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# 1. Import pandas as pd, numpy as np, and assign the result of pd.read_csv()the CFPB CSV to 'df'.
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# 2. Use the dataframe's groupby() method to create a groupby object on 'Product'. Assign it to 'gb'.
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# 3. Look at your groupby object's .groups attribute. What type of is it? Use this attribute's keys() method.
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# 4. Now trigger the .groups.values() method. What are those values for?
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# 5. Assign gb.groups.keys() to variable 'groups'. Use the Python built-in list to turn this into group_list.
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# 6. Use the gb.get_group() method to pull up a dataframe of 'Debt collection' items
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# 7. Use the size() method to get a count of each and every product
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# 8. The gb object has columns like a dataframe. Get the max of each group for 'Amount Received'.
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# 9. Use the gb's agg() method to run a list of functions [np.mean, np.std, np.median] and create a dataframe.
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# 10. Make a new groupby from 'df' using the list['Product', 'Sub-product] and assign it to 'gb2'
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# 11. Take gb2 and get the size(). Put the result in 'gb2_size'.
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# 12. Get the mean of gb2. Save it using the to_excel method of the dataframe.
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# 13. Take gb2_size and call its unstack() method. You can fillna() and stack() for an easy way to add zeros.
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# 14. Create a groupby object using a function or attribute.
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# 15. Get average monthly results by groupby df[DATE_COLUMN].dt.month