【发布时间】：2016-11-25 18:08:08
【问题描述】：
我正在尝试为 S&P 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）来自具有 15 分钟数据的几只股票的表（all15）。 all15 在“dt”（时间戳）和“instr”（股票代码）上有一个索引。我希望 spy30new 在 'dt' 上有一个索引。
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')
这给出了错误“ValueError: Cannot cast DatetimeIndex to dtype datetime64[us]”
到目前为止我尝试过的（我已经使用 pandas 成功将 CSV 文件推送到 PG。但这里的源是一个 PG 数据库）：
-
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 -
使用
to_pydatetime()将'dt'从pandas.tslib.Timestamp类型转换为datetime.datetime（如果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') -
使用
datetime.utcfromtimestamp()timesAsDt = (spy0030Df['dt']).apply(lambda d: datetime.utcfromtimestamp(d.tolist()/1e9)) -
使用
pd.to_datetime()timesAsDt = pd.to_datetime(spy0030Df['dt'])
【问题讨论】：
标签： python postgresql pandas