Supposed the 'date' column in the DataFrame shown has a dtype of object (i.e. string). How can it be converted to Pandas's datetime64 datatype?

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BlackTom AI · Accepted Answer

Question restated: The DataFrame has a 'date' column with dtype object (strings). We want to convert it to Pandas datetime64.
Option 1: df['date']=pd.to_datetime(df['date'],format='%Y').year
This would attempt to parse with a four-digit year and then access the .year attribute, producing a scalar or a series of integers, not a datetime64 dtype. It effectively strips the time information and returns years, so it does not yield a proper datetime64 column.
Option 2: df['date']=pd.to_datetime(df['date'],format='%Y').dt.year
Here, the parsing step creates datetimes, but .dt.year extracts only the year as integers, again transforming the column into a numeric year series rather than preserving full datetime values. The result is not datetime64.
Option 3: df['date']=pd.to_datetime(df['date'],format='%Y')
This uses pandas.to_datetime with a specified format of a four-digit year and returns a datetime64[ns] dtype for the entire column, preserving the date information in a proper datetime type. This aligns with the goal of converting to datetime64.
Option 4: df['date']=pd.to_datetime(df['date'],format='%y')
Using a two-digit year format ('%y') could misinterpret values or fail if the strings aren’t two-digit years. Even if it parsed, it would not reliably produce the intended full-year datetime values and is more error-prone than using '%Y'.
In summary, the approach that directly yields a datetime64 column by parsing the strings with an appropriate format is the one in Option 3, while the other options either extract only parts of the date (like the year) or misformat the input, leading to incorrect or unintended results.

Supposed the 'date' column in the DataFrame shown has a dtype of object (i.e. string). How can it be converted to Pandas's datetime64 datatype?

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