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我正在处理交易数据，并希望获得过去 12 个月的滚动活跃客户群，但按月计算。

以下是我拥有的交易数据示例 -交易数据

Cust-ID Date-of-Purchase    Quantity    Customer-Tag

N01847  01-04-18    10  B2C
N01847  15-04-18    20  B2C
N01847  25-06-19    20  B2C
N01847  12-02-20    100 B2C
N01847  25-03-20    150 B2C
N02341  01-12-19    20  B2B
N02341  02-02-20    150 B2B
N02011  25-01-19    10  B2B
N01033  02-06-18    30  B2C
N01033  02-04-19    40  B2C
N01033  15-04-19    50  B2C
N01033  15-02-20    20  B2C
N01010  16-01-18    100 B2C
N01010  25-02-18    250 B2C
N01010  03-09-18    10  B2C
N01010  04-04-19    250 B2C
N01010  15-06-19    100 B2C
N01010  26-07-19    40  B2C
N01010  17-09-19    10  B2C
N01010  17-09-19    10  B2C
N01010  20-03-20    20  B2C
N09100  20-01-18    20  B2B
N09100  20-01-18    20  B2B
N09100  25-01-20    20  B2B
N09100  25-01-20    20  B2B
N09100  25-01-20    20  B2B   ֿ
N09100  25-01-20    20  B2B

下面是我期望用 python 得到的 -所需的输出

Month-Year  B2C-Rolling-past-12months-Active-Customers  Monthly-Active-Customers    Monthly-Active-Rate

201801  100230  25058   25.0%

201802  100524  25634   25.5%

201803  100810  25213   25.0%

201804  101253  25495   25.2%

201805  101351  25525   25.2%

201806  103210  25998   25.2%

201807  103678  26122   25.2%

201808  103977  26202   25.2%

201809  104512  26342   25.2%

201810  104624  26376   25.2%

201811  105479  26597   25.2%

201812  111256  28059   26.2%

201901  112247  28314   25.2%

201902  112947  28497   25.2%

201903  113508  28644   25.2%

201904  113857  28737   25.2%

201905  114572  28924   25.2%

201906  115443  29149   25.3%

201907  116056  29310   25.3%

201908  116528  29435   25.3%

201909  116971  29553   25.3%

201910  117647  29729   25.3%

201911  118492  29949   25.3%

201912  124095  31371   26.3%

202001  124895  31580   25.3%

202002  125653  31778   25.3%

202003  126320  31953   25.3%

我非常感谢任何有助于获得所需结果的 python (spyder) 代码的帮助。

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1 回答 1

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您可能想使用 pandas，然后执行以下操作：

df["Date-of-Purchase"] = pd.to_datetime(df["Date-of-Purchase"], dayfirst=True)
df["Month"] = df["Date-of-Purchase"].dt.month
df["Year"] = df["Date-of-Purchase"].dt.year
res = df.groupby(["Year", "Month"])["Cust-ID"].nunique()

这将为您提供每月唯一客户的数量。假设您有所有月份的数据，您现在可以使用rolling12 个月的滚动总和（我在代码中使用了 3 个月以便于调试）：

monthly_customers = df.groupby(["Year", "Month"])["Cust-ID"].apply(lambda x: frozenset(x.values))
monthly_customers = monthly_customers.reset_index()

monthly_customers = pd.concat([monthly_customers] + [monthly_customers["Cust-ID"].shift(i) for i in range(1, 3)], axis ="columns")
monthly_customers.columns = ["Year", "Month"] + [ f"shift_{i}" for i in range(3) ]

def count_unique(row):
    total_set = frozenset()
    columns = [ f"shift_{i}" for i in range(3) ]
    for col in columns:
        if row.get(col) and type(row.get(col)) == frozenset:
            total_set = total_set | row.get(col)
    return len(total_set)

monthly_customers["N_month_count"] = monthly_customers.apply(count_unique, axis=1)
monthly_customers

如果您没有所有月份的数据，则需要填写缺失的月份。

于 2020-05-16T08:34:35.497 回答