ValueError:无法将DatetimeIndex强制转换为dtype datetime64[us]

Question

我正试图为标普500 ETF创建一个包含30分钟数据的PostgreSQL表 （spy30new，用于测试新插入的数据）来自一个包含15分钟数据（全部15分钟）的多个股票的表格。所有15个都有一个关于“dt”（时间戳）和“instr”（股票符号）的索引。我想让spy30new有一个关于“dt”的索引。

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 & 30 minute time points
# spy1545Df contains the 15-minute data at 15 & 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')

这会产生错误“ValueError:无法将DatetimeIndex强制转换为dtype datetime64[us]”
到目前为止我已经尝试过（我已经成功地使用pandas将CSV文件推送到PG。但这里的来源是一个PG数据库）：

1. 不在'dt'上放置索引

   spy30Df.set_index(['dt'], inplace=True)  # Remove this line
   spy30Df.to_sql('spy30new', engine, if_exists='append')  # Delete the index_label option
   

2. 使用to_pydatetime()将'dt'从pandas.tslib.Timestamp类型转换为datetime.datetime（以防psycopg2可以使用python dt，但不能使用pandas Timestamp）

   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')
   

3. 使用datetime.utcfromtimestamp()

   timesAsDt = (spy0030Df['dt']).apply(lambda d: datetime.utcfromtimestamp(d.tolist()/1e9))
   

4. 使用pd.to_datetime()

   timesAsDt = pd.to_datetime(spy0030Df['dt'])
   

Answer 1

对每个工作的元素使用pd.to_datetime（）。选项4不起作用，将pd.to_datetime（）应用于整个序列。或许Postgres驱动程序理解python的datetime，但不理解pandas&numpy中的datetime64。选项4产生了正确的输出，但是我在将DF发送到Postgres时得到ValueError（参见标题）

timesAsPyDt = (spy0030Df['dt']).apply(lambda d: pd.to_datetime(str(d)))