How to Fix ValueError: [day,month,year] is missing

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Last Updated on March 1, 2022 by Jay

You might see this error message “ValueError: to assemble mappings requires at least that [year, month, day] be specified: [day,month,year] is missing” when trying to convert data into datetime in pandas.

The probable cause of this error is that you are trying to use pd.to_datetime() method to convert some data into a datetime date type.

Sample Dataset

We’ll use the below sample dataframe that contains three columns of integer numbers. They present:

  • ‘a’ – year
  • ‘b’ – month
  • ‘c’ day
import pandas as pd

df = pd.DataFrame({'a': [2022,2022,2022],
                   'b': [1,2,3],
                   'c': [1,1,1] })

We know that we can convert data into datetime object in pandas, so if we try pd.to_datetime(df). this is what will happen:


ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29580/ in <module>
----> 1 pd.to_datetime(df)

~\Desktop\PythonInOffice\pandas_profiling\venv\lib\site-packages\pandas\core\tools\ in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
    888             result = arg._constructor(values, index=arg.index,
    889     elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
--> 890         result = _assemble_from_unit_mappings(arg, errors, tz)
    891     elif isinstance(arg, Index):
    892         cache_array = _maybe_cache(arg, format, cache, convert_listlike)

~\Desktop\PythonInOffice\pandas_profiling\venv\lib\site-packages\pandas\core\tools\ in _assemble_from_unit_mappings(arg, errors, tz)
    994     if len(req):
    995         _required = ",".join(req)
--> 996         raise ValueError(
    997             "to assemble mappings requires at least that "
    998             f"[year, month, day] be specified: [{_required}] is missing"

ValueError: to assemble mappings requires at least that [year, month, day] be specified: [day,month,year] is missing

How to fix it

This is literally saying “you are missing the ‘year’, ‘month’ and ‘day’ columns! So let’s add these names to the dataframe by changing the existing column names.

df.rename(columns = {'a':'year','b':'month','c':'day'}, inplace=True)

Another way to change the column names is by altering the df.columns attribute. Note this way we are modifying the column names directly, so there’s no need to use ‘inplace’ anywhere.

Index(['a', 'b', 'c'], dtype='object')

df.columns = ['year','month','day']
Index(['year', 'month', 'day'], dtype='object')

Then, by calling pd.to_datetime(df) will convert the dataframe to a datetime data type column.

Additional Resources

How to Convert Column to Datetime in Pandas

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