Question

我有两个查询正在为我完成工作，但我想将它们组合在一起以将结果存储在一个表中，而不是将结果复制到excel中。

第一个查询向我提供了2018年每月至少一个订阅已过期的用户数量：

    WITH UniqueUsers AS
    (
        SELECT DISTINCT MONTH(ValidTo) ExpireMonth, UserId
        FROM UserInAppPurchase 
        WHERE YEAR(ValidTo) = 2018
    )
    SELECT ExpireMonth, COUNT(UserId) UserCount
    FROM UniqueUsers
    GROUP BY ExpireMonth order by ExpireMonth;

第二个查询提供了我在2018年每月至少购买一次订阅的用户数量：

    WITH UniqueUsers AS
    (
        SELECT DISTINCT MONTH(PurchaseDate) PurchaseMonth, UserId
        FROM UserInAppPurchase 
        WHERE YEAR(PurchaseDate) = 2018
    )

   SELECT PurchaseMonth, COUNT(UserId) UserCount
   FROM UniqueUsers
   GROUP BY PurchaseMonth order by PurchaseMonth;

实际上PurchaseMonth和ExpireMonth是相同的。

我的预期输出是：第一栏：2018年的几个月
第二列：第一个查询的结果
第三列：第二个查询的结果

仅复制两个结果并手动组合它们并没有什么大的危害，但我很好奇如何直接在SQL中进行操作。

感谢您的帮助

Answer 1

我将取消显示日期，然后进行汇总：

SELECT MONTH(dte) as mon,
       COUNT(DISTINCT ExpireUserId) as numExpiredUsers,
       COUNT(DISTINCT PurchaseUserId) as numPurchaseUsers
FROM UserInAppPurchase uiap CROSS APPLY
     (VALUES (ValidTo, UserId, NULL),
             (PurchaseDate, NULL, UserId)
    ) v(dte, ExpireUserId, PurchaseUserId)
WHERE dte >= '2018-01-01' AND dte < '2019-01-01'
GROUP BY MONTH(dte) 
ORDER BY MONTH(dte);

此逻辑实际上并不需要子查询，显式JOIN或CTE。

Answer 2

您在这里，您可以加入CTE

 ;WITH UniqueUsers1
AS (
    SELECT DISTINCT MONTH(ValidTo) ExpireMonth
        ,UserId
    FROM UserInAppPurchase
    WHERE YEAR(ValidTo) = 2018
    )
    ,UniqueUsers2
AS (
    SELECT DISTINCT MONTH(PurchaseDate) PurchaseMonth
        ,UserId
    FROM UserInAppPurchase
    WHERE YEAR(PurchaseDate) = 2018
    )
SELECT *
FROM (
    SELECT ExpireMonth
        ,COUNT(UserId) UserCount
    FROM UniqueUsers1 c1
    GROUP BY ExpireMonth
    ) First
INNER JOIN (
    SELECT PurchaseMonth
        ,COUNT(UserId) UserCount
    FROM UniqueUsers2
    GROUP BY PurchaseMonth
    ) Sec ON First.ExpireMonth = Sec.PurchaseMonth

Answer 3

您可以将每个结果存储在单独的临时表中，例如，创建两个临时表，如下所示：

import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
import numpy as np
from PIL import Image

def hists(x, y):

    histx,_ = np.histogram(np.arange(len(x)), bins=len(x), weights=x)
    histy,_ = np.histogram(np.arange(len(y)), bins=len(y), weights=y)
    return histx, histy

def plot(ndimg):

    w, h = ndimg.shape

    x = np.mean(ndimg, axis=0)
    x -= np.mean(x)
    y = np.mean(ndimg, axis=1)
    y -= np.mean(y)

    nullfmt = NullFormatter()

    left, width = 0.1, 0.65*h/w if w > h else 0.65
    bottom, height = 0.1, 0.65*w/h if h > w else 0.65
    left_h = left + width + 0.02
    bottom_h = bottom + height + 0.02

    rect_img = [left, bottom, width, height]
    rect_histx = [left, bottom_h, width, 0.2]
    rect_histy = [left_h, bottom, 0.2, height]

    plt.figure(1, figsize=(8, 8))

    axImg = plt.axes(rect_img)
    axHistx = plt.axes(rect_histx)
    axHisty = plt.axes(rect_histy)

    axHistx.xaxis.set_major_formatter(nullfmt)
    axHisty.yaxis.set_major_formatter(nullfmt)

    axImg.imshow(ndimg, cmap=plt.get_cmap('gray'))

    axHistx.hist(np.arange(len(x)), bins=int(0.03*len(x)), weights=x)
    axHisty.hist(np.arange(len(y)), bins=int(0.03*len(y)), weights=y, 
                 orientation='horizontal')

    axHistx.set_xlim(axImg.get_xlim())
    axHisty.set_ylim(axImg.get_ylim())

    plt.show()

def mask(ndimg, bw_threshhold=0.6, mask_threshhold=5e-3):
    ndimg = ndimg / np.max(ndimg)
    ndimg = np.where(ndimg < bw_threshhold, 0.0, 1.0)
    #ndimg = np.exp(-np.logaddexp(0, -10*(ndimg-0.6)))

    x = np.mean(ndimg, axis=0)
    #x = x - np.mean(x)
    y = np.mean(ndimg, axis=1)
    #y = y - np.mean(y)

    histx, histy = hists(x, y)
    histx = histx - np.mean(histx)
    histy = histy - np.mean(histy)
    #histx -= (histx.max() + histx.min())/2
    #histy -= (histy.max() + histy.min())/2

    maskx = np.where(histx < mask_threshhold, False, True)
    masky = np.where(histy < mask_threshhold, False, True)

    ndimg[masky, :] = 0.
    ndimg[:, maskx] = 0.
    return ndimg

img = Image.open('C:/Users/maxid/Desktop/Pics/7_1.jpg')
                 .convert(mode='L')
w, h = img.size
img = img.crop((0.25*w, 0.25*h, 0.75*w, 0.75*h))
ndimg = np.asarray(img)
plot(ndimg)
ndimg = mask(ndimg, )
plot(ndimg)
a, b = hists(np.mean(ndimg, axis=0), np.mean(ndimg, axis=1))
print((a.max()+a.min())/2, np.mean(a), np.median(a))
plt.plot(a)

然后将第一个查询结果存储到临时表#ExpirayDates

CREATE Table #ExpiryDates (ExpireMonth int, TotalUsers int)
CREATE Table #PurchaseDates (PurchaseMonth int, TotalUsers int)

第二个查询将是这样：

;WITH UniqueUsers AS
(
    SELECT DISTINCT MONTH(ValidTo) ExpireMonth, UserId
    FROM UserInAppPurchase 
    WHERE YEAR(ValidTo) = 2018
)
INSERT INTO #ExpiryDates (ExpireMonth, TotalUsers)
SELECT ExpireMonth, COUNT(UserId) UserCount
FROM UniqueUsers
GROUP BY ExpireMonth order by ExpireMonth;

最后，将它们组合成一个查询

;WITH UniqueUsers AS
(
    SELECT DISTINCT MONTH(PurchaseDate) PurchaseMonth, UserId
    FROM UserInAppPurchase 
    WHERE YEAR(PurchaseDate) = 2018
)
INSERT INTO #PurchaseDates (PurchaseMonth, TotalUsers)
SELECT PurchaseMonth, COUNT(UserId) UserCount
FROM UniqueUsers
GROUP BY PurchaseMonth order by PurchaseMonth;

结果将是：

SELECT p.PurchaseMonth MonthNumber, p.TotalUsers As UsersMadePurchases, 
       e.ExpireMonth, e.TotalUsers As UsersExpiredSubs 
FROM #PurchaseDates p
LEFT JOIN #ExpiryDates e
    ON e.ExpireMonth = p.PurchaseMonth

让我知道这是否可以解决您的问题。

Answer 4

您可以像这样使用smth：

g++ -fPIC -c a.cc b.cc 
g++ a.o b.o -shared -o libA.so


g++ -fPIC -c c.cc
g++ c.o -shared -lA -L. -o libB.so 

g++ main.cc -lA -L.
g++ main.cc -lB -L.
/tmp/cci2foLo.o: undefined reference to symbol '_Z5hellov'
./libA.so: error adding symbols: DSO missing from command line
collect2: error: ld returned 1 exit status

ldd libB.so 
linux-vdso.so.1 =>  (0x00007fffa733d000)
libA.so => ./libA.so (0x00002b0f9a92a000)
libstdc++.so.6 => /usr/lib64/libstdc++.so.6 (0x00002b0f9ab5e000)
libm.so.6 => /lib64/libm.so.6 (0x00002b0f9ae64000)
libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00002b0f9b0e8000)
libc.so.6 => /lib64/libc.so.6 (0x00002b0f9b2ff000)
/lib64/ld-linux-x86-64.so.2 (0x00002b0f9a504000)

但是我不确定您的with months as ( SELECT MONTH(ValidTo) as month_ FROM UniqueUsers WHERE YEAR(ValidTo) = 2018 union SELECT MONTH(PurchaseDate) FROM UniqueUsers WHERE YEAR(PurchaseDate) = 2018 ), UniqueUsers AS ( SELECT MONTH(PurchaseDate) as ExpireMonth, COUNT(UserId) UserCount FROM UniqueUsers WHERE YEAR(ValidTo) = 2018 GROUP BY MONTH(PurchaseDate) ), UniqueUsers1 AS ( SELECT MONTH(PurchaseDate) as PurchaseMonth, COUNT(UserId) UserCount1 FROM UniqueUsers WHERE YEAR(PurchaseDate) = 2018 GROUP BY MONTH(PurchaseDate) ) select m.month_, u.UserCount, u1.UserCount1 from months m left join UniqueUsers u on m.month_ = u.ExpireMonth left join UniqueUsers1 u1 on m.month_ = u1.PurchaseMonth order by m.month_;子句是否是您想要的。您的月份将被排序为1、11、12。也许您想要另一个订单，我只是重写了您的查询以将其合并。

结合两个具有每个with子句的sql查询

4 个答案: