# The script MUST contain a function named azureml_main
# which is the entry point for this module.

# imports up here can be used to
import pandas as pd

# The entry point function can contain up to two input arguments:
#   Param<dataframe1>: a pandas.DataFrame
#   Param<dataframe2>: a pandas.DataFrame
def azureml_main(dataframe1):

    # Execution logic goes here
    print('Input pandas.DataFrame #1:\r\n\r\n{0}'.format(dataframe1))

    # If a zip file is connected to the third input port is connected,
    # it is unzipped under ".\Script Bundle". This directory is added
    # to sys.path. Therefore, if your zip file contains a Python file
    # mymodule.py you can import it using:
    # import mymodule

    products = {}
    for i in range(len(dataframe1['Product'])):
        name = dataframe1['Product'][i]
        if name in products:
            products[name] += dataframe1['Income'][i]
        else:
            products[name] = dataframe1['Income'][i]
    data = {}
    data['Product'] = list(products.keys())
    result = pd.DataFrame(data)
    result['Income'] = list(products.values())

    periods = {}
    for i in range(1996 * 12 + 7, 1998 * 12 + 6):
        periods[i] = {}
    for i in range(len(dataframe1['Product'])):
        date = dataframe1['Date'][i]
        arr = date.split('.')
        m = int(arr[1])
        y = int(arr[2])
        name = dataframe1['Product'][i]
        income = dataframe1['Income'][i]
        month = y * 12 + m
        if name in periods[month]:
            periods[month][name] += income
        else:
            periods[month][name] = income

    xyz = []
    for name in products:
        mean = 0.0
        for month in periods:
            if name in periods[month]:
                mean += periods[month][name]
        mean /= len(periods)
        sigma = 0.0
        for month in periods:
            if name in periods[month]:
                sigma += (periods[month][name] - mean) ** 2
            else:
                sigma += mean ** 2
        sigma /= len(periods)
        sigma = sigma ** 0.5

        var = 1.0 * sigma / mean
        if 100 * var <= 85:
            xyz.append('X')
            continue
        if 100 * var <= 100:
            xyz.append('Y')
            continue
        else:
            xyz.append('Z')
            continue

    result['ABC'] = pd.Series(len(products), index=result.index)
    result['XYZ'] = pd.Series(len(products), index=result.index)
    for i in range(len(products)):
        result['XYZ'][i] = xyz[i]
    result.sort_values(by=['Income'], ascending=False, inplace=True)
    sumOfIncomes = result['Income'].sum()
    result = result.reset_index(drop=True)
    accumulate = 0
    s = result['Income']
    for i in range(len(products)):
        accumulate += s[i]
        if (accumulate <= 0.7 * sumOfIncomes):
            result['ABC'][i] = 'A'
        elif (accumulate <= 0.9 * sumOfIncomes):
            result['ABC'][i] = 'B'
        else:
            result['ABC'][i] = 'C'

    # Return value must be of a sequence of pandas.DataFrame
    return result