Try doing this:

week_grouped = df.groupby('week')
week_grouped.sum().reset_index().to_csv('week_grouped.csv')

That'll write the entire dataframe to the file. If you only want those two columns then,

week_grouped = df.groupby('week')
week_grouped.sum().reset_index()[['week', 'count']].to_csv('week_grouped.csv')

Here's a line by line explanation of the original code:

# This creates a "groupby" object (not a dataframe object) 
# and you store it in the week_grouped variable.
week_grouped = df.groupby('week')

# This instructs pandas to sum up all the numeric type columns in each 
# group. This returns a dataframe where each row is the sum of the 
# group's numeric columns. You're not storing this dataframe in your 
# example.
week_grouped.sum() 

# Here you're calling the to_csv method on a groupby object... but
# that object type doesn't have that method. Dataframes have that method. 
# So we should store the previous line's result (a dataframe) into a variable 
# and then call its to_csv method.
week_grouped.to_csv('week_grouped.csv')

# Like this:
summed_weeks = week_grouped.sum()
summed_weeks.to_csv('...')

# Or with less typing simply
week_grouped.sum().to_csv('...')

I mean, isn't it groupby(), not group_by()?

The function pd.read_csv() is already a DataFrame and thus that kind of object does not support calling .to_dataframe().

You can check the type of your variable ds using print(type(ds)), you will see that it is a pandas DataFrame type.

It's case-sensitive, should be df.to_csv(...)

DataFrame has no attribute group. However, it is possible to access data in a column in your dataframe with the same syntax used to access attributes and methods, i.e. if you have a column col, you may access the series related to this column through

df.col

What happened here is that your data is probably different from what she used in the tutorial. Or at least, the columns she has are different than the columns you have.

To solve that problem, you can either (I) simply rename your columns to match the columns from the tutorial or (II) replace data.group with the corresponding column name that you have in your df