Extract multiple date/time values from text field into new variable columns

Question

I have dataframe - see below. This is just a snippet of the full dateframe, there are more text and date/times in each respective rows/IDS. As you can see the text before and after each date/time is random.

ID        RESULT
1         Patients Discharged Home :   12/07/2022 11:19 Bob Melciv   Appt 12/07/2022 12:19 Medicaid...
2         Stawword Geraldio -    12/17/2022 11:00 Bob Melciv   Appt 12/10/2022 12:09 Risk Factors...

I would like to pull all date/times where the format is MM/DD/YYYY HH:MM from the RESULT column and make each of those respective date/times into their own column.

ID    DATE_TIME_1              DATE_TIME_2        DATE_TIME_3 .....
1     12/07/2022 11:19         12/07/2022 12:19
2     12/17/2022 11:00         12/10/2022 12:09

Answer 1

How about:

• \d{2}/\d{2}/\d{4} \d{2}:\d{2}

Of course this doesn't cover nonsensical dates such as 55/55/1023 , but it should get you 99% of the way there.

Answer 2

From @David542's regex, you can use str.extractall :

pattern = r'(\d{2}/\d{2}/\d{4} \d{2}:\d{2})'
out = pd.concat([df['ID'],
                 df['RESULT'].str.extractall(pattern).squeeze()
                             .unstack().rename(columns=lambda x: f'DATE_TIME_{x+1}')
                             .rename_axis(columns=None)], axis=1)
print(out)

# Output
   ID       DATE_TIME_1       DATE_TIME_2
0   1  12/07/2022 11:19  12/07/2022 12:19
1   2  12/17/2022 11:00  12/10/2022 12:09

A slightly modified version to convert extracted date/time to pd.DatetimeIndex :

pattern = r'(\d{2}/\d{2}/\d{4} \d{2}:\d{2})'
out = pd.concat([df['ID'],
                 df['RESULT'].str.extractall(pattern).squeeze().apply(pd.to_datetime)
                             .unstack().rename(columns=lambda x: f'DATE_TIME_{x+1}')
                             .rename_axis(columns=None)], axis=1)
print(out)

# Output
   ID         DATE_TIME_1         DATE_TIME_2
0   1 2022-12-07 11:19:00 2022-12-07 12:19:00
1   2 2022-12-17 11:00:00 2022-12-10 12:09:00

Step by step:

# 1. Date extraction (and squeeze DataFrame with 1 column to Series)
>>> out = df['RESULT'].str.extractall(pattern)
   match
0  0        12/07/2022 11:19
   1        12/07/2022 12:19
1  0        12/17/2022 11:00
   1        12/10/2022 12:09
Name: 0, dtype: object

# 2. Move second index level as column (and add the prefix DATE_TIME_N)
>>> out = out.unstack().rename(columns=lambda x: f'DATE_TIME_{x+1}')
match       DATE_TIME_1       DATE_TIME_2
0      12/07/2022 11:19  12/07/2022 12:19
1      12/17/2022 11:00  12/10/2022 12:09

# 3. Remove the 'match' title on column axis
>>> out = out.rename_axis(columns=None)
            DATE_TIME_1       DATE_TIME_2
0      12/07/2022 11:19  12/07/2022 12:19
1      12/17/2022 11:00  12/10/2022 12:09

Finally concatenate original ID with this new dataframe along column axis.