iLocation based boolean indexing on an integer type is not available

Question

I have an issue, i Want to get those rows which contains missing values. Using iloc and pd.isnull, for column \'Mileage\' in my table.

import p         


        

Answer 1

You need use DataFrame.loc, because select by labels Bike and Mileage:

d2 = df.loc[pd.isnull(df['Mileage']),['Bike','Mileage']]

Or use Series.isna:

d2 = df.loc[df['Mileage'].isna(),['Bike','Mileage']]

If need DataFrame.iloc is necessary convert boolean mask to numpy array, but also columns to positions of columns by Index.get_indexer:

d2 = df.iloc[pd.isnull(df['Mileage']).values, df.columns.get_indexer(['Bike','Mileage'])]

Sample:

df = pd.DataFrame({
        'A':list('abcdef'),
         'Mileage':[np.nan,5,4,5,5,np.nan],
         'Bike':[7,8,9,4,2,3],
         'D':[1,3,5,7,1,0],
         'E':[5,3,6,9,2,4],
         'F':list('aaabbb')
})

print (df)
   A  Mileage  Bike  D  E  F
0  a      NaN     7  1  5  a
1  b      5.0     8  3  3  a
2  c      4.0     9  5  6  a
3  d      5.0     4  7  9  b
4  e      5.0     2  1  2  b
5  f      NaN     3  0  4  b

d2 = df.loc[pd.isnull(df['Mileage']),['Bike','Mileage']]
print (d2)
   Bike  Mileage
0     7      NaN
5     3      NaN

d2 = df.iloc[pd.isnull(df['Mileage']).values, df.columns.get_indexer(['Bike','Mileage'])]
print (d2)
   Bike  Mileage
0     7      NaN
5     3      NaN