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Combining columns of years, months and days in Pandas

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schedule Jul 1, 2022
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Consider the following DataFrame:

df = pd.DataFrame({"Year":["2020","2020"], "Month":["10","11"], "Day":["25","26"]})
df
Year Month Day
0 2020 10 25
1 2020 11 26

Solution

To combine the Year, Month and Day columns to form another column:

df["Date"] = pd.to_datetime(df["Year"] + "/" + df["Month"] + "/" + df["Day"])
df
Year Month Day Date
0 2020 10 25 2020-10-25
1 2020 11 26 2020-11-26

Here, column Date is of type datetime.

Explanation

We first combine Year, Month and Day columns via simple string concatenation:

df["Year"] + "/" + df["Month"] + "/" + df["Day"]
0 2020/10/25
1 2020/11/26
dtype: object

Here, the combined column (Series) is of type string. We can convert its data type to datetime using pd.to_datetime(~):

pd.to_datetime(df["Year"] + "/" + df["Month"] + "/" + df["Day"])
0 2020-10-25
1 2020-11-26
dtype: datetime64[ns]
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Published by Isshin Inada
Edited by 0 others
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