tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 773602d1647953246-how-to-lookup-a-value-with-two-criteria-cars
Sheet: Sheet1

 |  |  | Price |  |  |  |  | I need a way to select the price for a combination of Year and Item
 |  |  | Item | B21* | F21*
 |  |  | Cars | 1 | 4
 |  |  | Bikes | 2 | 5
 |  |  | Scooters | 3 | 6
 |  |  | Transactios |  |  |  |  | Desired Result
 |  |  | Item | Qty | Year | Price |  | Price
 |  |  | Cars | 10 | B21* |  |  | 1
 |  |  | Bikes | 20 | F21* |  |  | 5
 |  |  | Bikes | 30 | F21* |  |  | 5
 |  |  | Scooters | 40 | F21* |  |  | 6
 |  |  | Bikes | 50 | B21* |  |  | 2
Spreadsheet: 868102d1714824423-better-alternatives-to-lookup-example
Sheet: Software export

ID | name
ID_1 | Name 1
ID_2 | Name 2
ID_3 | Name 3
ID_4 | Name 4
ID_5 | Name 5
ID_6 | Name 6
ID_7 | Name 7
ID_8 | Name 8
ID_9 | Name 9
ID_10 | Name 10
Spreadsheet: kenneth_lay__19489__2001 Checking Account
Sheet: Sheet1

2001-01-03 00:00:00 | Beginning Bal. |  |  | 63.3
January Activity |  | Service Charges |  | -20
 |  | Bank Fees |  | -12
 |  | January Dues Deposits |  | 4000
February Activity |  | Pye-2000 Christmas Party |  | -62.63
 |  | Lorenzo-2000 Christmas Party |  | -1010.99
 |  | February Dues Deposits |  | 5600
 |  | Bank Fee |  | -2
 |  |  |  | 8600
March Activity |  | March Dues Deposits |  | 4200
 |  |  |  | 1400
 |  | Bank Fee |  | -2
April Activity |  | Dannenbaum-March dinner |  | -1060.3
 |  | Dussler-April dinner |  | -1591.66
 |  | Check order |  | -22.39
 |  | Bank Fee |  | -2
May Activity |  | Bank Fee |  | -2
June Activity |  | Klein-May dinner |  | -996.42
 |  | Pye-June dinner |  | -907.8
 |  | Bank Fee |  | -2
July Activity |  | Bank Fee |  | -2
August Activity |  | Bank Fee |  | -2
September Activity |  | Bank Fee |  | -2
October Activity |  | [NAME] dinner |  | -1665.33
 |  | Bank Fee |  | -2
Nov. 12, 2001 | Ending Bal.
Spreadsheet: 486225d1477391601-sort-data-smallest-to-largest-with-formula-index-match-sample
Sheet: Sheet2

LIST 1 | LIST 2 |  | TWO COLUMNS
A | BB
A | CC
B | CC |  |  |  |  |  |  |  |  |  |  |  |  | ONE COLUMN
C | D |  |  |  |  |  |  |  |  |  |  |  |  | A | BB
C | E |  |  |  |  |  |  |  |  |  |  |  |  | A | CC
 |  |  |  |  |  |  |  |  |  |  |  |  |  | B | CC
 |  |  |  |  |  |  |  |  |  |  |  |  |  | C | D
 |  |  |  |  |  |  |  |  |  |  |  |  |  | C | E
Spreadsheet: 720314d1614101642-looking-for-a-possible-simpler-solution-formula-to-calculate-cashflow-from-customers-monthly-revenues-v2
Sheet: Sheet1

Customer | Start Date | Payment |  |  | Assume revenue growth for each 'Trading' month (Not Calendar month) is as follows:
Customer 1 | 2021-01-10 00:00:00 | 10 |  |  | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 | Month 6 | Month 7 | Month 8 | Month 9 | Month 10 | Month 11 | Month 12
Customer 2 | 2021-02-08 00:00:00 | 10 |  |  | 10 | 11 | 12.1 | 13.31 | 14.641 | 16.1051 | 17.71561000000001 | 19.48717100000001 | 21.43588810000001 | 23.57947691000001 | 25.93742460100002 | 28.53116706110002
Customer 3 | 2021-02-21 00:00:00 | 10
Customer 1 | 2021-03-10 00:00:00 | 11
Customer 2 | 2021-03-11 00:00:00 | 11 |  |  | 2021-01-01 00:00:00 | 2021-02-01 00:00:00 | 2021-03-01 00:00:00 | 2021-04-01 00:00:00 | 2021-05-01 00:00:00 | 2021-06-01 00:00:00 | 2021-07-01 00:00:00 | 2021-08-01 00:00:00 | 2021-09-01 00:00:00 | 2021-10-01 00:00:00 | 2021-11-01 00:00:00 | 2021-12-01 00:00:00
Customer 4 | 2021-03-11 00:00:00 | 10 |  | Trading customers
Customer 3 | 2021-03-20 00:00:00 | 11 |  | Monthly revenue
Customer 2 | 2021-04-20 00:00:00 | 12.1 |  | Cum Revenue
Customer 3 | 2021-04-20 00:00:00 | 12.1
Customer 1 | 2021-05-10 00:00:00 | 12.1
Customer 3 | 2021-05-14 00:00:00 | 13.31 |  | Revenue pm
Customer 4 | 2021-05-16 00:00:00 | 11 |  |  | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Grand Total
Customer 2 | 2021-05-19 00:00:00 | 13.31 |  | Customer 1 | 10 |  | 11 |  | 12.1 |  |  | 13.31 |  | 14.641 |  |  | 61.05100000000001
Customer 2 | 2021-06-16 00:00:00 | 14.641 |  | Customer 2 |  | 10 | 11 | 12.1 | 13.31 | 14.641 |  |  | 16.1051 | 17.71561000000001 | 19.48717100000001 |  | 114.358881
Customer 4 | 2021-07-20 00:00:00 | 12.1 |  | Customer 3 |  | 10 | 11 | 12.1 | 13.31 |  |  | 14.641 | 16.1051 |  |  | 17.71561000000001 | 94.87171000000001
Customer 1 | 2021-08-10 00:00:00 | 13.31 |  | Customer 4 |  |  | 10 |  | 11 |  | 12.1 |  | 13.31 |  | 14.641 |  | 61.05100000000001
Customer 3 | 2021-08-20 00:00:00 | 14.641 |  | Grand Total | 10 | 20 | 43 | 24.2 | 49.72000000000001 | 14.641 | 12.1 | 27.95100000000001 | 45.52020000000001 | 32.35661000000001 | 34.12817100000001 | 17.71561000000001 | 331.332591
Customer 2 | 2021-09-02 00:00:00 | 16.1051
Customer 3 | 2021-09-14 00:00:00 | 16.1051
Customer 4 | 2021-09-24 00:00:00 | 13.31
Customer 2 | 2021-10-04 00:00:00 | 17.71561000000001
Customer 1 | 2021-10-10 00:00:00 | 14.641
Customer 4 | 2021-11-08 00:00:00 | 14.641
Customer 2 | 2021-11-28 00:00:00 | 19.48717100000001
Customer 3 | 2021-12-14 00:00:00 | 17.71561000000001
Checksum
Spreadsheet: 750522d1633507658-calculate-downtime-while-some-time-ranges-are-excluded-sample_v2
Sheet: Sheet1

 | Repair Start Date | Repair Start Time | Repair End Date | Repair End Time |  | Start | End | Total Hours | Downtime Hours
 | 2021-09-02 00:00:00 | 11:40:00 | 2021-09-02 00:00:00 | 13:00:00
 | 2021-09-06 00:00:00 | 07:58:04 | 2021-09-06 00:00:00 | 08:58:04
 | 2021-09-07 00:00:00 | 01:00:00 | 2021-09-07 00:00:00 | 02:00:00
 | 2021-09-06 00:00:00 | 15:50:00 | 2021-09-06 00:00:00 | 16:20:00
 | 2021-09-08 00:00:00 | 02:10:00 | 2021-09-08 00:00:00 | 02:15:00
 | 2021-09-07 00:00:00 | 23:00:00 | 2021-09-07 00:00:00 | 23:45:00
 | 2021-09-08 00:00:00 | 00:00:00 | 2021-09-08 00:00:00 | 03:00:00
 | 2021-09-08 00:00:00 | 23:00:00 | 2021-09-09 00:00:00 | 03:00:00
 | 2021-09-09 00:00:00 | 06:25:28 | 2021-09-09 00:00:00 | 10:25:28
 | 2021-09-09 00:00:00 | 00:00:00 | 2021-09-09 00:00:00 | 07:00:00
 | 2021-09-08 00:00:00 | 20:00:00 | 2021-09-09 00:00:00 | 22:30:00
Spreadsheet: 02b4bbe9-bcf1-4fc7-8ff5-f9872ff603c1
Sheet: Questions

# | Specific Questions | Answer Choices | Additional Notes
1 | How well is the agency's inventory of both wireless devices and service plans known? | < Select> |  | INFORMATION PROVIDED IS FOR MARKET RESEARCH PURPOSES ONLY
2 | How are cellular products and services purchased today? | < Select>
3 | How does or will your agency manage wireless service costs and usage? | < Select>
4 | How many wireless devices are currently in-use agency wide? | < Select> | If more details are known, please enter on "Data" worksheet
5 | How important is pricing as a consideration for using the FSSI BPAs? | < Select>
6 | How will your service plans be divided among these plan types? | Must total 100%
 | -  Voice Only service plans |  | <--- Enter percent values for each type
 | - Voice and Data service plans
 | -  Data Only service plans
7 | How would you describe your agency's future wireless data needs and usage? | < Select>
8 | How would most of your employess rate the level of satisfaction from your leading cellular provider? | < Select>
9 | What is the average monthly cost per plan your Agency pays for wireless services? | < Select> | If more details are known, please enter on "Data" worksheet
10 | How significant are your security-related concerns with cellular technologies? | < Select>
11 | With what tools does or will your agency manage wireless service costs and usage? | < Select>
 | Open Ended Questions
12 | What are the most-requested cellular products and services employees are interested in?
13 | In general, what improvement would most employees like to see from their cellular provider?
14 | How soon is the agency ready to order on the FSSI Wireless BPAs?
15 | What wireless security requirements or concerns does your agency have?
Spreadsheet: a8b32823-f1e5-4f57-84d9-ebcc2d3552b7
Sheet: Sheet1

Securities | % of Assets
Veresen Inc. | 3.11%
Capital Power Corp | 2.86%
Crescent Point Energy Crp | 2.82%
Baytex Energy Corp. | 2.75%
Canadian Oil Sands Ltd | 2.73%
BCE Inc. | 2.61%
Enbridge Income Fund Hld | 2.60%
National Bank of Canada | 2.54%
Northland Power Inc | 2.49%
Inter Pipeline Ltd. | 2.43%
Pembina Pipeline Inc | 2.41%
Great-West Life Company | 2.39%
TransCanada Corp. | 2.35%
Bank of Montreal | 2.34%
Cdn Imperial Bk of Commrc | 2.32%
Keyera Corp | 2.26%
Emera Inc. | 2.25%
Power Financial Corp. | 2.25%
IGM Financial Inc. | 2.21%
Rogers Communications Inc | 2.21%
Freehold Royalties Ltd | 2.19%
Fortis Inc. | 2.19%
Power Corporation Canada | 2.18%
Royal Bank of Canada | 2.14%
Shaw Communicatn Cl B Nvs | 2.12%
Sun Life Financial Inc. | 2.10%
Bank of Nova Scotia | 2.06%
Potash Corp of Saskatchwn | 2.03%
Thomson Reuters Corp | 2.01%
[NAME] Cl B Nvs | 1.94%
Toronto-Dominion Bank | 1.89%
TELUS Corp. | 1.85%
Transcontinental Inc Cl A | 1.83%
ARC Resources Ltd | 1.76%
Wajax Corporation | 1.67%
Enbridge Inc. | 1.63%
AGF Management Ltd Cl B | 1.59%
Cineplex Inc. | 1.59%
Mullen Group Ltd. | 1.55%
Vermilion Energy Inc. | 1.54%
Husky Energy Inc. | 1.52%
Genworth MI Canada Inc | 1.46%
CI Financial Corp | 1.43%
Brookfield Property Ptner | 1.42%
Aimia Inc. | 1.22%
First Capital Realty Inc | 1.17%
Dorel Industries Inc Cl B | 1.11%
Ensign Energy Srvcs Inc | 0.93%
Teck Resources Ltd Cl B | 0.91%
Canexus Corporation | 0.62%
Cash | 0.38%
Total Holdings in Portfolio | 51
Spreadsheet: kate_symes__18258__Deal Correction 4-2
Sheet: 3-27

Date of Inquiry | Adjustment date | Date of applied calc | Deal Date | Desk Involved | Reason | Correction located by: | Broker | Change | Details of reason | Deal # | Delivery | Term | Hours p/op/rtc | MW | Price | Counterparty
 | 2001-03-27 00:00:00 | 2001-03-27 00:00:00 | 2001-03-26 00:00:00 | LTNW | KS | Houston | N/A | Adjusted hours for exercised options |  | 428861.2/426659.2 | Mid-C | J-01 | [NAME] | 25 | 65 | Snohomish
 | 2001-03-27 00:00:00 | 2001-03-27 00:00:00 | 2001-03-26 00:00:00 | LTSW | KS | MD | N/A | Adjusted hours for exercised options |  | 547682.2 | Palo Verde | J-01 | On [NAME] | 50 | 212 | El Paso
 | 2001-03-27 00:00:00 | 2001-03-27 00:00:00 | 2001-03-26 00:00:00 | STCA | JR | Houston | Prebon | Added late deals |  | 562162/ 164 | NP-15 | M-01 | On [NAME] | 25 | 260/261 | Duke/Calpine
 |  |  |  | Grand Count
Spreadsheet: c9ad3370-df5d-4114-8ba6-7c2dd79b0716
Sheet: Sheet1

Acute Myocardial Infarction Deaths, by Sex and Age, Maine, 2009 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6
 |  | Number | Crude Rate |  | Age-adjusted Rate
 |  |  | Rate | 95% CI | Rate | 95% CI
Maine Total |  | 569 | 43.2 | 39.7 - 46.9 | 33.4 | 30.7 - 36.3
US |  |  |  |  | 37.8 | 37.6 - 38.0
Gender
 | Male | 323 | 50.2 | 44.9 - 56.0 | 46 | 41.1 - 51.5
 | Female | 246 | 36.5 | 32.0 - 41.3 | 23.9 | 21.0 - 27.2
Age
 | Less than 45 | 10 | 1.4* | 0.7 - 2.6 | NApp | NApp
 | 45-64 | 115 | 28.7 | 23.7 - 34.4 | NApp | NApp
 | 65 and older | 444 | 215.9 | 196.3 - 237.0 | NApp | NApp
Acute Myocardial Infarction Deaths, by Year, Maine, 1999-2009
Year |  | Number of Deaths | Crude Rate |  | Age-adjusted Rate
 |  |  | Rate | 95% CI | Rate | 95% CI
1999 |  | 937 | 74 | 69.3 - 78.9 | 65.6 | 61.5 - 70.0
2000 |  | 886 | 69.4 | 64.9 - 74.1 | 60.6 | 56.6 - 64.7
2001 |  | 840 | 65.4 | 61.0 - 70.0 | 56.4 | 52.6 - 60.3
2002 |  | 813 | 62.8 | 58.6 - 67.3 | 53.7 | 50.0 - 57.5
2003 |  | 786 | 60.2 | 56.1 - 64.6 | 50.7 | 47.2 - 54.4
2004 |  | 706 | 53.6 | 49.7 - 57.7 | 44.8 | 41.6 - 48.3
2005 |  | 685 | 51.8 | 48.0 - 55.9 | 42.3 | 39.1 - 45.6
2006 |  | 655 | 49.6 | 45.8 - 53.5 | 40.4 | 37.3 - 43.6
2007 |  | 635 | 48.2 | 44.5 - 52.1 | 38.5 | 35.6 - 41.7
2008 |  | 605 | 46 | 42.4 - 49.8 | 36.1 | 33.3 - 39.2
2009 |  | 569 | 43.2 | 39.7 - 46.9 | 33.4 | 30.7 - 36.3
Data Source: Maine Mortality Database; Data, Research, and Vital Records Program, [NAME]. Acute myocardial infarction deaths are defined as deaths in which the underlying cause of death is coded as I21-I22. Crude rates are deaths per 100,000 population. Age-adjusted rates are deaths per 100,000 population age-adjusted to the U.S. 2000 standard population. 95% CI: 95% confidence interval of the rate. *Rates are flagged as unreliable when the rate is calculated with a numerator of 20 or less
Napp:  Not applicableage-adjusted rates are not calculated for age-specific groups.
Spreadsheet: 719737d1613771513-how-to-extract-the-average-price-sss-1-copy
Sheet: Arkusz1

 |  |  | [NAME]:
 |  |  | I |  | II |  | III |  | IV |  | V
 |  |  | Price | Amount | Price | Amount | Price | Amount | Price | Amount | Price | Amount |  | AVERAGE PRICE:
 |  |  | 33.1 | 25 | 27.5 | 50 | 45.2 | 40 | 17.7 | 15 | 20.35 | 70 |  |  |  |  | Can i copy this function       down here and count another product?
 |  |  | 33.1 | 7 | 15.2 | 12 | 17.2 | 2 | 70 | 1 | 16 | 88
Spreadsheet: 526075d1499103315-extract-data-with-multiple-criteria-invoiced-june-2017
Sheet: Sheet2

Row Labels | Sum of Pending
[NAME] | 10
[NAME] | 30
Chowk Munda  WH | 0
[NAME] pur  WH | 0
Hasilpur HW IV | 0
Jehania WH | 22.5
[NAME] | 2.5
[NAME] | 40
Khanewal WH | 0
[NAME] | 0.5
[NAME] | 0
[NAME] | 0
[NAME] | 0
[NAME] | 38.45
Mailsi WH | 5
Mian Channu WHII | 60
Mian ChannuWHIII | 15
Multan WH | 140.4
Shujabad WH II | 0
Vehari WH | 60
Vehari WH-2 | 0
Yazman WH | 0
Grand Total | 424.35
Spreadsheet: 688363d1595748919-extract-list-with-formulas-data-label-list-by-month
Sheet: Sheet1

Launch Date | Brand | Item Name
2019-01-01 00:00:00 | Brand A | A0001
2019-01-01 00:00:00 | Brand A | A0002
2019-01-01 00:00:00 | Brand A | A0003
2019-01-01 00:00:00 | Brand C | C0001
2019-01-01 00:00:00 | Brand C | C0002
2019-05-01 00:00:00 | Brand C | C0003
2019-05-01 00:00:00 | Brand C | C0004
2019-05-01 00:00:00 | Brand D | D0001
2019-05-01 00:00:00 | Brand D | [NAME]
2019-05-01 00:00:00 | Brand D | D0003
2019-07-01 00:00:00 | Brand B | B0001
2019-07-01 00:00:00 | Brand B | B0002
2019-07-01 00:00:00 | Brand B | [NAME]
2019-07-01 00:00:00 | Brand B | B0004 |  |  | 2019-01-01 00:00:00
2019-08-01 00:00:00 | Brand A | A0004 |  | New SKUs
2019-08-01 00:00:00 | Brand A | A0005 |  | Cum. # SKUs
2019-08-01 00:00:00 | Brand A | A0006
2019-08-01 00:00:00 | Brand B | B0005
2019-09-01 00:00:00 | Brand A | A0007
2019-09-01 00:00:00 | Brand E | E0003
2019-09-01 00:00:00 | Brand E | E0004
2019-11-01 00:00:00 | Brand D | D0004
2019-11-01 00:00:00 | Brand E | E0001
2019-11-01 00:00:00 | Brand E | E0002
2019-12-01 00:00:00 | Brand D | D0005
 |  |  |  |  | Brand A |  |  |  | Brand C |  | Brand B | Brand A | Brand A |  | Brand D | Brand D
 |  |  |  |  | A0001 |  |  |  | C0003 |  | B0001 | A0004 | A0007 |  | D0004 | D0005
 |  |  |  |  | A0002 |  |  |  | C0004 |  | B0002 | A0005
 |  |  |  |  | A0003 |  |  |  |  |  | [NAME] | A0006 | Brand E |  | Brand E
 |  |  |  |  |  |  |  |  | Brand D |  | B0004 |  | E0003 |  | E0001
 |  |  |  |  | Brand C |  |  |  | D0001 |  |  | Brand B | E0004 |  | E0002
 |  |  |  |  | C0001 |  |  |  | [NAME] |  |  | B0005
 |  |  |  |  | C0002 |  |  |  | D0003
Spreadsheet: 163802d1340463232-dynamic-range-inside-array-formula-with-use-of-match-crosspostanswer
Sheet: Summary

Case | Category A
Spreadsheet: 33965d1230931295-suming-two-if-values-sales
Sheet: Sales

CITY | SALE DATE | SALE VALUE
City A | 2007-06-09 00:00:00 | 1000
City A | 2007-12-08 00:00:00 | 1000
City A | 2008-01-02 00:00:00 | 1000
City B | 2008-02-08 00:00:00 | 2000
City B | 2008-05-09 00:00:00 | 2000
City B | 2008-06-17 00:00:00 | 2000
City B | 2008-06-30 00:00:00 | 2000
City C | 2008-11-20 00:00:00 | 3000
City C | 2009-01-09 00:00:00 | 3000
City C | 2009-01-21 00:00:00 | 3000
City C | 2009-05-08 00:00:00 | 3000
Spreadsheet: danny_mccarty__4660__IdleAire 10-2001 Valuation
Sheet: 12-2000 Value

IDLEAIRE 12-2000 VALUATION SUMMARY
 |  | PRE-MONEY |  |  | POST-MONEY
Valuation Technique
 | Discounted Cash Flow Valuation
 | Comparable Public Company Analysis
 | Comparable Transaction Analysis
 | Average Valuation
 | Discount
 | New Equity |  |  |  | 5000000
 | Discounted Average Valuation
Common Shares
 | Founders | 36000000
 | Seed Investors | 3410000
 | Key Employees | 1110000
 | 1st Round Investors
 | 2nd Round Investors
 | Public Round
 | Total Common Shares Outstanding
 | Total Prefered Shares Outstanding | 0
 | Options & Warrants
 | Fully Diluted Shares
 | Price Per Fully Diluted Share
Spreadsheet: 215349d1361355093-prevents-line-graphs-plotting-zeros-workouttracker
Sheet: Progress

Progress Report |  |  |  | Set 1 |  |  | Set 2 |  |  | Set 3 |  |  | Set 4 |  |  | Set 5 |  |  | Total
Category: | Chest |  |  | Rep | Weight | Total | Rep | Weight | Total | Rep | Weight | Total | Rep | Weight | Total | Rep | Weight | Total | Rep | Weight | Total
Exercise: | Bench press |  | Week1
 |  |  | Week2
 |  |  | Week3
 |  |  | Week4
 |  |  | Week5
 |  |  | Week6
 |  |  | Week7
 |  |  | Week8
 |  |  | Week9
 |  |  | Week10
 |  |  | Week11
 |  |  | [NAME]
Spreadsheet: 146597d1332265865-excel-2007-conditional-formatting-mixed-data-conditional-format-2-condition
Sheet: Sheet1

STATUS | NEXT APPROVAL DATE
N/A | N/A
approved | 2012-10-01 00:00:00
approved | 2012-10-01 00:00:00
approved | 2012-10-01 00:00:00
N/A | N/A
 | 2012-10-01 00:00:00
approved | 2012-10-01 00:00:00
N/A | N/A
N/A | N/A
approved | 2012-10-01 00:00:00
N/A | N/A
N/A | N/A
 | 2012-10-01 00:00:00
N/A | N/A
N/A | N/A
 | 2012-03-22 00:00:00
approved | 2012-10-01 00:00:00
approved | 2012-10-01 00:00:00
approved | 2012-10-02 00:00:00
N/A | N/A
N/A | N/A
 | 2012-10-01 00:00:00
 | 2012-10-01 00:00:00
approved | 2012-10-01 00:00:00
Spreadsheet: louise_kitchen__24242__JulYTD Orig w notes
Sheet: North East Power

Northeast Power
Originator |  | Commodity |  | Team |  | Total |  | %Total
[NAME] |  | Power |  | Northeast
Wood |  | Power |  | Northeast
Hammond |  | Power |  | Northeast |  | 552600
Ader |  | Power |  | Northeast |  | 456000
Bernstein |  | Power |  | Northeast |  | 125000
[NAME] |  | Power |  | Northeast
Schener |  | Power |  | Northeast |  | 50000
Wheeler |  | Power |  | Northeast |  | 30000
Wheeler |  | Power |  | Northeast |  | 10000
Valderama |  | Power |  | Northeast |  | 8200
Total *
*  $150,000 related to Old Dominion(Dalton) is included in Midwest Power.
Spreadsheet: 73750d1274342712-viewing-details-with-code-number-staff-details
Sheet: Sheet1

S/N | Employee, Names | Designations | Department | Sub Depts | Date of Employ. | Date of Birth | Sex | Job Group | Job Step | ABS | LEAVE ENTITL. | CODE NUMNER
1 | AMEDU,  MONDAY | Driver | COMMERCIAL | COMM-EAST | 2009-04-01 00:00:00 | 1974-10-16 00:00:00 | M | Junior | 4.01 | 404240 | 18 | CB01052
2 | ESSIEN, [NAME] | Driver | COMMERCIAL | COMMERCIAL EAST | 1997-09-08 00:00:00 | 1961-05-12 00:00:00 | M | Junior | 4.01 | 404240 | 23 | CB00247
3 | OBIYOR, MIRACLE | Driver | COMMERCIAL | COMMERCIAL EAST | 2008-07-01 00:00:00 | 1974-07-17 00:00:00 | M | Junior | 4.01 | 404240 | 18 | CB00935
4 | NWOSU, GLORY | Secretary/Typist | COMMERCIAL | COMMERCIAL EAST | 2006-11-06 00:00:00 | 1974-07-19 00:00:00 | F | Junior | 4.02 | 418760 | 18 | CB00774
5 | ALAUKWU, CHICHEBEM | [NAME] | COMMERCIAL | COMMERCIAL NORTH | 1991-12-09 00:00:00 | 1963-09-27 00:00:00 | M | Junior | 4.01 | 404240 | 23 | CB00085
6 | AMANFO, NAPOLEON | Driver | COMMERCIAL | COMMERCIAL NORTH | 2003-11-27 00:00:00 | 1966-08-22 00:00:00 | M | Junior | 4.01 | 404240 | 18 | CB00615
7 | UZOBUIFE, EMEKA | Driver | COMMERCIAL | COMMERCIAL NORTH | 2000-05-15 00:00:00 | 1971-06-07 00:00:00 | M | Junior | 4.03 | 433280 | 23 | CB00476
8 | EZEJI, OBIOMA | Secretary/Typist | COMMERCIAL | COMMERCIAL NORTH | 2002-06-10 00:00:00 | 1976-03-03 00:00:00 | F | Junior | 4.01 | 404240 | 18 | CB00554
9 | OSSAI, MELETUS | Cashier | COMMERCIAL | DEPOT-EAST | 1995-05-17 00:00:00 | 1969-03-03 00:00:00 | M | Junior | 5.01 | 443840 | 23 | CB00173
10 | ALARIBE, ANTHONY | Cashier | COMMERCIAL | DEPOT-EAST | 1996-02-21 00:00:00 | 1958-03-03 00:00:00 | M | Junior | 5.01 | 443840 | 23 | CB00190
11 | OGBONNAYA, OKORONKWO | Cashier | COMMERCIAL | DEPOT-EAST | 1998-03-16 00:00:00 | 1970-08-20 00:00:00 | M | Junior | 5.01 | 443840 | 23 | CB00257
12 | NNENU, [NAME] | Cashier | COMMERCIAL | DEPOT-EAST | 1998-04-01 00:00:00 | 1961-04-15 00:00:00 | M | Junior | 5.01 | 443840 | 23 | CB00262
13 | EZE, FRANKLIN | Cashier | COMMERCIAL | DEPOT-EAST | 2000-12-04 00:00:00 | 1963-12-17 00:00:00 | M | Junior | 5.01 | 443840 | 23 | CB00498
14 | AZUWIKE, SUNDAY | Cashier | COMMERCIAL | DEPOT-EAST | 2001-11-26 00:00:00 | 1964-06-20 00:00:00 | M | Junior | 5.01 | 443840 | 23 | CB00531
Spreadsheet: cara_semperger__1451__ENA_PGE_DEC08_2001
Sheet: JD-MAL TRANS

From: | EPMI
To: | PGE System Transactions
Date:
Total Transmission
HE
ENDING | PGE TRANNY | PGE TRANNY | TOTAL
Pacific Prevailing
Time | NOT FOR TOM, ET AL | NOT FOR TOM, ET AL | [NAME] USED
(PPT) |  |  | Total In To PGE Trans
0100
0200
0300
0400
0500
0600
0700 | 50 | 25
0800 | 50 | 25
0900 | 50 | 25
1000 | 50 | 25
1100 | 50 | 25
1200 | 50 | 25
1300 | 50 | 25
1400 | 50 | 25
1500 | 50 | 25
1600 | 50 | 25
1700 | 50 | 25
1800 | 50 | 25
1900 | 50 | 25
2000 | 50 | 25
2100 | 50 | 25
2200 | 50 | 25
2300
2400
 | IDAC(G,FS,CA) BOARDMAN | [NAME],FS, CA) MIDC
 | IPC(T)BRDM/SLATT | AVISTA(PSE)
 | SCL(T)BPA(T) SLATT/[NAME] HNF | PWX(PSE)
 | IDAC(PSE) | IDAC(PSE)
 | AQUILA(PSE) | TEMU(PSE)
 | DYPMI(PSE) | DETM(PSE)
 | EPMI(PSE) | NRG(PSE)
 | PGE(T)JD/MAL5 | CONAGRA(PSE)
 | EPMI(PSE) | TEMI(PSE)
 | CISO(T)MAL5/NP15 | PGET(PSE)
 | EPMI(PSE) | DYPMI(PSE)
 | EPMI (L, FS) CISO(CA) | EPMI (PSE)
 |  | [NAME]
 |  | PGE(T)JD/MAL5
 |  | EPMI(PSE)
 |  | CISO(T)MAL5/NP15
 |  | EPMI(PSE)
 |  | EPMI (L, FS) CISO(CA)
Spreadsheet: 240926d1370452589-need-totals-in-child-tables-sheets-linked-into-parent-table-sheet-example
Sheet: System Setup

 |  |  |  | Understanding "Panels" Table
 | Panel | Fed By |  | Supply |  |  |  |  |  | For those that don't understand electrical distribution, think of Supply like data that's reported yearly (or YTD)... It includes data that's both reported quarterly (MDP1 and MDP2) and data that's reported monthly (1A, 1B, 2A, and 2B).  Then considering quarterly reports (MDP1 and MDP2), they will also include monthly reported data (1A, 1B, 2A, and 2B).  And finally, there's monthly reported date (1A, 1B, 2A, and 2B), but these must also include (if any) data that's reported weekly (names TBD).  Now go to sheet MDP1...
 | MDP1 | Supply |  | Supplies both MDP1 and MDP2
 | MDP2 | Supply |  | MDP1 |  |  | MDP2
 | 1A | MDP1 |  | Supplies direct loads plus 1A and 1B |  |  | Supplies direct loads plus 2A and 2B
 | 1B | MDP1
 | 2A | MDP2 |  | 1A | 1B |  | 2A | 2B
 | 2B | MDP2 |  | Will supply direct loads but may also supply added panels. | Will supply direct loads but may also supply added panels. |  | Will supply direct loads but may also supply added panels. | Will supply direct loads but may also supply added panels.
 |  |  |  |  |  |  |  |  |  | There will be a "report" for "Supply", I just didn't make one in this example.
Spreadsheet: 288830d1389309914-sort-mfmfmfm-instead-of-mmmfff-seating-plan
Sheet: 4

 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Teacher desk
Spreadsheet: 888830d1739422414-countif-function-excel-file
Sheet: Sheet1

 | Category | Status
 | DEMO | Live
 | DEMO | WIP
 | Paid | WIP
 | DEMO | Live
 | DEMO | WIP
 | Paid | Live
 | DEMO | Live
 | DEMO | WIP
 | Paid | Live
 | DEMO | Live
 | Paid | WIP
 |  | [NAME]
 | Count of Demo Live
 | Count of Paid Live
 | Count of Demo WIP
 | Count of Paid WIP
Spreadsheet: 749004d1632456922-optimize-value-while-staying-in-budget-book2_optimize
Sheet: Sheet1

List | A |  |  |  |  |  | List | B |  |  |  |  |  | List | C
Items | Value | Cost | R | RV | RC |  | Items | Value | Cost | R | RV | RC |  | Items | Value | Cost | R | RV | RC
A | 110 | 100 |  |  |  |  | Q | 150 | 150 |  |  |  |  | W | 125 | 110
B | 90 | 75 |  |  |  |  | R | 115 | 100 |  |  |  |  | X | 100 | 100
C | 85 | 74 |  |  |  |  | S | 100 | 90 |  |  |  |  | Y | 75 | 30
D | 70 | 70 |  |  |  |  | T | 50 | 60 |  |  |  |  | Z | 50 | 10
E | 60 | 50 |  |  |  |  | U | 45 | 50
F | 45 | 40 |  |  |  |  | V | 30 | 10
G | 40 | 30
H | 39 | 30
I | 35 | 30
J | 34 | 20
K | 33 | 10
L | 20 | 9
M | 19 | 8
N | 18 | 7
O | 17 | 6
P | 15 | 5