tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 02750ac9-d79a-4c4e-b0ca-0afc6b1de969
Sheet: Sheet1

Glendale Community College District |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | REPORT OF OVERTIME SERVICE
Payroll Department
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Month
Employee: |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Department
Last
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Account Number
First |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Fund (2) |  | Program    (4) |  |  | Sub-Program (1) |  | TOPS         (6) |  |  | Object (4) |  | Account Description |  |  |  |  |  |  | Amount
Social Security Number
Date of |  |  |  |  | Hours |  |  |  |  |  | Total |  |  |  |  | Requested By |  |  |  |  |  |  |  |  |  | Activity and Location
Service |  |  |  |  | From |  |  | To |  |  | Hours and Minutes
 |  |  |  |  | Total Hours |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Service Approved by:
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Signature of Department Head
Compensation is given in the form of overtime pay.
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [NAME]
Employee Signature |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Signature of Vice President
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [NAME]
Spreadsheet: 328464d1403902824-sumifs-with-multiple-criteria-and-sumranges-book2
Sheet: Sheet1

YTD Spend
Category | Department | Account | Jul | Aug | Sep | Oct |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | #[NAME]!
Consulting | 340 | 84100 | 3239.985872854265 | 3239.985872854265 | 3239.985872854265 | 3239.985872854265
[NAME] | 150 | 55000 | 1215 | 1215 | 1215 | 1215
Consulting | 250 | 89000 | 17500 | 0 | 0 | 0
Consulting | 150 | 83250 | 5953.06375 | 5953.06375 | 5953.06375 | 5953.06375
[NAME] | 150 | 51210 | 1286.8325 | 1286.8325 | 1286.8325 | 1286.8325
Consulting | 160 | 87000 | 144.80125 | 144.80125 | 144.80125 | 144.80125
[NAME] | 120 | 51410 | 280.89125 | 280.89125 | 280.89125 | 280.89125
Consulting | 150 | 88654 | 659.5775 | 659.5775 | 659.5775 | 659.5775
[NAME] | 150 | 51110 | 5953.06375 | 5953.06375 | 5953.06375 | 5953.06375
T&M | 180 | 61600 | 1286.8325 | 1286.8325 | 1286.8325 | 1286.8325
[NAME] | 150 | 51310 | 144.80125 | 144.80125 | 144.80125 | 144.80125
T&M | 280 | 61400 | 280.89125 | 280.89125 | 280.89125 | 280.89125
[NAME] | 150 | 51510 | 659.5775 | 659.5775 | 659.5775 | 659.5775
Current Period | 3
I want to know how muuch has been spent on [NAME] by dept 150 YTD which in this case would be for Jul-Sep
Is there a formula that would say look up everything through current period for [NAME] and dept 150, maybe a sumifs? I can do sumifs with offset but only for one period.
Spreadsheet: 8f7d6442-1fdd-4635-9e9a-68758f9bb6d0
Sheet: Sheet1

Predicted probability of childhood obesity from traditional risk factors. | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8
Enter the variables values and get the predicted probability.
 |  |  | Maternal BMI |  |  |  | 35
 |  |  | Paternal BMI |  |  |  | 30
 |  |  | Number of household members |  |  |  | 3
 |  |  | Maternal professional category (1-4) |  |  |  | 2
 |  |  | Gestational smoking (0=no, 1=yes) |  |  |  | 0
 |  |  | Birth weight (kg) |  |  |  | 4
 |  |  | Predicted probability |  |  |  | 42.06757478512507 | %
Spreadsheet: 9309bf14-e9d2-4c41-9e3b-ba39054fde05
Sheet: Sheet1

R10 Metabolite | Count | Unnamed: 2 | Unnamed: 3 | R10 Reaction | Number of uses (averaged) | Unnamed: 6 | Note about reaction usage in the R10 EFM network: Because the EFM method also gives MBP degeneracy, each set of EFMs representing one MBP were averaged together, giving an average reaction usage for each input output pair. For example, there are 4 degenerate MBPs that break down a10 into a1. Of these, three use the reaction [NAME] a2 + a2, so this MBP, on average, uses that reaction 75% of the time. This allows for an appropriate comparison with both the MILP R19 and KEGG datasets without unfairly weighting those functions that have a disproportionate number of degenerate MBPs. Also note that because there are 100 input/output pairs in the R10 network, the number of uses here is also the percentage of MBPs (averaged) that use each reaction.
a1 | 105.2385 |  | 1 | [NAME] | 56.9516
a2 | 174.022 |  | 2 | [NAME] a1 + a1 | 51.5923
a3 | 71.2245 |  | 3 | [NAME] a4 + a4 | 22.7017
a4 | 134.0113 |  | 4 | [NAME] a1 + a2 | 22.2395
a5 | 40.9806 |  | 5 | a6 ↔ a2 + a4 | 18.9354
a6 | 57.0286 |  | 6 | a6 ↔ a3 + a3 | 17.0158
a7 | 20.8453 |  | 7 | [NAME] a1 + a4 | 12.0724
[NAME] | 39.845 |  | 8 | a5 ↔ a2 + a3 | 9.8265
a9 | 19.9878 |  | 9 | a9 ↔ a3 + a6 | 7.4439
a10 | 21.8178 |  | 10 | a10 ↔ a5 + a5 | 6.7373
 |  |  | 11 | a7 ↔ a3 + a4 | 6.6275
 |  |  | 12 | a10 ↔ a4 + a6 | 6.5691
 |  |  | 13 | a10 ↔ a2 + a8 | 5.8039
 |  |  | 14 | [NAME] [NAME] | 5.3744
 |  |  | 15 | a9 ↔ a4 + a5 | 4.7792
 |  |  | 16 | a7 [NAME] | 4.6445
 |  |  | 17 | a9 ↔ [NAME] | 4.5063
 |  |  | 18 | a7 ↔ a1 + a6 | 3.3635
 |  |  | 19 | [NAME] a1 + a7 | 3.1589
 |  |  | 20 | [NAME] | 2.2273
 |  |  | 21 | a9 ↔ a2 + a7 | 1.801
 |  |  | 22 | a6 ↔ a1 + a5 | 1.4737
 |  |  | 23 | a10 ↔ a1 + a9 | 1.4575
 |  |  | 24 | [NAME] a3 + a5 | 1.447
 |  |  | 25 | a10 ↔ a3 + a7 | 1.25
Spreadsheet: 881713d1729853711-sum-cummulate-subtotal-numbers-for-a-lot-of-different-project-numbers-test-nw
Sheet: report

Resource | Hours | Project no | Price | Subtotal
Resource 1 | 0.75 | O/056710 | 20
Resource 1 | 2.25 | O/056710 | 20
Resource 1 | 5 | O/056710 | 20
Resource 2 | 24.25 | O/056803 | 1
Resource 2 | 24.25 | O/056803 | 1
Resource 2 | 24.25 | O/056803 | 1
Resource 2 | 24.25 | O/078255 | 1
Resource 3 | 6 | O/078255 | 1
Resource 3 | 48 | O/090381 | 1
Resource 4 | 48 | O/090381 | 1
Resource 4 | 20 | O/090381 | 1
Resource 2 | 72 | O/670229 | 1
Resource 2 | 72.25 | O/670229 | 1
Resource 3 | 72 | O/670229 | 1
Resource 4 | 72.25 | O/670229 | 1
Resource 2 | 16.75 | O/680131 | 1
Resource 3 | 18 | O/680131 | 1
Resource 4 | 22.25 | O/680131 | 1
Resource 1 | 71.5 | O/825469 | 40
Spreadsheet: 493140d1481547832-removing-chinese-characters-from-certain-rows-in-a-column-sample01-ak
Sheet: Sheet1

1
00:00:31,960 --> 00:00:34,720
你覺得它很可愛  對不對
You think this is cute, don't you?
2
00:00:34,800 --> 00:00:38,000
你覺得你們有真正的情感共鳴
You think there's a real connection there.
3
00:00:38,120 --> 00:00:40,720
你喂它吃東西
So you feed it,
4
00:00:40,800 --> 00:00:42,840
給它穿衣服
you dress it up.
5
00:00:43,050 --> 00:00:46,840
《 九條命 》
<<  Nine Lives  >>
6
00:00:46,920 --> 00:00:48,570
但是貓并不在乎你的死活
But a cat doesn't care if you live or die
7
00:00:48,640 --> 00:00:51,450
只要有人每天給它吃兩頓飯就行
as long as someone gives it Friskies twice a day.
Spreadsheet: 786446d1657197046-moving-data-from-multiple-rows-into-single-row-multiple-columns-formula-help
Sheet: ORDERS

Name | Item Name | Unit Cost | Quantity | Total Cost
Person A | Steak | 12 | 10
Person A | Hamburger | 1.5 | 100
Person A | Sausage | 0.75 | 1000
Person B | Steak | 12 | 100
Person B | Hamburger | 1.5 | 1500
Person C | Steak | 12 | 50
Person D | Steak | 12 | 75
Person D | Hamburger | 1.5 | 5000
Person D | Sausage | 0.75 | 1500
Person E | Sausage | 0.75 | 3000
Person F | Hamburger | 1.5 | 1200
Spreadsheet: frank_ermis__11033__Expansion Offer Sheet
Sheet: Manual Offer

Wild Goose Storage Inc. |  |  | April 1, 2004 Expansion Open Season |  |  |  | Manual Offer Sheet
Term | commencing at |  | and ending on
Inventory Capacity : |  |  | Dth. |  |  | [NAME] is 2:00 MST, Tuesday May 22, 2001
 | Maximum |  |  | Maximum Daily
 | Daily Injection |  |  | Withdrawal
Month | Quantity |  |  | Quantity
 | Dth/day/month |  |  | Dth/day/month
April
May
June
July |  |  |  |  |  | Total Storage Demand Charge (US$)
August
September
October
November |  |  |  |  |  | Annual Demand charge effective April 1, 2004
December
January
February
March
Injection Commodity Rate: |  |  |  | $US/Dth
Withdrawal Commodity Rate: |  |  |  | $US/Dth
This offer is open for acceptance by Wild Goose Storage Inc. until 2:00 pm MST, Thursday June 28, 2001.
 |  |  |  |  |  | Fax Offer Sheets to: [PHONE]
 |  |  |  |  |  | Attention: [NAME]
Company Name
Per:
 |  |  |  |  |  |  |  | 3900, 421 - 7th Avenue S.W.
 |  |  |  |  |  |  |  | Calgary, Alberta
 |  |  |  |  |  |  |  | Canada
Name: |  |  |  |  |  |  |  | T2P 4K9
Title:
Spreadsheet: 813702d1673944583-formula-giving-wrong-sum-amount-when-summing-a-percentage-sample-1-
Sheet: Sheet2

Fortnightly Dates |  |  | Start Date | 2022-10-17 00:00:00 | End Date | 2022-10-30 00:00:00 |  |  |  |  | Total: |  |  | Rates
 |  |  |  |  |  |  |  |  |  |  |  |  |  | Rate | New Amount Percentage | New Amount
 |  |  |  |  |  |  |  |  |  |  |  |  |  | 32.66 | 0.175
Week | Day | Date | Rate | Hours | Cost | Rate | Hours | Cost
One | Monday | 2022-10-17 00:00:00 | New Amount | 8
Spreadsheet: 66655d1267301791-searching-in-excel-with-a-table-searchinginexcel
Sheet: Sheet1

This is info copied and pasted into excel from a bank statement. My goal is for excel to "recognize: the name… for instance… american express.. Home depot…within the cell look below
 |  |  |  |  |  |  |  |  |  |  |  |  | I was thinking about using a search function??? And a vlookup..
 |  |  |  |  |  |  |  |  |  |  |  |  | Also, I thought a table might help
Copied and pasted |  |  |  |  |  |  |  |  | Goal |  |  |  |  | Excel, look for these "blue" words/If cell contains "___" | then ouput these red words
Date Description Debit Credit Balance |  |  |  |  |  |  |  |  |  |  |  |  |  | american express-settlement | american express
08/03/09 AC-AMERICAN EXPRESS-SETTLEMENT $10,282.47 $25,788.56 |  |  |  |  |  |  |  |  | american express
08/03/09 CKCD CREDIT 08/01 THE HOME $34.92 $25,823.48 |  |  |  |  |  |  |  |  | home depot |  |  |  |  | the home | home depot
DEPOT 906WATCHUNG NJ
08/03/09 AC-BANKCARD-8740 -MTOT DISC $92.95 $25,730.53 |  |  |  |  |  |  |  |  | bankcard |  |  |  |  | bankcard & mtot | bankcard
08/03/09 AC-ADP TX/FINCL SVC-ADP - TAX $1,526.65 $24,203.88 |  |  |  |  |  |  |  |  | [NAME] |  |  |  |  | adp tx/fincl | svc
08/03/09 CKCD DEBIT 08/02 FINDLEGALFOR $4.99 $24,198.89 |  |  |  |  |  |  |  |  | findlegalfor |  |  |  |  | findlegalfor | findlegalforms.com
MS.COM760-322-6900 CA |  |  |  |  |  |  |  |  |  | (or findlegalforms.com)
08/03/09 CKCD DEBIT 07/31 NACIREMA $13.50 $24,185.39 |  |  |  |  |  |  |  |  | nacirema |  |  |  |  | nacirema | nacirema
INDUSTRIEBAYONNE NJ
08/03/09 CKCD DEBIT 07/31 THE HOME $35.32 $24,150.07 |  |  |  |  |  |  |  |  | home depot |  |  |  |  | the home | home depot
DEPOT 906WATCHUNG NJ
08/03/09 CKCD DEBIT 08/01 THE HOME $50.95 $24,099.12 |  |  |  |  |  |  |  |  | home depot
DEPOT 906WATCHUNG NJ
08/04/09 CKCD DEBIT 07/31 THE HOME $84.12 $24,015.00 |  |  |  |  |  |  |  |  | home depot
DEPOT 926HAZLET NJ
08/03/09 CKCD DEBIT 07/31 THE HOME $93.94 $23,921.06 |  |  |  |  |  |  |  |  | home depot
DEPOT 926HAZLET NJ
08/03/09 CKCD DEBIT 07/31 HUSTON $156.88 $23,764.18 |  |  |  |  |  |  |  |  | [NAME] |  |  |  |  | [NAME] | [NAME] lumber
LUMBER [PHONE] NJ |  |  |  |  |  |  |  |  |  | (or huston lumber)
08/03/09 CKCD DEBIT 07/30 THE HOME $190.97 $23,573.21 |  |  |  |  |  |  |  |  | etc…. |  |  |  |  | <add any new account to search for> | <add new output for account>
DEPOT 906WATCHUNG NJ
08/03/09 CKCD DEBIT 07/31 THE HOME $270.67 $23,302.54
DEPOT 926HAZLET NJ |  |  |  |  |  |  |  |  |  |  | **additional note:
08/03/09 CKCD DEBIT 07/30 THE HOME $366.03 $22,936.51 |  |  |  |  |  |  |  |  |  |  |  | I figured out a way to combine the lines so I can get row 32 to say…
DEPOT 906WATCHUNG NJ
08/03/09 CKCD DEBIT 07/30 THE HOME $419.23 $22,517.28 |  |  |  |  |  |  |  |  |  |  |  | CKCD DEBIT 07/30 THE HOME DEPOT 906WATCHUNG NJ
DEPOT 906WATCHUNG NJ
08/03/09 CKCD DEBIT 07/30 THE HOME $616.32 $21,900.96 |  |  |  |  |  |  |  |  |  |  |  | I think this can help out in that "search" part
DEPOT 906WATCHUNG NJ
08/03/09 CKCD DEBIT 07/31 NACIREMA $1,121.74 $20,779.22
Spreadsheet: 115198d1310793206-excel-2007-need-help-with-summing-up-the-data-in-sheet-tally-sheet
Sheet: Sheet1

CITY | ITEM | SALES
HOUSTON | SHOES | 500
 | SHIRTS | 300
 | PANTS | 250
 | SOCKS | 140
 |  |  | DAILY TOTAL SALES
DALLAS | SHOES | 300
 | SHIRTS | 125
 | PANTS | 141
 | SOCKS | 35
 |  |  | DAILY TOTAL SALES
AUSTIN | SHOES | 650
 | SHIRTS | 120
 | PANTS | 285
 | SOCKS | 55
 |  |  | DAILY TOTAL SALES
Spreadsheet: 455661d1460456492-two-dimensional-conditional-counting-2d-conditional-counting
Sheet: Sheet1

 |  | Q1 | Q2 | Q3 | Q4
Office1 | 2016-03-01 00:00:00 | YES | NO | YES | NO
Office2 | 2016-03-01 00:00:00 | NO | NO | NO | NO
Office3 | 2016-03-01 00:00:00 | NO | YES | NO | YES
Office4 | 2016-03-01 00:00:00 | NO | YES | YES | YES
 | 2016-04-01 00:00:00 | NO | NO | NO | NO
 | 2016-04-01 00:00:00 | YES | NO | NO | YES
 | 2016-04-01 00:00:00 | YES | NO | YES | NO
 | 2016-04-01 00:00:00 | NO | YES | NO | YES
2016-04-01 00:00:00 | Q1 |  | Q2 |  | Q3 |  | Q4
 | YES | NO | YES | NO | YES | NO | YES | NO
OFFICE1
OFFICE2
OFFICE3
OFFICE4
Spreadsheet: 80368d1280531142-how-to-apply-a-combination-of-index-and-match-functions-template-test
Sheet: Sheet2

 | Development: |  |  | 0 |  | Unit Mix |  |  | # of Units | Size (SF)
 | Address: |  |  | 0 |  |  | Studio
 | Cross Streets: |  |  |  |  |  | 1-Bedroom
 | City: |  |  |  |  |  | 2-Bedroom
 | County: |  |  |  |  |  | 3-Bedroom
 | Tax I.D. |  |  |  |  |  | 4-Bedroom
 | Community Type: |  |  |  |  | Project Amenities
 | Building Description:
 |  | Living Area (SF): |  | 192450
 |  | Year Built: |  | 1988 |  | Laundry
 |  | Number of Units: |  | 188 |  | Hook-ups
 |  | Avg. Unit [NAME] (SF): |  |  |  | Sale Analysis:
 |  | Unit Style: |  |  |  |  | Grantor:
 |  | Stories: |  |  |  |  | Grantee:
 |  | Effective Age: |  |  |  |  | Verification:
 | Site Description: |  |  |  |  |  | Sale Date:
 |  | [NAME] ([NAME]): |  |  |  |  | Sale Price:
 |  | Shape: |  |  |  |  | Occupancy at Sale:
 |  | Zoning: |  |  |  |  | Property Rights Transferred:
 |  | Parking: |  |  |  |  | Conditions of Sale:
 |  | Land to Building Ratio: |  |  |  |  | Sale Terms:
 | Financial Analysis |  |  |  |  |  | Sale Price/Unit:
 |  | Potential Gross Income: |  |  |  |  | Sale Price/SF:
 |  | Vacancy & Credit Loss: |  |  |  |  | GIM:
 |  | Effective Gross Income: |  |  |  |  | EGIM:
 |  | Expenses & Reserves: |  |  |  |  | Op. Expense Ratio:
 |  | Net Operating Income |  |  |  |  | Capitalization Rate:
 | Comments:
 | 0
 | Source:  Allen & Associates
Spreadsheet: 86065d1286064589-help-im-super-stuck-brain-is-now-mush-bid-and-payout
Sheet: PandP_Schedule

 |  | Cost | Payout yr 1 | Payout yr 2
 | Package 1 | 10000 | 7000 | 6000
 | Package 2 | 15000 | 12000 | 7000
 | Package 3 | 12000 | 9000 | 5000
 | Package 4 | 10000 | 7000 | 6000 |  | Cost= The amount the seller would like to receive
 | Package 5 | 12000 | 9000 | 5000 |  | Payout yr x = The amount the investment pays out each year
 | Package 6 | 15000 | 12000 | 7000
 | Package 7 | 10000 | 7000 | 6000
 | Package 8 | 15000 | 12000 | 7000
 | Package 9 | 12000 | 9000 | 5000
 | Package 10 | 10000 | 7000 | 6000
 | Package 11 | 12000 | 9000 | 5000
 | Package 12 | 15000 | 12000 | 7000
 | Package 13 | 10000 | 7000 | 6000
 | Package 14 | 15000 | 12000 | 7000
 | Package 15 | 12000 | 9000 | 5000
 | Package 16 | 10000 | 7000 | 6000
 | Package 17 | 12000 | 9000 | 5000
 | Package 18 | 15000 | 12000 | 7000
 | Package 19 | 10000 | 7000 | 6000
 | Package 20 | 15000 | 12000 | 7000
 | Package 21 | 12000 | 9000 | 5000
 | Package 22 | 10000 | 7000 | 6000
 | Package 23 | 12000 | 9000 | 5000
 | Package 24 | 15000 | 12000 | 7000
 | Package 25 | 10000 | 7000 | 6000
 | Package 26 | 15000 | 12000 | 7000
 | Package 27 | 12000 | 9000 | 5000
 | Package 28 | 10000 | 7000 | 6000
 | Package 29 | 12000 | 9000 | 5000
 | Package 30 | 15000 | 12000 | 7000
 | Package 31 | 10000 | 7000 | 6000
 | Package 32 | 15000 | 12000 | 7000
 | Package 33 | 12000 | 9000 | 5000
 | Package 34 | 10000 | 7000 | 6000
 | Package 35 | 12000 | 9000 | 5000
 | Package 36 | 15000 | 12000 | 7000
 | Package 37 | 10000 | 7000 | 6000
 | Package 38 | 15000 | 12000 | 7000
 | Package 39 | 12000 | 9000 | 5000
 | Package 40 | 10000 | 7000 | 6000
Spreadsheet: 804459d1668086588-sum-the-number-of-rows-within-a-range-in-a-table-containing-any-data-example-spreadsheet
Sheet: Sheet1

 | Table showing how many hours each person attended for each week.
 | [NAME] needed to find how many people attended at least once in any given period.
 | For example, for  8 Oct - 29 Oct, the formula would show that 8 different people attended.
 | i.e. How many rows in column '08-Oct' to column '29-Oct' contain at least one entry?
 | Person | 01-Oct | 08-Oct | 15-Oct | 22-Oct | 29-Oct | 05-Nov | 12-Nov
 | Nicola
 | Anna | 2 |  | 3
 | [NAME]
 | [NAME] |  |  | 2 |  |  | 3
 | [NAME] | 3 | 4 |  | 2
 | [NAME] |  |  | 1
 | [NAME] |  | 3
 | [NAME] | 3 | 1 | 2 |  |  | 2
 | [NAME] |  |  |  | 2
 | Ray
 | [NAME] | 1 |  | 2 | 3 |  |  | 1
 | [NAME] |  |  |  |  | 2 | 3 | 3
 | [NAME]
Spreadsheet: 649061d1573457921-run-time-error-in-macro-time-series-upper-aquifer-qw-spreadsheet
Sheet: Well 14

Well | Date | pH | EC | Ca | [NAME] | Na | Alk as CaCO3 | HCO3  as  CaCO3 | CO3  as  CaCO3 | SO4 | Cl | TDS | SAR | SARadj | B | NO3-N | Cu | Fe | Mn | Zn | L.I. |  | Sum cat | [NAME] | % dif
 | Sampled | unit | dS/m | meq/L | meq/L | meq/L | meq/L | meq/L | meq/L | meq/L | meq/L | mg/L |  |  | mg/L | mg/L | mg/L | mg/L | mg/L | mg/L | Calc
14 | 2007-02-19 00:00:00 | 7.6 | 1.17 | 5.4 | 2.1 | 5.9 | 5.6 |  |  |  | 6 |  | 4.308750785707307 | 4.1 | 0 | 5 |  |  |  |  | 0.7
MTTR Well 14A  1000gpm | 2012-03-29 00:00:00 | 7.8 | 1.41 | 5.53 | 2.19 | 5.4 | 6.5 |  |  | 0.9 | 6.2 |  | 2.7 | 6.9 | 0.04 | 2.2 |  | <0.01 | <0.01 |  | 0.9
Well #14 | 2013-10-11 00:00:00 | 7.7 | 1.49 | 5.75 | 2.15 | 5 | 6.5 |  |  | 0.9 | 6.8 |  | 2.5 | 6.3 | 0.05 | 2.4 |  | <0.10 | <0.02 |  | 0.8
Well - 14 | 2014-01-27 00:00:00 | 7.8 | 1.51 | 6.91 | 2.76 | 6.2 | 6.2 |  |  | 0.9 | 7.7 |  | 2.8 | 7.3 | 0.04 | 3 |  | <0.10 | <0.02 |  | 1
Well 14 | 2015-04-02 00:00:00 | 7.6 | 1.32 | 6.36 | 2.34 | 5.4 | 6.5 |  |  | 1 | 6.7 |  | 2.6 | 6.7 | <0.05 | 2.9 |  | <0.10 | <0.02 |  | 0.7
Well 14 | 2016-04-01 00:00:00 | 7.93 | 1.449 | 6.850000000000001 | 2.55 | 5.54 | 6.228518903365039 | 6.22851890336504 | 0 | 1.35 | 7.6724 | 928 | 2.56 | 6.17 | 0.022 | 3.4123 | 0.015 | 0 | 0.001 | 0.022
W14 | 2018-05-29 00:00:00 | 7.78 | 1.284 | 6.98 | 2.57 | 6.37 |  | 6.796019502837503 | 0 | 1.02 | 6.537 | 821.76 |  |  | 0.056 | 0.137 | 0.031 | 0.088 | 0.001 | 0.008
Spreadsheet: 701225d1603632391-hyperlink-to-locate-multiple-common-max-value-hyperlink-sample
Sheet: Sheet1

30+ | 60+ | 90+ | 120+
0 | 0 | 0 | 0
9 | 1 | 10 | 4 |  | Category | [NAME]
6 | 0 | 0 | 0 |  | 30+
27 | 34 | 5 | 1 |  | 60+
1 | 3 | 0 | 5 |  | 90+ |  | Only looks up first [NAME] value. Can this be tweaked to locate both values when hyperlinked is clicked. Please suggest an alternative.
2 | 2 | 0 | 1 |  | 90+
4 | 5 | 3 | 9
0 | 0 | 0 | 0 |  | 120+
1 | 0 | 10 | 6
Spreadsheet: 877206d1723924505-count-number-of-entries-with-duplicates-once-in-a-filtered-worksheet-count-duplicates-once-3
Sheet: Sheet1

 |  | Total Regd. Issued
Sl. No. | Year | Registration No. | Student Name | Father's Name
1 | 2022 | 1001-070204 | RAHUL ROY | ROHIT ROY
2 | 2022 | 1001-070205 | ISHAN MALLICK | PRACIR MALLICK
3 | 2022 | 1001-070206 | ROHIT BAL | RATAN BAL
4 | 2022 | 1001-070207 | SANJEEV KAPOOR | JITENDRA KAPOOR
5 | 2022 | 1001-070220 | NAYAN ARORA | SUNIL ARORA
6 | 2022 | 1001-070209 | SAYAN GHOSH | JIBAN GHOSH
7 | 2022 | NOT REGISTERED | NAVEEN DAS | TAPAN DAS
8 | 2023 | 1001-070210 | [NAME] | KABIR CHHETRI
9 | 2022 | 1001-070207 | SANJEEV KAPOOR | JITENDRA KAPOOR
10 | 2023 | 1001-070212 | JHUMA PAL | SOHAN PAL
11 | 2023 | 1001-070213 | RAHUL ROY | ROHIT ROY
12 | 2023 | 1001-070214 | KARAN KAPOOR | ARJUN KAPOOR
13 | 2023 | 1001-070215 | [NAME] | [NAME]
14 | 2023 | NOT REGISTERED | GOPAL GHOSH | KUNTAL GHOSH
15 | 2023 | 1001-070216 | PRIYA SANYAL | KAMAL SANYAL
16 | 2023 | 1001-070210 | [NAME] | KABIR CHHETRI
17 | 2023 | 1001-070218 | RAJEEV KUMAR | KIRAN KUMAR
18 | 2023 | 1001-070219 | SAYAN GHOSH | SUKUMAR GHOSH
19 | 2023 | 1001-070220 | PRIYANKA SINGH | MANJEET SINGH
20 | 2023 | 1001-070222 | SUNIL KUMAR | KAPIL KUMAR
21 | 2024 | NOT REGISTERED | RAJEEV KUMAR | KIRAN KUMAR
22 | 2024 | 1001-070222 | RONIT DAS | RATUL DAS
23 | 2024 | 1001-070223 | PUJA BANERJEE | DIBAKAR BANERJEE
24 | 2023 | 1001-070215 | [NAME] | [NAME]
25 | 2024 | 1001-070225 | ANUJ AGARWAL | BISHAL AGARWAL
There is a little problem. See there are 5 sets of duplicate numbers and 3 marked "non registered". Total 8 in column [NAME] outcome is 25-8=17. It's ok. But if I filter the data and select year 2024 please see what's happening. It's 2.5 whereas it should be 3. How to cope with this problem?
Spreadsheet: 677465d1589369612-sum-column-based-on-single-criteria-across-multiple-columns-sumif-example
Sheet: Sheet1

[NAME] |  |  | quantity | items
apple |  |  | 5 | orange | apple | peach | grapes
orange |  |  | 8 | peach | apple
peach |  |  | 2 | grapes | apple | peach
grapes |  |  | 13 | grapes | orange | peach
Spreadsheet: 818544d1676906284-create-multiple-lists-based-on-criteria-from-single-column-s666-pq-create-multiple-lists-based-on-criteria-from-single-column
Sheet: Sheet1

col1 | [NAME] |  | Billing Admin | Receptionist |  |  | https://www.excelforum.com/excel-formulas-and-functions/1399914-create-multiple-lists-based-on-criteria-from-single-column.html
Billing Admin | View |  | View | View
Billing Admin | Edit |  | Edit | Edit
Billing Admin | Invoice |  | Invoice | Schedule
Receptionist | View |  |  | Create
Receptionist | Edit
Receptionist | Schedule
Receptionist | Edit
Receptionist | Create