Analyzing restaurant data using python. Need help merging two datasets on both check # and date

I am running a diff in diff analysis for my restaurant that changed the format of their menus a while back. The menus we changed are only used for smaller parties and the menus we use for larger parties have remained the same for the entire period of analysis. I am essentially using the larger parties as a point of comparison to see if the change in the smaller party menu has reduced our per person revenue.

I have made a df for each of the time frames and distinct menus used as follows:

dfLargePre = Large party in the period before menus were changed
dfSmallPre = Small party in the period before menus were changed
dfLargePost = Large party after menus were changed
dfSmallPost = Small party after menus were changed

These dfs have columns for 'Date', 'Check_number', and 'Total_payment'

However, I only want to analyze the food sales and no alcohol (which makes up a great deal of each check). Because of how our sales reporting system works, I had to create a new df called dfFood that has columns 'Date', 'Check_number', and 'Total_food'. This data frame is huge and has way more checks than the ones I need to analyze but I need the 'Total_food' column to be in each of the party size and time distinguished data frames.

'Check_number' is unique within any given day but there are several repeat check numbers within the many months time frame being analyzed. Therefore, I need to merge based on 'Date' and 'Check_number'.

Does anyone know how I can best complete these merges?

df_merged = pd.merge(dfLargePre, dfFood, how='left', on=['Check_Number', 'Date'])

I tried the above line and I got an error saying I need to use pd.concat but I am unsure if that is the best plan of action and I am not familiar with the pd.concat coding procedures.

############ EDIT ############# (Adding traceback for attempted merge)

Traceback Screenshot

You are merging a pair of dataframes.
Each has Check_Number and Date columns.

Now look at the .dtype of each dataframe.
The Date should be of datetime64[ns].
Presumably Check_Number should be an integer,
or perhaps "object" if you're modeling that as a string.

The Check_Number type in both dataframes must be identical.
The Date type in both dataframes must be identical.
If they don't match, your .merge() won't succeed.

Thank you all. I found the error. I had not used parse_dates when loading one of my csv files. I fixed the csv upload and the original line of code worked perfectly.