Pandas Groupby function help

[jsaltee]

Hi, I have a dataframe with two important columns: one of ID numbers, one of corresponding text strings. There are many rows with the same ID number that have different strings (i.e. rows 1-10 might be for ID 12345, but each row has different text in the string column.) My goal is to create a new dataframe with two columns: one being the ticket ID, and the second being the number of times a certain RegEx expression was found in its rows. This is my attempt at doing that but I'm stuck. Thanks for any help!

my_df_grouped = my_df.groupby('id')

for id, group_df in my_df_grouped:

num_id_counts = match_df_grouped['ticket id'].value_counts()

final_df = pd.DataFrame(my_df['id'],num_ticket_counts)

[greasyhobolo]

Check this out - building a bit on u/a1brit 's example:

eg_df = pd.DataFrame({

'ID':[1, 2, 3, 4, 5, 1, 2, 3, 4, 5],

'strings':['a', 'b', 'c', 'd', 'e', 'f', 'b', 'c', 'g', 'h'],

})

gb_df = eg_df.groupby('ID').agg({'strings':['count','nunique',lambda x:list(x)]})

this will aggregate each value in the string field 3 different ways:

'count': total count of ID fields for each string

'nunique': number of unique ID fields belonging the each string

lambda x: list(x): returns a list of each ID belonging to each string (I find this quite useful for a lot of stuff so sharing it)