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# Combining columns of years, months and days in Pandas

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Time Series Cookbook
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 Day0 2020 10 251 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 Date0 2020 10 25 2020-10-251 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 2020/10/251 2020/11/26dtype: 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 2020-10-251 2020-11-26dtype: datetime64[ns] ```
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Published by Isshin Inada
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