human
I am working on the file "11/homework-11-schuetz.ipynb".
The first task is:
How I Tried to Do It Before I Made 'Fee for This Precinct'
I want:
    [
    {'DEFINITION': 'Street Cleaning...', 'freq': 140329, 'All Other Areas Int': 45},
    {'DEFINITION': 'Failing to show a receipt', 'freq': 125716, 'All Other Areas Int': 35},
    ...
    ]
Then I'll turn that to a DataFrame.
<p> _Then I'll multiply 'freq' and 'All Other Areas Int' to make a 'How Lucrative' column_
Can you write Python program for it?

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Can you provide a the Python program you requested?
assistant
#Here is the Python program you requested.

# def_list = df_merged['DEFINITION'].unique()

# def first_fee_result(partial_df):
#     number = 0
#     for row in partial_df:
#         number += 1
#         if number == 1:
#             return row

# lucrative_list = []
# lucrative_dict = {}
# for definition in def_list:
#     all_rows_in_df_with_def = df_merged[df_merged['DEFINITION'] == definition]
#     how_many_rows = all_rows_in_df_with_def['DEFINITION'].count()
#     fee = first_fee_result(all_rows_in_df_with_def['All Other Areas Int'])
#     if how_many_rows > 0:
#         lucrative_dict['DEFINITION'] = definition
#         lucrative_dict['freq'] = how_many_rows
#         lucrative_dict['All Other Areas Int'] = fee
#         lucrative_dict['How Lucrative'] = how_many_rows * fee
#         lucrative_list.append(lucrative_dict)
#         lucrative_dict = {}

# df_lucrative = pd.DataFrame(lucrative_list)

# df_lucrative.sort_values('How Lucrative', ascending=False).head()