
2 3 1 None B2 S 5 None
3 4 1 c3 None None 6 2
4 5 1 None B4 S 7 2
This is a dropped dataframe due to those rows contain nan values. However,I wanna know why they be dropped? Which column is the "first nan value column" made the row been dropped ? I need a dropped reason for report.
the output should be
['E','C','D','C']
I know I can do dropna
by each column then record it as the reason
but it's really non-efficient.
Is any more efficient way to solve this problem? Thank you
The head() method returns the first n rows. By default, the first 5 rows are returned. You can specify the number of rows.
I think you can create boolean dataframe by DataFrame.isnull
, then filter by boolean indexing
with mask where are at least one True
by any
and last idxmax
- you get column names of first True
values of DataFrame
:
booldf = df1.isnull()
print (booldf)
A B C D E F G
0 False False False False False False False
1 False False False False True False False
2 False False True False False False True
3 False False False True True False False
4 False False True False False False False
print (booldf.any(axis=1))
0 False
1 True
2 True
3 True
4 True
dtype: bool
print (booldf[booldf.any(axis=1)].idxmax(axis=1))
1 E
2 C
3 D
4 C
dtype: object
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