Combine pandas df [datetime] time and date columns into one datetime column

December 12, 2023

I’m a bit confused trying to combine pandas DataFrame columns with datetime.date format and datetime.time format.
DF looks like this:

    VJNo    VJIdx   lnTime  lnDate
0   32613   1   05:00:00    2023-04-18
1   32613   2   05:01:00    2023-04-18
2   32613   3   05:02:30    2023-04-18
3   32613   5   05:05:30    2023-04-18
4   32613   6   05:06:30    2023-04-18
5   32613   8   05:07:30    2023-04-18
6   32613   9   05:08:30    2023-04-18
7   32613   11  05:10:30    2023-04-18

I wanted to use pandas.Timestamp.combine(date, time), but apparently it doesn’t want to work for DataSeries…(?) Running:

import pandas as pd

# Defining the data
data = {'VJNo': [32613, 32613, 32613, 32613, 32613, 32613, 32613, 32613],
        'VJIdx': [1, 2, 3, 5, 6, 8, 9, 11],
        'lnTime': ['05:00:00', '05:01:00', '05:02:30', '05:05:30', '05:06:30', '05:07:30', '05:08:30', '05:10:30'],
        'lnDate': ['2023-04-18', '2023-04-18', '2023-04-18', '2023-04-18', '2023-04-18', '2023-04-18', '2023-04-18', '2023-04-18']}

# Create pandas dataframe
df = pd.DataFrame(data)

df['tmp'] = pd.Timestamp.combine( df['lnDate'], df['lnTime'])

Returns error: combine() argument 1 must be datetime.date, not Series, while it is datetime.date, but series of it…
Unfortunately other solutions like found here also doesn’t work (probably due to changes in pandas):

df['tmp'] = df.apply(pd.Timestamp.combine, df['lnDate'], df['lnTime'])

df['tmp'] = df.apply(lambda x: pd.Timestamp.combine(x['lnDate'], x['lnTime']))

Am I doing something wrong?
Last resort would be probably to convert date and time to strings and then use pd.to_datetime on them, but… I think it’s not the right way.

>Solution :

You need to vectorize your function:

import numpy as np

f = np.vectorize(pd.Timestamp.combine)
# f = np.vectorize(lambda d,t: pd.Timestamp.combine(d, t))

dfP['out'] = f(dfP['lnDate'], dfP['lnTime'])

Output:

    VJNo  VJIdx    lnTime      lnDate                 out
0  32613      1  05:00:00  2023-04-18 2023-04-18 05:00:00
1  32613      2  05:01:00  2023-04-18 2023-04-18 05:01:00
2  32613      3  05:02:30  2023-04-18 2023-04-18 05:02:30
3  32613      5  05:05:30  2023-04-18 2023-04-18 05:05:30
4  32613      6  05:06:30  2023-04-18 2023-04-18 05:06:30
5  32613      8  05:07:30  2023-04-18 2023-04-18 05:07:30
6  32613      9  05:08:30  2023-04-18 2023-04-18 05:08:30
7  32613     11  05:10:30  2023-04-18 2023-04-18 05:10:30