tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: daren_farmer__6606__AA_Report - Feb
Sheet: Sheet1

Last_Name | First_Name | Reference Identity | Company | RC | Title | Team_Name
[NAME] | [NAME] | 5 | 413 | 1638 | Analyst | Group - Energy Operations
Bucalo | [NAME] | 14 | 413 | 2677 | Analyst | Group - Energy Operations
[NAME] | Tobin | 18 | 413 | 469 | Analyst | Group - Energy Operations
Crook | [NAME] | 24 | 413 | 2655 | Analyst | Group - Energy Operations
Estrada | Israel | 35 | 413 | 1742 | Analyst | Group - Energy Operations
Etter | [NAME] | 36 | 413 | 1892 | Analyst | Group - Energy Operations
Falcone | [NAME] | 37 | 413 | 469 | Analyst | Group - Energy Operations
Guggenheim | Victor | 46 | 413 | 1892 | Analyst | Group - Energy Operations
Hiemstra | [NAME] | 56 | 413 | 2374 | Analyst | Group - Energy Operations
Hull | [NAME] | 63 | 413 | 1638 | Analyst | Group - Energy Operations
Hyde | Crystal | 65 | 413 | 1893 | Analyst | Group - Energy Operations
Lafuze | [NAME] | 83 | 413 | 719 | Analyst | Group - Energy Operations
[NAME] | Homer | 89 | 413 | 2182 | Analyst | Group - Energy Operations
Malone | Lynette | 93 | 413 | 2372 | Associate | Group - Energy Operations
[NAME] | [NAME] | 94 | 413 | 720 | Analyst | Group - Energy Operations
Martin | Gregory | 95 | 413 | 1631 | Analyst | Group - Energy Operations
Mathews | [NAME] | 99 | 413 | 1893 | Analyst | Group - Energy Operations
Monroy | [NAME] | 106 | 413 | 1839 | Analyst | Group - Energy Operations
Mundim, [NAME] | Milson | 108 | 413 | 2372 | Associate | Group - Energy Operations
[NAME] | Sheetal | 115 | 413 | 463 | Analyst | Group - Energy Operations
[NAME] | Purvi | 114 | 413 | 1839 | Analyst | Group - Energy Operations
[NAME] | [NAME] | 113 | 413 | 1742 | Analyst | Group - Energy Operations
Perkins | Alicia | 116 | 413 | 1893 | Analyst | Group - Energy Operations
Reside | [NAME] | 127 | 413 | 2655 | Analyst | Group - Energy Operations
Scott | [NAME] | 141 | 413 | 469 | Analyst | Group - Energy Operations
Southard | Carrie | 154 | 413 | 2662 | Analyst | Group - Energy Operations
Waldhauser | Michelle | 168 | 413 | 1840 | Analyst | Group - Energy Operations
Weakly | [NAME] | 170 | 413 | 1631 | Analyst | Group - Energy Operations
Zeman | Angie | 177 | 413 | 719 | Analyst | Group - Energy Operations
Spreadsheet: 829846d1684487526-using-a-sumifs-formula-to-add-up-invoices-purchase-orders-in-a-date-range-test-dashboard
Sheet: PTHeadline

Vendor | Purchase Order No | Years (Period Date) | $ [NAME] | $ [NAME] | $ PO Amount | $ [NAME] | $ Forecast Amount
Test Company 1 | 106198 | 2021 | 549295.2300000001 |  | 549295.2400000001 |  |  |  | Purchase Orders
 | 106205 | 2021 | 1023244.96 |  | 4165922.5517 |  |  |  | Invoices
 |  | 2022 | 1288107.49 |  |  |  |  |  | Credit Memo
 |  | 2023 | 378320.7399999999 | -62132.69 |  | 119081.74 |  |  | Accrual
Test Company 2 | 106152 | 2022 | 27850.03 |  |  |  |  |  | Forecast
 | 106202 | 2021 | 10445.75 |  | 101240.01
 |  | 2022 | 40684.70999999997 |  | 29517.54
 |  | 2023 | 3479.71
Spreadsheet: 159916d1338903582-lunch-break-timings-calculations-lunch-break-formulae
Sheet: Sheet3

1st | in
0 | 0 |  | 0 | 0 |  | 0 | 0
1 | 0.25 |  | 1 | 0.01666666666666667 |  | 1 | 0
2 | 0.25 |  | 2 | 0.03333333333333333 |  | 2 | 0
3 | 0.25 |  | 3 | 0.05 |  | 3 | 0
4 | 0.25 |  | 4 | 0.06666666666666667 |  | 4 | 0
5 | 0.25 |  | 5 | 0.08333333333333331 |  | 5 | 0
6 | 0.25 |  | 6 | 0.1 |  | 6 | 0
7 | 0.25 |  | 7 | 0.1166666666666667 |  | 7 | 0
8 | 0.25 |  | 8 | 0.1333333333333333 |  | 8 | 0
9 | 0.25 |  | 9 | 0.15 |  | 9 | 0
10 | 0.25 |  | 10 | 0.1666666666666666 |  | 10 | 0
11 | 0.25 |  | 11 | 0.1833333333333333 |  | 11 | 0
12 | 0.25 |  | 12 | 0.2 |  | 12 | 0
13 | 0.25 |  | 13 | 0.2166666666666667 |  | 13 | 0
14 | 0.25 |  | 14 | 0.2333333333333333 |  | 14 | 0
15 | 0.25 |  | 15 | 0.25 |  | 15 | 0.25
16 | 0.5 |  | 16 | 0.2666666666666667 |  | 16 | 0.25
17 | 0.5 |  | 17 | 0.2833333333333333 |  | 17 | 0.25
18 | 0.5 |  | 18 | 0.3 |  | 18 | 0.25
19 | 0.5 |  | 19 | 0.3166666666666667 |  | 19 | 0.25
20 | 0.5 |  | 20 | 0.3333333333333333 |  | 20 | 0.25
21 | 0.5 |  | 21 | 0.35 |  | 21 | 0.25
22 | 0.5 |  | 22 | 0.3666666666666666 |  | 22 | 0.25
23 | 0.5 |  | 23 | 0.3833333333333334 |  | 23 | 0.25
24 | 0.5 |  | 24 | 0.4 |  | 24 | 0.25
25 | 0.5 |  | 25 | 0.4166666666666667 |  | 25 | 0.25
26 | 0.5 |  | 26 | 0.4333333333333333 |  | 26 | 0.25
27 | 0.5 |  | 27 | 0.45 |  | 27 | 0.25
28 | 0.5 |  | 28 | 0.4666666666666666 |  | 28 | 0.25
29 | 0.5 |  | 29 | 0.4833333333333333 |  | 29 | 0.25
30 | 0.5 |  | 30 | 0.5 |  | 30 | 0.5
31 | 0.75 |  | 31 | 0.5166666666666667 |  | 31 | 0.5
32 | 0.75 |  | 32 | 0.5333333333333333 |  | 32 | 0.5
33 | 0.75 |  | 33 | 0.55 |  | 33 | 0.5
34 | 0.75 |  | 34 | 0.5666666666666667 |  | 34 | 0.5
35 | 0.75 |  | 35 | 0.5833333333333334 |  | 35 | 0.5
36 | 0.75 |  | 36 | 0.6 |  | 36 | 0.5
37 | 0.75 |  | 37 | 0.6166666666666667 |  | 37 | 0.5
38 | 0.75 |  | 38 | 0.6333333333333333 |  | 38 | 0.5
39 | 0.75 |  | 39 | 0.65 |  | 39 | 0.5
40 | 0.75 |  | 40 | 0.6666666666666665 |  | 40 | 0.5
41 | 0.75 |  | 41 | 0.6833333333333332 |  | 41 | 0.5
42 | 0.75 |  | 42 | 0.7 |  | 42 | 0.5
43 | 0.75 |  | 43 | 0.7166666666666667 |  | 43 | 0.5
44 | 0.75 |  | 44 | 0.7333333333333333 |  | 44 | 0.5
45 | 0.75 |  | 45 | 0.75 |  | 45 | 0.75
46 | 1 |  | 46 | 0.7666666666666667 |  | 46 | 0.75
47 | 1 |  | 47 | 0.7833333333333333 |  | 47 | 0.75
48 | 1 |  | 48 | 0.8 |  | 48 | 0.75
49 | 1 |  | 49 | 0.8166666666666668 |  | 49 | 0.75
50 | 1 |  | 50 | 0.8333333333333335 |  | 50 | 0.75
51 | 1 |  | 51 | 0.85 |  | 51 | 0.75
52 | 1 |  | 52 | 0.8666666666666667 |  | 52 | 0.75
53 | 1 |  | 53 | 0.8833333333333333 |  | 53 | 0.75
54 | 1 |  | 54 | 0.9 |  | 54 | 0.75
55 | 1 |  | 55 | 0.9166666666666665 |  | 55 | 0.75
56 | 1 |  | 56 | 0.9333333333333332 |  | 56 | 0.75
57 | 1 |  | 57 | 0.95 |  | 57 | 0.75
58 | 1 |  | 58 | 0.9666666666666667 |  | 58 | 0.75
59 | 1 |  | 59 | 0.9833333333333333 |  | 59 | 0.75
0.001 | 0.15
SICK | 0
HOLS | 0
MAT | 0
OFF | 0
Spreadsheet: 233089d1367608381-add-digits-if-marked-book1-stews-oeldere-
Sheet: Sheet1

mark | value
x | 1 | < list of digits |  |  | x |  |  | sumproduct
 | 1
 | 0 |  |  |  | x |  |  | sumif
x | 2
 | 1
 | 1
 | 0 |  |  |  |  | Rijlabels | Som van value | pivot table
 | 1 |  |  |  |  | x | 3
 | 1 |  |  |  |  | Eindtotaal | 3
x | 0
 | 3 | < Total here
^
marked for adding
Spreadsheet: 272652d1382453995-consolidating-my-workbook-consolidating-workbook
Sheet: Sheet1

 |  |  |  |  |  |  |  |  |  | What I am looking to do is consolidate my workbook. I have a lot of different venders with many different items that I need to order. [NAME] is just a basic example.
 |  |  |  |  |  | Line # | Items |  |  |  | Vender |  | Cost |  | What I need it to do is pull out everything that needs to be ordered from Wal-Mart and bring it to another Sheet. It also needs to do that same for each target and Publix if possible. Currently I am just individually copying each item to another sheet to consolidate it.
 |  |  |  |  |  | 1 | Cups |  |  |  | Wal-Mart |  | $$$
 |  |  |  |  |  | 2 | Plates |  |  |  | Target |  | $$$
 |  |  |  |  |  | 3 | Forks |  |  |  | Wal-Mart |  | $$$
 |  |  |  |  |  | 4 | Spoons |  |  |  | Target |  | $$$
 |  |  |  |  |  | 5 | Apples |  |  |  | Wal-Mart |  | $$$
 |  |  |  |  |  | 6 | Oranges |  |  |  | Wal-Mart |  | $$$
 |  |  |  |  |  | 7 | Chicken |  |  |  | Target |  | $$$
 |  |  |  |  |  | 8 | Broccoli |  |  |  | Publix |  | $$$
 |  |  |  |  |  | 9 | [NAME] |  |  |  | Publix |  | $$$
 |  |  |  |  |  | 10 | Mustard |  |  |  | Publix |  | $$$
Spreadsheet: 756430d1637541970-parking-fees-parking_fees
Sheet: Rates

Min | [NAME] | Hours | Monday-Friday (Peak)
0 | 1 |  | 7.5
1.01 | 2 | 0.0 - 1.0 | 15
2.01 | 3 | 1.0 - 2.0 | 22.5
3.01 | 4 | 2.0 - 3.0 | 33
4.01 | 5 | 3.0 - 4.0 | 43.5
5.01 | 5.01 | 4.0 - 5.0 | 54
 |  | 5+
Off-Peak
Weekend Rate | 9
Night Rate (17:00-7:00) | 9
Spreadsheet: 101096d1299251772-trying-to-return-a-year-to-date-amount-book1
Sheet: Sheet1

 | JAN | FEB | MAR | APR | MAY | JUN | JUL | AUG | SEP | OCT | NOV | DEC | total
MTC | 10000 | 500 | 1000 | 5000 | 2000 | 3000 | 400 | 500 | 800 | 1000 | 1000 | 100 | 25300
RENT | 10000 | 10000 | 10000 | 10000 | 10000 | 10000 | 10000 | 10000 | 10000 | 10000 | 10000 | 10000 | 120000
CARS | 500 | 500 | 500 | 250 | 250 | 250 | 250 | 500 | 250 | 250 | 250 | 500 | 4250
TOTAL | 20500 | 11000 | 11500 | 15250 | 12250 | 13250 | 10650 | 11000 | 11050 | 11250 | 11250 | 10600 | 149550
 | FEB | FEB | FEB |  |  | FEB YTD | FEB YTD | FEB YTD
 | PLAN | ACT | VAR |  |  | PLAN | ACT | VAR
MTC | 500 | 8000 | -7500 |  |  | 10500 | 16000 | -5500
RENT | 10000 | 1000 | 9000 |  |  | 20000 | 19800 | 200
CARS | 500 | 550 | -50 |  |  | 1000 | 550 | 450
total | 11000 | 9550 | 1450 |  |  | 31500 | 36350 | -4850
Spreadsheet: 158446d1338246708-is-this-possible-reading-data-exception-tracking-2012
Sheet: December by Shift

AUDIT DESCREPANCIES BY SHIFT DECEMBER
 | NOVEMBER
AUDIT DESCREPANCIES / EXCEPTIONS | DAY | SWING | GRAVE
Illegible or missing signature/ badge number
Any void not completed properly
Improper Error Corrections
Inconsistent Key Return
Incorrect/ Missing Information/ Totals
Missing Time Stamp/ Manual Date
Paperwork not received in Audit
Compliance Exceptions
Totals
Total Audit Exceptions
Total Compliance Exceptions
THESE ARE THE AUDIT DISCREPANCIES RECEIVED IN DECEMBER!
THE GOAL FOR DECEMBER IS A 10% REDUCTION. |  |  |  | JANUARY GOAL
Spreadsheet: 57898d1258115315-autofit-a-row-of-merged-cells-that-have-wrapped-text-temp
Sheet: Example

Shift Report: |  |  |  |  |  | 2009-01-01 00:00:00
Day Shift
Example: |  |  |  |  | Basically we wanted to have these cells wrap and auto fit, we can't have this as one cell as you can see below.
Example:
Example:
Example:
Example:
Example:
Example:
Example:
Example:
Swing Shift
Example:
Example:
Example:
Example:
Example:
Example:
Example:
Example:
Example:
Grave Shift
Example:
Example:
Example:
Example:
Example:
Example:
Example:
Example:
Example:
[NAME]:
Variances |  |  |  | Day Shift |  |  |  |  |  |  |  | Variances |  |  |  | Swing Shift |  |  |  |  |  |  |  | Variances |  |  |  | Grave Shift
Bank |  |  |  | Employee Name |  |  |  | Amount |  |  |  | Bank |  |  |  | Employee Name |  |  |  | Amount |  |  |  | Bank |  |  |  | Employee Name |  |  |  | Amount
Location #1 |  |  |  |  |  |  |  |  |  |  |  | Location #1 |  |  |  |  |  |  |  |  |  |  |  | Location #1
Location #2 |  |  |  |  |  |  |  |  |  |  |  | Location #2 |  |  |  |  |  |  |  |  |  |  |  | Location #2
Location #3 |  |  |  |  |  |  |  |  |  |  |  | Location #3 |  |  |  |  |  |  |  |  |  |  |  | Location #3
Location #4 |  |  |  |  |  |  |  |  |  |  |  | Location #4 |  |  |  |  |  |  |  |  |  |  |  | Location #4
Location #5 |  |  |  |  |  |  |  |  |  |  |  | Location #5 |  |  |  |  |  |  |  |  |  |  |  | Location #5
Location #6 |  |  |  |  |  |  |  |  |  |  |  | Location #6 |  |  |  |  |  |  |  |  |  |  |  | Location #6
Location #7 |  |  |  |  |  |  |  |  |  |  |  | Location #7 |  |  |  |  |  |  |  |  |  |  |  | Location #7
Location #8 |  |  |  |  |  |  |  |  |  |  |  | Location #8 |  |  |  |  |  |  |  |  |  |  |  | Location #8
Location #9 |  |  |  |  |  |  |  |  |  |  |  | Location #9 |  |  |  |  |  |  |  |  |  |  |  | Location #9
Location #10 |  |  |  |  |  |  |  |  |  |  |  | Location #10 |  |  |  |  |  |  |  |  |  |  |  | Location #10
Location #11 |  |  |  |  |  |  |  |  |  |  |  | Location #11 |  |  |  |  |  |  |  |  |  |  |  | Location #11
Location #12 |  |  |  |  |  |  |  |  |  |  |  | Location #12 |  |  |  |  |  |  |  |  |  |  |  | Location #12
Location #13 |  |  |  |  |  |  |  |  |  |  |  | Location #13 |  |  |  |  |  |  |  |  |  |  |  | Location #13
Location #14 |  |  |  |  |  |  |  |  |  |  |  | Location #14 |  |  |  |  |  |  |  |  |  |  |  | Location #14
Location #15 |  |  |  |  |  |  |  |  |  |  |  | Location #15 |  |  |  |  |  |  |  |  |  |  |  | Location #15
Location #16 |  |  |  |  |  |  |  |  |  |  |  | Location #16 |  |  |  |  |  |  |  |  |  |  |  | Location #16
Grand Total |  |  |  |  |  |  |  |  |  |  |  | Grand Total |  |  |  |  |  |  |  |  |  |  |  | Grand Total
Comments |  |  |  |  |  |  |  |  |  |  |  | Comments |  |  |  |  |  |  |  |  |  |  |  | Comments
Spreadsheet: c3da160f-c701-4b48-a10b-58b8e8b4dca3
Sheet: Sheet1

Table 8--U.S. cotton textile imports, by origin | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8
 | Oct. | Nov. | Dec. |  | Dec.
Region/country | 2013 | 2013 | 2013 |  | 2012
 | 1,000 pounds 1/
North America | 142853.649 | 126548.189 | 126147.956 |  | 128561.284
Canada | 2367.157 | 3051.128 | 2650.07 |  | 2607.225
Costa Rica | 342.579 | 331.098 | 352.284 |  | 961.033
Dominican Republic | 5543.468 | 5837.042 | 4836.861 |  | 5924.564
El Salvador | 20769.94 | 17369.602 | 21454.375 |  | 20594.708
Guatemala | 9926.174 | 7760.285 | 7625.182 |  | 6976.458
Haiti | 11067.811 | 10101.988 | 9870.263 |  | 12417.199
Honduras | 26795.355 | 25155.866 | 26406.457 |  | 31609.029
Mexico | 50248.045 | 42732.526 | 38215.884 |  | 33682.466
Nicaragua | 15775.949 | 14186.174 | 14719.551 |  | 13760.184
South America | 4371.576 | 3627.323 | 4038.24 |  | 3804.262
Brazil | 220.78 | 201.933 | 161.762 |  | 121.364
Colombia | 1815.151 | 1519.032 | 1637.477 |  | 1480.093
Peru | 2290.557 | 1874.647 | 2208.495 |  | 2139.697
Europe | 19023.974 | 12973.31 | 11844.843 |  | 9550.135
Germany | 1246.225 | 925.34 | 1002.564 |  | 789.844
Italy | 1665.175 | 1736.338 | 1783.291 |  | 1627.681
Portugal | 1895.874 | 1700.429 | 1198.517 |  | 1085.556
Turkey | 11069.684 | 6120.26 | 5260.313 |  | 3805.249
Asia | 621854.621 | 504923.688 | 464528.488 |  | 457745.032
Bahrain | 1061.746 | 897.785 | 1638.068 |  | 985.505
Bangladesh | 54344.48 | 43205.38 | 35791.506 |  | 35129.594
Cambodia | 23414.474 | 17713.164 | 14151.178 |  | 16769.271
China | 268202.304 | 219329.719 | 204402.039 |  | 207309.192
Hong Kong | 1973.009 | 1368.809 | 724.772 |  | 927.597
India | 76380.751 | 61617.173 | 60226.036 |  | 49763.097
Indonesia | 31910.576 | 25955.998 | 20361.075 |  | 26866.206
Israel | 736.086 | 771.095 | 625.84 |  | 814.994
Japan | 1140.984 | 1007.177 | 1119.785 |  | 1009.387
Jordan | 4777.443 | 4310.543 | 4517.307 |  | 4199.536
Malaysia | 2841.113 | 2589.225 | 2608.321 |  | 2612.682
Pakistan | 72087.216 | 54501.304 | 52410.165 |  | 46314.679
Philippines | 4264.312 | 3312.851 | 3506.532 |  | 4450.578
South Korea | 5598.013 | 5222.777 | 5127.101 |  | 4989.27
Sri Lanka | 9763.29 | 8009.494 | 7204.405 |  | 6376.124
Taiwan | 2660.403 | 1833.09 | 1970.411 |  | 2643.202
Thailand | 6603.715 | 5948.006 | 5810.107 |  | 7074.895
Vietnam | 52540.039 | 46119.173 | 41098.262 |  | 38583.156
Oceania | 98.907 | 46.531 | 17.797 |  | 28.721
Africa | 17021.376 | 12606.393 | 14364.683 |  | 13078.707
Egypt | 8361.427 | 6181.281 | 7415.911 |  | 7985.532
Kenya | 2373.149 | 2494.03 | 2076.901 |  | 1703.138
[NAME] | 3850.369 | 2281.772 | 2009.654 |  | 1880.87
[NAME] | 1169.989 | 571.575 | 1366.258 |  | 488.26
World 2/ | 805224.104 | 660725.435 | 620942.007 |  | 612768.141
 |  |  |  |  |  |  |  | 95
1/ Raw-fiber equivalent.  2/ Totals may not add due to rounding.
Sources: USDA, Economic Research Service; and
U.S. Dept. of Commerce, U.S. Census Bureau.
Last update:  02/24/14.
Errata: On February 26, 2014, this table was revised with the correct
data for October and November 2013.
Spreadsheet: 72336d1273024836-sumif-for-percentage-averages-book1
Sheet: Sheet1

 | [NAME] | 0.11
 | [NAME] | 0.03
 | [NAME]
 | [NAME] | 0.12
 | [NAME]
 | [NAME]
 | [NAME] | 0.44
 | AREA8 | 0.12
 | AREA8
 | AREA8 | 0.22
 | AREA8 | 0.43
 | AREA8
 | AREA8 | 0.11
 | AREA8
 | AREA8
 | [NAME]
 | [NAME] | 0.23
 | [NAME]
 | I want to find the average of cells where column B is "AREA1" and where column C is bigger than 0
Spreadsheet: 89457d1289249665-help-with-scheduling-machine-schedule
Sheet: Sheet1

 | Parameters |  |  | [NAME] | Job Ref | Description | Run Time | Completed | Days Left | Estimated End Date
 | Machine | 1 |  | 1 |  |  | 56 | 0.55
 | Start Time | 08:30:00 |  | 2 |  |  | 64 | 0
 | End Time | 17:30:00
 | Days / Week | 6
 | Hours Per Day
 | Hours Per Week
 | Time Left Today
Spreadsheet: 37cab6f0-5f87-48d3-9f0c-0cc60c70c8de
Sheet: dinsosnakertrans

.
PA/KPA |  | :   [NAME]
K/L/D/I |  | :   -
SATKER/SKPD |  | :   [NAME] [NAME]
PPK |  | :   [NAME]
TAHUN ANGGARAN |  | :   2013
NO | KEGIATAN | SUB KEGIATAN/PEKERJAAN | NAMA PAKET PEKERJAAN | VOLUME | LOKASI PEKERJAAN (PROVINSI/KAB/KOTA | RENCANA PENGANGGARAN |  |  | CARA PENGADAAN | PERKIRAAN WAKTU MULAI PELAKSANAAN PENGADAAN | WAKTU PELAKSANAAN | KET
 |  |  |  |  |  | PERKIRAAN BIAYA (Rp.) | [NAME] (APBN/APBD) | RENCANA PEMBIAYAAN
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13
1 | Belanja Modal | Pengadaan | Lemari | 5 | Dinsosnakertrans | 12500000 | APBD | - | [NAME] | Maret | Maret
 |  |  | AC | 5 | Dinsosnakertrans | 15000000 | APBD | - | Kontrak | [NAME] | [NAME]
 |  |  | [NAME]/PC | 4 | Dinsosnakertrans | 30000000 | APBD | - | Kontrak | [NAME] | [NAME]
 |  |  | Printer | 6 | Dinsosnakertrans | 7600000 | APBD | - | Kontrak | [NAME] | [NAME]
 |  |  | UPS/Stabilizer | 4 | Dinsosnakertrans | 4000000 | APBD | - | Kontrak | [NAME] | [NAME]
 |  |  | Kamera Digital | 1 | Dinsosnakertrans | 3500000 | APBD | - | Kontrak | [NAME] | [NAME]
 |  |  | Alat Ukur GPS | 1 | Dinsosnakertrans | 8000000 | APBD | - | Kontrak | Oktober | Oktober
[NAME] |  |  |  |  |  | 80600000
 |  |  |  |  |  |  |  |  | Batulicin, 20 Desember 2012
 |  |  |  |  |  |  |  |  | PA/KPA,
 |  |  |  |  |  |  |  |  | ABD. HARIS, S.Sos.M.Si
 |  |  |  |  |  |  |  |  | [NAME]
 |  |  |  |  |  |  |  |  | NIP. 19630115 198503 1 021
Spreadsheet: 214189d1360841025-lowest-code-first-followed-by-name-and-dollar-amount-example-1-
Sheet: Sheet1

Code | ADD_1 | ADD_2 | ADD_3 | Name | Amount |  |  | [NAME] |  | MAX |  |  |  |  | Min
1 |  |  |  | Tri. | 1000 |  | 1 |  |  |  |  |  |  | 1
1 |  |  |  | [NAME]. | 600 |  | 2 |  |  |  |  |  |  | 2
1 |  |  |  | [NAME]. | 250 |  | 3 |  |  |  |  |  |  | 3
1 |  |  |  | [NAME]. | 900 |  | 4 |  |  |  |  |  |  | 4
1 |  |  |  | Hector. | 150 |  | 5 |  |  |  |  |  |  | 5
1 |  |  |  | Nick. | 75 |  | 6 |  |  |  |  |  |  | 6
1 |  |  |  | [NAME]. | 850
2 |  |  |  | [NAME]. | 3000
2 |  |  |  | [NAME]. | 200
2 |  |  |  | [NAME]. | 450
2 |  |  |  | [NAME]. | 375
2 |  |  |  | [NAME]. | 225
3 |  |  |  | [NAME]. | 123
4 |  |  |  | Sue | 254
5 |  |  |  | [NAME] | 642
6 |  |  |  | [NAME] | 179
3 |  |  |  | [NAME] | 217
4 |  |  |  | Sylvie | 305
5 |  |  |  | [NAME] | 300
6 |  |  |  | [NAME] | 100
Spreadsheet: fletcher_sturm__11005__Power Trading Start2
Sheet: Master Trading Agreements

Counterparty | Master | Master | Master ISDA | Master | Legal Lead | Credit | Commercial Lead 1 | Commercial | Target Completion Date | Status
 | Gas | EEI |  | Netting |  | Lead |  | Lead 2 |  | (C or I)
Williams Energy Marketing & Trading |  | YES | YES |  | Sager |  | Belden | Sturm
Aquilla Risk Management Corporation |  | YES | YES |  |  |  | Duran | Sturm
El Paso Merchant Energy |  | YES | YES |  |  |  | Duran | [NAME]
AEP Energy Services |  | YES | YES |  |  |  | Curry | [NAME]
Reliant Energy Services |  | YES | YES |  |  |  | Curry | [NAME]
Duke Energy Trading & Marketing |  | YES | YES |  |  |  | [NAME]
Dynegy Marketing & Trade |  | YES | YES |  |  |  | [NAME] | Presto
Constellation Power Source |  | YES | YES |  |  |  | Calger | [NAME]
BP Energy Company |  | YES | YES |  |  |  | Curry | DGS
Mirant Americas Energy Marketing |  | YES | YES |  |  |  | [NAME]
PG&E Energy Trading |  | YES | YES |  |  |  | Benson
Morgan Stanley Capital Group |  | YES | YES |  |  |  | DGS
Coral Power, LLC |  | YES | YES |  |  |  | Duran
Calpine Energy Services |  | YES | YES |  |  |  | Presto
Allegheny Energy Supply Company |  | YES | YES |  |  |  | Calger
Entergy-Koch Trading L.P. |  | YES | YES |  |  |  | [NAME]
PSEG Energy Resources & Trade |  | YES | YES |  |  |  | Duran
Virginia Electric & Power Company |  | YES | YES |  |  |  | Presto
Cinergy Services, Inc. |  | YES | YES |  |  |  | [NAME]
Tractebal Energy Marketing, Inc. |  | YES | YES |  |  |  | Duran
Sempra Energy Trading Corp. |  | YES | YES |  |  |  | Rosman
Cargill-Alliant, LLC |  | YES | YES |  |  |  | [NAME]
CMS Marketing Services & Trading Co. |  | YES | YES |  |  |  | [NAME]
NRG Marketing, Inc. |  | YES | YES |  |  |  | [NAME]
Select Energy, Inc |  | YES | YES |  |  |  | [NAME]
DTE Energy Trading, Inc. |  | YES | YES |  |  |  | [NAME]
Conectiv Energy Supply, Inc. |  | YES | YES |  |  |  | Wood
Exelon Generation Company, LLC |  | YES | YES |  |  |  | [NAME]
Counterparty |  | Master | Master ISDA | Master | Legal Lead | Credit | Commercial Lead 1 | Commercial | Target Completion Date | Status
 | Master Physical Gas | EEI |  | Netting |  | Lead |  | Lead 2 |  | (C or I)
HQ Energy Services (US) inc. |  | YES | YES |  |  |  | [NAME]
Northern Indiana Public Service Company |  | YES | YES |  |  |  | [NAME]
PPL Energy Plus, L.L.C. |  | YES | YES |  |  |  | Wood
TXU Energy Services |  | YES | YES |  |  |  | DGS
FPL Energy Services |  | YES | YES |  |  |  | Presto | [NAME]
Spreadsheet: 809297d1671012897-find-and-show-the-last-value-from-a-row-examplefile-2
Sheet: Sheet1

testheader 1 | testheader 2 | testheader 3 | testheader 4 | testheader 5 | testheader 6 | testheader 7 | testheader 8 | testheader 9 | testheader 10 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Past header info here
Info1 | Info2 | [NAME] | [NAME]
Info1 | Info2 | [NAME] | [NAME] | Info 5 | Info 6 | [NAME]
Info1 | Info2 | [NAME] | [NAME]
Info1 | Info2 | [NAME] | [NAME]
Info1 | Info2 | [NAME] | [NAME]
Info1 | Info2 | [NAME] | [NAME] | Info 5 | Info 6 | [NAME] | [NAME] 8 | [NAME] 9
Info1 | Info2 | [NAME] | [NAME]
Info1 | Info2 | [NAME] | [NAME]
Info1 | Info2 | [NAME] | [NAME]
Info1 | Info2 | [NAME] | [NAME] | Info 5 | Info 6 | [NAME] | [NAME] 8 | [NAME] 9 | [NAME] 10
Spreadsheet: 179425d1347258568-extract-the-top-item-excel-question-1-
Sheet: Sheet1

City | cost | count | vendor | Name | I need a way to extract the top (?)items in the cost column |  |  |  |  |  |  |  | [NAME] | [NAME]
Portland | 82.23 | 1 | 8383 | [NAME] | (?) is the variable in count column |  |  |  |  |  |  |  | LA | [NAME]
[NAME] | 83.66 | 1 | 883 | [NAME] |  |  |  |  |  |  |  |  | NY | [NAME]
Seattle | 88.95333333333333 | 5 | 379 | [NAME] | In this example the top 5 items in City Seattle, 9 items in city LA |  |  |  |  |  |  |  | Portland
Seattle | 89.31199999999998 |  | 1781 | [NAME] | and 4 items in city of NY are highlighted |  |  |  |  |  |  |  | Seattle
Seattle | 89.37333333333333 |  | 6842 | [NAME]
Seattle | 87.64 |  | 6837 | [NAME]
Seattle | 89.44333333333333 |  | 6483 | [NAME] | [NAME] | LA
Seattle | 89.55333333333333 |  | 450 | [NAME]
Seattle | 87.64 |  | 446 | [NAME] | City | cost | count | vendor | Name
Seattle | 87.71333333333332 |  | 7483 | [NAME]
Seattle | 87.846 |  | 198 | [NAME]
Seattle | 88.075 |  | 6881 | [NAME]
LA | 90.04499999999999 | 9 | 5926 | [NAME]
LA | 90.4125 |  | 1109 | [NAME]
LA | 90.46666666666665 |  | 285 | [NAME]
LA | 88.7825 |  | 124 | [NAME]
LA | 88.95333333333333 |  | 1863 | [NAME]
LA | 89.31199999999998 |  | 6648 | [NAME]
LA | 89.3733333333333 |  | 1592 | [NAME]
LA | 89.39666666666666 |  | 1471 | [NAME]
LA | 89.44333333333333 |  | 6377 | [NAME]
LA | 89.55333333333333 |  | 1209 | [NAME]
LA | 89.875 |  | 858 | [NAME]
LA | 89.89750000000001 |  | 397 | [NAME]
NY | 89.9375 | 4 | 7399 | [NAME]
NY | 90.04499999999999 |  | 266 | [NAME]
NY | 90.4125 |  | 639 | [NAME]
NY | 89.39666666666666 |  | 1780 | [NAME]
NY | 89.44333333333333 |  | 591 | [NAME]
NY | 91.13 |  | 999 | [NAME]
Spreadsheet: 888357d1738848100-conditional-formatting-based-on-days-remaining-enquiry-sheet
Sheet: Enquiries

 |  | Overdue
 |  | Less than 3 days until tender return |  | Todays Date:
 |  | More than 3 days until tender return
 | Enquiries
EQ Number | Project Name | Client | Enquiry Received | Tender Deadline | Comments
EQ3384 | Job1 | Client1 | 2024-10-23 00:00:00 | 2024-11-06 00:00:00
EQ3405 | Job2 | Client2 | 2024-11-27 00:00:00 | 2025-02-15 00:00:00
EQ3415 | Job3 | Client3 | 2025-01-06 00:00:00 | 2025-01-20 00:00:00
EQ3418 | Job4 | Client4 | 2025-01-13 00:00:00 | 2025-01-27 00:00:00
EQ3426 | [NAME] | Client5 | 2025-01-20 00:00:00 | 2025-02-03 00:00:00
EQ3430 | Job6 | Client6 | 2025-01-30 00:00:00 | 2025-02-13 00:00:00
EQ3431 | Job7 | Client7 | 2025-02-04 00:00:00
EQ3433
EQ3434
EQ3435
EQ3436
EQ3437
EQ3438
EQ3439
EQ3440