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Adding new column containing the difference between two date columnsCombining columns containing date and timeCombining columns of years, months and daysConverting a column of strings to datetimeConverting dates to stringsConverting datetime column to date and time columnsConverting DatetimeIndex to Series of datetimeConverting index to datetimeConverting UNIX timestamp to datetimeCreating a column of datesCreating a range of datesExtracting month and year from Datetime columnGetting all weekdays between two datesGetting all weekends between two datesGetting day unit from date columnGetting month unit from date columnGetting name of months in date columnGetting week numbers from a date columnGetting day of week of date columnsGetting year unit from date columnModifying datesOffsetting datetimeRemoving time unit from datesSetting date to beginning of monthSorting DataFrame by datesUsing dates as the index of a DataFrame
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Combining columns containing date and time in Pandas
schedule Aug 12, 2023
Last updated local_offer
Tags Python●Pandas
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Consider the following DataFrame:
df = pd.DataFrame({"A":["2020-12-22","2020-12-23"], "B":["15:30:00","16:30:00"]})df
A B0 2020-12-22 15:30:001 2020-12-23 16:30:00
Here, column A represents the date unit while B represents the time unit. They are both of type string.
Solution
To add a new column C of type datetime64 that combines A and B:
df["C"] = pd.to_datetime(df["A"] + " " + df["B"])df
A B C0 2020-12-22 15:30:00 2020-12-22 15:30:001 2020-12-23 16:30:00 2020-12-23 16:30:00
Explanation
We first make a Series of datetime strings using concatenation:
df["A"] + " " + df["B"]
0 2020-12-22 15:30:001 2020-12-23 16:30:00dtype: object
The space " " is essential - the conversion to datetime64 is not possible without it.
We then use to_datetime(~) method to convert the datetime strings into type datetime64.
Published by Isshin Inada
Edited by 0 others
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