Remove row in Dataframe if Contains Question Mark Python 3

Question

I am newbie here so please forgive for any mistakes. I am trying to work on adult census dataset. I am finding it hard to remove the question marks in the dataset.

Link to the dataset :- https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data

I have also tried the 1st answer in the given link:- Drop rows with a 'question mark' value in any column in a pandas dataframe

But I am getting an error

~/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py in wrapper(self, other, axis)
   1251 
   1252             with np.errstate(all='ignore'):
-> 1253                 res = na_op(values, other)
   1254             if is_scalar(res):
   1255                 raise TypeError('Could not compare {typ} type with Series'

~/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py in na_op(x, y)
   1164                     result = method(y)
   1165                 if result is NotImplemented:
-> 1166                     raise TypeError("invalid type comparison")
   1167             else:
   1168                 result = op(x, y)

TypeError: invalid type comparison

Please tell me how to solve this issue. I am using Python 3.6

Thank You!!

Edit 1:- This is also called Census Income Dataset.

Answer 1

First cast to strings and then filter by boolean indexing:

df = df[(df.astype(str) != '?').all(axis=1)]
#alternative solution
#df = df[~(df.astype(str) == '?').any(axis=1)]
print (df)
   X  Y  Z
1  1  2  3
3  4  4  4

Or compare numpy array:

df = df[(df.values != '?').all(axis=1)]

Details:

Compare all converted strings by astype with change condition to !=:

print (df.astype(str) != '?')
       X      Y      Z
0   True   True  False
1   True   True   True
2  False  False   True
3   True   True   True
4  False   True   True

And then check if all True values per row:

print ((df.astype(str) != '?').all(axis=1))
0    False
1     True
2    False
3     True
4    False
dtype: bool