tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: aa35518c-de84-426a-bbd6-10c5df109997
Sheet: Sheet1

Unnamed: 0 | Permit No.: | WQ0001077 | Unnamed: 3 | Facility Name: | Unnamed: 5 | Innospec Performance Chemicals | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | County: | Unnamed: 13 | [NAME] | Unnamed: 15 | Month: | Unnamed: 17 | Unnamed: 18 | Unnamed: 19 | Year: | Unnamed: 21
 |  | Field Name: |  | 1 |  | Field Name: |  | 2-1 |  | Field Name: |  | 2-2 |  | Field Name: |  | 2-4 |  | Field Name: |  | 2-5
 |  | Area (acres): |  | 4 |  | Area (acres): |  | 1.14 |  | Area (acres): |  | 0.92 |  | Area (acres): |  | 1.44 |  | Area (acres): |  | 1.09
 |  | Cover Crop: |  | Fescue |  | Cover Crop: |  | Fescue |  | Cover Crop: |  | Fescue |  | Cover Crop: |  | Fescue |  | Cover Crop: |  | Fescue
 |  | Load Type: |  | PAN |  | Load Type: |  | PAN |  | Load Type: |  | PAN |  | Load Type: |  | PAN |  | Load Type: |  | PAN
 |  | [NAME]? |  |  |  | [NAME]? |  |  |  | [NAME]? |  |  |  | [NAME]? |  |  |  | [NAME]?
 | Date | Volume Applied | Average PAN Concentration | Monthly PAN Load | Cumulative PAN Load | Volume Applied | Average PAN Concentration | Monthly PAN Load | Cumulative PAN Load | Volume Applied | Average PAN Concentration | Monthly PAN Load | Cumulative PAN Load | Volume Applied | Average PAN Concentration | Monthly PAN Load | Cumulative PAN Load | Volume Applied | Average PAN Concentration | Monthly PAN Load | Cumulative PAN Load
 | Month | gal | mg/L | lbs/ac | lbs/ac | gal | mg/L | lbs/ac | lbs/ac | gal | mg/L | lbs/ac | lbs/ac | gal | mg/L | lbs/ac | lbs/ac | gal | mg/L | lbs/ac | lbs/ac
 | 12 Month Floating PAN Load (lbs/ac/yr):
 | Annual PAN Load Limit (lbs/ac/yr): |  |  | 300 |  |  |  | 300 |  |  |  | 300 |  |  |  | 300 |  |  |  | 300
Spreadsheet: 880991d1728898152-how-do-i-rewrite-the-filter-formula-in-cell-l2-to-display-duplicate-x-y-axis-filter-x-y-duplicates-a
Sheet: Sheet1

X | Y | X | Y | X | Y | X | Y | X | Y |  | X | Y | X | Y | X | Y | X | Y | X | Y
-2 | -7 | -7 | -3 | -2 | -7 | -4 | 1 |  |  |  |  | -7 | -7 | -3 | -4 | 1
-3 | -5 | -2 | -3 | 9 | -6 | 6 | -1 |  |  |  |  | -5 | -2 | -3 | 9 | -6 | 6 | -1
0 | -6 | -5 | 3 | 6 | 0 |  |  |  |  |  |  | -6 | -5 | 3 | 6 | 0
-6 | -2 | -5 | -1 | -6 | -2 | -6 | -2 |  |  |  |  | -2 | -5 | -1
DATA IS RANGE A2:J5. |  |  |  |  |  |  |  |  |  |  | RESULTS RANGE L2:U5
=LET(f,FILTER(A2:J2,A2:J2<>""),s,SEQUENCE(COLUMNS(f)/2,,0),u,UNIQUE(INDEX(f,,s*2+{1,2})),ss,SEQUENCE(COUNT(u),,0),TRANSPOSE(INDEX(u,INT(ss/2)+1,MOD(ss,2)+1)))
The FILTER formula above (In cell L2:L5) will display non duplicates x,y axis in range L2:U2. HOWEVER, I want to do the opposite. I  want to display only the duplicate (x,y) axis.
Then I can copy the FILTER formula down to resolve the ranges for each row which are ranges:  L3:U3, L4:U4, and L5:U5.
Even if the any data is change, the FILTER formula should recalculate accordingly. This following is only a sample: [NAME] is 2. Cell B1 is 3.  Cell C1 formula is =SUM(A1:B1). The answer will be 5. However, if B1 is change to -3, the answer will now be -1
 | So the FILTER formula must work accordingly whenever the data is change.
IF you can revised the FILTER formula above and enter into cel L2. I can copied it down it should display only x,y axis that are duplicates in range L2:Y5.
Once the FILTER formula is written to do the opposite, the results will display duplicates that are in each row:
Row 2: Range L2:M2 is -2,-7 (Because -2,-7 is repeated two times)
Row 3: blank (Because there are no duplicates)
Row 4: blank (Because there are no duplicates)
Row 5: Range L5:M5 is -6.-2 (Because -6,-2 is repeated three times)
Spreadsheet: 752329d1634720424-how-to-sum-data-between-two-dates-by-project-type-financial-model-extract
Sheet: Sheet1

 |  | 2022-12-31 00:00:00
 | Project 1
 | Project 2
 | Project 3
 | Project 4
 | Project 5
 | Project 6
 | Project 8
 | Project 10
 | Project 11
 | Project 13
 | Project 14
 | Project 15
 | Project 16
 | Project 17
 | Project 18
 | Project 19
 | Total
 |  |  |  |  |  |  |  | ***HELP HERE
 |  |  |  |  |  |  |  | From | To | Project 1 | Project 2 | Project 3 | Project 4 | Project 5 | Project 6 | Project 8 | Project 10 | Project 11 | Project 13 | Project 14 | Project 15 | Project 16 | Project 17 | Project 18 | Project 19
 |  |  |  |  |  |  |  | 2023-01-31 00:00:00 | 2023-12-31 00:00:00
 |  |  |  |  |  |  |  | 2024-01-31 00:00:00 | 2024-12-31 00:00:00
 |  |  |  |  |  |  |  | 2025-01-31 00:00:00 | 2025-12-31 00:00:00
 |  |  |  |  |  |  |  | 2026-01-31 00:00:00 | 2026-12-31 00:00:00
Spreadsheet: 154141d1336243463-conditional-formatting-highlight-cells-in-a-column-that-contain-1-or-more-words-book1
Sheet: Sheet1

 |  |  |  |  | Cat
 |  |  |  |  | Monkey
 |  |  |  |  | Bull
Cataaaa | 1 | 1
aaaaMonkeyaaa | 1 | 1
Bul | 0 | 0
Snake | 0 | 0
dddCatdddd | 1 | 1
This is a Bull | 1 | 1
Look Monkey ahead | 1 | 1
Spreadsheet: 4436b02f-53d0-4c5c-94fd-94f610cea8dc
Sheet: Sheet1

Table 4--Wheat: Monthly food disappearance estimates (1,000 grain-equivalent bushels), 12/12/2013 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11
Mkt year and month 1/
 |  |  | Wheat ground for flour | + | Food imports 2/ | + | Nonmilled food use 3/ | - | Food exports 2/ | = | Food use 4/
2011/12 | Jun |  | 70554 |  | 2237 |  | 2000 |  | 1743 |  | 73048
 | Jul |  | 72573 |  | 2098 |  | 2000 |  | 1327 |  | 75344
 | Aug |  | 79317 |  | 2308 |  | 2000 |  | 2390 |  | 81235
 | Sep |  | 76269 |  | 2245 |  | 2000 |  | 1652 |  | 78863
 | Oct |  | 81402 |  | 2247 |  | 2000 |  | 1487 |  | 84162
 | Nov |  | 77915 |  | 2568 |  | 2000 |  | 1763 |  | 80720
 | Dec |  | 73135 |  | 2464 |  | 2000 |  | 1291 |  | 76308
 | Jan |  | 74522 |  | 2579 |  | 2000 |  | 1233 |  | 77868
 | Feb |  | 73931 |  | 2057 |  | 2000 |  | 1330 |  | 76658
 | Mar |  | 78437 |  | 2555 |  | 2000 |  | 1843 |  | 81149
 | Apr |  | 74497 |  | 2622 |  | 2000 |  | 1513 |  | 77606
 | May |  | 76171 |  | 2530 |  | 2000 |  | 2310 |  | 78390
2012/13 | Jun |  | 72876 |  | 2173 |  | 2000 |  | 1760 |  | 75290
 | Jul |  | 75861 |  | 2296 |  | 2000 |  | 2912 |  | 77245
 | Aug |  | 82910 |  | 2345 |  | 2000 |  | 2193 |  | 85063
 | Sep |  | 79725 |  | 2069 |  | 2000 |  | 2283 |  | 81511
 | Oct |  | 81567 |  | 2462 |  | 2000 |  | 1840 |  | 84189
 | Nov |  | 78073 |  | 2438 |  | 2000 |  | 1613 |  | 80897
 | Dec |  | 73283 |  | 2369 |  | 2000 |  | 1442 |  | 76210
 | Jan |  | 72290 |  | 2191 |  | 2000 |  | 1550 |  | 74931
 | Feb |  | 71716 |  | 2101 |  | 2000 |  | 1674 |  | 74143
 | Mar |  | 76088 |  | 2391 |  | 2000 |  | 1744 |  | 78734
 | Apr |  | 74750 |  | 2581 |  | 2000 |  | 1432 |  | 77899
 | May |  | 76429 |  | 2530 |  | 2000 |  | 2042 |  | 78917
2013/14 | Jun |  | 73123 |  | 2277 |  | 2000 |  | 2430 |  | 74970
 | Jul |  | 74417 |  | 2519 |  | 2000 |  | 1474 |  | 77461
 | Aug |  | 81332 |  | 2548 |  | 2000 |  | 1450 |  | 84431
 | Sep |  | 78207 |  | 2271 |  | 2000 |  | 1498 |  | 80981
 | Oct |  | 0 |  | 2700 |  | 0 |  | 1845 |  | 855
1/ Current year is preliminary. Previous year is preliminary through August of current year, estimated afterwards. 2/ Food imports and exports used to calculate total food use. Includes all categories of wheat flour, semolina, bulgur, and couscous and selected categories of pasta. 3/ Wheat prepared for food use by processes other than milling.
4/ Estimated food use equals wheat ground for flour plus food imports plus nonmilled food use minus food exports. See http://www.ers.usda.gov/Briefing/Wheat/wheatfooduse.htm for more information. Source: Data through the 2nd quarter of 2011 was calculated using data from U.S. Department of Commerce, Bureau of the Census’ Flour Milling Products (MQ311A) and U.S. Department of Commerce, Bureau of Economic Analysis’ Foreign Trade Statistics. Subsequent flour milling calculations are based on data from the North American Millers Association.
Date run: [NAME]
Spreadsheet: danny_mccarty__4660__IdleAire 10-2001 Valuation
Sheet: PNV

 | Closing | Shares | Market | Revenues | EBITDA | Earnings | Price to
 | Price | Outstanding | Capitalization | 4 x ttm | 4 x ttm | 4 x ttm | Sales | EBITDA | Income
 |  | (M) | ($M) | ($M) | ($M) | ($M)
PNV.net (PNVN)
1Q01 | 0.09375 | 16
4Q00 | 0.1875 | 16
3Q00 | 1 | 16
2Q00 | 1.375 | 16
1Q00 | 4.5 | 16
4Q99 | 8.1875 | 16
IPO | 18 | 16
3Q99
2Q99
1Q99
4Q98 |  |  |  | 7.6 | -26 | -32
3Q98 |  |  |  | 5.6 | -21.5 | -30.3
2Q98 |  |  |  | 3.2 | -20 | -16
1Q98 |  |  |  | 2.8 | -16 | -14
Spreadsheet: 92744d1292307101-iteration-continuously-increases-before
Sheet: Week6

Week | Day | Date | Winner/tie | PtsW |  | [NAME]/tie | PtsL
6 | Sun | 2010-10-17 00:00:00 | Minnesota Vikings | 24 |  | Dallas Cowboys | 21
6 | Sun | 2010-10-17 00:00:00 | New England Patriots | 23 |  | Baltimore Ravens | 20
6 | Sun | 2010-10-17 00:00:00 | New York Giants | 28 |  | Detroit Lions | 20
6 | Sun | 2010-10-17 00:00:00 | [NAME] | 23 | @ | Green Bay Packers | 20
6 | Sun | 2010-10-17 00:00:00 | St. Louis Rams | 20 |  | San Diego Chargers | 17
6 | Sun | 2010-10-17 00:00:00 | New Orleans Saints | 31 | @ | Tampa Bay Buccaneers | 6
6 | Sun | 2010-10-17 00:00:00 | Philadelphia Eagles | 31 |  | Atlanta Falcons | 17
6 | Sun | 2010-10-17 00:00:00 | Houston Texans | 35 |  | Kansas City Chiefs | 31
6 | Sun | 2010-10-17 00:00:00 | Pittsburgh Steelers | 28 |  | [NAME] | 10
6 | Sun | 2010-10-17 00:00:00 | [NAME] | 23 | @ | Chicago Bears | 20
6 | Sun | 2010-10-17 00:00:00 | Indianapolis Colts | 27 | @ | Washington Redskins | 24
6 | Sun | 2010-10-17 00:00:00 | San Francisco 49ers | 17 |  | Oakland Raiders | 9
6 | Sun | 2010-10-17 00:00:00 | New York Jets | 24 | @ | Denver Broncos | 20
6 | Mon | 2010-10-18 00:00:00 | Tennessee Titans | 30 | @ | [NAME] | 3
Spreadsheet: 868600d1715280746-return-value-from-cols-and-rows-us-states-workdays-for-a-given-period-example4
Sheet: Working (2)

Period
2024-04-01 00:00:00
2024-04-30 00:00:00
Name |  |  |  |  | State | Workdays in month of Prov. State
Ahmed, [NAME]
Aibak, Haneen
Albors, [NAME]
[NAME], [NAME]
Alphonse, [NAME]
[NAME], [NAME]
Aref, [NAME]
Bahrami, [NAME]
Barck, Tawna
Bhama, [NAME]
Bhasin, [NAME]
[NAME], [NAME]
Brainard, Meredith
Brown, Miariesha
Bryant, Nichell
Bunal, [NAME]
[NAME], [NAME]
Cartier, [NAME]
Catrell, [NAME]
Cha, [NAME]
Chambers, [NAME]
Chheda, Neel
[NAME], [NAME]
[NAME], [NAME]
Conte, [NAME]
Cortes, Frances
Cribley, Iryna
Cruz, [NAME]
Culver, Tori
[NAME], [NAME]
Dadhania, Akruti
Danner, Kimberly
[NAME], [NAME]
Denor, [NAME]
DeWire, [NAME]
Dhaliwal, Navdeep
Dhaliwal, Rajandeep
Dockendorf, Salome
[NAME], Cecily
Dupre, [NAME]
Duvall, Charlotte
[NAME], [NAME]
Estes, [NAME]
Fagbemi, Olumide
[NAME], [NAME]
Fletcher, Yukari
[NAME], [NAME]
Fox, Nikolous
[NAME], [NAME]
[NAME], [NAME]
[NAME], Elissa
Fricks, [NAME]
Gallaway, Rebecca
Gandia, Victoria
Gates, Shaleena
Gayles, Keyona
Gilliver, [NAME]
Go, [NAME]
Golez, Pauline
Goodman, [NAME]
Gorazd, [NAME]
Grandhi, [NAME]
[NAME], [NAME]
Gross, Amanda
Guirguis, [NAME]
[NAME], [NAME]
Haberbush, [NAME]
Hall, Charlene
Hamblen, [NAME]
Hartwig-Mullally, Corinna
Heard, [NAME]
[NAME], Grayce
[NAME], [NAME]
Hoener, [NAME]
Holloway, Micahl
[NAME], [NAME]
Homolka, Saydee
Hooper, [NAME]
[NAME], [NAME]
[NAME], Sheriva
Iredia, [NAME]
Ivy, Mellissa
Jacks, [NAME]
Jelks, [NAME]
[NAME], [NAME]
Joachim, Carthia
Jobe, [NAME]
[NAME], [NAME]
[NAME], [NAME]
[NAME], Olivia
[NAME], [NAME]
Jones, [NAME]
Jones, [NAME]
[NAME], [NAME]
Kam, [NAME]
Kamau, [NAME]
[NAME], [NAME]
[NAME], Gibril
Kaur, Jasdeep
[NAME], Rajwinder
Kaur, Ramandeep
Kemper, Malinda
Keya, Dominic
Khavshaba, Elena
Klein, [NAME]
Kwon, [NAME]
Lambie, Ianthe
[NAME], [NAME]
Liauw, Shannon
Lopez, Heather
Louis, Makenson
Macadangdang, Cesar
Manoharan, Arun
McCalister, Kenzi
McKenzie, [NAME]
Medina, [NAME]
Mershon, Sharelle
Milford, Danielle
Monk-Beckley, [NAME]
[NAME], [NAME]
Montgomery, Autumn
Moonat, Ashish
Mote, [NAME]
Mudey, Yonis
Mussa, Zaina
Myers, [NAME]
Nebrija, Kristine
[NAME], [NAME]
[NAME], [NAME]
Njenga, [NAME]
[NAME], [NAME]
[NAME], [NAME]
Ocfemia, Rommel
Oke, Adenike
Oliveira, Adrienne
[NAME], [NAME]
[NAME], [NAME]
Panlasigui, Leonico
[NAME], [NAME]
Parker, Selby
Patrito, Gina
Patton, Miciah
Petiote, Fredely
Pribble, [NAME]
Quetgles, Tabatha
Quinones, Angelica
Raney, Tiffany
Reed, [NAME]
Richards, Shirmel
Rook, Jodi
Rozul, Laisa
[NAME], Soheila
Sanchious, Felecia
Sandhu, [NAME]
[NAME], [NAME]
[NAME], [NAME]
Schutz, [NAME]
Sharma, Aishwarya
Shepherd, Tess
Sherrill, [NAME]
Shillcutt, Kayla
Shuff, Lorelei
Singh, Supriya
Smith, [NAME]
Soames, Garrett
[NAME], Leo
Swint, [NAME]
[NAME], [NAME]
Taleghani, Masoud
[NAME], [NAME]
[NAME], [NAME]
Tran, Mi
Tran, [NAME]
Trino, [NAME]
Troeh, [NAME]
[NAME], [NAME]
Udo, Bernarde
Urraca, Priscila
[NAME], Donis
Watts, [NAME]
White, [NAME]
Wilkins, Denita
Wilson, Cawonda
Windsor, Patrice
Witherspoon, Fabienne
[NAME], [NAME]
[NAME], [NAME]
0
[NAME], [NAME]
Spreadsheet: 472745d1469725906-employee-attendance-tracker-viewing-all-together-on-a-year-calendar-welse
Sheet: Sheet1

Employee Name | Start Date | End Date | Type of Leave | Sum of Days
Employee 1 | 2011-11-14 00:00:00 | 2011-11-18 00:00:00 | Vacation | 5
 | 2011-11-24 00:00:00 | 2011-11-29 00:00:00 | [NAME] | 4
 | 2011-12-14 00:00:00 | 2011-12-16 00:00:00 | Vacation | 3
 | 2011-12-21 00:00:00 | 2011-12-22 00:00:00 | Other | 2
 | 2012-01-31 00:00:00 | 2012-02-02 00:00:00 | Bereavement | 3
Employee 2 | 2011-11-29 00:00:00 | 2011-12-06 00:00:00 | Bereavement | 6
 | 2012-01-13 00:00:00 | 2012-01-15 00:00:00 | [NAME] | 1
 | 2012-06-13 00:00:00 | 2012-06-15 00:00:00 | Bereavement | 3
 | 2012-12-05 00:00:00 | 2012-12-09 00:00:00 | Vacation | 3
Employee 3 | 2011-12-12 00:00:00 | 2011-12-17 00:00:00 | Bereavement | 5
 | 2012-01-17 00:00:00 | 2012-01-18 00:00:00 | Vacation | 2
Employee 4 | 2011-12-01 00:00:00 | 2011-12-02 00:00:00 | [NAME] | 2
 |  | 2011-12-06 00:00:00 | Other | 4
 | 2011-12-10 00:00:00 | 2011-12-16 00:00:00 | Other | 5
 | 2012-01-31 00:00:00 | 2012-02-04 00:00:00 | [NAME] | 4
Employee 5 | 2011-12-03 00:00:00 | 2011-12-07 00:00:00 | Vacation | 3
 | 2012-01-15 00:00:00 | 2012-01-20 00:00:00 | [NAME] | 5
 | 2012-01-27 00:00:00 | 2012-02-03 00:00:00 | Bereavement | 6
 | 2012-04-11 00:00:00 | 2012-04-19 00:00:00 | Vacation | 7
[NAME] | 2012-01-03 00:00:00 | 2012-01-03 00:00:00 | [NAME] | 1
 | 2012-01-17 00:00:00 | 2012-01-18 00:00:00 | Other | 2
 | 2012-01-18 00:00:00 | 2012-01-21 00:00:00 | Other | 3
[NAME] | 2011-12-10 00:00:00 | 2011-12-16 00:00:00 | Bereavement | 5
Spreadsheet: 11c5e348-fa4f-4322-8deb-80b9693a858a
Sheet: TumbleBooks

TumbleBooks 2014
 | My Cloud Registrations
 | New this Month | Total | Materials Viewed
January | 20 | 76 | 31377
February |  | 85 | 29722
March | 2 | 87 | 26664
April | 6 | 93 | 34272
May | 5 | 98 | 25509
June | 2 | 100 | 7277
July | 3 | 103 | 2804
August | 8 | 111 | 12952
September | 6 | 117 | 36137
October |  | 122 | 38728
November | 5 | 127 | 29602
December
Year to date
Notes
My Cloud is an optional registration to save favorites and does not indicate actual number of patrons.
Spreadsheet: phillip_m_love__30259__OntFRED
Sheet: AVar

Financial Liquidation Variances
GL Perspective: Economics to Review
TAGG | GL Amount | [NAME] | [NAME] |  | Desk Adjustment | GL Adjustment/Reclass | Comments
NL3318.B | -3049644 | -3049644
NK4144.1 | 77399.87 | -131579.871
NL9853.1 | 90706 | -90700
NL0076.4 | 87341.1 | -87344.12700000001
[NAME] | 45255 | -45250
[NAME] | 44253 | -44250
[NAME] | 35280 | -35275
NJ9548.1 | 29610 | -29600
NL8089.1 | -26910 | -26900
NL1138.2 | 25452 | -25450
NL1165.2 | -13426 | -13425
NL1165.1 | -12726 | -12725
NM9636.2 | 7755 | -7750
NO1121.2 | 2474.5 | -2475
EW4045.A | -374721.1018 | 0
NL9651.6 | 72500 | 0
NK3697.1 | -4890 | 4900
NN0937.2 | -4995 | 5000
NL0764.1 | 8204 | 8200
NN1072.1 | 9255 | 9250
[NAME] | 10208 | 10212.5
NK3755.1 | -12390 | 12400
NN6701.1 | -931200 | 14225
NM7023.2 | -23154 | 23150
NL2918.2 | -25452 | 25450
NM7651.1 | 28352 | 28350
NM7361.2 | -32352 | 32350
NM8493.2 | -38752 | 38750
NK9451.1 | 42891 | 42900
NJ8350.1 | -49860 | 49850
NK4980.2 | -58290 | 58300
NI0331.1 | -58290.03 | 58300.03
NK5531.2 | -71790 | 71800
NK5853.2 | -77790 | 77800
NK6115.2 | 79141 | 79150
NK6490.2 | -84941 | 84950
NK6115.1 | 92191 | 92200
NK5155.2 | -118661.63 | 118661.5604
NJ7495.1 | -179220 | 179200
NK4224.1 | -211726.91 | 211794.58
NI6055.1 | 537750 | 537750
NL3287.1 | 613768 | 613768
Spreadsheet: 791401d1660323544-metre-squared-to-lineal-metres-calculator-m2-to-lm-calc
Sheet: Timber

 | Timber | Price
 | 50mm Merbau | 5
 | 70mm [NAME] | 6.5
 | 90mm Merbau | 9
 | 50mm Pine | 3
 | 75mm Pine | 4.5
 | 90mm Pine | 6
 | 42mm Gum | 7
 | 67mm Gum | 8
 | 92mm Gum | 10.75
Spreadsheet: 67372d1268019894-string-concatenation-of-values-sample-data
Sheet: Sample

Group | Subgroup | Members | Remarks | Status | In-charge | Level | Units
Lopez | A | 1 | Sec A | Completed | [NAME] | Beginner | 12
[NAME] | B | 7 | Sec B | On-going | Siy | Proficient | 13
Martinez | A, B | 1, 2, 3, 4, 5, 6, 7 | Sec C | Not started | [NAME] | Beginner | 21
[NAME] | A, B | 1, 2, 3, 4, 5, 6, 7 | Sec D | Completed | [NAME] | Beginner | 20
Ramos | A | 1, 2, 3, 4, 5, 6, 7 | Sec E | Not started | Dy | Beginner | 16
Uy | A, B, C | 1,2,3,4,7,8 | Sec F | On-going | Han | Proficient | 11
[NAME] | B | 7 | Sec G | Started | Niy | Proficient | 13
[NAME] | C | 8 | Sec H | Completed | [NAME] | Proficient | 11
Spreadsheet: 204531d1357568027-production-line-calculation-production-sample
Sheet: Database

Workings:
Daily Minute workings:
Workers | 30
Hours every worker | 8
Total:
[NAME] |  |  |  |  |  |  |  | Workings for the delivery dates fo amount of doors
Flush Door |  |  |  |  |  |  | Job: | Qty | Product: | Minutes needed | Days needed | Worker 1 total time:
 |  |  |  |  |  |  | 123456 | 56
 |  |  |  |  |  |  | 123457 | 56
 |  |  |  |  |  |  | 123458 | 56
Calculating calender days for delivery dates:
Job: | Days to completion: | Worker 1 time | Start Date: | Delivery Date: | Client's Confirmation Date:
 |  |  | 2013-01-04 00:00:00 |  | 2012-12-12 00:00:00
 |  |  |  |  | 2012-12-03 00:00:00
 |  |  |  |  | 2012-12-19 00:00:00
The problem is when calculating delivery dates, the dates should include public holidays and even the weekends, The other problem is however I need to make a constant of input date to plan automaticly how my Job listed with quantities,
It will constantly calculate between jobs listed by work person For example:
Product: | Worker 1 | Worker 2 | Worker 3 | Worker 4 | Worker 5 | Worker 6 | Worker 7 | Worker 8 | Worker 9 | Worker 10 | Worker 11 | Worker 12 | Worker 13 | Worker 14 | Worker 15 | Worker 16 | Worker 17 | Worker 18 | Worker 19 | Total
Flush Door | 20 | 50 | 24 | 29 | 21 | 5 | 30 | 10 | 6 | 8 | 10 | 25 | 34 | 9 | 4 | 50 | 30 | 10 | 10
Therefore i need to calculate the Start date of a job depending on Worker 1 (The cutting proccess so it will calculate how much time he needs to start another job) if he needs 2 hours he still have time to do another job or begin it,
This part i cant really imagine how i can do it by any ways, would apreciate some help.
Is there a way to priorotise by Confirmation Date?
Spreadsheet: 721808d1614871239-multiple-nested-functions-multiple-conditions-2
Sheet: Sheet1

 |  |  | [NAME] | Compa-Ratio Proposed | Promotion Yes/No | Manual | [NAME] | Var
 |  |  | Meets | 0.948 | Yes | 0.015
 |  |  | Exceeds Some | 0.948 | No
 |  |  | Meets | 0.947 | Yes | 0.015
 |  |  | Meets | 0.942 | No
 |  |  | Far Exceeds | 0.941 | Yes | 0.05
 |  |  | Far Exceeds | 0.938 | Yes | 0.05
 |  |  | Exceeds Some | 0.937 | No
 |  |  | Meets | 0.937 | No
 |  |  | Meets | 0.935 | No
 |  |  | Exceeds Some | 0.934 | Yes | 0.03
 |  |  | Meets | 0.933 | No
 |  |  | Meets | 0.931 | Yes | 0.015
 |  |  | Far Exceeds | 0.921 | No
 |  |  | Meets | 0.92 | No
 |  |  | Exceeds Some | 0.916 | No
 |  |  | Meets | 0.913 | No
 |  |  | Exceeds Some | 0.908 | No
 |  |  | Meets | 0.906 | No
 |  |  | Meets | 0.903 | No
 |  |  | Meets | 0.9 | No
 |  |  | Meets | 0.897 | No
Spreadsheet: phillip_m_love__30138__200007_OA
Sheet: MidMarket-FLSH

ENRON CAPITAL & TRADE RESOURCES
OPERATIONAL ANALYSIS - REPORTED
COMPANY:  ECTR (EGM)
 |  |  | NGP&L |  | CPR Adjustments |  | 3rd Day CPR |  | [NAME] |  | [NAME]
 |  |  | Volume | $'s | Volume | $'s | Volume | $'s | Volume | $'s | Volume | $'s
 | Sales:
1 |  | 3rd Party / Inter Enron | 4145149 | 4914010
2 |  | Liquidations | 0 | 0
3 |  | Interdesk | 4829510 | 20529658
4 |  | Option Premiums | 0 | 0
5 |  | Dmd Reimbursements | 0 | 0
 | Total Sales: |  | 8974659 | 25443668
 |  |  | 0 | 0
 | Purchases: |  | 0 | 0
6 |  | 3rd Party / Inter Enron | -4863089 | -20350210 |  |  |  |  | 0 | 0
7 |  | Liquidations | 0 | 0
8 |  | Interdesk | -4107551 | -16674885
9 |  | Option Premiums | 0 | 0
10 |  | [NAME] | 0 | 0
 | Total Purchases: |  | -8970640 | -37025095
 |  |  | 0 | 0
 | Intra CTS (EGM / HPL / LRC): |  | 0 | 0
11 |  | Sales | 0 | 0
12 |  | Purchases | 0 | 0
 | Total Intra CTS Sales / Purchases |  | 0 | 0
 |  |  | 0 | 0
 | Imbalances: |  | 0 | 0
13 |  | In Kind | 0 | 0
14 |  | Cash Out | 0 | 0
15 |  | Cash In | 0 | 0
16 |  | Synthetic Storage | 0 | 0 |  |  |  |  | 0 | 0
 | Total Imbalances: |  | 0 | 0
 |  |  | 0 | 0
 | Storage: |  | 0 | 0
17 |  | Injection (Sales to Stg) | 0 | 0
18 |  | W/D (Purchase from Stg) | 0 | 0
19 |  | Inventory Profit | 0 | 0
 |  | Total W/D | 0 | 0
 | Total Storage: |  | 0 | 0
 |  |  | 0 | 0
20 | Linepack: |  | 0 | 0
 |  |  | 0 | 0
21 | Shrinkage: |  | 0 | 0
 |  |  | 0 | 0
22 | UA4: |  | -4019 | -17080.75
 |  |  | 0 | 0
23 | Fuel: |  | 0 | 0
 |  |  | 0 | 0
 | Transport Expense: |  | 0 | 0
24 |  | Commodity | 0 | 0
25 |  | Demand | 0 | 0
 | Total Transport Expense: |  | 0 | 0
 |  |  | 0 | 0
 | Transport Revenue: |  | 0 | 0
26 |  | Interstate | 0 | 0
27 |  | Intrastate | 0 | 0
 | Total Transport Revenue: |  | 0 | 0
 |  |  | 0 | 0
 | Intra CTS (EGM / HPL / LRC): |  | 0 | 0
28 |  | Transport Expense | 0 | 0
29 |  | Transport Revenue | 0 | 0
 | Total Intra CTS Transport |  | 0 | 0
 |  |  | 0 | 0
 | Tie Out Schedule Items: |  | 0 | 0
 |  | MTM Income: | 0 | 0
30 |  | Current Gross Book Balance | 0 | 13801398.9478
31 |  | Prior Month Book Balance | 0 | -16978937
 |  | Total MTM Income | 0 | -3177538.052199999
32 |  | Prudency | 0 | 0
33 |  | Financial Liquidations | 0 | 15351593
34 |  | Originations | 0 | 22718
35 |  | Broker Fees | 0 | 0
36 |  | Asset Group Demand Charges | 0 | 0
37 |  | [NAME] | 0 | 0
38 |  | Interdesk Charges | 0 | 0
39 |  | Canada | 0 | 0
40 |  | Miscellaneous | 0 | 0
 |  | TOTAL INTRA-MTH
Spreadsheet: 476977d1472089883-labelling-things-dynamically-excel-question2
Sheet: Worksheet

 |  |  |  | BC | Long/short Label | Long Equities/Bonds Flag |  |  |  | Long/short Label
 |  |  |  | Equities | Short | Short |  |  |  | BC | Short
 |  |  |  | Cash | Short | Short |  |  |  | I2 | Long
 |  |  |  | I2 |  |  |  |  |  | BL | Long
 |  |  |  | Equities | Long | Long |  |  |  | BS | Short
 |  |  |  | Loans/Deposits | Long | Short |  |  |  | KL | Long
 |  |  |  | BL |  |  |  |  |  | SWEEP | Short
 |  |  |  | Equities | Long | Long |  |  |  | XP | Short
 |  |  |  | Loans/Deposits | Long | Short
 |  |  |  | Cash | Long | Short |  |  |  | Long Equities/Bonds Flag (variable)
 |  |  |  | BS |  |  |  |  |  | Equities | If label is long, then long; if label is short, then short
 |  |  |  | Equities | Short | Short |  |  |  | Equity Swaps | If label is long, then long; if label is short, then short
 |  |  |  | Equity Swaps | Short | Short |  |  |  | Interest Rate Swaps | If label is long, then long; if label is short, then short
 |  |  |  | Interest Rate Swaps | Short | Short |  |  |  | Loans/Deposits | If label is long, then long; if label is short, then short
 |  |  |  | Loans/Deposits | Short | Short |  |  |  | Bonds CORP | If label is long, then long; if label is short, then short
 |  |  |  | Simple Cash Flow | Short | Short
 |  |  |  | Cash | Short | Short |  |  |  | Long Equities/Bonds Flag (fixed)
 |  |  |  | KL |  |  |  |  |  | NAV SWEEP | Short
 |  |  |  | Bond CORP | Long | Short |  |  |  | Simple Cash Flow | Shoet
 |  |  |  | Loans/Deposits | Long | Short |  |  |  | Cash | Short
 |  |  |  | Cash | Long | Short
 |  |  |  | SWEEP
 |  |  |  | NAV SWEEP | Short | Short