Combine Date and Time columns using pandas

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)

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).

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)

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,

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']

You can also convert to datetime without string concatenation, by combining to_datetime and to_timedelta, which create datetime and timedeltea objects, respectively. 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

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"]

Use the combine function:

datetime.datetime.combine(date, time)

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.

DATA:

<TICKER>,<PER>,<DATE>,<TIME>,<OPEN>,<HIGH>,<LOW>,<CLOSE>,<VOL>
SPFB.RTS,1,20190103,100100,106580.0000000,107260.0000000,106570.0000000,107230.0000000,3726

CODE:

data.columns = ['ticker', 'per', 'date', 'time', 'open', 'high', 'low', 'close', 'vol']    
data.datetime = pd.to_datetime(data.date.astype(str) + ' ' + data.time.astype(str), format='%Y%m%d %H%M%S')

Here is a one liner, to do it. You simply concatenate the two string in each of the column with a " " space in between.

Say df is your dataframe and columns are ‘Time’ and ‘Date’. And your new column is DateAndTime.

df['DateAndTime'] = df['Date'].str.cat(df['Time'],sep=" ")

And if you also wanna handle entries like datetime objects, you can do this. You can tweak the formatting as per your needs.

df['DateAndTime'] = pd.to_datetime(df['DateAndTime'], format="%m/%d/%Y %I:%M:%S %p")

Cheers!! Happy Data Crunching.

I think the best solution is to parse dates within read_csv (or other read_ functions) directly. It is not obvious how to manage two columns in date_parser but here it is:

date_parser = lambda x,y: datetime.strptime(f"{x}T{y}", "%d-%m-%YT%H:%M:%S")
date = pd.read_csv('data.csv', parse_dates={'date': ['Date', 'Time']}, date_parser=date_parser)