title: numpy equivalent of pandas.Timestamp.floor('15min')
body: <p>I try to calculate <code>floor</code> of <code>datetime64</code> type pandas series to obtain equivalent of <code>pandas.Timestamp.round('15min')</code> for '1D', '1H', '15min', '5min', '1min' intervals.</p>
  <p>I can do it if I convert datetime64 to pandas Timestamp directly:</p>
  <p><code>pd.to_datetime(df.DATA_CZAS.to_numpy()).floor('15min')</code></p>
  <p>But how to do that without conversion to pandas (which is quite slow) ?</p>
  <p>Remark, I can't convert datetime64[ns] to int as :</p>
  <pre><code>df.time_variable.astype(int)
  &gt;&gt;&gt; cannot astype a datetimelike from [datetime64[ns]] to [int32]
  type(df.time_variable)
  &gt;&gt;&gt; pandas.core.series.Series
  df.time_variable.dtypes
  &gt;&gt;&gt; dtype('&lt;M8[ns]')
  </code></pre>