AttributeError: Can only use .dt accessor with datetimelike values

Hi I am using pandas to convert a column to month. When I read my data they are objects:

Date object dtype: object 

So I am first making them to date time and then try to make them as months:

import pandas as pd file = '/pathtocsv.csv' df = pd.read_csv(file, sep = ',', encoding='utf-8-sig', usecols= ['Date', 'ids']) df['Date'] = pd.to_datetime(df['Date']) df['Month'] = df['Date'].dt.month 

Also if that helps:

In [10]: df['Date'].dtype Out[10]: dtype('O') 

So, the error I get is like this:

/Library/Frameworks/ in _make_dt_accessor(self) 2526 return maybe_to_datetimelike(self) 2527 except Exception: -> 2528 raise AttributeError("Can only use .dt accessor with datetimelike " 2529 "values") 2530 AttributeError: Can only use .dt accessor with datetimelike values 

EDITED:

Date columns are like this:

0 2014-01-01 1 2014-01-01 2 2014-01-01 3 2014-01-01 4 2014-01-03 5 2014-01-03 6 2014-01-03 7 2014-01-07 8 2014-01-08 9 2014-01-09 

Do you have any ideas? Thank you very much!

5 Answers

Your problem here is that to_datetime silently failed so the dtype remained as str/object, if you set param errors='coerce' then if the conversion fails for any particular string then those rows are set to NaT.

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

So you need to find out what is wrong with those specific row values.

See the docs

First you need to define the format of date column.

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

For your case base format can be set to;

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

After that you can set/change your desired output as follows;

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

Your problem here is that the dtype of 'Date' remained as str/object. You can use the parse_dates parameter when using read_csv

import pandas as pd file = '/pathtocsv.csv' df = pd.read_csv(file, sep = ',', parse_dates= [col],encoding='utf-8-sig', usecols= ['Date', 'ids'],) df['Month'] = df['Date'].dt.month 

From the documentation for the parse_dates parameter

parse_dates : bool or list of int or names or list of lists or dict, default False

The behavior is as follows:

• boolean. If True -> try parsing the index.
• list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.
• list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.
• dict, e.g. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’

If a column or index cannot be represented as an array of datetimes, say because of an unparseable value or a mixture of timezones, the column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use pd.to_datetime after pd.read_csv. To parse an index or column with a mixture of timezones, specify date_parser to be a partially-applied pandas.to_datetime() with utc=True. See Parsing a CSV with mixed timezones for more.

Note: A fast-path exists for iso8601-formatted dates.

The relevant case for this question is the "list of int or names" one.

col is the columns index of 'Date' which parses as a separate date column.

#Convert date into the proper format so that date time operation can be easily performed

df_Time_Table["Date"] = pd.to_datetime(df_Time_Table["Date"]) # Cal Year df_Time_Table['Year'] = df_Time_Table['Date'].dt.strftime('%Y') 

When you write

df['Date'] = pd.to_datetime(df['Date'], errors='coerce') df['Date'] = df['Date'].dt.strftime('%m/%d') 

It can fixed