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How can I check if a pandas series contains timestamps?

It looks like the job gets done, at least in some cases:

series.dtype == np.dtype('<M8[ns]')

but it doesn't look pretty and I'm not sure if it always works (with all (?) kinds of timestamps).

Is there a better way to check if a column contains timestamps?

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2 answers


try this, it should work:

pd.core.dtypes.common.is_datetime_or_timedelta_dtype(series)


if you go through pd.core.dtypes.common.is_

you will find many options for checking timestamps. If you want to combine them, you can use boolean operators, for example:

pd.core.dtypes.common.is_datetime64_ns_dtype(ser)|pd.core.dtypes.common.is_timedelta64_ns_dtype(ser)
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The approach in @ shivsn's answer is simpler and most likely better. This is more difficult, but can be informative nonetheless.

More about dtypes see. Here . But in a nutshell, the second character of the dtype string should be "M" for Datetime and "m" for Timedelta. Hence, you can test dtype.str[1] == 'M'

to detect only Datetime, or dtype.str[1].lower() == 'm'

to detect either Datetime or Timedelta.



>>> dr = pd.date_range('1-1-2017',periods=4,freq='d')
>>> df = pd.DataFrame({ 'i':range(3), 'x':[1.1,2.2,3.3], 
                        'ts':dr[:3], 'td':dr[1:]-dr[:3] })

   i     td         ts    x
0  0 1 days 2017-01-01  1.1
1  1 1 days 2017-01-02  2.2
2  2 1 days 2017-01-03  3.3

>>> for v in df.columns:  
        print( '\ncolumn ' + v + ': ')
        print( 'dtype.str: ', df[v].dtype.str )
        print( 'dtype:     ', df[v].dtype )
        print( 'timestamp? ', df[v].dtype.str[1] == 'M' )

column i: 
dtype.str:  <i8
dtype:      int64
timestamp?  False

column td: 
dtype.str:  <m8[ns]
dtype:      timedelta64[ns]
timestamp?  False

column ts: 
dtype.str:  <M8[ns]
dtype:      datetime64[ns]
timestamp?  True

column x: 
dtype.str:  <f8
dtype:      float64
timestamp?  False
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