title: Issue with Overflow in int64 addition in pandas
body: <p>Similar to <a href="https://stackoverflow.com/q/56720783/6051639">this issue</a>, only instead of subtracting 2 dates I am trying to add days to dates. I import the data from a csv and then convert <code>date</code> to datetime and <code>time</code> to a timedelta in days:</p>
  <pre><code>df['date']=pd.to_datetime(df['date'])
  df['days']=pd.to_timedelta(df['time'],'days')
  </code></pre>
  <p>The resulting dtypes are:</p>
  <p>df.dtypes:</p>
  <pre><code>date               datetime64[ns]
  days               timedelta64[ns]
  </code></pre>
  <p>My command is:</p>
  <pre><code>df['new_date']=df['date']+df['days']
  </code></pre>
  <p>The error I get is:</p>
  <pre><code>File &quot;C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\pandas\core\ops\common.py&quot;, line 69, in new_method
      return method(self, other)
  
    File &quot;C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\pandas\core\arraylike.py&quot;, line 100, in __sub__
      return self._arith_method(other, operator.sub)
  
    File &quot;C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\pandas\core\series.py&quot;, line 5526, in _arith_method
      result = ops.arithmetic_op(lvalues, rvalues, op)
  
    File &quot;C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\pandas\core\ops\array_ops.py&quot;, line 218, in arithmetic_op
      res_values = op(left, right)
  
    File &quot;C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\pandas\core\ops\common.py&quot;, line 69, in new_method
      return method(self, other)
  
    File &quot;C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\pandas\core\arrays\datetimelike.py&quot;, line 1330, in __sub__
      result = self._add_timedelta_arraylike(-other)
  
    File &quot;C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\pandas\core\arrays\datetimelike.py&quot;, line 1126, in _add_timedelta_arraylike
      self_i8, other_i8, arr_mask=self._isnan, b_mask=other._isnan
  
    File &quot;C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\pandas\core\algorithms.py&quot;, line 1112, in checked_add_with_arr
      raise OverflowError(&quot;Overflow in int64 addition&quot;)
  
  OverflowError: Overflow in int64 addition
  </code></pre>
  <p>I can get it to work with the csv if turn my date into a (datetime) object first, as gleaned from the linked question:</p>
  <pre><code>obs['date']=pd.to_datetime(obs['date']).dt.date
  </code></pre>
  <p>df.dtypes:</p>
  <pre><code>days    timedelta64[ns]
  date             object
  </code></pre>
  <p>But with this warning:</p>
  <pre><code>PerformanceWarning: Adding/subtracting object-dtype array to TimedeltaArray not vectorized
    PerformanceWarning,
  </code></pre>
  <p>Strangely I cannot reproduce the Overflow error when I create my own df:</p>
  <pre><code>df=pd.DataFrame([range(1965,1970)]).T
  df.columns=['year']
  df['date']=pd.to_datetime(df.year,format='%Y')
  df['days']=pd.to_timedelta(30,'days')
  df.dtypes
  </code></pre>
  <p>Note the dtypes are the same as those that give the Overflow error:</p>
  <pre><code>date    datetime64[ns]
  days   timedelta64[ns]
  </code></pre>
  <p>Yet, the same command gives me exactly what I expect, rather than the Overflow error:</p>
  <pre><code>df['new_date']=df['date']+df['days']
  </code></pre>
  <p>df:</p>
  <pre><code>   year       date    days   new_date
  0  1965 1965-01-01 30 days 1965-01-31
  1  1966 1966-01-01 30 days 1966-01-31
  2  1967 1967-01-01 30 days 1967-01-31
  3  1968 1968-01-01 30 days 1968-01-31
  4  1969 1969-01-01 30 days 1969-01-31
  </code></pre>
  <p>And if I make date a (datetime) object base on year:</p>
  <pre><code>df['date']=pd.to_datetime(df['year']).dt.date
  </code></pre>
  <p>I get the PerformanceWarning again:</p>
  <pre><code>PerformanceWarning: Adding/subtracting object-dtype array to TimedeltaArray not vectorized
    PerformanceWarning,
  </code></pre>
  <p>This behavior seems inconsistent. I am struggling to figure out when I need <code>.dt.date</code> (i.e. the example when I read csv) and when I should not use it (i.e. performance warning. What is the best way to consistently handle datetime objects in pandas? Any help understanding what I am doing &quot;wrong&quot; would be greatly appreciated.</p>