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Time Series Cookbook
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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Adding new column containing the difference between two date columns in Pandas DataFrame
Pandas
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schedule Jul 1, 2022
Last updated local_offer Python●Pandas
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expand_more Consider the following DataFrame:
dates1 = pd.date_range(start="2020/12/15", periods=3)dates2 = pd.date_range(start="2020/12/20", periods=3, freq="2D")df = pd.DataFrame({"A":dates1, "B":dates2})df
A B0 2020-12-15 2020-12-201 2020-12-16 2020-12-222 2020-12-17 2020-12-24
Pandas allows for date arithmetics using the standard binary operators (e.g. +, -):
df["C"] = df["B"] - df["A"]df
A B C0 2020-12-15 2020-12-20 5 days1 2020-12-16 2020-12-22 6 days2 2020-12-17 2020-12-24 7 days
Here, column C is of type timedelta64[ns], which explains why the term "days" is appended. To remove the "days", use dt.days to extract the day unit as an int64:
df["C"] = (df["B"] - df["A"]).dt.daysdf
A B C0 2020-12-15 2020-12-20 51 2020-12-16 2020-12-22 62 2020-12-17 2020-12-24 7
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Published by Isshin Inada
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