Pandas Gives Incorrect Result When Asking If Timestamp Column Values Have Attr Astype

With a column containing Timestamp values, I am getting inconsistent results about whether the elements have the attribute astype: In [30]: o.head().datetime.map(lambda x: hasattr(

Solution 1:

Timestamps do not have an astype method. But numpy.datetime64's do.

NDFrame.values returns a numpy array.o.head().datetime.values returns a numpy array of dtype numpy.datetime64, which is why

In [31]: map(lambda x: hasattr(x, 'astype'), o.head().datetime.values)
Out[31]: [True, True, True, True, True]

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Note that Series.__iter__ is defined this way:

def__iter__(self):
    if  com.is_categorical_dtype(self.dtype):
        returniter(self.values)
    elif np.issubdtype(self.dtype, np.datetime64):
        return (lib.Timestamp(x) for x in self.values)
    elif np.issubdtype(self.dtype, np.timedelta64):
        return (lib.Timedelta(x) for x in self.values)
    else:
        returniter(self.values)

So, when the dtype of the Series is np.datetime64, iteration over the Series returns Timestamps. This is where the implicit conversion takes place.