Pandas - 将时间戳四舍五入到最接近的秒
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Pandas - Rounding off timestamps to the nearest second
提问by Jetman
I am struggling to round off timestamps using pandas.
我正在努力使用Pandas来完善时间戳。
The timestamps look like this:
时间戳如下所示：
datetime.datetime(2017,06,25,00,31,53,993000)
datetime.datetime(2017,06,25,00,32,31,224000)
datetime.datetime(2017,06,25,00,33,11,223000)
datetime.datetime(2017,06,25,00,33,53,876000)
datetime.datetime(2017,06,25,00,34,31,219000)
datetime.datetime(2017,06,25,00,35,12,634000)
How do I round off to the nearest second?
如何四舍五入到最接近的秒？
Previously iv tried suggestions in this post but they didn't work: Rounding time off to the nearest second - Python
以前 iv 尝试过这篇文章中的建议，但没有奏效： 四舍五入到最接近的秒数 - Python
My code looks like this so far:
到目前为止，我的代码如下所示：
import pandas as pd
filename = 'data.csv'
readcsv = pd.read_csv(filename)
Import data according to file header info
根据文件头信息导入数据
log_date = readcsv.date
log_time = readcsv.time
log_lon = readcsv.lon
log_lat = readcsv.lat
log_heading = readcsv.heading
readcsv['date'] = pd.to_datetime(readcsv['date']).dt.date
readcsv['time'] = pd.to_datetime(readcsv['time']).dt.time
Combine date and time into one variable
将日期和时间合并为一个变量
timestamp = [datetime.datetime.combine(log_date[i],log_time[i]) for i in range(len(log_date))]
Create dataframe
创建数据框
data = {'timestamp':timestamp,'log_lon':log_lon,'log_lat':log_lat,'log_heading':log_heading}
log_data = pd.DataFrame(data,columns=['timestamp','log_lon','log_lat','log_heading'])
log_data.index = log_data['timestamp']
I am still quite new to python so please excuse my ignorance
我对 python 还是很陌生，所以请原谅我的无知
采纳答案by jezrael
You can use first read_csvwith parameter parse_datesfor create datetimes from column dateand timeand then dt.roundfor round datetimes:
你可以先使用read_csv带有参数parse_dates的创造datetime从列Sdate和time再dt.round圆datetimeS：
import pandas as pd
temp=u"""date,time,lon,lat,heading
2017-06-25,00:31:53.993000,48.1254,17.1458,a
2017-06-25,00:32:31.224000,48.1254,17.1458,a
2017-06-25,00:33:11.223000,48.1254,17.1458,a
2017-06-25,00:33:53.876000,48.1254,17.1458,a
2017-06-25,00:34:31.219000,48.1254,17.1458,a
2017-06-25,00:35:12.634000,48.1254,17.1458,a"""
#after testing replace 'pd.compat.StringIO(temp)' to 'filename.csv'
df = pd.read_csv(pd.compat.StringIO(temp), parse_dates={'timestamp':['date','time']})
print (df)
timestamp lon lat heading
0 2017-06-25 00:31:53.993 48.1254 17.1458 a
1 2017-06-25 00:32:31.224 48.1254 17.1458 a
2 2017-06-25 00:33:11.223 48.1254 17.1458 a
3 2017-06-25 00:33:53.876 48.1254 17.1458 a
4 2017-06-25 00:34:31.219 48.1254 17.1458 a
5 2017-06-25 00:35:12.634 48.1254 17.1458 a
print (df.dtypes)
timestamp datetime64[ns]
lon float64
lat float64
heading object
dtype: object
df['timestamp'] = df['timestamp'].dt.round('1s')
print (df)
timestamp lon lat heading
0 2017-06-25 00:31:54 48.1254 17.1458 a
1 2017-06-25 00:32:31 48.1254 17.1458 a
2 2017-06-25 00:33:11 48.1254 17.1458 a
3 2017-06-25 00:33:54 48.1254 17.1458 a
4 2017-06-25 00:34:31 48.1254 17.1458 a
5 2017-06-25 00:35:13 48.1254 17.1458 a
EDIT:
编辑：
If you want set column with datetimes to indexalso:
如果您还想将带有日期时间的列设置为index：
import pandas as pd
temp=u"""date,time,lon,lat,heading
2017-06-25,00:31:53.993000,48.1254,17.1458,a
2017-06-25,00:32:31.224000,48.1254,17.1458,a
2017-06-25,00:33:11.223000,48.1254,17.1458,a
2017-06-25,00:33:53.876000,48.1254,17.1458,a
2017-06-25,00:34:31.219000,48.1254,17.1458,a
2017-06-25,00:35:12.634000,48.1254,17.1458,a"""
#after testing replace 'pd.compat.StringIO(temp)' to 'filename.csv'
df = pd.read_csv(pd.compat.StringIO(temp), parse_dates={'timestamp':['date','time']}, index_col=['timestamp'])
print (df)
lon lat heading
timestamp
2017-06-25 00:31:53.993 48.1254 17.1458 a
2017-06-25 00:32:31.224 48.1254 17.1458 a
2017-06-25 00:33:11.223 48.1254 17.1458 a
2017-06-25 00:33:53.876 48.1254 17.1458 a
2017-06-25 00:34:31.219 48.1254 17.1458 a
2017-06-25 00:35:12.634 48.1254 17.1458 a
print (df.index)
DatetimeIndex(['2017-06-25 00:31:53.993000', '2017-06-25 00:32:31.224000',
'2017-06-25 00:33:11.223000', '2017-06-25 00:33:53.876000',
'2017-06-25 00:34:31.219000', '2017-06-25 00:35:12.634000'],
dtype='datetime64[ns]', name='timestamp', freq=None)
df.index = df.index.round('1s')
print (df)
lon lat heading
timestamp
2017-06-25 00:31:54 48.1254 17.1458 a
2017-06-25 00:32:31 48.1254 17.1458 a
2017-06-25 00:33:11 48.1254 17.1458 a
2017-06-25 00:33:54 48.1254 17.1458 a
2017-06-25 00:34:31 48.1254 17.1458 a
2017-06-25 00:35:13 48.1254 17.1458 a
回答by FatihAkici
dt.round is what you are looking for. I'll just create a smaller version of your DataFrame, please comment if you can't modify it to fully fit your case, I can help with that also.
dt.round 就是你要找的。我只会创建一个较小版本的 DataFrame，如果您无法修改它以完全适合您的情况，请发表评论，我也可以提供帮助。
import datetime
import pandas as pd
ts1 = datetime.datetime(2017,06,25,00,31,53,993000)
ts2 = datetime.datetime(2017,06,25,00,32,31,224000)
ts3 = datetime.datetime(2017,06,25,00,33,11,223000)
df = pd.DataFrame({'timestamp':[ts1, ts2, ts3]})
df.timestamp.dt.round('1s')
Gives you the following:
为您提供以下内容：
Out[89]:
0 2017-06-25 00:31:54
1 2017-06-25 00:32:31
2 2017-06-25 00:33:11
Name: timestamp, dtype: datetime64[ns]
