tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 269190d1381017908-adding-multiply-taxes-to-cell-sample-sheet
Sheet: Sheet1

 | Price | Amount | Total
Product
Apple | 2.55 | 6 | 15.3
 |  |  | 15.3 | Subtotal
 |  |  | ? | Manitoba GST 13%
 | Total Amount Purchase |  |  |  |  | PROVINCE | TAX
 |  |  |  |  |  | Alberta GST 5% | 0.05
 |  |  |  |  |  | British Columbia GST 7% | 0.07
 |  |  |  |  |  | Manitoba GST 13% | 0.13
 |  |  |  |  |  | New Brunswick GST 13% | 0.13
 |  |  |  |  |  | Newfoundland GST 13% | 0.13
 |  |  |  |  |  | NWT  GST 5% | 0.05
 |  |  |  |  |  | Ontario GST 13% | 0.13
 |  |  |  |  |  | PEI GST 14% | 0.14
 |  |  |  |  |  | Quebec GST 14.975% | 0.07
 |  |  |  |  |  | Saskatchewan GST 10% | 0.1
Spreadsheet: 704506d1605606787-retrieving-missing-values-from-a-list-list-example-excel
Sheet: Sheet1

 |  |  | List A |  |  | List B | List C
 |  |  | [NAME] |  |  | [NAME]
 |  |  | [NAME] |  |  | Lucas
 |  |  | [NAME] |  |  | [NAME]
 |  |  | Zlatan |  |  | [NAME]
 |  |  | [NAME] |  |  | Patrik
 |  |  | [NAME] |  |  | Patrik Junior
 |  |  | Lucas |  |  | [NAME]
 |  |  | Maria |  |  | Lennart
 |  |  | [NAME] |  |  | Kennet
 |  |  | [NAME]
 |  |  | Patrik
 |  |  | Patrik Junior
Spreadsheet: 768535d1645016832-formula-to-calculate-a-price-based-on-quantity-thresholds-matrix-estimating-master-test
Sheet: Sheet1

 | INPUT CHOSEN QUANTITY HERE
Choose Option via drop down menu below | 25 | 75 | 750
Option 1 | 250 | 675 | 4500
Option 3 | 750 | 2175 | 19500 |  | *FORMULA NEEDED TO CALCULATE HERE
Option 4 | 1000 | 2925 | 27000
PRICE MATRIX | 50 or less | 51 to 100 | 101 to 250 | 251 to 500 | 501 and above
Drop Down Menu  (using Data Validation List) | 50 | 100 | 250 | 500 | 501
Option 1 | 10 | 9 | 8 | 7 | 6
Option 2 | 20 | 19 | 18 | 17 | 16
Option 3 | 30 | 29 | 28 | 27 | 26
Option 4 | 40 | 39 | 38 | 37 | 36
Spreadsheet: 809491d1671116147-ranking-formular-rank333
Sheet: WANAFUNZI 5 BORA NA 5 DUNI

HALMASHAURI YA WILAYA [NAME]
MTIHANI WA MUHULA WA PILI DARSA LA III NOVEMBA, 2022
[NAME] BORA
NA | JINA | JINSI 1=MV 2=MS | SHULE | KATA | MASOMO |  |  |  |  |  |  |  |  |  |  |  | JUMLA | WASTANI | DARAJA
 |  |  |  |  | KISW |  | ENGL |  | HIS |  | SAY |  | M/JAM |  | URA
 |  |  |  |  | A | D | A | D | A | D | A | D | A | D | A | D
1
2
3
4
5
H. WANAFUNZI 5 DUNI
NA | JINA | JINSI 1=MV 2=MS | SHULE | KATA | MASOMO |  |  |  |  |  |  |  |  |  |  |  | JUMLA | WASTANI | DARAJA
 |  |  |  |  | KISW |  | ENGL |  | HIS |  | SAY |  | M/JAM |  | URA
 |  |  |  |  | A | D | A | D | A | D | A | D | A | D | A | D
1
2
3
4
5
Spreadsheet: 688190d1595609597-using-index-to-find-matches-across-multiple-worksheets-with-multiple-matches-test1
Sheet: Sheet4

FRUIT | PRICE | QUALITY
APPLE | 12 | 4342
GRAPE | 19 | 5453
ORANGE | 8 | 6453
APPLE | 11 | 3566
PEACH | 18 | 4654
ORANGE | 9 | 7365
APPLE | 17 | 3456
PEACH | 20 | 7534
Apple
Spreadsheet: 62e8b253-ecce-4e77-98c7-6699c0ba7f6d
Sheet: Sheet1

Adresseliste fra www.proff.no | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7
[NAME]:Alarm- og sikringsanlegg
FIRMANAVN | ADRESSE | POSTNR. | POSTSTED | TELEFON | TELEFAKS
Beslag-Consult AS | Industriveien 57 | 7080 | Heimdal | 72 89 67 60
Sector [NAME] | Wiels plass 1 | 1771 | Halden | 03033
Lian Alarmservice | Mellomila 64 | 7018 | Trondheim | 484 03 701
Varanger Security AS | Ørtangen 10 | 9800 | Vadsø | 400 01 553 | 78 95 00 01
Proffpartner Security AS avd Bærum | Vøyenengtunet 7 | 1339 | Vøyenenga | 67 17 22 22
Proffpartner Security AS avd Follo | Osloveien 717 | 1914 | [NAME] | 08911
Proffpartner Security AS avd Lørenskog |  |  |  | 08911
Hemer Lås & Dørtelefon AS | Vogts gate 56 | 0477 | Oslo | 06714
911-Tjenesten AS | Skippergata 12 | 3921 | Porsgrunn | 35 93 12 50
Kaba MøllerUndall AS | Hamborggata 21 | 3018 | Drammen | 06866
Alarm-elektro | Wilsters vei 1B | 0580 | Oslo | 920 38 040
Sikkerhetskompaniet AS | Slagenveien 7 | 3110 | [NAME] | 33 35 27 37
Comsafe AS | Gudeberg alle 21 | 1632 | Gamle Fredrikstad | 924 04 444
Automatikk-Service AS | Strømsveien 346 | 1081 | Oslo | 950 60 138
Securinet AS | Strandveien 33 | 1366 | Lysaker | 61 28 70 00 | 22 30 55 70
[NAME] | [NAME] 66 | 3182 | Horten | 33 04 95 94
AlltidFokus | Fløisbonnveien 5 | 1412 | Sofiemyr | 905 30 976 | 66 99 58 41
Kaba Møllerundall AS avd Lillehammer | Storgata 136 | 2615 | Lillehammer | 06866
Bygg og Boligsikring AS | Sønsterudveien 26 | 1412 | Sofiemyr | 22 00 60 10 | 66 99 45 49
Viste & Sømme AS | Prinsens vei 10A | 4315 | Sandnes | 51 96 12 00 | 51 96 12 01
Prosec Bergen AS | Leirvikåsen 43B | 5179 | Godvik | 478 32 100 | 55 50 07 46
Selmatec Securities AS | Vilbergveien 71 | 2060 | Gardermoen | 63 92 84 00
Solland Elektro A/S |  | 4014 | Stavanger | 51 85 82 00
Vicon Norway A/S | Munkerudtunet 10 | 1164 | Oslo | 22 74 10 01
Bergen Låsservice AS | Alvehaugen 2 | 5179 | Godvik
Trenger du oppdaterte adresselister til DM /TM? [NAME] (www.forvalt.no) tilbyr ett segmenterings- og uttekksverktøy hvor du [NAME] frem til din målgruppe og eksportere lister til Excel. Du kan gjøre oppslag så ofte du vil, med en begrensning på 3500 selskap per søk. Våre telefonnummere er hentet fra Telefonkatalogen og [NAME].  Med segmenteringsverktøy kan du: - Hente ut adresseliste før en DM utsendelse - Lage ringelister - Hente ut bransjestatistikker - Oppdatere din kundedatabase  Se http://forvalt.no/segmenteringsverktoy/
Spreadsheet: 221219d1363425466-advance-formula-to-keep-calcuating-dates-of-expected-date-of-completion-of-order-trialformulas
Sheet: Sheet1

 | Table A
 | column A : Represent item
 | c9: m9 respresent net stock in ware house for that item
 |  | Net estimated stock in Ware House against expected arrivals daily basis
 |  | 2013-03-01 00:00:00
 | Apple | 24000 | 11022 | 0 | 1112 | 30000 | 4000 | 50000 | 34340 | 244654 | 54545 | 45450
 | Mango | 30000 | 32200 | 22222 | 111 | 222334 | 0 | 2323 | 67676 | 46565 | 676878 | 99898
 | Table B | Despatch plan against orders in hand
 | Customer | Order seq | Item | Quantity | Despatch date
 | ABCD | 1 | Apple | 35000 | ???
 | SSS | 2 | Apple | 112 | ???
 | ssdd | 3 | Apple | 34445 | ??
 | In table B  , I wish to find a formula to calculate despatch date based on stock in Table A
 | For example customer ABCD needs 35000 apples , So it can be despatched on 3 mar as total stock of 1 March
 | and 2 March is more than 35000. Then Next customer
 | SSS order can be shipped only on 5Mar as balance of 2 mar 22 and 4 mar Stock is more than delivery
 | Like this it should keep on calculating in E14 ,E15 ,E16
Spreadsheet: 75668d1276200568-need-to-sum-only-certain-numbers-with-a-date-restriction-example
Sheet: Sheet1

 | 2010-06-07 00:00:00 | 2010-06-08 00:00:00 | 2010-06-09 00:00:00 | 2010-06-10 00:00:00 | 2010-06-11 00:00:00 | 2010-06-12 00:00:00 | 2010-06-13 00:00:00
 | 15119.89 | 3399.58 | 7409.000000000001 | 1044 | 4176 | 8453 | 0
34/36+ | 0 | 0 | 0 | 0 | 0 | 0 | 0 |  | 0 |  |  |  |  | 0
40/44 | 0 | 0 | 10.2312 | 10.2312 | 40.9248 | 20.4624 | 0 |  | 81.8496 |  |  |  |  | 0.005986221019527536
48/50 | 1058.3923 | 237.9706 | 738.392 | 292.842 | 1171.368 | 1031.234 | 0 |  | 4530.1989 |  |  |  |  | 0.3313244277042346
54/56 | 4082.3703 | 917.8866 | 2200.0428 | 481.4928 | 1925.9712 | 2681.5356 | 0 |  | 12289.2993 |  |  |  |  | 0.8988005046441891
60- | 9979.1274 | 2243.7228 | 4460.334000000001 | 259.434 | 1037.736 | 4719.768000000001 | 0 |  | 22700.1222 |  |  |  |  | 1.660215183207782
 | 0 | 0 | 0 | 13673 | 12629 | 8453 | 0 |  | 13673 |  |  |  |  | 1
 |  |  |  | As each day passes, the grey cells remain the same, while the row immediately below counts down.  The row immediately above is simply a sum of the grey cells below.  In this case, if you subtract E3 from E9, you get F9.  If you subtract E3 and F3 from E9, you get G9.  It's a quantity countdown as the week progresses so that we can see both how much we have done, and how much we still expect to do by the end of the week. |  |  |  |  | In another tab, the week is collapsed into a summary, which uses =sum() for each size line, and uses a lookup to find the current day's number for what remains in the week. |  |  |  |  | The final section takes the summary size and divides it by the lookup return value (for example, O8 = J8/J9.  This causes an obvious problem, as we go over 100%.
 |  |  |  | What I need to be able to do is have the summary lines in the second section only count what remains in the week for each size.  For example, today, I would need cell J6 to ONLY sum up cells E6:H6.  That would fix the percentage problem.
Spreadsheet: 753159d1635269845-lookup-multiple-criteria-in-one-cell-lookup-multiple-values-in-one-cell
Sheet: Sheet1

 | Date | Serial | CC | Cost
 | 2021-03-11 00:00:00 | 648 | 4505656791 | 5562
 | 2021-03-11 00:00:00 | 617 | 4505656789 | 5312 |  |  |  |  |  | CC | Date | Serial | Cost
 | 2021-03-10 00:00:00 | 535 | 4505645222 | 5541 |  |  |  |  |  | [PHONE] [PHONE] [PHONE] | 10-03-2021 25-03-2021 26-03-2021 | 535 536 831 | 5541 5541 5496
 | 2021-03-25 00:00:00 | 536 | 4505679686 | 5541 |  |  |  |  |  | [PHONE] [PHONE] [PHONE] | 11-03-2021 25-03-2021 25-03-2021 | 648 743 537 | 5562 5452 5541
 | 2021-03-25 00:00:00 | 596 | 4505679662 | 5541 |  |  |  |  |  | [PHONE] [PHONE] [PHONE] | 25-03-2021 25-03-2021 25-03-2021 | 596 708 675 | 5541 5587 5547
 | 2021-03-25 00:00:00 | 743 | 4505679672 | 5452
 | 2021-03-25 00:00:00 | 708 | 4505679675 | 5587
 | 2021-03-24 00:00:00 | 570 | 4505679726 | 5172
 | 2021-03-25 00:00:00 | 537 | 4505679792 | 5541
 | 2021-03-25 00:00:00 | 675 | 4505679731 | 5547
 | 2021-03-26 00:00:00 | 831 | 4505685314 | 5496
Spreadsheet: 863056d1710613899-need-help-with-formula-or-function-pulling-data-to-another-tab-copy-of-2023-budget-031024
Sheet: Income-Year

 |  | Income | 2023-01-01 00:00:00 |  |  |  |  |  |  |  |  |  |  |  | Total | Average
Payday |  | Monthly totals:
 |  | Cox | 3487.13 | 3917.49 | 3608.26 | 3096 | 3782.83 | 5478.39 | 3668.95 | 3544.4 | 3711.85 | 3386.13 | 3623.2 | 4793.65
 |  | Cox-Bonus |  |  |  | 1250 |  |  |  |  |  |  |  | 250
 |  | Cox-PTO (40 hrs) | 1080
Other |  | Monthly totals:
 |  | Gigs
 |  | Sold
 |  | Refunds |  |  |  | 1593.04 |  | 31.48 |  |  |  |  |  | 50.2
 |  | Misc
Advances |  | Monthly totals:
 |  | Equity |  |  |  |  |  |  | 2000 | 2500
 |  | Savings |  |  |  |  |  |  |  |  |  | 10.1 |  | 3000
Investing |  | Monthly totals:
 |  | Dividends
 |  | Interest | 0.04 | 0.04 | 0.05 | 0.03 | 0.04 | 0.03 | 0.02 | 0.02 | 0.03 | 0.03 | 0.02 | 0.02
TOTAL |  | Monthly totals:
Spreadsheet: 112022d1308216780-summary-spreadsheet-table-invites
Sheet: Sheet2

Name | Invites Past Week | Invites Past 4 Weeks | Invites Lifetime
Amanda | 1 | 0.5 | 0.4871794871794872
[NAME] | 1 | 0.5 | 0.6923076923076923
Athena Sans | 1 | 1 | 0.9487179487179487
Chantel Rodriguez | 1 | 0.5 | 0.1282051282051282
[NAME] | 1 | 1 | 0.6153846153846154
Cristina Torres | 1 | 1 | 0.7692307692307693
[NAME] | 85 | 46.25 | 30.66666666666667
[NAME] | 0 | 0.25 | 0.4615384615384616
[NAME]
Desiree
Domingo
Dorina
Hylana Almeida
[NAME]
[NAME]
[NAME]
[NAME]
Jonzi
[NAME]
Karina
[NAME]
[NAME]
[NAME]
Lucianos
[NAME]
[NAME]
[NAME]
[NAME]
[NAME]
[NAME]
[NAME] (coatcheck)
Priscilla
[NAME]
[NAME]
[NAME]
[NAME]
[NAME]
[NAME]
[NAME]
[NAME]
Tiffany
[NAME]
[NAME]
Veronika
[NAME]
[NAME]
Boris - CONFIRMED
Ylenia
Anthony - NO FB
[NAME]
[NAME] FBK
[NAME] BROKE
Sharif Ahfan
Taffy
Yessica - Maternity Leave
Spreadsheet: 882437d1730821662-distribute-students-evenly-and-only-once-in-repeating-sessions-according-to-wishes-wishes-reading-day
Sheet: All wishes

ID | Name | Grade | spøkelseshistorier | historier rundt bålapanna | faktabøker med mange forskjellige tema | høytlesning med leseoppdrag | [NAME] | skrive slutten på en billedbok | mørkelesing | stillelesing | film fra bok [NAME] | [NAME] hus, hjelpe lille Larsend med å lage ting han trenger for å overleve |  | first calculation | second calculation | third calculation | etc. |  | [NAME]) to be with | Student(s) to avoid
1 | Ole | 4 | 1 |  | 2 | 3 | 4 |  |  | 5 |  |  |  |  |  |  |  |  | "=B3"
2 | Per | 3 | 2 | 4 | 2 | 1
3 | [NAME] | 6 | 1 | 3 | 2 | 4 |  |  |  | 5
4 | Hans | 7 | 2 |  | 1 | 3 | 4
5 | [NAME] | 1 | 1 | 2 |  | 3 |  |  | 5
6 | [NAME] | 4 |  | 3 | 4 |  | 1 |  |  |  |  | 2 |  |  |  |  |  |  |  | "=B11"
7 | [NAME] | 4 | 3 | 2 |  |  | 1 |  | 5
8 | Stacy | 4 | 1 |  | 3 | 4 |  |  |  | 5
9 | [NAME] | 2 |  | 1 | 4 | 3 |  |  |  |  | 5
10 | [NAME] | 7 |  |  | 4 | 3 | 2 |  |  |  | 1
11 | George | 6 |  |  |  | 1 | 2 | 4 | 5
12 | [NAME] | 2 | 4 | 3 | 1 |  |  |  | 2
13 | Stuart | 4 | 3 |  | 4 | 2 |  |  | 5 |  | 1
14 | THOMAS | 4 | 1 | 3 | 3 | 4
15 | ARTHUR | 2 | 2 |  | 1 | 3 | 4
16 | [NAME] | 7 | 1 | 2 |  | 3 |  |  | 5
17 | [NAME] | 6 |  | 3 | 4 |  | 1 |  |  |  |  | 2
18 | FREDERICK | 2 | 3 | 2 |  |  | 1 |  | 5
19 | ALBERT | 7 | 1 |  | 3 | 4 |  |  |  | 5
20 | ERNEST | 6 |  | 1 | 4 | 3 |  |  |  |  | 5
21 | ALFRED | 2 |  |  | 4 | 3 | 2 |  |  |  | 1
22 | EDWARD | 4 |  |  |  | 1 | 2 | 4 | 5
23 | [NAME] | 4 | 4 | 3 | 1 |  |  |  | 2
24 | HAROLD | 7 | 3 |  | 4 | 2 |  |  |  |  | 1
25 | [NAME] | 6 | 1 | 2 |  | 3 |  |  | 5
26 | FRANK | 2 |  | 3 | 4 |  | 1 |  |  |  |  | 2
27 | HENRY | 4 | 3 | 2 |  |  | 1 |  | 5
28 | HARRY | 4 | 1 |  | 3 | 4 |  |  |  | 5
29 | WALTER | 3 |  | 1 | 4 | 3 |  |  |  |  | 5
30 | REGINALD | 5 |  |  | 4 | 3 | 2 |  |  |  | 1
31 | HERBERT | 4 | 3 | 2 |  |  | 1 |  | 5
32 | LEONARD | 7 | 1 |  | 3 | 4 |  |  |  | 5
33 | STANLEY | 6 |  | 1 | 4 | 3 |  |  |  |  | 5
34 | LESLIE | 1 |  |  | 4 | 3 | 2 |  |  |  | 1
35 | RICHARD | 2 | 1 |  | 2 | 3 | 4
36 | CYRIL | 4 | 2 | 4 | 2 | 1 |  |  |  | 3 | 5
37 | SIDNEY | 3 | 1 | 3 | 3 | 4
38 | FRANCIS | 5 | 2 |  | 1 | 3 | 4
39 | [NAME] | 6 | 1 | 2 |  | 3 |  |  | 5
40 | [NAME] | 4 |  | 3 | 4 |  | 1 |  |  |  |  | 2
41 | WILFRED | 6 | 3 | 2 |  |  | 1 |  | 5
42 | SAMUEL | 4 | 1 |  | 3 | 4 |  |  |  | 5
43 | NORMAN | 5 |  | 1 | 4 | 3 |  |  |  |  | 5
44 | CECIL | 7 |  |  | 4 | 3 | 2 |  |  |  | 1
45 | [NAME] | 6 | 1 |  | 2 | 3 | 4 |  | 5
46 | SYDNEY | 4 | 2 | 4 | 2 | 1 |  |  |  | 3
47 | HORACE | 2 | 1 | 3 | 3 | 4
48 | FRED | 4 | 2 |  | 1 | 3 | 4
49 | JACK | 5 | 1 | 2 |  | 3 |  |  | 5
50 | EDWIN | 6 |  | 3 | 4 |  | 1 |  |  |  |  | 2
51 | RONALD | 2 | 3 | 2 |  |  | 1 |  | 5
52 | ALEXANDER | 3 | 1 |  | 3 | 4 |  |  |  | 5 | 2
53 | CLIFFORD | 1 |  | 1 | 4 | 3 |  |  |  |  | 5
54 | PATRICK | 2 |  |  | 4 | 3 | 2 |  |  |  | 1
55 | BERNARD | 3 | 3 | 2 |  |  | 1 |  | 5
56 | [NAME] | 4 | 1 |  | 3 | 4 |  | 2 |  | 5
57 | VICTOR | 5 |  | 1 | 4 | 3 |  |  | 2 |  | 5
58 | PHILIP | 3 |  |  | 4 | 3 | 2 |  | 5 |  | 1
Spreadsheet: 810755d1672038233-how-to-get-the-maximum-value-of-the-sum-of-which-day-from-a-certain-row-tr
Sheet: Sheet1

 | 2
Day | platform | Total number of fans
1 | AA | 22
1 | BB | 55
1 | CC | 66
1 | DD | 44
1 | EE | 560
1 | FF | 480
2 | AA | 22
2 | BB | 550
2 | DD | 44
2 | EE | 560
2 | FF | 480
3 | AA | 22
3 | BB | 55
3 | CC | 66
3 | DD | 44
3 | EE | 560
3 | FF | 480
4 | AA | 23
4 | DD | 46
4 | EE | 566
4 | FF | 480
Spreadsheet: 02d1dae1-973a-43a7-8632-adf467bd0460
Sheet: EC

Table | Ecuador |  | Latin America and the Caribbean
 | Grain | Root | Commercial | Food aid |  | Aggregate
Year | production | production | imports | receipts |  | availability
 |  | (grain equiv.) | (grains) | (grain equiv.) |  | of all food
 |  |  | ---1,000 tons ---
2002 | 1588.004 | 140.9054476677215 | 786.0602759999999 | 83.805224 |  | 3307.167777073301
2003 | 1593.891 | 114.6949682193427 | 851.847569 | 5.777997999999999 |  | 3279.735169459495
2004 | 2015.022 | 123.7779617425308 | 1007.80861 | 21.061211 |  | 3412.379841114443
2005 | 1814.107 | 111.9291090894187 | 950.224974 | 43.191013 |  | 3504.326442887775
2006 | 1777.708 | 107.2121814378582 | 1134.032747 | 31.305282 |  | 3526.885605823828
2007 | 2148.405 | 95.72893477618416 | 1187.526354 | 0.759476 |  | 3553.017056465147
2008 | 1666.997 | 95.78504663624176 | 1097.042916 | 2.63908 |  | 3864.400632829159
2009 | 1773.258 | 91.83393426109272 | 1078 | 0.3943 |  | 3829.765395393316
2010(e) | 1756 | 104.9354164495135 | 1082.587656222242 | 1.264285333333333 |  | 3911.941157473514
Projections |  |  |  | Food gaps*
 |  |  |  | NG | DG
2011 | 1944.158668772914 | 106.21913848981 | 1053.465030841146 | 0 | 97.73396866000516 | 3977.578190680601
2016 | 2065.742829351925 | 112.8015725315689 | 1176.953590745671 | 0 | 46.47615426973904 | 4422.804741116306
2021 | 2179.110293123708 | 119.6660435685636 | 1280.837413904039 | 0 | 18.85450654205745 | 4873.519381476173
Source: Economic Research Service, using data from FAOSTAT (grain and root and tuber production and imports), and the World Food Program (food aid).
*NR stands for nutritional requirements and describes the amount of grain equivalent needed to support nutritional standards on
*NG stands for nutritional gap and describes the amount of grain equivalent needed to support nutritional standards on
income quintile to reach the nutrional requirement.
Spreadsheet: 63437d1264363875-excel-2007-linking-sheets-within-files-daily-cashflow-blank-template
Sheet: Dec 2009

Date |  | Receipts | Unpaid Chqs | P/Ledger | Salaries | PAYE | Savings | Dividends | Motor | [NAME] | Tax Res | Managers Res | S/O | S/L Refunds | Personal Exp | S/L Healthcare | Daily Totals |  | Acc Balance (carried forward):
1 |  | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 | 123 |  |  | O/Bal (carry forward):
2
3 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Total Income:
4 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Total Expenditure:
5
6 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Month Total:
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
 |  | Receipts | Unpaid Chqs | P/Ledger | Salaries | PAYE | MAS Savings | Dividends | Motor | [NAME] | Tax Res | Managers Res | [NAME] | S/L Refunds | RGS Exp | S/L Healthcare
 | Monthly Total
Spreadsheet: 130994d1322680372-summing-cells-from-a-column-if-an-adjacent-column-contains-a-specified-value-copy-of-2011-11-29-detailed-1
Sheet: 11.29

Date: | 2011-11-29 00:00:00
Meals
Meal | Item Brand | Item Name | Your Servings | Your Total Calories | Your Total Sugars | Your Total Carbs | Your Total Fats | Your Total Protein | Your Total Cholesterol | Your Total Sodium | Your Total Dietary Fiber | Calories | Sugars | Carbs | Fats | Protein | Cholesterol | Sodium | Dietary Fiber
breakfast |  | 1 Large Egg White | 1 | 17 | 0 | 0 | 0 | 4 | 0 | 55 | 0 | 17 | 0g | 0g | 0g | 4g | 0mg | 55mg | 0g
breakfast |  | California [NAME] | 1 | 275 |  | 15 | 25 | 5 |  |  | 15 | 275 |  | 15g | 25g | 5g |  |  | 15g
breakfast |  | [NAME] | 1.14 | 23.94 | 0 | 3.534 | 0.228 | 3.762 | 0 | 0 | 0 | 21 |  | 3.1g | 0.2g | 3.3g
breakfast |  | Large Navel Orange | 1 | 100 | 28 | 32 | 0.2 | 2.8 | 0 | 0 | 7 | 100 | 28g | 32g | .2g | 2.8g |  | 0mg | 7g
breakfast |  | Red Onion | 1 | 15 | 0 | 3 | 0 | 1 | 0 | 0 | 1 | 15 |  | 3g | 0g | 1g |  |  | 1g
breakfast |  | egg | 2 | 140 | 0 | 2 | 9 | 12 | 430 | 130 | 0 | 70 | 0g | 1g | 4.5g | 6g | 215mg | 65mg | 0g
morning snack |  | Coffee | 1 | 2 | 0 | 0 | 0 | 0.3 | 0 | 5 | 0 | 2 |  | 0 | 0g | 0.3g | 0mg | 5.0mg
lunch |  | Starkist [NAME] Tuna | 2.5 | 150 | 0 | 0 | 3.75 | 25 | 50 | 500 | 0 | 60 | 0g | 0g | 1.5g | 10g | 20mg | 200mg | 0g
lunch |  | [NAME], baked with skin | 2 | 360 | 33.8 | 82.8 | 0.6 |  | 0 | 144 | 13.2 | 180 | 16.9g | 41.4g | 0.3g |  | 0mg | 72mg | 6.6g
afternoon snack |  | Lactaid Milk | 3 | 195 | 18 | 19.5 | 7.5 | 12 | 30 | 186 | 0 | 65 | 6g | 6.5g | 2.5g | 4g | 10mg | 62mg | 0g
pre-workout |  | Boneless Skinless [NAME] | 1 | 190 | 0 | 0 | 2.5 | 50 | 140 | 65 | 0 | 190 |  | 0g | 2.5g | 50g | 140mg | 65mg
pre-workout | Carapelli | Extra Virgin Olive Oil | 2 | 240 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 120 | 0g | 0g | 13.5g | 0g | 0mg | 0mg | 0g
pre-workout |  | Sundried Tomatoes | 14 | 70 | 14 | 14 | 0 | 0 | 0 | 588 | 0 | 5 | 1g | 1g | 0g | 0g | 0mg | 42mg
post-workout | [NAME] | [NAME] | 1 | 20 | 1 | 2.5 | 0 | 3 | 0 | 15 | 1 | 20 | 1g | 2.5g | 0g | 3g | 0mg | 15mg | 1g
post-workout |  | Boneless Skinless [NAME] | 1 | 190 | 0 | 0 | 2.5 | 50 | 140 | 65 | 0 | 190 |  | 0g | 2.5g | 50g | 140mg | 65mg
post-workout |  | Green Bell Pepper | 1 | 33 | 3.9 | 7.6 | 0.3 | 1.4 | 0 | 4.9 | 2.8 | 33 | 3.9g | 7.6g | 0.3g | 1.4g |  | 4.9mg | 2.8g
post-workout |  | Olive Oil | 3 | 360 | 0 | 0 | 40.5 | 0 | 0 | 0 | 0 | 120 | 0g | 0g | 13.5g | 0g | 0mg | 0mg | 0g
post-workout |  | Red Onion | 2.5 | 37.5 | 0 | 7.5 | 0 | 2.5 | 0 | 0 | 2.5 | 15 |  | 3g | 0g | 1g |  |  | 1g
Fitness
Exercise Done | Minutes | Calories Burned | Heart Rate | Distance
Cross Fit | 60 | 652 | 0 | 0.00
Totals:
 |  |  |  | Calories | Sugars | Carbohydrates | Fat | Protein | Cholesterol | Sodium | Dietary Fiber
 |  |  |  | 2418.44 | 98.7 | 189.43 | 119.08 | 172.76 | 790 | 1,757.90 | 42.5
 |  |  |  | Calories Allowed | 3,782.00
 |  |  |  | Calories Consumed | 2,418.44
 |  |  |  | Calories Burned | 652
 |  |  |  | Net Calories | 1766.44
Weight
Date : | November 29th, 2011
Weight | 205.00 - Pounds
Water
Date |  | Glasses
2011-11-29 |  | 7
TOTAL |  | 7
Spreadsheet: peter_keavey__28664__BMO Curve
Sheet: Sheet1

 | 2001-11-10 00:00:00 | 2.61
 | 2001-11-11 00:00:00 | 2.61
 | 2001-11-12 00:00:00 | 2.61
 | 2001-11-13 00:00:00
 | 2001-11-14 00:00:00
 | 2001-11-15 00:00:00 | 2.7
 | 2001-11-16 00:00:00
 | 2001-11-17 00:00:00
 | 2001-11-18 00:00:00
 | 2001-11-19 00:00:00
 | 2001-11-20 00:00:00
 | 2001-11-21 00:00:00
 | 2001-11-22 00:00:00
 | 2001-11-23 00:00:00
 | 2001-11-24 00:00:00
 | 2001-11-25 00:00:00
 | 2001-11-26 00:00:00
 | 2001-11-27 00:00:00
 | 2001-11-28 00:00:00
 | 2001-11-29 00:00:00
 | 2001-11-30 00:00:00
Spreadsheet: ec942641-c34d-4e8f-9d31-a8b1ce1bc2e9
Sheet: Sheet1

Test Referenced | Entry Type | Mayo ID | Description | Reporting Description | Method | Collection Container | Temperature | Stability | [NAME] | Unit of Measure | [NAME] | Minimum Volume | Reject Due To | Reference Values | Days/Times Performed | Analytic Time | Maximum Lab Time | CPT Units | CPT Code | LOINC® Code | LOINC® Attributes | Always Performed | Orderable Separately | Performing Location
AMOBS | Test | AMOBS | Amobarbital, Serum | Amobarbital, S | Gas Chromatography-Mass Spectrometry (GC-MS) | Plain, Red-Top Tube: Red |  |  | 1.5 | mL | Container/Tube: Red top  [NAME]: 1.5 mL Collection Instructions: Spin down within 2 hours of draw. | 0.6 mL | Hemolysis	Mild OK; Gross reject Thawing	Warm OK; Cold OK  Lipemia	Mild OK; Gross reject Icterus	Mild OK; Gross reject Other	Serum gel tube | Therapeutic concentration: 1.0-5.0 mcg/mL Toxic concentration: >10.0 mcg/mL Concentration at which toxicity occurs varies and should be interpreted in light of clinical situation. | Tuesday | Same day/1 day | 8 days | 1 | 82205 |  |  |  |  | Rochester
AMOBS | [NAME] | SRED | Serum Red |  |  |  | Refrigerated | 14 days
AMOBS | Result | 8325 | Amobarbital, S | Amobarbital, S | Numeric |  |  |  |  | mcg/mL |  |  |  |  |  |  |  |  |  | 3338-1
Spreadsheet: 177962d1346567518-counting-data-on-another-worksheet-scorecard
Sheet: Score Board

Customers | Extremely likely (9 and 10) | Neutral  (7 and 8) | Not likely at all  (0 and 6) | Total # of Surveys
Adam
[NAME]
Edmond
[NAME]
[NAME]
I'm trying to get how many customer provided the answer 9 and 10-- Extremely likely on each student on Worksheet named Data and placed it on Column B. I'm having problem since the word "-- Extremely likely" is attached to number 10.
I'm trying to get how many customer provided the answer 7 and 8 on Worksheet named Data and placed it on Column C
I'm trying to get how many customer provided the answer 0-- Not likely at all and 6 on Worksheet named Data and placed it on Column D
I'm trying to get on how many customer participate on the surveys in Worksheet Data and placed it on Column E