I have a pivot table that is being created like this:
date Services Uptime Services Downtime Centers Downtime Centers Uptime
----- --------- - ------------------ ---------------- ---------------
12/5/14 100.00% 0.00% 100.00% 100.00%
12/12/14 100.00% 0.00% 0.00% 0.00%
12/19/14 100.00% 0.00% 100.00% 0.00%
12/26/14 100.00% 0.00% 100.00% 0.00%
I would like it to come out as a pivot table, like this:
Date Name Uptime Downtime
----- ------ --------- -------------
12/5/14 Services 100.00% 0.00%
12/5/14 Center 100.00% 100.00%
12/12/14 services 100.00% 0.00%
12/12/14 Center 0.00% 0.00%
If you only have those 2 values, try a UNION:
select [date]
,'Services'
,[Services Uptime] as Uptime
,[Services Dowtime] as Downtime
from myTable
union all
select [date]
,'Center'
,[Centers Uptime] as Uptime
,[Centers Dowtime] as Downtime
from myTable
Edited: to include Jason suggestion about "union all"
May be you need to unpivot instead of pivoting. I will do this using cross apply with tables valued constructor.
Performance wise this will be better than Union All if you have some more names
SELECT [date],NAME, uptime, downtime
FROM Yourtable
CROSS apply (VALUES ('service',Services_Uptime,Services_Downtime),
('center',Centers_Uptime,Centers_Downtime) )
cs (NAME, uptime, downtime)
Related
Table A
date
flight
airport
2012-10-01
oneway
ATL, GA
2012-10-01
oneway
LAX, CA
2012-10-01
oneway
SAN, CA
2012-10-01
oneway
DTW, MI
2012-10-02
round
SFO, CA
Table B
date
temp
precip
2012-10-01
67
0.02
2012-10-01
65
0.32
2012-10-01
86
0.18
2012-10-01
87
0.04
2012-10-02
78
0.24
The actual tables have more than 100k rows.
Exepected outcome has two columns temp and ratio
For each temp, I am trying to get the a ratio of flight = oneway where airport have "CA" in it.
I need to first filter rows that the average of precip is greater than 0.2 and cast ratio to interger.
I tried to join on date and group by temp that is having average precip < 0.2 but I am getting fixed wrong value on ratio.
How can I do CTE or CASE WHEN to merge these two tables to compute ratio?
Ratio is should be the (total count of all rows where flight = 'oneway' per each temperature after all filtering) / (total counts of rows)
In the query below I join A and B records over their Date matching as well as B.airport ending with CA, grouped by temperature. The total number of such pairs is the result of COUNT(*) that I divide with. The value I am dividing is the number of items from the pairs which have a oneway flight. It's possible that I did not fully understand the question, in which case we may need to move the airport criteria from the where into the case-when.
SELECT b.temp,
CAST(SUM(
CASE
WHEN A.flight = 'oneway'
THEN 1
ELSE 0
END
) AS FLOAT) / COUNT(*)
FROM A
JOIN B
ON A.`Date` = B.`Date` AND
B.airport LIKE '%CA'
GROUP BY B.temp
I have been trying to gather data using beautiful soup and selenium and have been unable to get more than the first 20 results on the table (there are over 2000 total). I saw some related questions and answers which suggested trying different parsers, so I tried lxml, html.parser and html5lib, but none worked. I also saw some answers suggesting to use selenium and webdriver but wasn't able to get the entire page using either. This is my code as of right now
import requests, time
import os
from selenium import webdriver
from selenium.webdriver.common.by import By
from webdriver_manager.chrome import ChromeDriverManager
import urllib.request
#import pandas as pd
from bs4 import BeautifulSoup
import requests, time
import lxml
driver = webdriver.Chrome(ChromeDriverManager().install())
driver.get('https://coinmarketcap.com/exchanges/uniswap-v2/')
#supposed to scroll to the bottom of the page
driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
time.sleep(5)
page = driver.page_source
soup = BeautifulSoup(''.join(page), 'lxml')
pairs = soup.find_all('div', attrs = {"class" : "hmd6df-0 kCRNNr"})
data = [i.find_all('a')[0] for i in pairs]
Any help would be awesome. Thanks.
I have come up with a purely selenium solution - the following python script successfully scrapes all the data from the table. I am just printing like this as an example, you will want to figure out how to organize your data.
Update: in order to expand and scrape all the rows, I am scrolling to the bottom of the list, then clicking the "Expand More" button, which expands 100 more rows. I am iterating this enough times to get all the way through the 1850 rows of data.
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
import time
driver = webdriver.Chrome(ChromeDriverManager().install())
driver.get('https://coinmarketcap.com/exchanges/uniswap-v2/')
total_height = int(driver.execute_script("return document.body.scrollHeight"))
for x in range(20):
for i in range(1, total_height, 130):
driver.execute_script("window.scrollTo(0, {});".format(i))
if x == 0:
driver.find_element_by_css_selector('div.cmc-cookie-policy-banner__close').click()
driver.find_element_by_xpath('//button[text() = "Load More"]').click()
time.sleep(2)
first_column = driver.find_elements_by_css_selector('td.cmc-table__cell.cmc-table__cell--sticky.cmc-table__cell--sortable.cmc-table__cell--left.cmc-table__cell--sort-by__rank > div')
second_column = driver.find_elements_by_css_selector('div.cwwgik-0.bCvAgC')
third_column = driver.find_elements_by_css_selector('div.hmd6df-0.kCRNNr')
fourth_column = driver.find_elements_by_css_selector('div.cmc-table__column-market-pair-volume-24h')
fifth_column = driver.find_elements_by_css_selector('div.cmc-table__column-market-pair-volume-percent')
sixth_column = driver.find_elements_by_css_selector('td.cmc-table__cell.cmc-table__cell--sortable.cmc-table__cell--right.cmc-table__cell--sort-by__quote-usd-effective-liquidity > div')
seventh_column = driver.find_elements_by_css_selector('td.cmc-table__cell.cmc-table__cell--sortable.cmc-table__cell--right.cmc-table__cell--sort-by__fee-type > div')
eighth_column = driver.find_elements_by_css_selector('div.ghkc60-0.fLaXDt')
for i in range(len(second_column)):
print(str(first_column[i].get_attribute("innerText")) + ' ' + str(second_column[i].get_attribute("innerText")) + ' ' + str(third_column[i].get_attribute("innerText")) + ' ' + str(fourth_column[i].get_attribute("innerText")) + ' ' + str(fifth_column[i].get_attribute("innerText")) + ' ' + str(sixth_column[i].get_attribute("innerText")) + ' ' + str(seventh_column[i].get_attribute("innerText")) + ' ' + str(eighth_column[i].get_attribute("innerText")))
Output:
1 USD Coin USDC/WETH $250,651,977 20.81% 935 Percentage Recently
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...
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I am exercising coding in MS.SQL Server. I have a small table below:
INV_NUM INV_AMOUNT
------- ----------
8000 235
8001 312
8002 528
8003 194
8004 619
I would like to write the query for all the invoices that will show:
the invoice number,
the invoice amount,
the average invoice amount, and
the difference between the average invoice amount and the actual invoice amount.
My code below:
SELECT I.INV_NUM,I.INV_AMOUNT,AVG(I.INV_AMOUNT) as AVERAGE,(AVG(INV_AMOUNT)-I.INV_AMOUNT) as DIFFER
FROM INVOICE I
GROUP BY I.INV_NUM,I.INV_AMOUNT;
The result in MS SQL did not generate the avg value, instead it shows INV_AMOUNT value in each row.
The result is :
INV_NUM INV_AMOUNT Average Differ
--------- ------------------------------------
8003 194 194 0
8000 235 235 0
8001 312 312 0
8002 528 528 0
8004 619 619 0
--------------- -------------------------------
I found if I have only one row in the table, it works fine.
Why this happened? Thanks!
I guess you want something like:
select
inv_num,
inv_amount,
avg(inv_amount) over() as average,
avg(inv_amount) over() - inv_amount as diff
from invoice
Your query is grouping your rows as separate groups. Instead you need to use a window function over a single group (using OVER).
Result:
inv_num inv_amount average diff
------- ---------- ------- ----
8000 235 377 142
8001 312 377 65
8002 528 377 -151
8003 194 377 183
8004 619 377 -242
You can use different solutions. As The Impaler mentioned you can use window functions. For diving deeper into this way of implementation you should read some tutorials about that.
Another way is using an CTE. You will first select the average invoice amount as own query. Afterwards you will use it in your "main" query. Looks like that:
WITH avg_inv AS
(
select avg(inv_amount) average
)
select
inv_num,
inv_amount,
(select average from avg_inv) as average,
(select average from avg_inv) - inv_amount as diff
from invoice;
I’m trying to sum the values in a column VAL for the last 14 days from T_DATE, by account.
My expression is
if([RND_FLG]=1 ,Sum([VAL]) over (Intersect([T_ACC],LastPeriods(14,[T_DATE]))),null)
9/10 the results are accurate, but this is not always the case.
Any help is appreciated.
Sample data below:
ALLDATE T_ACC VAL 14DAYVAL
12/13/2016 1501313137 500000 500000
12/15/2016 1501313137 800000 1300000
12/19/2016 1501313137 500000 1800000
12/20/2016 1501313137 500000 2300000
12/21/2016 1501313137 500000 2300000
12/22/2016 1501313137 500000 3300000
12/30/2016 1501313137 200000 3500000
You are probably getting incorrect results when you have gaps in your dates. LastPeriods() isn't the same as n - days so it's aggregating over n number of rows versus days. You can normalize your data to have 1 row per date to get around this.
Try adding a rank column like Rank([T_DATE],[T_ACC]) Then you can sum using over intersect and lastperiods
Recently I got a new batch of dumps to identify the HighMemory usage in 3 of our WCF Services. Which are hosted on 64Bit AppPool and Windows Server 2012.
Application one :
ProcessUp Time : 22 days
GC Heap usage : 2.69 Gb
Loaded Modules : 220 Mb
Commited Memory : 3.08 Gb
Native memory : 2 Gb
Issue identified as large GC heap usage is due to un closed WCF client proxy objects. Which are accounting for almost 2.26 Gb and rest for cache in GC heap.
Application Two :
ProcessUp Time : 9 Hours
GC Heap usage : 4.43 Gb
Cache size : 2.45 Gb
Loaded Modules : 224 Mb
Commited Memory : 5.13 Gb
Native memory heap : 2 Gb
Issue identified as most of the objects are of System.Web.CaheEntry, as they are due to large cache size. 2.2 Gb of String object on Gc heap has roots to CacheRef root objects.
Application Three :
Cache size : 950 Mb
GC heap : 1.2 Gb
Native Heap : 2 Gb
We recently upgrade to Windows Server 2012, I had old dumps as well. Those dumps does not show the native heap for the same application. It was only around 90 Mb.
I also use WinDbg to explore the Native heap with !heap -s command.
which shows very minimal native heap sizes as shown below.
I am just confused Why DebugDiag 2.0 is showing 2Gb of Native Heap in every WCF service. My understand is that !heap -s should also dump the same native heaps and it should match the debug diag reports graphs. Report also shows values in Thoushand of TBytes.
0:000> !heap -s
LFH Key : 0x53144a890e31e98b
Termination on corruption : ENABLED
Heap Flags Reserv Commit Virt Free List UCR Virt Lock Fast
(k) (k) (k) (k) length blocks cont. heap
-------------------------------------------------------------------------------------
000000fc42c10000 00000002 32656 31260 32552 2885 497 6 2 f LFH
000000fc42a40000 00008000 64 4 64 2 1 1 0 0
000000fc42bf0000 00001002 3228 1612 3124 43 9 3 0 0 LFH
000000fc43400000 00001002 1184 76 1080 1 5 2 0 0 LFH
000000fc43390000 00001002 1184 148 1080 41 7 2 0 0 LFH
000000fc43d80000 00001002 60 8 60 5 1 1 0 0
000000fc433f0000 00001002 60 8 60 5 1 1 0 0
000000fc442a0000 00001002 1184 196 1080 1 6 2 0 0 LFH
000000fc44470000 00041002 60 8 60 5 1 1 0 0
000001008e9f0000 00041002 164 40 60 3 1 1 0 0 LFH
000001008f450000 00001002 3124 1076 3124 1073 3 3 0 0
External fragmentation 99 % (3 free blocks)
-------------------------------------------------------------------------------------
Can anybody explain me why WinDbg command !heap -s and DebugDiag report is varing. Or I have incorrect knowledge of above command.
I also use Pykd script to dump native object stats. Which does not show much large number of objects.
Also what is mean by External fragmentation 99 % (3 free blocks)
in above output. I understand that fragmented memory has less large block of continuous memory place. But fail to relate it with Percentage.
Edit 1 :
Application 2 :
0:000> !address -summary
Mapping file section regions...
Mapping module regions...
Mapping PEB regions...
Mapping TEB and stack regions...
Mapping heap regions...
Mapping page heap regions...
Mapping other regions...
Mapping stack trace database regions...
Mapping activation context regions...
--- Usage Summary ---------------- RgnCount ----------- Total Size -------- %ofBusy %ofTotal
Free 363 7ffb`04a14000 ( 127.981 Tb) 99.98%
<unknown> 952 4`e8c0c000 ( 19.637 Gb) 98.54% 0.01%
Image 2122 0`0e08d000 ( 224.551 Mb) 1.10% 0.00%
Heap 88 0`03372000 ( 51.445 Mb) 0.25% 0.00%
Stack 124 0`013c0000 ( 19.750 Mb) 0.10% 0.00%
Other 7 0`001be000 ( 1.742 Mb) 0.01% 0.00%
TEB 41 0`00052000 ( 328.000 kb) 0.00% 0.00%
PEB 1 0`00001000 ( 4.000 kb) 0.00% 0.00%
--- Type Summary (for busy) ------ RgnCount ----------- Total Size -------- %ofBusy %ofTotal
MEM_PRIVATE 643 4`ebf44000 ( 19.687 Gb) 98.79% 0.02%
MEM_IMAGE 2655 0`0eb96000 ( 235.586 Mb) 1.15% 0.00%
MEM_MAPPED 37 0`00b02000 ( 11.008 Mb) 0.05% 0.00%
--- State Summary ---------------- RgnCount ----------- Total Size -------- %ofBusy %ofTotal
MEM_FREE 363 7ffb`04a14000 ( 127.981 Tb) 99.98%
MEM_RESERVE 725 3`b300d000 ( 14.797 Gb) 74.25% 0.01%
MEM_COMMIT 2610 1`485cf000 ( 5.131 Gb) 25.75% 0.00%
--- Protect Summary (for commit) - RgnCount ----------- Total Size -------- %ofBusy %ofTotal
PAGE_READWRITE 868 1`3939d000 ( 4.894 Gb) 24.56% 0.00%
PAGE_EXECUTE_READ 157 0`09f10000 ( 159.063 Mb) 0.78% 0.00%
PAGE_READONLY 890 0`035ed000 ( 53.926 Mb) 0.26% 0.00%
PAGE_WRITECOPY 433 0`0149c000 ( 20.609 Mb) 0.10% 0.00%
PAGE_EXECUTE_READWRITE 148 0`0065d000 ( 6.363 Mb) 0.03% 0.00%
PAGE_EXECUTE_WRITECOPY 67 0`0017c000 ( 1.484 Mb) 0.01% 0.00%
PAGE_READWRITE|PAGE_GUARD 41 0`000b9000 ( 740.000 kb) 0.00% 0.00%
PAGE_NOACCESS 4 0`00004000 ( 16.000 kb) 0.00% 0.00%
PAGE_EXECUTE 2 0`00003000 ( 12.000 kb) 0.00% 0.00%
--- Largest Region by Usage ----------- Base Address -------- Region Size ----------
Free 101`070a0000 7ef6`e77b2000 ( 126.964 Tb)
<unknown> fd`72f14000 0`d156c000 ( 3.271 Gb)
Image 7ff9`91344000 0`012e8000 ( 18.906 Mb)
Heap 100`928a0000 0`00544000 ( 5.266 Mb)
Stack fc`43240000 0`0007b000 ( 492.000 kb)
Other fc`42ea0000 0`00181000 ( 1.504 Mb)
TEB 7ff7`ee852000 0`00002000 ( 8.000 kb)
PEB 7ff7`eeaaf000 0`00001000 ( 4.000 kb)
Application Three :
0:000> !address -summary
--- Usage Summary ---------------- RgnCount ----------- Total Size -------- %ofBusy %ofTotal
Free 323 7ffb`9f8ea000 ( 127.983 Tb) 99.99%
<unknown> 832 4`4bbb6000 ( 17.183 Gb) 98.15% 0.01%
Image 2057 0`0e5ab000 ( 229.668 Mb) 1.28% 0.00%
Heap 196 0`04f52000 ( 79.320 Mb) 0.44% 0.00%
Stack 127 0`01440000 ( 20.250 Mb) 0.11% 0.00%
Other 7 0`001be000 ( 1.742 Mb) 0.01% 0.00%
TEB 42 0`00054000 ( 336.000 kb) 0.00% 0.00%
PEB 1 0`00001000 ( 4.000 kb) 0.00% 0.00%
--- Type Summary (for busy) ------ RgnCount ----------- Total Size -------- %ofBusy %ofTotal
MEM_PRIVATE 783 4`51099000 ( 17.266 Gb) 98.63% 0.01%
MEM_IMAGE 2444 0`0ec06000 ( 236.023 Mb) 1.32% 0.00%
MEM_MAPPED 35 0`00a67000 ( 10.402 Mb) 0.06% 0.00%
--- State Summary ---------------- RgnCount ----------- Total Size -------- %ofBusy %ofTotal
MEM_FREE 323 7ffb`9f8ea000 ( 127.983 Tb) 99.99%
MEM_RESERVE 621 3`e3504000 ( 15.552 Gb) 88.83% 0.01%
MEM_COMMIT 2641 0`7d202000 ( 1.955 Gb) 11.17% 0.00%
--- Protect Summary (for commit) - RgnCount ----------- Total Size -------- %ofBusy %ofTotal
PAGE_READWRITE 919 0`6dc07000 ( 1.715 Gb) 9.80% 0.00%
PAGE_EXECUTE_READ 153 0`0a545000 ( 165.270 Mb) 0.92% 0.00%
PAGE_READONLY 734 0`02cf5000 ( 44.957 Mb) 0.25% 0.00%
PAGE_WRITECOPY 470 0`01767000 ( 23.402 Mb) 0.13% 0.00%
PAGE_EXECUTE_READWRITE 240 0`009cf000 ( 9.809 Mb) 0.05% 0.00%
PAGE_EXECUTE_WRITECOPY 76 0`001c5000 ( 1.770 Mb) 0.01% 0.00%
PAGE_READWRITE|PAGE_GUARD 42 0`000be000 ( 760.000 kb) 0.00% 0.00%
PAGE_NOACCESS 5 0`00005000 ( 20.000 kb) 0.00% 0.00%
PAGE_EXECUTE 2 0`00003000 ( 12.000 kb) 0.00% 0.00%
--- Largest Region by Usage ----------- Base Address -------- Region Size ----------
Free 52`892e0000 7fa5`65548000 ( 127.646 Tb)
<unknown> 4f`4ec81000 0`e9c3f000 ( 3.653 Gb)
Image 7ff9`91344000 0`012e8000 ( 18.906 Mb)
Heap 52`8833b000 0`00fa4000 ( 15.641 Mb)
Stack 4e`37a70000 0`0007b000 ( 492.000 kb)
Other 4e`37720000 0`00181000 ( 1.504 Mb)
TEB 7ff7`ee828000 0`00002000 ( 8.000 kb)
PEB 7ff7`eea43000 0`00001000 ( 4.000 kb)