ValueError: Cannot cast DatetimeIndex to dtype datetime64[us]
<p>I'm trying to create a PostgreSQL table of 30-minute data for the S&amp;P 500 ETF
(spy30new, for testing freshly inserted data) from a table of several stocks with 15-minute data (all15). all15 has an index on 'dt' (timestamp) and 'instr' (stock symbol). I would like spy30new to have an index on 'dt'.</p>

<pre><code>import numpy as np
import pandas as pd
from datetime import datetime, date, time, timedelta
from dateutil import parser
from sqlalchemy import create_engine

# Query all15
engine = create_engine('postgresql://user:passwd@localhost:5432/stocks')
new15Df = (pd.read_sql_query("SELECT dt, o, h, l, c, v FROM all15 WHERE (instr = 'SPY') AND (date(dt) BETWEEN '2016-06-27' AND '2016-07-15');", engine)).sort_values('dt')
# Correct for Time Zone.
new15Df['dt'] = (new15Df['dt'].copy()).apply(lambda d: d + timedelta(hours=-4))

# spy0030Df contains the 15-minute data at 00 &amp; 30 minute time points
# spy1545Df contains the 15-minute data at 15 &amp; 45 minute time points
spy0030Df = (new15Df[new15Df['dt'].apply(lambda d: d.minute % 30) == 0]).reset_index(drop=True)
spy1545Df = (new15Df[new15Df['dt'].apply(lambda d: d.minute % 30) == 15]).reset_index(drop=True)

high = pd.concat([spy1545Df['h'], spy0030Df['h']], axis=1).max(axis=1)
low = pd.concat([spy1545Df['l'], spy0030Df['l']], axis=1).min(axis=1)
volume = spy1545Df['v'] + spy0030Df['v']

# spy30Df assembled and pushed to PostgreSQL as table spy30new
spy30Df = pd.concat([spy0030Df['dt'], spy1545Df['o'], high, low, spy0030Df['c'], volume], ignore_index = True, axis=1)
spy30Df.columns = ['d', 'o', 'h', 'l', 'c', 'v']
spy30Df.set_index(['dt'], inplace=True)
spy30Df.to_sql('spy30new', engine, if_exists='append', index_label='dt')
</code></pre>

<p>This gives the error "ValueError: Cannot cast DatetimeIndex to dtype datetime64[us]"<br>
What I've tried so far (I have successfully pushed CSV files to PG using pandas. But here the source is a PG database):  </p>

<ol>
<li><p><strong>Not placing an index on <code>'dt'</code></strong></p>

<pre><code>spy30Df.set_index(['dt'], inplace=True)  # Remove this line
spy30Df.to_sql('spy30new', engine, if_exists='append')  # Delete the index_label option
</code></pre></li>
<li><p><strong>Converting 'dt' from type pandas.tslib.Timestamp to datetime.datetime using <code>to_pydatetime()</code></strong> (in case psycopg2 can work with python dt, but not pandas Timestamp)</p>

<pre><code>u = (spy0030Df['dt']).tolist()
timesAsPyDt = np.asarray(map((lambda d: d.to_pydatetime()), u))
spy30Df = pd.concat([spy1545Df['o'], high, low, spy0030Df['c'], volume], ignore_index = True, axis=1)
newArray = np.c_[timesAsPyDt, spy30Df.values]
colNames = ['dt', 'o', 'h', 'l', 'c', 'v']
newDf = pd.DataFrame(newArray, columns=colNames)
newDf.set_index(['dt'], inplace=True)
newDf.to_sql('spy30new', engine, if_exists='append', index_label='dt')
</code></pre></li>
<li><p><strong>Using <code>datetime.utcfromtimestamp()</code></strong></p>

<pre><code>timesAsDt = (spy0030Df['dt']).apply(lambda d: datetime.utcfromtimestamp(d.tolist()/1e9))
</code></pre></li>
<li><p><strong>Using <code>pd.to_datetime()</code></strong></p>

<pre><code>timesAsDt = pd.to_datetime(spy0030Df['dt'])
</code></pre></li>
</ol>