calculate.py

def calculate_good_bad_average(data):

    result = data.groupby(['Competency','Status'])['Status'].count()
    keys = result.to_dict().keys()
    competency= list() 
    for item in keys:
        a = dict()
        a = {
            "Competency" : item[0],
            item[1].lower() : result[item]
        }
        competency.append(a)
        
    return competency

def get_total_competency_scores(data):
    result_list = list()
    data =  data.groupby(['Competency','Status'])["Status"].count().groupby('Competency').count().to_dict()
    
    for item in data.keys():
        result = dict()
        result["Competency"]=item
        result["No moves"]=data[item]
        result["bad"]=int()
        result["intermediate"]=int()
        result["good"]=int()
        result_list.append(result)
    return result_list

def main(data):     
    total_scores = get_total_competency_scores(data)
    individual_scores = calculate_good_bad_average(data)
    
    
    for item_1 in total_scores:
        for item_2 in individual_scores:
            if item_1['Competency'] == item_2 ['Competency']:
                try:
                    item_1['bad'] = item_2['bad']
                    item_1['good'] = item_2['good']
                    item_1['intermediate'] = item_2['intermediate']
                except:                
                    try:
                        item_1['good'] = item_2['good']
                        item_1['bad'] = item_2['bad']
                        item_1['intermediate'] = item_2['intermediate']
                    except Exception as e:
                        try:
                            item_1['intermediate'] = item_2['intermediate']
                            item_1['good'] = item_2['good']
                            item_1['bad'] = item_2['bad']
                        except Exception as e:
                            pass
    
    return total_scores