Python Pandas를 사용하여 날짜 및 시간 열 결합

Python Pandas를 사용하여 날짜 및 시간 열 결합

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다음 열이있는 pandas 데이터 프레임이 있습니다.

Date              Time
01-06-2013      23:00:00
02-06-2013      01:00:00
02-06-2013      21:00:00
02-06-2013      22:00:00
02-06-2013      23:00:00
03-06-2013      01:00:00
03-06-2013      21:00:00
03-06-2013      22:00:00
03-06-2013      23:00:00
04-06-2013      01:00:00

데이터 [ '날짜']와 데이터 [ '시간']을 결합하여 다음을 얻으려면 어떻게해야합니까? 사용하는 방법이 pd.to_datetime있습니까?

Date
01-06-2013 23:00:00
02-06-2013 01:00:00
02-06-2013 21:00:00
02-06-2013 22:00:00
02-06-2013 23:00:00
03-06-2013 01:00:00
03-06-2013 21:00:00
03-06-2013 22:00:00
03-06-2013 23:00:00
04-06-2013 01:00:00

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예를 들어 using을 사용하는 경우 직접 읽을 수 있었을 수도 있다는 점을 언급 할 가치가 있습니다 .read_csvparse_dates=[['Date', 'Time']]

이것들이 단지 문자열이라고 가정하면 (공백으로) 간단히 추가 할 수 있으므로 다음을 적용 할 수 있습니다 to_datetime.

In [11]: df['Date'] + ' ' + df['Time']
Out[11]:
0    01-06-2013 23:00:00
1    02-06-2013 01:00:00
2    02-06-2013 21:00:00
3    02-06-2013 22:00:00
4    02-06-2013 23:00:00
5    03-06-2013 01:00:00
6    03-06-2013 21:00:00
7    03-06-2013 22:00:00
8    03-06-2013 23:00:00
9    04-06-2013 01:00:00
dtype: object

In [12]: pd.to_datetime(df['Date'] + ' ' + df['Time'])
Out[12]:
0   2013-01-06 23:00:00
1   2013-02-06 01:00:00
2   2013-02-06 21:00:00
3   2013-02-06 22:00:00
4   2013-02-06 23:00:00
5   2013-03-06 01:00:00
6   2013-03-06 21:00:00
7   2013-03-06 22:00:00
8   2013-03-06 23:00:00
9   2013-04-06 01:00:00
dtype: datetime64[ns]

참고 : 놀랍게도 (저에게는) NaN이 NaT로 변환되면 잘 작동하지만 변환 (아마도 raise인수 사용)에 대해 걱정할 가치가 있습니다.

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The accepted answer works for columns that are of datatype string. For completeness: I come across this question when searching how to do this when the columns are of datatypes: date and time.

df.apply(lambda r : pd.datetime.combine(r['date_column_name'],r['time_column_name']),1)

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You can use this to merge date and time into the same column of dataframe.

import pandas as pd    
data_file = 'data.csv' #path of your file

Reading .csv file with merged columns Date_Time:

data = pd.read_csv(data_file, parse_dates=[['Date', 'Time']]) 

You can use this line to keep both other columns also.

data.set_index(['Date', 'Time'], drop=False)

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I don't have enough reputation to comment on jka.ne so:

I had to amend jka.ne's line for it to work:

df.apply(lambda r : pd.datetime.combine(r['date_column_name'],r['time_column_name']).time(),1)

This might help others.

Also, I have tested a different approach, using replace instead of combine:

def combine_date_time(df, datecol, timecol):
    return df.apply(lambda row: row[datecol].replace(
                                hour=row[timecol].hour,
                                minute=row[timecol].minute),
                    axis=1)

which in the OP's case would be:

combine_date_time(df, 'Date', 'Time')

I have timed both approaches for a relatively large dataset (>500.000 rows), and they both have similar runtimes, but using combine is faster (59s for replace vs 50s for combine).

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You can cast the columns if the types are different (datetime and timestamp or str) and use to_datetime :

df.loc[:,'Date'] = pd.to_datetime(df.Date.astype(str)+' '+df.Time.astype(str))

Result :

0   2013-01-06 23:00:00
1   2013-02-06 01:00:00
2   2013-02-06 21:00:00
3   2013-02-06 22:00:00
4   2013-02-06 23:00:00
5   2013-03-06 01:00:00
6   2013-03-06 21:00:00
7   2013-03-06 22:00:00
8   2013-03-06 23:00:00
9   2013-04-06 01:00:00

Best,

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The answer really depends on what your column types are. In my case, I had datetime and timedelta.

> df[['Date','Time']].dtypes
Date     datetime64[ns]
Time    timedelta64[ns]

If this is your case, then you just need to add the columns:

> df['Date'] + df['Time']

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First make sure to have the right data types:

df["Date"] = pd.to_datetime(df["Date"])
df["Time"] = pd.to_timedelta(df["Time"])

Then you easily combine them:

df["DateTime"] = df["Date"] + df["Time"]

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You can also convert to datetime without string concatenation, by combining datetime and timedelta objects. Combined with pd.DataFrame.pop, you can remove the source series simultaneously:

df['DateTime'] = pd.to_datetime(df.pop('Date')) + pd.to_timedelta(df.pop('Time'))

print(df)

             DateTime
0 2013-01-06 23:00:00
1 2013-02-06 01:00:00
2 2013-02-06 21:00:00
3 2013-02-06 22:00:00
4 2013-02-06 23:00:00
5 2013-03-06 01:00:00
6 2013-03-06 21:00:00
7 2013-03-06 22:00:00
8 2013-03-06 23:00:00
9 2013-04-06 01:00:00

print(df.dtypes)

DateTime    datetime64[ns]
dtype: object

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Use the combine function:

datetime.datetime.combine(date, time)

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My dataset had 1second resolution data for a few days and parsing by the suggested methods here was very slow. Instead I used:

dates = pandas.to_datetime(df.Date, cache=True)
times = pandas.to_timedelta(df.Time)
datetimes  = dates + times

Note the use of cache=True makes parsing the dates very efficient since there are only a couple unique dates in my files, which is not true for a combined date and time column.

참고URL : https://stackoverflow.com/questions/17978092/combine-date-and-time-columns-using-python-pandas