pd.DataFrame.select_dtypes（）包含timedelta dtype

Question

Why is it expected behavior that this test code:

test = pd.DataFrame({'bool' :[False, True], 'int':[-1,2], 'float': [-2.5, 3.4],
                     'compl':np.array([1-1j, 5]),
                     'dt'   :[pd.Timestamp('2013-01-02'), pd.Timestamp('2016-10-20')],
                     'td'   :[pd.Timestamp('2012-03-02')- pd.Timestamp('2016-10-20'),
                              pd.Timestamp('2010-07-12')- pd.Timestamp('2000-11-10')]})
test.dtypes
test.select_dtypes(np.number)

Produces DataFrame with TimeDelta column included?

>>> bool                bool
>>> int                int64
>>> float            float64
>>> compl         complex128
>>> dt        datetime64[ns]
>>> td       timedelta64[ns]
>>> dtype: object

>>>     int     float   compl   td
>>> 0    -1     -2.5    (1-1j)  -1693 days
>>> 1     2      3.4    (5+0j)   3531 days

EDIT:

For someone (including me) the following may be helpful:

I've also found the reason why this behavior was unexpected for me at first. The reason was another way to check if dtype of pd.DataFrame is numeric. Namely via pd.api.types.is_numeric_dtype :

for col in test.columns:
    if pd.api.types.is_numeric_dtype(test[col]):
        print (test[col].dtype)

>>> bool
>>> int64
>>> float64
>>> complex128

Which produces more 'human-desired' output.

Answer 1

Because that's how it has been implemented:

np.issubdtype(np.timedelta64, np.number)
# True

More specifically,

np.issubdtype(np.timedelta64, np.integer)
# True

timedelta and datetime dtypes in numpy are internally represented by integer. This makes it easy to represent in memory, and makes arithmetic on datetimes fast .

If you want to exclude these types from your checks, you can specify an exclude argument:

test.select_dtypes(include=['number'], exclude=['datetime', 'timedelta'])

   int  float   compl
0   -1   -2.5  (1-1j)
1    2    3.4  (5+0j)

Answer 2

Since numpy.timedelta is belong to numpy.number , if you only want the number numeric columns return

num= ['int16', 'int32', 'int64', 'float16', 'float32', 'float64','complex128']
test.select_dtypes(include=num)
Out[715]: 
    compl  float  int
0  (1-1j)   -2.5   -1
1  (5+0j)    3.4    2