Question

我是StackOverflow和pandas的新手。我正在尝试使用以下格式读取包含股票市场仓库数据的大型CSV文件：

date,time,open,high,low,close,volume,splits,earnings,dividends,sym
20130625,715,49.2634,49.2634,49.2634,49.2634,156.293,1,0,0,JPM
20130625,730,49.273,49.273,49.273,49.273,208.39,1,0,0,JPM
20130625,740,49.1866,49.1866,49.1866,49.1866,224.019,1,0,0,JPM
20130625,745,49.321,49.321,49.321,49.321,208.39,1,0,0,JPM
20130625,750,49.3306,49.369,49.3306,49.369,4583.54,1,0,0,JPM
20130625,755,49.369,49.369,49.369,49.369,416.78,1,0,0,JPM
20130625,800,49.369,49.369,49.3594,49.3594,1715.05,1,0,0,JPM
20130625,805,49.369,49.369,49.3306,49.3306,1333.7,1,0,0,JPM
20130625,810,49.3306,49.3786,49.3306,49.3786,1567.09,1,0,0,JPM

我有以下代码将其读入Pandas中的DataFrame

import numpy as np
import scipy as sp
import pandas as pd
import datetime as dt
fname  = 'bindat.csv'
df     = pd.read_csv(fname, header=0, sep=',')

问题是日期和时间列是作为int64读入的。我想将这两个时间戳合并为一个时间戳，例如：2013-06-25 07:15:00。

我正在努力使用正确的时间阅读：

df['date'] = pd.to_datetime(df['date'].astype(str))
df['time'] = pd.to_datetime(df['time'].astype(str))

第一个命令可以转换，但时间似乎很奇怪。

df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 9999 entries, 0 to 9998
Data columns (total 11 columns):
date         9999 non-null datetime64[ns]
time         9999 non-null object
open         9999 non-null float64
high         9999 non-null float64
low          9999 non-null float64
close        9999 non-null float64
volume       9999 non-null float64
splits       9999 non-null float64
earnings     9999 non-null int64
dividends    9999 non-null float64
sym          9999 non-null object
dtypes: datetime64[ns](1), float64(7), int64(1), object(2)None

然后我想要合并到一个DatetimeIndex。

非常感谢任何建议。

干杯！

Answer 1

有很多方法可以做到这一点。在read_csv期间执行此操作的一种方法是使用parse_dates和date_parser参数，告诉parse_dates组合日期和时间列并定义内联函数来解析日期：

>>> df = pd.read_csv("bindat.csv", parse_dates=[["date", "time"]],
date_parser=lambda x: pd.to_datetime(x, format="%Y%m%d %H%M"), 
index_col="date_time")
>>> df
                        open     high      low    close    volume  splits  earnings  dividends  sym
date_time                                                                                          
2013-06-25 07:15:00  49.2634  49.2634  49.2634  49.2634   156.293       1         0          0  JPM
2013-06-25 07:30:00  49.2730  49.2730  49.2730  49.2730   208.390       1         0          0  JPM
2013-06-25 07:40:00  49.1866  49.1866  49.1866  49.1866   224.019       1         0          0  JPM
2013-06-25 07:45:00  49.3210  49.3210  49.3210  49.3210   208.390       1         0          0  JPM
2013-06-25 07:50:00  49.3306  49.3690  49.3306  49.3690  4583.540       1         0          0  JPM
2013-06-25 07:55:00  49.3690  49.3690  49.3690  49.3690   416.780       1         0          0  JPM
2013-06-25 08:00:00  49.3690  49.3690  49.3594  49.3594  1715.050       1         0          0  JPM
2013-06-25 08:05:00  49.3690  49.3690  49.3306  49.3306  1333.700       1         0          0  JPM
2013-06-25 08:10:00  49.3306  49.3786  49.3306  49.3786  1567.090       1         0          0  JPM
2013-06-25 16:10:00  49.3306  49.3786  49.3306  49.3786  1567.090       1         0          0  JPM

我在最后添加了一行以确保工作时间。

Pandas将csv dateint列读取到datetime

1 个答案: