Recently, I had to work with an Excel file that has 2 columns, with headers 'Dog Breed' and 'Dog Name'. I came up with the following code (tested with Python 3.11.0) that uses groupby() and prints the grouped data into a .csv file.

from pathlib import Path
import pandas as pd

p = Path(__file__).with_name('data.xlsx')
q = Path(__file__).with_name('data-grouped.csv')

df = pd.read_excel(p)
groups = df.groupby('Dog Breed', sort=False)

with q.open('w') as foutput:
for g in groups: # For each group
    foutput.write(f"{g[0]}, {len(g[1])}") # Record the number of dogs in each group
    for e, (index, row) in enumerate(g[1].iterrows()): # Iterating over the group's dataframe
        name = str(row['Dog Name'])
        if(e == 0):
            mystr = f",{name}\n"
        else:
            mystr = f",,{name}\n"
        foutput.write(mystr)

data.xlsx:

data-grouped.csv:

Group By returns key, value pairs where key is the identifier of the group and the value is the group itself, i.e. a subset of an original df that matched the key.

In your example week_grouped = df.groupby('week') is set of groups (pandas.core.groupby.DataFrameGroupBy object) which you can explore in detail as follows:

for k, gr in week_grouped:
    # do your stuff instead of print
    print(k)
    print(type(gr)) # This will output <class 'pandas.core.frame.DataFrame'>
    print(gr)
    # You can save each 'gr' in a csv as follows
    gr.to_csv('{}.csv'.format(k))

Or alternatively you can compute aggregation function on your grouped object

result = week_grouped.sum()
# This will be already one row per key and its aggregation result
result.to_csv('result.csv') 

In your example you need to assign the function result to some variable as by default pandas objects are immutable.

some_variable = week_grouped.sum() 
some_variable.to_csv('week_grouped.csv') # This will work

basically result.csv and week_grouped.csv are meant to be same

I agree with chrisb's determination of the problem. As a cleaner way, consider having your user_grouper() function not save any values, but instead return these. With a structure as

def user_grouper(df, ...):
    (...)
    df['max_tag_count'] = some_calculation
    return df

results = df.groupby(...).apply(user_grouper, ...)
for i,row in results.iterrows():
    # calculate raw score
    raw_score = (tag_counts[row['tag']]-1) / row['max_tag_count']
    # write to file
    out.write('\t'.join(map(str,[row['uid'],row['iid'],row['tag'],raw_score,weight]))+'\n')

This answer was very helpful to me - thanks @mkln.

I just wanted to add something specific to my own use case, which relates to the original point about file-naming ('Some Text' + name = group).

You could add the name and additional text, for example current date, to each csv filename, so I will create a function to return the current date and then use this for the filename.

Therefore:

from datetime import datetime

def cur_date():
    return datetime.now().strftime("%Y-%m-%d")

for name, group in grouped:
    group.to_csv('{}_{}.csv'.format(name, cur_date()))

you can use groupby with head

df.groupby(['AA1', 'AA2']).head(1)