tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 121593d1316185598-consolidate-data-from-multiple-tabs-in-the-same-excel-file-classsignupsconsolidate
Sheet: listings

FMEA01_Studies | 0
FMEA01_PT | 0
FMEA01_CM | 0
FMEA01_Others | 0
FMEA02_Studies | 0
FMEA02_PT | 0
FMEA02_CM | 0
FMEA02_Others | 0
FMEA03_Studies | 0
FMEA03_PT | 0
FMEA03_CM | 0
FMEA03_Others | 0
FMEA04_Studies | 0
FMEA04_PT | 0
FMEA04_CM | 0
FMEA04_Others | 0
FMEA05_Studies | 0
FMEA05_PT | 0
FMEA05_CM | 0
FMEA05_Others | 0
FMEA06_Studies | 0
FMEA06_PT | 0
FMEA06_CM | 0
FMEA06_Others | 0
Spreadsheet: 789776d1659360450-need-help-with-tax-tables-tax-sample-hd
Sheet: Sheet2

0 - 50 000 | Not taxable | 50010
50 0001 - 100 000 | 18% for each N$ above 50 000
100 001 - 300 000 | 9 000 + 25% for each N$ above 100 000
300 001 - 500 000 | 59 000 + 28% for each N$ above 300 000
500 001 - 800 000 | 115 000 + 30% for each N$ above 500 000
801 001 - 1 500 000 | 205 000 + 32% for each N$ above 800 000
Above 1 500 000 | 429 000 + 37% for each N$ above 1 500 000
0 | 0
50000 | 0.18 |  |  |  | 25572.58
100000 | 0.25 |  |  |  | 7541.04
300000 | 0.28
500000 | 0.3 |  |  |  | 1156
800000 | 0.32
1500000
 |  |  |  |  | 1790.08
411235.44 |  |  |  |  | Tax table | 300000
 |  |  |  |  |  |  | x | 0.28
 |  |  |  |  |  | 59000
Spreadsheet: 75935d1276538829-removing-and-consolidating-rows-in-excel-disposal-and-hauling-costs
Sheet: disposal_and_hauling_costs

CCUST. | Customer | WO. | HAULING | DISPOSAL
22706 | [NAME] | 1865201 | 310 | 0
22706 | [NAME] | 1865201 | 310 | 0
22706 | [NAME] | 1865201 | 310 | 0
22706 | [NAME] | 1865201 | 310 | 0
22706 | [NAME] | 1865201 | 310 | 0
22706 | [NAME] | 1865201 | 310 | 0
22706 | [NAME] | 1865201 |  | 160
22706 | [NAME] | 1865201 |  | 160
22706 | [NAME] | 1865201 |  | 160
22706 | [NAME] | 1865201 |  | 160
22706 | [NAME] | 1865201 |  | 160
22706 | [NAME] | 1865201 |  | 160
22706 | [NAME] | 1865201 |  | 160
31569 | THOR EQUITIES LLC | 1586024 | 185 | 0
31569 | THOR EQUITIES LLC | 1586024 | 185 | 0
31569 | THOR EQUITIES LLC | 1586024 | 185 | 0
31569 | THOR EQUITIES LLC | 1586024 | 185 | 0
31569 | THOR EQUITIES LLC | 1586024 | 185 | 0
31569 | THOR EQUITIES LLC | 1586024 |  | 424.54
31569 | THOR EQUITIES LLC | 1586024 |  | 424.54
31569 | THOR EQUITIES LLC | 1586024 |  | 424.54
31569 | THOR EQUITIES LLC | 1586024 |  | 424.54
31569 | THOR EQUITIES LLC | 1586024 |  | 424.54
31569 | THOR EQUITIES LLC | 1586024 |  | 424.54
31569 | THOR EQUITIES LLC | 1586024 |  | 424.54
31569 | THOR EQUITIES LLC | 1586024 |  | 424.54
Spreadsheet: 80216d1280413204-unique-concatenate-book1
Sheet: Sheet1

ref | total nr | nr
Couldn't be changed these cells |  |  | CONCATENATE | required result
556 | 2 | 1 | 556/1 | 556/1
 |  | 2 | /2 | 556/2
554 | 4 | 1 | 554/1 | 554/1
 |  | 2 | /2 | 554/2
 |  | 3 | /3 | 554/3
 |  | 4 | /4 | 554/4
538 | 2 | 1 | 538/1 | 538/1
 |  | 2 | /2 | 538/2
557 | 9 | 1 | 557/1 | 557/1
 |  | 2 | /2 | 557/2
 |  | 3 | /3 | 557/3
 |  | 4 | /4 | 557/4
 |  | 5 | /5 | 557/5
 |  | 6 | /6 | 557/6
 |  | 7 | [NAME] | 557/8
 |  | 8 | /8 | 557/8
 |  | TB | /TB | 557/TB
1159 | 4 | 1 | 1159/1 | 1159/1
 |  | 2 | /2 | 1159/2
 |  | 3 | /3 | 1159/3
 |  | 4 | /4 | 1159/4
1279 | 6 | 1 | 1279/1 | 1279/1
 |  | 2 | /2 | 1279/2
 |  | 3 | /3 | 1279/3
 |  | 4 | /4 | 1279/4
 |  | 5 | /5 | 1279/5
 |  | 6 | /6 | 1279/6
569 | 1 | 1 | 569/1 | 569/1
567 | 1 | 1 | 567/1 | 567/1
Spreadsheet: 672402d1586863251-autofil-pairs-of-rows-example-abcpairs
Sheet: Sheet1

 | Datset I have :
 | A |  | B |  | C |  |  | Matrix of pairs of 1 (green)
 | a | b | a | b | a | b |  |  | A | B | C
1 |  |  | 3 |  | 1 |  |  | A | 8 |  |  | I already have this matrix
2 |  |  | 3 |  |  | 1 |  | B | 6 | 11
3 |  |  | 3 |  | 1 |  |  | C | 3 | 3 | 11
4 | 1 |  | 3 |  |  | 1
5 | 1 |  | 1 |  |  |  |  | Matrix of neither has a 3 (non reds) |  |  |  | number of lines where there is no 3 either in A or B, A or C, B or C
6 | 1 |  | 1 |  |  |  |  |  | A | B | C
7 |  | 1 |  | 1 |  |  |  | A | 14 |  |  | I'm looking for a way to create this one
8 |  |  |  |  |  |  |  | B | 10 | 16 |  | Ex: for AB I count entries 5 to 12 then 19 and 20, total = 10
9 | 1 |  | 1 |  | 3 |  |  | C | 6 | 11 | 15
10 |  |  |  | 1 | 3 |  |  | so I count the number of line that has no 3 in either Aa or Ba, Aa or Ca, Ba or Ca etc… over the entire row (here from line 4 to line 23).
11 | 1 |  | 1 |  | 3 |  |  | Everytime there is a 3, do not count. I'm thinking playing with COUNTIF and a step dataset but I don't know how to autofil all the pairs
12 |  |  |  | 1 | 3 |  |  | we would need to autofil with pairs like this : (row)B and (row)D, then B and F, then D and F. 3s are always in the "a" rows.
13 | 3 |  | 1 |  | 3 |  |  | for this simple dataset it's easy, for for 31 entries that leave me with a lot of pairs so I really need to automate this.
14 | 3 |  | 1 |  | 1 |  |  | Even if I could simply create a step dataset with the sum of each pair for each line then I can COUNTIF(row;<3) over the entire rows and have my answers.
15 | 3 |  |  |  | 1 |  |  | that would leave me with my answers in a line instead of a matrix but I can manually refill if no better options.
16 | 3 |  |  |  | 1
17 | 3 |  |  |  |  | 1 |  | Comments :
18 | 3 |  | 1 |  | 1 |  |  | - "3" can be changed to anything preferable with a simple "replace" over the dataset.
19 |  | 1 |  | 1 |  | 1 |  | - there are 31 (a+b) so 62 rows
20 | 1 |  |  |  | 1 |  |  | - there is over 650 lines and growing potentially
 |  |  |  |  |  |  |  | - we can delete all the "b" row if important for autofilling
Seen total (1s) | 6 | 2 | 7 | 4 | 7 | 4
Over x potential (non-red) | 14 |  | 16 |  | 15
Spreadsheet: 881132d1729066968-problem-with-vlookup-with-2-variables-sklep
Sheet: Arkusz1

[NAME] |  | sklep | cena | cena 2 |  | sklep | cena 1 najniższa | [NAME] | cena 2 najniższa
VW |  | LDZ | 394 | 210 |  | LDZ
VW |  | PAB | 390 | 390 |  | PAB
VW |  | KOL | 388 | 388 |  | KOL
VW |  | PIOTR | 385 | 210 |  | PIOTR
VW |  | SIER | 382 | 382 |  | SIER
Opel |  | LDZ | 391 | 391
Opel |  | PAB | 400 | 400
Opel |  | KOL | 370 | 370
Opel |  | PIOTR | 360 | 150
Opel |  | SIER | 360 | 360
Citroen |  | LDZ | 380 | 380
Citroen |  | PAB | 365 | 220
Citroen |  | KOL | 350 | 350
Citroen |  | PIOTR | 340 | 340
Citroen |  | SIER | 330 | 330
Skoda |  | LDZ | 355 | 355
Skoda |  | PAB | 310 | 290
Skoda |  | KOL | 355 | 355
Skoda |  | PIOTR | 310 | 310
Skoda |  | SIER | 400 | 400
Spreadsheet: david_delainey__8095__Cultivation
Sheet: Sheet1

Y | [NAME], Nadeem | [PHONE] | [EMAIL]
Y | Burnes, JaCee | [PHONE] | [EMAIL]
Y | Conner, Scott | [PHONE] | [EMAIL]
Y | [NAME], [NAME] | [PHONE] | [EMAIL]
Y | [NAME], [NAME] | [PHONE] | [EMAIL]
Y | Parikh, Niraj | [PHONE] | [EMAIL]
Y | Preheim, Todd | [PHONE] | [EMAIL]
Y | Salazar, [NAME] | [PHONE] | [EMAIL]
Y | [NAME], Roman | [PHONE] | [EMAIL]
N | Giles, Jeremy | [PHONE] | [EMAIL]
N | Hutcheson, Stephen |  | [EMAIL]
N | Matthews, [NAME] | [PHONE] | [EMAIL]
N | Sirna, [NAME] | [PHONE] | [EMAIL]
N | Tso, Kin | [PHONE] | [NAME]
N | [NAME], [NAME] | [PHONE] | [EMAIL]
N | Zhang, [NAME] | [PHONE] | [EMAIL]
N | [NAME], Yaqin | [PHONE] | [EMAIL]
Spreadsheet: 327988d1403753334-find-number-in-array-that-is-less-than-index-match-excel-question
Sheet: Xcommission

 |  |  |  |  |  |  | Actual | Actual | Actual | Actual
 |  |  |  |  |  |  | 2014-01-31 00:00:00 | 2014-02-28 00:00:00 | 2014-03-31 00:00:00 | 2014-04-30 00:00:00
vs Target | CommScale | Accel/Decel | Over/Under $ | Payout |  | EBITDA Target
0.06 |  | 0.035 |  |  |  | Target Cumulative
0.05 |  | 0.03
0.04 |  | 0.025 |  |  |  | EBITDA Actual
0.03 |  | 0.02 |  |  |  | Actual Cumulative
0.02 |  | 0.015
0.01 |  | 0.005 |  |  |  | Cumulative Total (over/under)
0 |  | 0 |  |  |  | % Diff
-0.01 |  | -0.25
-0.02 |  | -0.35
-0.03 |  | -0.45 |  |  |  | Commission Target | 8000
-0.04 |  | -0.5 |  |  |  | Commission Payout
-0.05 |  | -0.55 |  |  |  |  | should return highlighted green in cell H18
-0.06 |  | -0.6
Spreadsheet: 22245393-0cdd-4bb7-bd5f-2a93b9173df6
Sheet: Sheet1

Table 4--U.S. fiber demand | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5
 | Sep. | Oct. | Nov. |  | Nov.
Item | 2013 | 2013 | 2013 |  | 2012
 | 1,000 480-lb. [NAME]
Cotton:
All consumed by mills 1/ | 309 | 315 | 282 |  | 278
Total since August 1 | 630 | 944 | 1226 |  | 1164
Daily rate | 14.7 | 13.7 | 13.4 |  | 12.6
Upland consumed by mills 1/ | 307 | 313 | 281 |  | 276
Total since August 1 | 627 | 940 | 1221 |  | 1159
Daily Rate | 14.6 | 13.6 | 13.4 |  | 12.6
Upland exports | 487 | 375 | 399 |  | 637
Total since August 1 | 1399 | 1774 | 2173 |  | 2514
Sales for next season | 22 | 13 | 73 |  | 227
Total since August 1 | 165 | 178 | 251 |  | 502
Extra-long staple exports | 43.1 | 39 | 83 |  | 48.3
Total since August 1 | 98.7 | 137.7 | 220.7 |  | 250.3
Sales for next season | 0 | 0 | 0 |  | 0
Total since August 1 | 0 | 0 | 0 |  | 4.4
 | Aug. | Sep. | Oct. |  | Oct.
 | 2013 | 2013 | 2013 |  | 2012
 | Million pounds
Manmade:
Raw fiber exports | 55.6 | 54.2 | 53.4 |  | 61.7
Noncellulosic | 55 | 53.6 | 52.7 |  | 61.3
Cellulosic | 0.6 | 0.6 | 0.7 |  | 0.4
Total since January 1 | 445.4 | 499.6 | 552.9 |  | 577.8
Total since January 1
 | 1,000 pounds
Wool and mohair:
Raw wool exports, clean | 1058.2 | 496.3 | 837.9 |  | 397.3
Total since January 1 | 7270.4 | 7766.7 | 8604.6 |  | 6226.4
Wool top exports | 222.8 | 152.3 | 129.3 |  | 125.8
Total since January 1 | 1608.9 | 1761.2 | 1890.5 |  | 729.3
Mohair exports, clean | 58.2 | 53.1 | 62.5 |  | 66.8
Total since January 1 | 569.5 | 622.6 | 685.1 |  | 417
1/  Estimated by USDA.
Sources: USDA, Farm Service Agency; USDA, Foreign Agricultural Service,
U.S. Export Sales; U.S. Dept. of Commerce, U.S. Census Bureau; and [NAME].
Last update: 1/14/14.
Spreadsheet: 837321d1690042132-how-do-you-provide-points-based-on-a-defined-lookup-targets-table-points-based-on-value
Sheet: Test

 | Test | Pts | Points Want |  | Test2 | Pts | Points want |  | Test3 | Pts | Points want
[NAME] | 0.2285714285714286 |  | 0 |  | 28 |  | 10 |  | 110 |  | 10
[NAME] | 0.1568627450980392 |  | 2 |  | 30 |  | 10 |  | 72 |  | 5
[NAME] | 0.0967741935483871 |  | 10 |  | 35 |  | 5 |  | 35 |  | 0
[NAME] | 0.2051282051282051 |  | 0 |  | 20 |  | 10 |  | 100 |  | 10
[NAME] | 0.06521739130434782 |  | 10 |  | 10 |  | 10 |  | 46 |  | 2
[NAME] |  |  |  |  | 5 |  | 10 |  | 5 |  | 0
Area2 | 0.09302325581395349 |  | 10 |  | 20 |  | 10 |  | 110 |  | 10
Area2 | 0.09090909090909091 |  | 10 |  | 30 |  | 7 |  | 60 |  | 7
Area2 | 0.15 |  | 7 |  | 15 |  | 10 |  | 35 |  | 1
Area2 | 0.19 |  | 4 |  | 35 |  | 4 |  | 20 |  | 0
Spreadsheet: sally_beck__35235__EGM_MgmtSum-Q4-1116
Sheet: CapChrg-AllocExp

PLAN2000
CAP_CHRG | ENRON GLOBAL MARKETS |  |  |  |  |  |  |  |  |  |  |  |  |  |  | DEALS IDENTIFIED
TOT_ALLOCATION | 4TH QUARTER 2000 CAPITAL CHARGE & ALLOCATED EXPENSES
2000-12-01 00:00:00
M.QTD
ENA
 |  |  | Capital Charge |  |  |  |  |  |  | Allocated Expenses
 | Business Team |  | Forecast | Plan | [NAME] | Explanation |  |  |  | Forecast | Plan | [NAME] | Explanation
 | Liquids |  | 0 | 0 |  |  |  |  |  |  | 7846 |  | Unplanned Legal & IT Expenses
COAL | Coal |  | 658
SO2 | Emissions
WEATHER | Weather
INSURANCE | Global Risk Markets |  | 0
ECT_INV_IRFX | Financial Trading
 | Transportation |  | 0 | 0 |  |  |  |  |  | 0 | 0
 | Subtotal
 | LNG |  |  |  |  |  |  |  |  | 0 | 0
 | Middle East |  |  | 652 |  |  |  |  |  | 0 | 0
 | Puerto Rico |  | 0 | 545 |  |  |  |  |  | 0 | 0
 | Subtotal Commercial
 | Office of the Chairman |  | 0 | 0 |  |  |  |  |  |  | 0
 | Total
 | [NAME] |  |  |  |  |  |  |  |  | 0 | 0
 | Group Allocated to Teams |  | 0 | 0
 | Total
Spreadsheet: 200340d1355501437-combining-two-data-bases-combine-example
Sheet: Sheet1

Date | Name | DATA A
2012-01-01 00:00:00 | [NAME] | abc
 | bob | abc
 | [NAME] | abc
 | [NAME] | abc
 | [NAME] | abc
 | [NAME] | abc
 | sue | abc
 | indie | abc
 | [NAME] | abc
 | [NAME] | abc
 | vader | abc
 | [NAME] | abc
 | [NAME] | abc
Spreadsheet: andy_zipper__168__DYNEGY-ICE VOL Apr12
Sheet: ICE-EPM

Enron Power Marketing
Activity on ICE
UOM | Deals | Volume
MWhs
 |  |  |  |  |  |  | START | END
Enron Power Marketing, Inc.
Commodity Type:  All
Instruments:  All
Trade Dates:  Apr-12-01 thru Apr-12-01
Trade Date | Deal ID | Leg ID | B/S | Product | Hub | Strip | START | END | Option | Strike | Style | Counterparty | Price | Price Units | Qty Per Period | Periods | Total Quantity | Qty Units | Trader
Apr-12-01 | 387932999 |  | Sold | Firm-LD Peak | PJM-W | Next Day | Apr-13-01 | Apr-16-01 |  |  |  | BP Energy Company | 44.5 | USD / MWh | 50 | Hourly | 1600 | MWhs | Carson , M
Apr-12-01 | 375467374 |  | Bought | Firm-LD Peak | Cin | Next Day | Apr-16-01 | Apr-16-01 |  |  |  | American Electric Power Service Corp. | 47.25 | USD / MWh | 500 | Hourly | 8000 | MWhs | [NAME], R
Apr-12-01  Deals
Spreadsheet: 726245d1617121223-dynamic-array-replacement-packaging-invoice
Sheet: Booking Detail

Length | [NAME] | Height | # Pallets | Volume | Total Weight |  | Pallet Number | Number of Boxes | Layers | Type | Length | [NAME] | Height | KG
 | 80 | 360 | 1 | 0.003456 | 29.96 |  |  | 24 | 6 | COV | 120 | 80 | 1150 | 46.04
120 | 80 | 600 | 1 | 0.00576 | 43.88 |  | 2 | 24 | 6 | COV | 120 | 80 | 1150 | 46.04
120 | 80 | 700 | 1 | 0.00672 | 34.52 |  | 3 | 18 | 5 | COV | 120 | 80 | 980 | 42.2
120 | 80 | 720 | 1 | 0.006912 | 50.84 |  | 4 | 21 | 6 | COV | 120 | 80 | 1150 | 46.04
120 | 80 | 780 | 1 | 0.007488 | 54.32 |  | 5 | 24 | 6 | COV | 120 | 80 | 1150 | 46.04
120 | 80 | 980 | 2 | 0.018816 | 84.4 |  | 6 | 24 | 6 | COV | 120 | 80 | 1150 | 46.04
120 | 80 | 1150 | 7 | 0.07728 | 322.28 |  | 7 | 24 | 6 | COV | 120 | 80 | 1150 | 46.04
- | - | Total | 14 | 0.126432 | 620.2 KG |  | 8 | 24 | 6 | COV | 120 | 80 | 1150 | 46.04
 |  |  |  |  |  |  | 9 | 20 | 5 | COV | 120 | 80 | 980 | 42.2
 |  |  |  |  |  |  | 10 | 12 | 3 | COV | 120 | 80 | 700 | 34.52
 |  |  |  |  |  |  | 11 | 23 | 6 | LHL | 120 | 80 | 600 | 43.88
 |  |  |  |  |  |  | 12 | 35 | 9 | N/L | 120 | 80 | 780 | 54.32
 |  |  |  |  |  |  | 13 | 30 | 8 | N/L | 120 | 80 | 720 | 50.84
 |  |  |  |  |  |  | 14 | 5 | 2 | RHL | 120 | 80 | 360 | 29.96
Spreadsheet: 790444d1659710425-calculate-the-difference-from-the-earlies-login-time-to-the-oldest-login-time-book3
Sheet: Sheet1

Name | [NAME] | Logout Time | Logout Date
Employee.Number1 | 07:30:00 | 07:30:00
Employee.Number1 | 07:31:00 | 07:46:00
Employee.Number1 | 08:09:00 | 08:09:00
Employee.Number1 | 08:10:00
Employee.Number2 | 07:12:00 | 07:12:00 | 2022-08-05 00:00:00
Employee.Number2 | 07:36:00 | 07:51:00 | 2022-08-05 00:00:00
Employee.Number2 | 07:55:00 | 08:26:00 | 2022-08-05 00:00:00
Employee.Number2 | 08:27:00
Spreadsheet: 70625d1271339263-distance-formula-adaptation-book1
Sheet: Map 1

5,1 |  | 5,3
Home 1
h8 |  | h10
Home 2
X | Y |  |  | - |  |  |  |  | - |  |  |  |  | - |  |  |  |  | - |  |  |  |  | - |  |  |  |  | -
→ | ↓ | 0 | 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
 | 0
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l | 2
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l | 7
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l | 12
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l | 17
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l | 22
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l | 27
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l | 32
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l | 37
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l | 42
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l | 47
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Spreadsheet: 818363d1676768771-recognizing-words-from-an-url-and-counting-how-many-of-them-appear-in-another-cell-formula-to-break-up-url-and-count-words-in-other-cells-1-2-
Sheet: Sheet1

new example
example url | extracted words (this is where I'd need the formula to extract the words from url) | word string for comparison (this is pre-defined) | how many of the words in c4 also appear in b4 (need this) | do all words appear? (1 if yes, 0 if no)
https://www.example.com/word1-word2-word3/ | word1 word2 word3 | word1 word2 word3 | 3 | 1 | word1 | word2 | word3
[NAME] | word1 something word3 | word1 word2 word3 | 2 | 0 | word1 | word2 | word3
https://www.example.com/word1-something-other-word2/ | word1 something other word2 | word1 word2 word3 | 2 | 0 | word1 | word2 | word3
https://www.example.com/word3-something-other-else/ | word3 something other else | word1 word2 word3 | 1 | 0 | word1 | word2 | word3
example url | group of words | what I need as an output - counting the number of words from the group that appear in the url slug (separated by "-")
https://www.example.com/word1-word2-word3/ | word1 word2 word3 | 3 | 3 | word1
https://www.example.com/word1-word2-something/ |  | 2 | 2 | word2
https://www.example.com/word1-something/ |  | 1 | 1 | word3
https://www.example.com/word1-something-word1/ |  | 2 | 1
[NAME] |  | 3 | 3
specific example
URL | word | count
https://www.fruit.com/green-apples/ | good green apples | 2 | 2 | good
https://www.fruit.com/awesome-green-apples/ |  | 2 | 2 | green
https://www.fruit.com/good-green-apples/ |  | 3 | 3 | apples
https://www.fruit.com/apples-for sale/ |  | 1 | 1
https://www.fruit.com/oranges |  | 0 | 0
Spreadsheet: 790771d1659984540-totalling-columns-dependent-upon-text-day-team-flexi-sheet
Sheet: Conversion Chart

% | Minutes
 | 1
 | 2
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 | 4
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 | 6
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 | 8
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Spreadsheet: 828784d1683730037-reverse-index-match-find-column-row-labels-based-on-a-value-book1gk
Sheet: Sheet1

 | -4.13 | -2.75 | -1.38 | 0 | 1.38 | 2.75 | 4.13
-0.06 | -355206 | -248021 | -138501 | -22435 | 106842 | 258218 | 448501
-0.04 | -368787 | -255334 | -139024 | -15174 | 123195 | 284626 | 485316
-0.02 | -380865 | -261544 | -138853 | -7625 | 139302 | 310038 | 520208
0 | -391735 | -266931 | -138228 | 0 | 154981 | 334344 | 553193
0.02 | -401671 | -271725 | -137328 | 7560 | 170134 | 357526 | 584361
0.04 | -410904 | -276109 | -136283 | 14970 | 184727 | 379624 | 613844
0.06 | -419621 | -280216 | -135175 | 22189 | 198768 | 400712 | 641793
 | Min | X | Y
Spreadsheet: john_arnold__15179__SSDSTF Datatables 97 June 00
Sheet: Table 17

TABLE 17
USE A SEPARATE SHEET FOR METRO & COUNTY
UTILITIES
Water & Sewer
Water Availability
Major Source
% of Population Increase Served from the Source
Water Treatment
Name of Provider
Rated Capacity (mgd)
Average Daily Demand (mgd)
Peak Demand (mgd)
Chemical Breakdown of Water
Cost per 1000 Gallons
Water Connection Fee
Sewer Treatment
Name of Provider
Type of Service
Rated Capacity (mgd)
Average Daily Demand (mgd)
Peak Demand (mgd)
Cost per 1000 Gallons
Sewer Connection Fee
Electric Power
Name of Company
Communities Served
Company-Net Importer or Exporter of Power
% of Reserve Margin Including Generation & Net Purchases
Name of Power Pool Membership
State Electric Power Deregulated (yes or no, if yes provide date of fully effective deregulation)
Average Cost per kWh (cents)
 | Small | Medium | Large
 | 28% Load Factor | 28% Load Factor | 28% Load Factor
Industrial Low Load Factor | 150 kW & 30,000 kWh | 500 kW & 100,000 kWh | 1,000 kW & 200,000 kWh
 | Small | Medium | Large
 | 92% Load Factor | 90% Load Factor | 90% Load Factor
Industrial High Load Factor | 150 kW & 100,000 kWh | 500 kW & 300,000 kWh | 1,000 kW & 650,000 kWh
 | Small | Medium | Large
 | 41% Load Factor | 41% Load Factor | 41% Load Factor
Commercial Low Load Factor | 100 kW & 30,000 kWh | 300 kW & 90,000 kWh | 500 kW & 150,000 kWh
 | Small | Medium | Large
 | 50% Load Factor | 70% Load Factor | 50% Load Factor
Commercial High Load Factor | 100 kW & 36,000 kWh | 300 kW & 150,000 kWh | 500 kW & 180,000 kWh
Natural Gas
Name of Provider
Average Cost for Industrial Users ($ per mcf)
Firm Contract Rates
Interruptible Service Rates
Telecommunications
No. of Local Service Providers ( local exchange carriers and competitive local
exchange carriers)
No. of Long Distance Service Providers
No. of Internet Service Providers
Central Offices of Local Service Providers
Name of Office | City | County | Address
Location
Service Characteristics of the Office
Switch Technology (Digital or Analog)
Fiber Service Provided (yes or no)
Monitored Remotely or On-Site
ISDN Available from this [NAME]
ADS2 Available from this [NAME]
DSL Available from this [NAME]
Central Office on Self-Healing Fiber Ring (yes or no)
Dual Feed from 2 Separate Switching Offices Likely Available
Points of Presence (POPs) of Long Distance Carriers
Carrier | City | County | Address
Microwave or Hardwire
Dual Fiber Fee from the Central Switching Office of the Local Exchange Carrier
Diverse Routing Available
Business Support Services (No.)
Tool & Die
Machine Shop
Welding
Office Equipment Service & Repair
Temporary Employment Services
United States Post Office (Including Performance Ratings)
Distance to Nearest General Mail Facility
% of Mail On-Time
Efficiency Rating
Guaranteed Service/Delivery Territory | First Class | Next Day | 2nd Day
Nearest Bulk Mail Facility
Location (City) | Location |  | Distance
Distance fom Community
Spreadsheet: 232262d1367364599-unique-records-but-only-want-to-see-records-that-didnt-contain-duplicates-0countuniquematch-sample-data-1-oeldere-for-other-forummember
Sheet: Formula

Reporting Periods
October 2012
November 2012 |  |  | Insert formula to count unique values in Data!C:C and filter Data!A:A by criteria A:2 for example
December 2012
January 2013
February 2013
March 2013
April 2013
May 2013
June 2013
July 2013
August 2013
September 2013
October 2013
November 2013
December 2013
January 2014 |  |  |  | May 2013
February 2014
March 2014
April 2014
May 2014
June 2014
July 2014
August 2014
September 2014
October 2014
November 2014
December 2014
January 2015
February 2015
March 2015
April 2015
May 2015
June 2015
July 2015
August 2015
September 2015
October 2015
November 2015
December 2015
Spreadsheet: 61935d1262829458-fill-row-header-based-on-cell-in-column-sample
Sheet: Sheet1

 | Initial report from system
 | Sales Rep: [NAME]
 | Product A | 2
 | Product B | 3
 | Product I | 5
 | Sales Rep: [NAME]
 | Product A | 1
 | Product J
 | Sales Rep: [NAME]
 | [NAME] | 4
 | Product Z | 2
 | Product Y | 5
 | Product A | 6
What I need to automatically generate
 | Sales Rep: [NAME]
Sales Rep: [NAME] | Product A | 2
Sales Rep: [NAME] | Product B | 3
Sales Rep: [NAME] | Product I | 5
 | Sales Rep: [NAME]
Sales Rep: [NAME] | Product A | 1
 | Product J
 | Sales Rep: [NAME]
Sales Rep: [NAME] | [NAME] | 4
Sales Rep: [NAME] | Product Z | 2
Sales Rep: [NAME] | Product Y | 5
Sales Rep: [NAME] | Product A | 6
Column A to automatically populate based on sales rep for the group
Would like to paste the same formula all the way down column A to achieve this as there could be a lot of data