Convert all numeric columns of dataframe to absolute value

Question

I want to convert all numeric columns in a dataframe to their absolute values and am doing this:

df = df.abs()

However, it gives the error:

*** TypeError: bad operand type for abs(): 'unicode'

How to fix this? I would really prefer not having to manually specify the column names

Answer 1

You could use np.issubdtype to check whether your dtype of the columns is np.number or not with apply. Using @Amy Tavory example:

df = pd.DataFrame({'a': ['-1', '2'], 'b': [-1, 2]})
res = df.apply(lambda x: x.abs() if np.issubdtype(x.dtype, np.number) else x)

In [14]: res
Out[14]:
    a  b
0  -1  1
1   2  2

Or you could use np.dtype.kind to check whether your dtype is numeric:

res1 = df.apply(lambda x: x.abs() if x.dtype.kind in 'iufc' else x)


In [20]: res1
Out[20]:
    a  b
0  -1  1
1   2  2

Note: You may be also interested in NumPy dtype hierarchy

Answer 2

Borrowing from an answer to this question, how about selecting the columns that are numeric?

Say you start with

df = pd.DataFrame({'a': ['-1', '2'], 'b': [-1, 2]})
>>> df        
    a   b
0   -1  -1
1   2   2

Then just do

numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
for c in [c for c in df.columns if df[c].dtype in numerics]:
    df[c] = df[c].abs()
>>> df
    a   b
0   -1  1
1   2   2