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Situation: This is my first question in this Q&A segment of stackoverflow. please
bear with me. Currently, my code works perfectly well but i would like
to make it cleaner for other users by removing the duplicate and similar lines of code into functions or for loops. Because I am still new learning
python, I still did not get a hang of functions and for loops. My data
frame rfm includes 5 columns:

• 
  Max Date (Latest transaction )


• 
  Id (unique identifier)


• 
  Recency (Today's date minus Latest Transaction Date)


• 
  Frequency (Total # of transactions per Id since its subscription)


• 
  Monetary (Total amount of $ spent by Id since its subscription)

Seperating the main data frame into 3 different df because the sort differs for each cumulative sum column. Frequency and Monetary dfs have identical calculations:

rfm_recency = rfm[['Max_Date', 'Id', 'Member_id', 'Recency']].copy()

rfm_recency = rfm_recency.sort_values(['Recency'], ascending=True)



rfm_frequency = rfm[['Id', 'Member_id', 'Frequency']].copy()

rfm_frequency = rfm_frequency.sort_values(['Frequency'], ascending=False)

rfm_frequency['cum_sum'] = rfm_frequency['Frequency'].cumsum()

rfm_frequency['cum_sum_perc'] = rfm_frequency['cum_sum'] / rfm_frequency['Frequency'].sum()



rfm_monetary = rfm[['Id', 'Member_id', 'Monetary']].copy()

rfm_monetary = rfm_monetary.sort_values(['Monetary'], ascending=False)

rfm_monetary['cum_sum'] = rfm_monetary['Monetary'].cumsum()

rfm_monetary['cum_sum_perc'] = rfm_monetary['cum_sum'] / rfm_monetary['Monetary'].sum()



def scorefm(x):

    """Function for separating data into 5 bins for Frequency & Monetary df """

    if x <= 0.20:

        return 5

    elif x <= 0.40:

        return 4

    elif x <= 0.60:

        return 3

    elif x <= 0.80:

        return 2

    else:

        return 1





# Divide the Recency df into equal quantiles

rfm_recency['r_score'] = 5 - pd.qcut(rfm_recency['Recency'], q=5, labels=False)



# Create scores from cum_sum_perc for Frequency and Monetary

rfm_frequency['f_score'] = rfm_frequency['cum_sum_perc'].apply(scorefm)

rfm_monetary['m_score'] = rfm_monetary['cum_sum_perc'].apply(scorefm)



# Resorting data frames by ID to merge

rfm_recency = rfm_recency.sort_values('Id')

rfm_frequency = rfm_frequency.sort_values('Id')

rfm_monetary = rfm_monetary.sort_values('Id')



# Merging data frames together

result = rfm_recency.copy(['Recency', 'r_score'])

result = result.join(rfm_frequency[['Frequency', 'f_score']])

result = result.join(rfm_monetary[['Monetary', 'm_score']])



# Create an FM and RFM score based on the individual R, F, M scores.

result['FM'] = (result['f_score'] + result['m_score']) / 2

result['RFM_Score'] = result['r_score'] * 10 + result['FM']

Goal: This is one of my first python script and would like to see how this could have been done with functions or for loops.

Thank you all for your time and help on this review.