can only use .dt accessor with datetimelike values ( Solved)


attributeerror: can only use .dt accessor with datetimelike values error occurs while converting string to datetime format in the specific situation. These specific situations are if are converting multiple string values into datetime format values (complete pandas dataframe column) and some of the values are having errors in conversion. Here we need to provide the error handling mechanism too with the syntax. Apart from this, In some scenario, the interpreter throws the same error when we do not pass the format for conversion. We face this error.

Attributeerror: Can only use .dt accessor with datetimelike values ( Solution ) –

As I already explained the root cause for the error. In this section we will see the best way to fix can only use .dt accessor with datetime like values error.

Approach 1: Error handler type –

While converting any string object to datatime object in python we all use the to_datetime() function. But we do add any error handler type and if it fails, The interpreter thows the same error. Actually In to_datetime() we can provide an error handler type while calling the same function. Here is an example.

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

Suppose you have multiple values in the dataframe column “col” in string format and you want to convert them into datetime format. Then you can use the same syntax. If there will be any error in any row, It will convert the same into NaN and rest will be converted.

Attributeerror can only use .dt accessor with datetimelike values
Attributeerror can only use .dt accessor with datetimelike values

 

There are two more possible arguments with errors –
1. Ignore – It will throw the same input
2. raise – It will throw the exception and halt the process there only.

Approach 2 :

In some scenarios, if we add the format of the date conversion in the to_datetime() function. We can get rid of the same error. Here is an example of this –

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

Here “col” is the column where we are Appling the same function.

 

Thanks 

Data Science Learner Team

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