tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 849855d1700229755-finding-unique-data-unique-list
Sheet: Sheet1

From the list below I want to be ablue to total value by client and by assignment, but then more specifically highlight the client where it only has one assignment associated to it, for example, PQR only has Education.
The idea is that I'm able to identify a client that is only engaged to complete one type of assignment and not a mixture of assignments.
Client | Assignment | Value |  | Client | Count of Categories |  | Client | Sum
ABC | Crafting | 5 |  | ABC | 4 |  | ABC | 75
ABC | Projects | 10 |  | DEF | 4 |  | DEF | 60
ABC | [NAME] | 15 |  | GHI | 3 |  | GHI | 75
ABC | Education | 20 |  | JKL | 3 |  | JKL | 50
ABC | Education | 25 |  | MNO | 2 |  | MNO | 55
DEF | Crafting | 30 |  | PQR | 1 |  | PQR | 110
DEF | Projects | 5 |  | STU | 3 |  | STU | 45
DEF | [NAME] | 10 |  | VWX | 4 |  | VWX | 165
DEF | Education | 15
GHI | Education | 20
GHI | Crafting | 25
GHI | Projects | 30
JKL | [NAME] | 5
JKL | Education | 10
JKL | Education | 15
JKL | Crafting | 20
MNO | Projects | 25
MNO | [NAME] | 30
PQR | Education | 5
PQR | Education | 10
PQR | Education | 15
PQR | Education | 20
PQR | Education | 25
PQR | Education | 30
PQR | Education | 5
STU | Education | 10
STU | Crafting | 15
STU | Projects | 20
VWX | [NAME] | 25
VWX | Education | 30
VWX | Education | 5
VWX | Crafting | 10
VWX | Projects | 15
VWX | [NAME] | 20
VWX | Education | 25
VWX | Education | 30
VWX | Crafting | 5
Spreadsheet: 142695d1329973390-excel-2007-percent-and-profit-formula-help-and-profit
Sheet: Sheet1

Sale |  | commission
1
2
3
4 | my sale | 0.75
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25 | 25th sale |  | bonus
26
27
28
29
30
Spreadsheet: 0fde8eec-df6f-4252-addb-d86ef7175203
Sheet: Sheet1

For more information contact [NAME] (337) 475-5378,   [EMAIL], or on the web at  www.mcneese.edu/hhp | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4
Fall |  | 1st year | Spring
BIOL 100 Medical Terminology $ | 2 |  | BIOL 225 - Anatomy & Physiology $ | 4
ENGL 101 - English Composition | 3 |  | ENGL 102 - English Composition | 3
FFND 101 - Freshman Foundations | 1 |  | MATH 170 or 231 | 3
MATH 113 - College Algebra | 3 |  | PSYC 101 - Psychology | 3
ORIN 101 - Orientation | 0 |  | HHP 124 -  Exercise Fit/Nutrition $ | 3
HHP 111 - First Aid / CPR $ | 2 |  | HHP 285 - Taping - Athletic Injuries $ | 2
HHP 115 - Intro to Athletic Training † | 2
HHP 122  Personal/Comm Health $ | 3 |  | Application Deadline April 30*
Semester hours | 16 |  | Semester hours | 18
Fall |  | 2nd year | Spring
BIOL 226 - Anatomy & Physiology II | 4 |  | NFSC 131 - Science Nutrition | 3
ENGL (201,202, or 221) | 3 |  | HHP 244 - Drugs & Society | 3
HHP 235 - Care & Prevent Ath Injuries | 3 |  | HHP 355 - Athletic Injury Asses. LE | 3
HHP 331 - Kinesiology | 3 |  | HHP 375 - Therapeutic Modalities | 3
HHP 288 - ATEP Clinical I | 2 |  | HHP 388 - ATEP Clinical II | 3
Semester hours | 15 |  | Semester hours | 15
Fall |  | 3rd Year | Spring
MAAP | 0
CSCI 241 - Computer Science | 3 |  | PHSC Elective - Physical Science | 3
HIST (201 or 202) - History | 3 |  | SOCI 201 - Sociology | 3
HHP 346 - Comm/Noncomm Disease | 3 |  | HHP 415 - TheraEx & Rehab Ath Inj | 3
HHP 365 - Athletic Injury Asses. UE | 3 |  | HHP 465 - Gen Med Conditions Athletic | 3
HHP 389 - ATEP Clinical III | 3 |  | HHP 488 - ATEP Clinical IV | 3
Semester hours | 15 |  | Semester hours | 15
 |  | 4th Year
Fall |  |  | Spring
COMM (201 or 205) - Speech | 3 |  | ART elective | 3
HHP 400 - Research Methods | 1 |  | HHP 401 - Independent Studies | 1
HHP 345 - Physiology of Exercise | 3 |  | HHP 435 - Fit/Ex Prescription | 3
HHP 430 - Advanced Athletic Training | 3 |  | HHP 490 - ATEP Clinical VI | 3
HHP 489 - ATEP Clinical V | 3 |  | HHP 495 - Preparatory BOC Exam | 3
Semester hours | 13 |  | Semester hours | 13
$ Bold Required Courses
* Application Deadline
† ATEP Application Process Begins |  |  | Total Hours | 120
HHP 288 - Pre-requisites
BIOL 100
BIOL 225
HHP 111
HHP 115
HHP 122
HHP 124
HHP 285
HHP 388 - Pre-requisites
BIOL 226
HHP 235
HHP 331
HHP 288
HHP 389 - Prerequisites
[NAME] 131
HHP 244
HHP 355
HHP 375
HHP 388
HHP 488 - Pre-requisites
HHP 346
HHP 365
HHP 389
HHP 489 - Pre-requisites
HHP 415
HHP 465
HHP 488
HHP 490 - Pre-requisites
HHP 400
HHP 345
HHP 430
HHP 489
Spreadsheet: 05dbc24e-141d-4751-b3fa-d39b76ad2742
Sheet: Sheet1

UNIVERSITY OF ARKANSAS AT PINE BLUFF | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8
PINE BLUFF, ARKANSAS 71601
REQUEST FOR USE OF MOTOR VEHICLES
 |  |  |  |  | Date of Request:
Name of Person(s) to Travel:
Driver(s) and License No(s).:
Give date and place of meeting, name of organization, and special purpose for making this trip:
Point of Travel |  |  |  | Stop over Points
Vehicle Type: |  | [NAME]: |  |  | Date(s) of Travel:
Vehicle pick up date and time prior to departure:
Hour of Departure |  |  | AM | PM |  | Return | AM | PM
Approximate number of days it will be necessary to be absent from instructional or Administrative duties:
 |  | Approximate Cost of Trip:  (This amount not to be exceeded)
 |  |  | Miles at |  | per mile |  | 0
 |  |  | Days at |  | per day |  | 0
 | *CDL Driver |  | Hours at |  | per hour |  | 0
 |  |  |  |  | Total |  | 0
*Include room and board, if necessary
Account Name:
Account Number:
 |  |  |  | Signature |  |  |  | Date
Grant/Project Budget Officer Approval:
Department Head Approval:
ORSP Grant Budget Officer Approval:
Finance & Adm. Budget Officer Approval :
[NAME]/Director [NAME]:
Motor Pool Supervisor Approval:
[NAME] |  |  |  |  | Ending Odometer Reading:
Vehicle and License Number |  |  |  |  | [NAME]
Price Schedule
[NAME]:  All vehicles (Van, Truck, SW, SUV, Car) |  |  |  |  | $25.00 per day |  | $0.50 per mile
[NAME] will cost $15.00 per hour driving time, plus room and board, if necessary.
Vehicle request should be submitted 2 weeks prior to the date of travel.  SAFHS personnel, must submit request at least 3 days prior to date of travel to guarantee availability of SAFHS vehicles.
elb:lc 04/07/2009
Spreadsheet: 479060d1473195410-vlookup-multiple-criteria-drop-downs-2
Sheet: Sheet1

Category | Sinks | Faucets | Accessories |  | Stainless | Porcalain | Silgranite | Cast_Iron
Sinks | Stainless | Bristol_F | Grids |  | Bristol_S | Bristol_P | Bristol_G | kindred_CI
Faucets | Porcalain | Delta_F | Basket Strainer |  | Teco_S | Teco_P | Teco_G
Accessories | Silgranite | [NAME] | Soap pump |  | Kindred_S | Kindred_P | Blanco_G
 | Cast_Iron | Teco_F | [NAME] shelves |  | Franke_S | Franke_P
 |  | Artisan_F |  |  | Blanco_S | Blanco_P
 |  |  |  |  | Artisan_S | Am_Standard_P
 |  |  |  |  |  | Artisan_P |  |  |  | w | d | count
Sinks | Stainless | Teco_S | Teco T05 topmount
 |  |  |  |  |  |  |  |  | Bristol_S | sinks!A:C
 |  |  |  |  |  |  |  |  | Teco_S | sinks!D:F
Spreadsheet: 882986d1731457734-index-rand-ignore-matching-qa-order-demo
Sheet: English

QA Order | Employee Name | Workgroup | Team | Evaluator | Column2
1 | A | CSR_E | ETM1
2 | A | CSR_E | ETM1
3 | A | CSR_E | ETM1
4 | A | CSR_E | ETM1
5 | A | CSR_E | ETM1
6 | A | CSR_E | ETM1 | ETM1
1 | B | TSS_E | [NAME]
2 | B | TSS_E | [NAME]
3 | B | TSS_E | [NAME]
4 | B | TSS_E | [NAME]
5 | B | TSS_E | [NAME]
6 | B | TSS_E | [NAME] | [NAME]
1 | C | TSS_E | ETM3
2 | C | TSS_E | ETM3
3 | C | TSS_E | ETM3
4 | C | TSS_E | ETM3
5 | C | TSS_E | ETM3
6 | C | TSS_E | ETM3 | ETM3
1 | D | TSS_E | ETM4
2 | D | TSS_E | ETM4
3 | D | TSS_E | ETM4
4 | D | TSS_E | ETM4
5 | D | TSS_E | ETM4
6 | D | TSS_E | ETM4 | ETM4
Spreadsheet: 140416d1328737708-excel-2007-help-to-add-an-extra-criteria-to-a-set-of-concatenate-formulas-without-v-or-h-lookup-2dlookup
Sheet: Type2

 | Lookup Table
 |  | RED | AMBER | GREEN
RED |  | RED | AMBER | AMBER
AMBER |  | AMBER | GREEN | GREEN
GREEN |  | AMBER | GREEN | GREEN
Inputs
Type A | Type B | Result
RED | RED
RED | GREEN
RED | AMBER
RED
GREEN | RED
GREEN | GREEN
GREEN | AMBER
GREEN
AMBER | RED
AMBER | GREEN
AMBER | AMBER
AMBER
Spreadsheet: 08172964-84f9-4cec-8ed6-dd8dc6c970ca
Sheet: 1RD

1RD RANKING OUT OF 227
ANON# | RANK
3617 | 36
3743 | 20
4181 | 48
4325 | 10
4913 | 43
5650 | 12
5670 | 8
5710 | 1
5749 | 41
5872 | 43
6098 | 24
6271 | 52
6308 | 45
6504 | 5
6720 | 54
7621 | 16
16664 | 11
16690 | 49
16807 | 49
16815 | 32
23108 | 53
23451 | 38
24078 | 20
24318 | 29
46244 | 22
46307 | 36
46339 | 2
46399 | 8
46442 | 4
46478 | 57
46486 | 19
46771 | 22
46946 | 57
53001 | 57
53036 | 24
53242 | 31
53563 | 18
69363 | 24
69375 | 45
69387 | 34
69903 | 12
69912 | 7
69975 | 42
76085 | 2
76124 | 54
76128 | 12
76191 | 17
76349 | 34
76714 | 28
82520 | 49
82590 | 33
82643 | 6
82905 | 47
82940 | 12
82980 | 24
83028 | 39
99247 | 39
99601 | 29
99796 | 57
99814 | 56
Spreadsheet: 2221c500-1a35-40aa-b8bc-d710e0f6acb9
Sheet: 9d

Product | 0-3** | 3-6 | 6-12 | 12-24 | 24-60 | 60-120 | 120+ | TOTAL
Basis* | 840163 | 1001056 | 758141 | 2192096 | 1483779 | 546278 | 221809 | 7043322
FRA | 14834159 | 8622919 | 4743398 | 4175156 | 62769 |  |  | 32438401
IRS- [NAME] | 6591597 | 5718923 | 5308011 | 21297366 | 33189663 | 26856866 | 13316089 | 112278515
OIS | 7350965 | 4889542 | 2402529 | 4566586 | 22700 |  |  | 19232322
VNS | 455 | 420 | <100 | <100 | 787 | 1094 | 1213 | 3969
Zero Coupon | 56023 | 26171 | 23832 | 44915 | 31012 | 6971 | 33067 | 221991
TOTAL | 29673362 | 20259033 | 13235912 | 32276118 | 34790710 | 27411210 | 13572179 | 171218524
Estimated gross notional amount outstanding, December 21 weekly snapshot, by product type and tenor (millions of USD), cleared swaps only, all participant types and tenors. Data are adjusted for double-counting of cleared trades to show a single exposure for each trade.
Total(s) may be less than total shown for this asset class in the All Swaps reports and/or other CFTC Swaps Report tables if products were excluded from this table that (a) presented disclosure risk or (b) did not belong in the categories shown. See Explanatory Notes for more detail.
*See Data Dictionary for product descriptions.
**See Explanatory Notes for tenor bucket descriptions.
The Commission requests feedback on the format and content of this CFTC Swaps Report table. Submit comments to [EMAIL].
Spreadsheet: 138032d1327492022-excel-2007-multiple-count-from-a-number-of-cells-into-1-cell-downanddegradedcount
Sheet: Sheet1

MONTH | ACTION | ACTIONS | type | PLACEMENT
JAN | OPERATOR | 1 | DOWN | Front
JAN | OUTSOURCE | 2 | DEGRADED | back |  |  | Count of type | Column Labels
JAN | REPAIR | 1 | DOWN | Front |  |  | Row Labels | DEGRADED | DOWN | Grand Total
JAN | REPAIR | 2 | DEGRADED | back |  |  | JAN | 4 | 3 | 7
JAN | OPERATOR | 1 | DEGRADED | Front |  |  | FEB |  | 1 | 1
JAN | OUTSOURCE | 2 | DOWN | back |  |  | MAR | 1 |  | 1
FEB | REPAIR | 1 | DOWN | Front |  |  | Grand Total | 5 | 4 | 9
JAN | REPAIR | 1 | DEGRADED | back
MAR | REPAIR | 1 | DEGRADED | back
How many Calls in:
 | January that are Down & degraded, with 1 as an action, and are placed in the Front
How do i add all the scenarios together to get the right answer in 1 formula???
solution
3
I am currently using this formula, but When I have added the red bits it now doesn't work?!!?
IF(B83=0,"",(SUMPRODUCT(('Raw Data'!$B$2:$B$2000='Response Time'!A83)*('Raw Data'!$P$2:$P$2000="Operator")*('Raw Data'!$AS$2:$AS$2000=1))+SUMPRODUCT(('Raw Data'!$B$2:$B$2000='Response Time'!A83)*('Raw Data'!$P$2:$P$2000="Repair")*('Raw Data'!$AS$2:$AS$2000=1))+SUMPRODUCT(('Raw Data'!$B$2:$B$2000='Response Time'!A83)*('Raw Data'!$N$2:$N$2000="DOWN")*('Raw Data'!$AS$2:$AS$2000=1))+SUMPRODUCT(('Raw Data'!$B$2:$B$2000='Response Time'!A83)*('Raw Data'!$AD$2:$AD$2000="FRONT")*('Raw Data'!$AS$2:$AS$2000=1))))
Spreadsheet: 70658d1271358941-npv-vs-manual-npv-npv
Sheet: Sheet2

 | Indexation | 0.02
 | Yield | 0.1 | Exit | 1000
 | Disc Date | 2010-01-01 00:00:00
 | [NAME] | 0.12
 |  |  | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10
 |  |  | 2010-01-01 00:00:00 | 2011-01-02 00:00:00 | 2012-01-03 00:00:00 | 2013-01-04 00:00:00 | 2014-01-05 00:00:00 | 2015-01-06 00:00:00 | 2016-01-07 00:00:00 | 2017-01-08 00:00:00 | 2018-01-09 00:00:00 | 2019-01-10 00:00:00
 |  |  | 100 | 102 | 104.04 | 106.1208 | 108.243216 | 110.40808032 | 112.6162419264 | 114.868566764928 | 117.1659381002266 | 119.5092568622311
 |  | Exit |  |  |  |  |  |  |  |  |  | 1195.092568622311
 |  | CF for NPV | 100 | 102 | 104.04 | 106.1208 | 108.243216 | 110.40808032 | 112.6162419264 | 114.868566764928 | 117.1659381002266 | 1314.601825484542
 |  | CAGR | 0.02000000000000002
 |  | NPV | 992.3042435531031
 |  | NPV | 100 | 91.04315623402385 | 82.8885629705288 | 75.44093662625131 | 68.68380979704894 | 62.53190826100706 | 56.93102293418512 | 51.81570942148669 | 47.17465728237195 | 472.4422662570073
 |  | Total NPV | 1108.952029783911
 |  | Difference | -116.6477862308079
Spreadsheet: 92958d1292472511-excel-2007-lookup-value-and-return-corresponding-values-in-the-row-forum-template
Sheet: Sheet1

Output
D23
D10
D15
D18
[NAME]
D25
D4
D2
D28
D28
D28
D28
D28
D1
D2
[NAME]
D13
D9
D33
D14
D30
D25
D2
D37
D14
D9
D20
D32
D23
D16
D3
D29
D11
D11
Spreadsheet: 148179d1333224262-formula-for-register-long-and-shorts-register-long-shorts
Sheet: Totals

Cashier | Quantity | Dollar Total
1
2
3
4
5
6
Spreadsheet: 7e427e41-58c9-4cc8-981b-3231bf28cbac
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
TIAG | Test | TIAG | Tiagabine Concentration, Serum | Tiagabine, S | Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) | Serum Gel Tube: SGT |  |  | 1 | mL | Collection Container/Tube: Red top  Submission Container/Tube: Plastic vial [NAME]: 1 mL Collection Instructions:  1. Serum for a peak concentration should be drawn 1 hour after administration of oral tiagabine. Serum for a trough concentration is usually drawn 30 minutes before dose.  2. Label specimen appropriately (peak or trough). | 0.6 mL | Hemolysis	Mild OK; Gross reject Thawing	Warm reject; Cold OK - refreeze Lipemia	Mild OK; Gross reject Icterus	Mild OK; Gross reject Other	Serum gel tube | Peak: 110-520 ng/mL Trough: 5-35 ng/mL | Monday, Wednesday | 2 days | 5 days | 1 | 80299 |  |  |  |  | Rochester
TIAG | [NAME] | SRED | Serum Red |  |  |  | Frozen | 7 days
TIAG | Result | 82524 | Tiagabine, S | Tiagabine, S | Numeric |  |  |  |  | ng/mL |  |  |  |  |  |  |  |  |  | 21565-7 | Tiagabine:MCnc:Pt:Ser/Plas:Qn:
Spreadsheet: 741997d1627475847-index-match-formula-how-to-return-subsequent-values-beneath-first-row-feas
Sheet: ANNUAL OUTPUT

 | 1 | 2 | 3 | 4 | 5
Expenses | <12 Months ($[NAME]) | 12-24 Months ($[NAME]) | 24-36 Months ($[NAME]) | 36-48 Months ($[NAME]) | 48-60 Months ($[NAME])
Advertising & Marketing
Metro | 9.09 | 7.76 | 8.53 | 6.83 | 3.52
Regional | 3.44 | 3.17 | 4.28 | 4.29 | 0.88
Electricity
Metro | 2.46 | 2.43 | 3.2 | 3.2 | 3.82
Regional | 1.24 | 1.8 | 1.78 | 1.4 | 1.72
Insurance
Metro | 3.9 | 3.03 | 2.66 | 2.58 | 1.92
Regional | 2.56 | 2.53 | 1.79 | 2.18 | 2.04
Land Tax
Metro | 12.64 | 10.55 | 13.08 | 10.1 | 9.58
Regional | 7.06 | 5.64 | 2.02 | 2.2 | 1.85
Printing & Postage
Metro | 0.54 | 0.72 | 0.83 | 0.87 | 0.79
Regional | 0.43 | 0.68 | 0.61 | 0.4 | 1.18
Rates
Metro | 6.69 | 6.26 | 6.89 | 6.25 | 6.49
Regional | 6.33 | 5.69 | 3.95 | 4.78 | 3.42
R&M
Metro | 5.82 | 4.78 | 4.96 | 3.38 | 3.71
Regional | 3.49 | 3.15 | 2.44 | 1.95 | 1.6
Salaries & Wages
Metro | 24.64 | 21.86 | 24.68 | 25.84 | 26.96
Regional | 16.95 | 16.87 | 19.49 | 16.63 | 23.15
Telco
Metro | 1.21 | 1.19 | 1.18 | 0.9 | 1.22
Regional | 0.62 | 0.55 | 0.77 | 0.82 | 0.77
Spreadsheet: 803001d1667212785-to-calculate-summary-of-total-qty-based-on-leadtime-copy-of-copy1-of-example-of-coloring-and-formulas-to-counting-the-input-date-within-the-lt
Sheet: Sheet4

 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Helper Table |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Expected Rsult
 |  |  | Delivery Date |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | ETA LeadTime Based |  |  | ETA Date |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | ETA
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | W45 | W46 | W47 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | W45 | W46 | W47
 | Area | Lead Time (days) | 2022-10-31 00:00:00 | 2022-11-01 00:00:00 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 2022-10-31 00:00:00 | 2022-11-01 00:00:00
 | A | 4 | 10 | 10 | 10 |  | 10 |  |  | 10 | 10 |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 10 | 10 | 10 |  | 10 |  |  | 10 | 10
 | B | 2 | 10 | 10 | 10 | 10 |  |  |  | 10 |  | 10 | 10 |  |  |  |  |  |  |  |  |  |  | 10 | 10 | 10 | 10 |  |  |  | 10 |  | 10 | 10
 | C | 3 |  | 10 |  |  | 10 |  |  | 10 |  | 10 | 10 |  |  |  |  |  |  |  |  |  |  |  |  | 10 |  |  | 10 |  |  | 10 |  | 10 | 10
Spreadsheet: 844554d1696336995-sum-or-sumproduct-or-with-dates-in-the-table-headers-products-sold-
Sheet: Blad1

Continent | Country | Product | 1-10-2023 | 2-10-2023 | 3-10-2023 | 4-10-2023 | 5-10-2023 | 6-10-2023 | 7-10-2023 | 8-10-2023 | 9-10-2023 | 10-10-2023
Northern Europe | UK | Apples | 1 | 4 | 1 | 4 | 4 | 4 | 2 | 3 | 1 | 2
Northern Europe | UK | Oranges | 4 | 2 | 1 | 4 | 3 | 3 | 2 | 2 | 3 | 1
Northern Europe | UK | Bananas | 4 | 2 | 2 | 4 | 2 | 4 | 1 | 2 | 2 | 3
Northern Europe | UK | Pears | 3 | 2 | 4 | 4 | 1 | 2 | 2 | 2 | 2 | 1
Northern Europe | UK | Strawberries | 2 | 2 | 2 | 2 | 4 | 4 | 4 | 3 | 3 | 3
Northern Europe | UK | Pineapples | 2 | 4 | 2 | 1 | 3 | 1 | 4 | 0 | 1 | 3
Northern Europe | UK | Melons | 4 | 3 | 4 | 4 | 1 | 1 | 2 | 1 | 1 | 0
Northern Europe | DE | Apples | 3 | 0 | 1 | 3 | 3 | 4 | 3 | 2 | 2 | 2
Northern Europe | DE | Oranges | 2 | 4 | 2 | 2 | 4 | 0 | 3 | 4 | 1 | 4
Northern Europe | DE | Bananas | 1 | 1 | 3 | 4 | 1 | 2 | 4 | 3 | 2 | 1
Northern Europe | DE | Pears | 3 | 3 | 4 | 2 | 1 | 4 | 2 | 1 | 4 | 1
Northern Europe | DE | Strawberries | 4 | 2 | 1 | 1 | 3 | 2 | 3 | 3 | 2 | 2
Northern Europe | DE | Pineapples | 2 | 1 | 3 | 0 | 4 | 1 | 1 | 1 | 2 | 3
Northern Europe | DE | Melons | 1 | 2 | 4 | 2 | 2 | 3 | 3 | 0 | 2 | 1
Southern Europe | IT | Apples | 3 | 1 | 1 | 2 | 4 | 1 | 2 | 2 | 3 | 4
Southern Europe | IT | Oranges | 2 | 3 | 4 | 1 | 3 | 4 | 4 | 1 | 2 | 4
Southern Europe | IT | Bananas | 4 | 1 | 2 | 4 | 3 | 1 | 3 | 1 | 1 | 4
Southern Europe | IT | Pears | 4 | 4 | 4 | 2 | 2 | 2 | 2 | 2 | 4 | 1
Southern Europe | IT | Strawberries | 4 | 0 | 4 | 1 | 1 | 3 | 1 | 1 | 3 | 2
Southern Europe | IT | Pineapples | 2 | 4 | 4 | 3 | 2 | 4 | 3 | 4 | 4 | 0
Southern Europe | IT | Melons | 3 | 3 | 4 | 0 | 4 | 3 | 1 | 2 | 3 | 4
Southern Europe | FR | Apples | 3 | 3 | 1 | 1 | 1 | 4 | 1 | 2 | 4 | 2
Southern Europe | FR | Oranges | 2 | 2 | 2 | 4 | 3 | 4 | 1 | 1 | 4 | 4
Southern Europe | FR | Bananas | 2 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 4 | 2
Southern Europe | FR | Pears | 4 | 1 | 3 | 2 | 2 | 2 | 2 | 4 | 4 | 4
Southern Europe | FR | Strawberries | 3 | 2 | 1 | 3 | 4 | 1 | 4 | 3 | 3 | 1
Southern Europe | FR | Pineapples | 4 | 1 | 2 | 1 | 4 | 3 | 2 | 2 | 4 | 1
Southern Europe | FR | Melons | 4 | 3 | 3 | 3 | 4 | 3 | 4 | 1 | 3 | 4
This is part of a table with products sold in many different countries. What formula would I need to sum the total amount of Apples sold in DE and FR in week 40 (2-8 okt 2023), or f.i. the total amount of Oranges sold in FR, IT and UK.
This list goes on for each day of the year and has eventually be summerised in month totals as well.
Spreadsheet: 160444d1339086705-count-occurrences-and-fill-cells-with-results-using-a-date-range-samplewb3
Sheet: Summary

Title
Area taken by other data
Area taken by other data |  |  |  | Whole Data | 35 |  | Defined List by Group (i.e. 06)
 |  |  |  | Area taken by other data |  |  | CodeID | # OCC | % OCC
Spreadsheet: 269393d1381153153-unique-list-upload_forum_list
Sheet: Sheet1

ubs
Sedol | Current PB | Current Actual Rate(%) |  |  |  | List
B734XQ | CS | 13.6202
B3Z5T1 | MS | 1.73
651053 | DB | 15
651053 | DB | 15
296817 | CITI | 11.74
309272 | DB | 1
234539 | RBC | 3.2313
B0B8Z1 | DB | 4.25
B0B8Z1 | DB | 4.25
B3Q1YB | MS | 30
B020QY | CS | 3
B0CC0M | CS | 3
B7WJ1F | RBC | 1.6229
690777 | UBS | 10
B4N97J | MS | 3.8792
B0B8Z1 | DB | 4.2385
B19NKB | DB | 1.75
660239 | UBS | 12
B7WJ1F | RBC | 1.9623
268565 | GS | 1.15
284825 | CITI | 5.74
671871 | DB | 2.75
B1PMQ6 | RBC | 1.0371
234539 | RBC | 3.2172
211590 | MS | 41.5859
774212 | JPM | 4
B1VZ48 | CITI | 0.9
B018BZ | UBS | 6
Spreadsheet: 849702d1700114922-stuck-with-sum-using-index-match-or-vlookup-total-shifts
Sheet: Duty Rota

DEPARTMENT: |  |  |  |  |  |  |  |  | MONTH/YEAR: |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | THR | TWD | OFF
NO | Name of Staff | ERP # | DESIGNATION | WE | TH | FR | SA | SU | MO | TU | WE | TH | FR | SA | SU | MO | TU | WE | TH | FR | SA | SU | MO | TU | WE | TH | FR | SA | SU | MO | TU | WE | TH
 |  |  |  | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |  |  |  | Day | Afternoon | Total Days Worked | Total Hrs
1 |  |  |  | D1 |  |  |  |  |  |  |  |  |  | D2
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Leave Legends
AL | Annual Leave
BL | Birthday Leave
APL | Appreciation Leave
CME | [NAME]
SL | [NAME]
WO | Weekly Leave
PH | Public Holiday
CO | Compensatory Off
EL | Emergency Leave
OL | On [NAME]
ML | Maternity Leave
PL | Parental Leave
LOP | Loss Of Pay
WO/PH | Week Off & Ph Same Day
MI | Mission
Spreadsheet: 730672d1619823226-find-the-percentage-a-cell-is-in-a-time-range-and-the-average-time-within-each-5x20-strategy
Sheet: RESULTS

 | TIME TRADED %
 | Average
 | 9:30-9:50
 | 9:50-10:30
 | 10:30-12:00
 | 12:00-4:00
 | GAIN/LOSS
 | Total Trading Days Span | 53
 | Trading Days
 | Wins
 | Total /share
 | Average /share
 | Win/Loss %
 | ENTRY REASON %
 | PM CONSOLIDATION
 | R2G
 | MOMO
 | CONSOLIDATION
 | PMH
 | BO
 | HIGHS %
 | Entry-High Ave |  |  | Percentage High above Entry
 | High-Close Ave |  |  | Percentage High above Close
Spreadsheet: 698379d1601966367-update-an-original-list-only-with-new-elements-from-other-source-s-stock-list-2
Sheet: Sheet1

Table 1 |  | Table 2 |  | Table 3
Original products list |  | Inventory list |  | Updated products list
PRODUCT |  | PRODUCT |  | PRODUCT
1 |  | 2 |  | 1
2 |  | 5 |  | 2
3 |  | 7 |  | 3
4 |  | 9 |  | 4
5 |  | 10 |  | 5
6 |  | 12 |  | 6
7 |  | 13 |  | 7
8 |  | 14 |  | 8
9 |  | 15 |  | 9
10 |  |  |  | 10
11 |  |  |  | 11
 |  |  |  | 12
 |  |  |  | 13
 |  |  |  | 14
 |  |  |  | 15
Spreadsheet: 293104d1390896071-goal-seeker-random-numbers-equal-a-number
Sheet: Sheet2

 |  |  |  |  |  | 0.1
 |  |  |  |  |  | 0.1
 |  |  |  |  |  | 0.1
 |  |  |  |  |  | 0.1
 |  |  |  |  |  | 0.05
 |  |  |  |  |  | 0.05
 |  |  |  |  |  |  |  |  | Number to achieve: | 7
 |  |  |  |  |  |  |  |  | Sum random numbers:
 |  |  |  |  |  |  |  |  | Factor:
 |  |  |  |  | http://www.excelforum.com/excel-general/984744-goal-seeker.html
Spreadsheet: 765337d1643285001-how-to-use-vlookup-for-multiples-rows-test-1-
Sheet: Sheet1

ID | ID No. | Employee Name |  |  | ID |  | ID No. | Employee Name
imti | 78 | Md.Imtiaz |  |  | [NAME]
imti | 72 | [NAME] |  |  | [NAME]
[NAME] | 96 | Md. Nasir |  |  | [NAME]
[NAME] | 100 | [NAME]
[NAME] | 17 | Anika |  |  | ID |  | ID No. | Employee Name
nabi | 47 | Masrur |  |  | nabi
nabi | 71 | Afroza |  |  | nabi
nabi | 81 | Mafuz |  |  | nabi
nabi | 90 | Md. [NAME] |  |  | nabi