attributeerror: can only use .dt accessor with datetimelike values

In this article, we will explain the solutions on how to solve can only use .dt accessor with datetimelike values? What are the causes and why it is occur.

Why the error can only use .dt accessor with datetimelike values occur?

The error “can only use .dt accessor with datetimelike values” occurs when you are trying to use the “.dt accessor” on a value that is not a datetime object in Python.

The “.dt accessor” is used to extract specific attributes such as day, month, year, etc. from a datetime object.

What are the causes of this error?

The AttributeError “can only use .dt accessor with datetimelike values” occurs if you’re trying to use the “.dt” accessor method in pandas on a variable or column that is not a datetime or datetime-like data type.

There are multiple possible causes of this error:

• The column you’re trying to access with the “.dt” accessor doesn’t contain datetime values.
• The column you’re trying to access with the “.dt” accessor consist of some missing or null values, which can cause pandas to figure out an incorrect data type.
• You have not imported the pandas library or another library that contains the “.dt” accessor method.
• You have misspelled the “.dt” accessor method or used it incorrectly.

Furthermore, you might interested to read or visit the other resolved attributeerror:

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• attributeerror int object has no attribute split [SOLVED]

How to solve the attributeerror: can only use .dt accessor with datetimelike values?

We already explained the possible cause for the error. In this section we will give the solutions to solve the attributeerror: can only use .dt accessor with datetimelike values error.

Solution 1: Check the type of the object

You will make sure that the object you are trying to use the “.dt” accessor is a datetime-like object, such as a pandas Timestamp, DatetimeIndex, or Period.

Solution 2: Convert the object to datetime

If the object is not already a datetime-like object, you can convert it using pandas’ to_datetime() function.

For example, if you have a column in a pandas DataFrame that contains dates as strings, you can convert it to datetime like this:

df['Date'] = pd.to_datetime(df['Date'], errors='coerce')

Your issue here is that to_datetime failed silently, it will remain the dtype as str/object. If you specify param errors=’coerce’, any rows for which the conversion fails will be set to NaT.

Solution 3: Check for null values

If the object contains null values, the “.dt” accessor may be not working.

You can use pandas’ fillna() function to fill any null values with a default value, or you can drop the null values using dropna().

For example, if you have a DataFrame with a column date_column that contains null values, you can drop the null values like this:

df = df.dropna(subset=['date_column'])

Solution 4: Define the format of date column

In some scenarios, if we add the format of the date conversion in the to_datetime() function. You can eliminate the same error.

For example:

First you will need to define the format of the date column.

df['Date'] = pd.to_datetime(df.Date, format='%Y-%m-%d %H:%M:%S')

For your case base format you can set to:

df['Date'] = pd.to_datetime(df.Date, format='%Y-%m-%d')

Following that, you can set/modify the desired output as follows:

df['Date'] = df['Date'].dt.strftime('%Y-%m-%d')

Conclusion

In conclusion, through following the solutions in this article, you should be able to solve the AttributeError: can only use .dt accessor with datetimelike values error.

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