Подсчет самых высоких последовательных значений во временных рядах

Question

у меня есть таблица продаж;Учетная запись, код продукта, стоимость, шт. И т. Д.

Я хочу быстро рассчитать количество макс. Последовательных месяцев, которые каждый уникальный номер счета заказал один или несколько раз.

У меня есть метод, но егомедленный, пока он перебирает каждую строку, есть ли лучший метод?

Вот мои данные:

first_purchase_df.head()


+-----------+------------+----------+------------+--------+---------+------------+-----------------------------+------------+------+-------+---------+----------+--------+-------------------+----------+
| SA_DACCNT | SA_TRDATE  | SA_TRREF | SA_TRVALUE | SA_QTY | SA_COST | SA_PRODUCT | SA_DESC                     | Month_year | Year | Month | CN_REF  | CN_CATAG | Margin | FirstPurchaseDate | UserType |
+-----------+------------+----------+------------+--------+---------+------------+-----------------------------+------------+------+-------+---------+----------+--------+-------------------+----------+
| GO63      | 2009-01-02 | 11587    | 0.980      | 1.000  | 0.580   | R613S/6    | BLACK BUFFALO GRAIN R613S/6 | 01-2009    | 2009 | 1     | R613S/6 | ZZZZ     | 0.400  | 01-2009           | New      |
| GO63      | 2009-01-02 | 11587    | 2.500      | 1.000  | 0.000   | POST3      | POSTAGE                     | 01-2009    | 2009 | 1     | POST3   | POST     | 2.500  | 01-2009           | New      |
| GO63      | 2009-01-02 | 11587    | 2.500      | 1.000  | 0.000   | POST3      | POSTAGE                     | 01-2009    | 2009 | 1     | POST3   | POST     | 2.500  | 01-2009           | New      |
+-----------+------------+----------+------------+--------+---------+------------+-----------------------------+------------+------+-------+---------+----------+--------+-------------------+----------+

Я сгруппирован по месяцу-дате:

retention = first_purchase_df.groupby(['Month_year','SA_DACCNT'])['Margin'].sum().astype(int).reset_index()
retention.head()

+------------+-----------+--------+
| Month_year | SA_DACCNT | Margin |
+------------+-----------+--------+
| 01-2009    | ABB1      | 199    |
| 01-2009    | ABB3      | 75     |
| 01-2009    | ACK1      | 49     |
| 01-2009    | ACR2      | 79     |
| 01-2009    | ADO1      | 210    |
+------------+-----------+--------+

Затем я использовал кросс-таблицу, чтобы сложить месяцы с двоичным классификатором, если они упорядочены или нет.

+------------+-----------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+
| Month_year | SA_DACCNT | 01-2009 | 01-2010 | 01-2011 | 01-2012 | 01-2013 | 01-2014 | 01-2015 | 01-2016 | 01-2017 | 01-2018 | 01-2019 | 02-2009 | 02-2010 | 02-2011 | 02-2012 | 02-2013 | 02-2014 | 02-2015 | 02-2016 | 02-2017 | 02-2018 | 02-2019 | 03-2009 | 03-2010 | 03-2011 | 03-2012 | 03-2013 | 03-2014 | 03-2015 | 03-2016 | 03-2017 | 03-2018 | 03-2019 | 04-2009 | 04-2010 | 04-2011 | 04-2012 | 04-2013 | 04-2014 | 04-2015 | 04-2016 | 04-2017 | 04-2018 | 04-2019 | 05-2009 | 05-2010 | 05-2011 | 05-2012 | 05-2013 | 05-2014 | 05-2015 | 05-2016 | 05-2017 | 05-2018 | 05-2019 | 06-2009 | 06-2010 | 06-2011 | 06-2012 | 06-2013 | 06-2014 | 06-2015 | 06-2016 | 06-2017 | 06-2018 | 06-2019 | 07-2009 | 07-2010 | 07-2011 | 07-2012 | 07-2013 | 07-2014 | 07-2015 | 07-2016 | 07-2017 | 07-2018 | 07-2019 | 08-2009 | 08-2010 | 08-2011 | 08-2012 | 08-2013 | 08-2014 | 08-2015 | 08-2016 | 08-2017 | 08-2018 | 08-2019 | 09-2009 | 09-2010 | 09-2011 | 09-2012 | 09-2013 | 09-2014 | 09-2015 | 09-2016 | 09-2017 | 09-2018 | 09-2019 | 10-2009 | 10-2010 | 10-2011 | 10-2012 | 10-2013 | 10-2014 | 10-2015 | 10-2016 | 10-2017 | 10-2018 | 10-2019 | 11-2009 | 11-2010 | 11-2011 | 11-2012 | 11-2013 | 11-2014 | 11-2015 | 11-2016 | 11-2017 | 11-2018 | 12-2009 | 12-2010 | 12-2011 | 12-2012 | 12-2013 | 12-2014 | 12-2015 | 12-2016 | 12-2017 | 12-2018 |
+------------+-----------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+
| CSU1       | 0         | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 1       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       |         |
| 1171       | 0         | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       |         |
| 1183       | 0         | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       |         |
| 1184       | 0         | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       |         |
| 1740       | 0         | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 1       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       |         |
| 1773       | 0         | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 1       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       |         |
+------------+-----------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+

Затем, наконец, написал эту функцию для подсчета месяцев с последовательным упорядочением.

def count_consec(vert,columns):
    max_val = 0
    cur_max_val = 0
    total_val = 0
    current_val = 0

    for x in columns:
        if vert[x] == 1 and current_val == 1:
            current_val = 1
            cur_max_val += 1
            total_val += 1
            if cur_max_val > max_val:
                max_val = cur_max_val
        elif vert[x] == 1:
            cur_max_val +=1
            current_val = 1

            total_val += 1
        else:
            current_val = 0
            cur_mav_val = 0

    return max_val+1

df_retention_count = pd.crosstab(retention['SA_DACCNT'], retention['Month_year']).reset_index()
columns = df_retention_count.columns

df_retention_count["max_month"] = df_retention_count.apply(lambda x: count_consec(x,columns),axis=1)

Answer 1

Решение в одну строку:

df_retention_count["max_month"] = df_retention_count.apply(lambda s: (~s.eq(1)).cumsum()[s.eq(1)].value_counts().max(), axis=1)

Давайте разбить процесс на 2 части:

1. преобразовать данные из результата кросс-таблицы в ряды True / False. Это можно сделать с помощью series.eq (1)

2. Благодаря этой записи мы можем посчитать самую длинную непрерывную последовательность в серии.