tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 883084d1731573241-merging-in-out-with-absent-dates-into-a-single-pivot-table-formula-power-query-sample-emp
Sheet: Sheet1

First Table |  |  |  |  |  |  | Second Table
Employee | Date | Time | Status | Column1 |  |  | Employee | 1/1/2024 | 1/2/2024 | 1/3/2024
1 | 2024-01-01 00:00:00 | 08:00:00 | IN |  |  |  | 1 |  |  | A
1 | 2024-01-01 00:00:00 | 17:00:00 | OUT |  |  |  | 2 |  | CL
1 | 2024-01-02 00:00:00 | 08:00:00 | IN |  |  |  | 3 | AL
1 | 2024-01-02 00:00:00 | 17:00:00 | OUT
2 | 2024-01-01 00:00:00 | 08:00:00 | IN
2 | 2024-01-01 00:00:00 | 17:00:00 | OUT |  |  |  | below is the table like what I love to extract from the 2 data tables (Excel or Power Query)
2 | 2024-01-03 00:00:00 | 08:00:00 | IN
2 | 2024-01-03 00:00:00 | 17:00:00 | OUT |  |  |  |  | 2024-01-01 00:00:00 |  | 2024-01-02 00:00:00 |  | 2024-01-03 00:00:00
3 | 2024-01-02 00:00:00 | 08:00:00 | IN |  |  |  | Employee Emp | IN | OUT | IN | OUT | IN | OUT
3 | 2024-01-02 00:00:00 | 17:00:00 | OUT |  |  |  | 1 | 08:00:00 | 17:00:00 | 08:00:00 | 17:00:00 | AL | AL
3 | 2024-01-03 00:00:00 | 08:00:00 | IN |  |  |  | 2 | 08:00:00 | 17:00:00 | CL | CL | 08:00:00 | 17:00:00
3 | 2024-01-03 00:00:00 | 17:00:00 | OUT |  |  |  | 3 | A | A | 08:00:00 | 17:00:00 | 08:00:00 | 17:00:00
I did compiled the both but still I don’t know how to extract the data what I need.
Employee | Date | Status | Time |  |  |  |  | 2024-01-01 00:00:00 | 2024-01-01 00:00:00 | 2024-01-02 00:00:00 | 2024-01-02 00:00:00 | 2024-01-03 00:00:00 | 2024-01-03 00:00:00
1 | 2024-01-01 00:00:00 | IN | 0.3333333333333333 |  |  |  |  | IN | OUT | IN | OUT | IN | OUT
1 | 2024-01-01 00:00:00 | OUT | 0.7083333333333334 |  |  |  | 1 | 08:00 | 17:00 | 08:00 | 17:00 | A | A
1 | 2024-01-02 00:00:00 | IN | 0.3333333333333333 |  |  |  | 2 | 08:00 | 17:00 | CL | CL | 08:00 | 17:00
1 | 2024-01-02 00:00:00 | OUT | 0.7083333333333334 |  |  |  | 3 | AL | AL | 08:00 | 17:00 | 08:00 | 17:00
2 | 2024-01-01 00:00:00 | IN | 0.3333333333333333
2 | 2024-01-01 00:00:00 | OUT | 0.7083333333333334
2 | 2024-01-03 00:00:00 | IN | 0.3333333333333333
2 | 2024-01-03 00:00:00 | OUT | 0.7083333333333334
3 | 2024-01-02 00:00:00 | IN | 0.3333333333333333
3 | 2024-01-02 00:00:00 | OUT | 0.7083333333333334
3 | 2024-01-03 00:00:00 | IN | 0.3333333333333333
3 | 2024-01-03 00:00:00 | OUT | 0.7083333333333334
1 | 2024-01-03 00:00:00 | IN | A
2 | 2024-01-02 00:00:00 | IN | CL
3 | 2024-01-01 00:00:00 | IN | AL
1 | 2024-01-03 00:00:00 | Out | A
2 | 2024-01-02 00:00:00 | Out | CL
3 | 2024-01-01 00:00:00 | Out | AL
Spreadsheet: 76442d1276972796-max-and-min-automatic-sorting-baby-genius-rev2
Sheet: Data

# | NAME | NATIONALITY | [NAME] | FACEBOOK
1 | FREDERICK ILAS | FILIPINO | NIGERIA | [EMAIL]
2 | [NAME] | BRITISH | BRITAIN
3 | [NAME] | FILIPINO | PHILIPPINES
4 | ROBERTO | ITALIAN | ITALY
5 | JACK | AMERICAN | NIGERIA
6 | BOB | AMERICAN | AMERICA
7 | CHU | south KOREAN | KOREA
8 | ALLAN | FILIPINO | PHILIPPINES
9
10
11
Spreadsheet: 675279d1588232891-group-data-by-values-groupdatabyvalue
Sheet: Feuil1

 | I would like to group data if they have the same value but write it if the value is different
 | Example 1 |  |  |  |  | Example 2
 | W1 | [NAME] | W3 | W4 |  | W1 | [NAME] | W3 | W4 | W5 | W6
 | 1 day, 11:00:00 | 1 day, 11:00:00 | 1 day, 11:00:00 | 1 day, 11:00:00 |  | 1 day, 11:00:00 | 1 day, 11:00:00 | 1 day, 11:00:00 | 1 day, 11:00:00 | 1 day, 6:00:00 | 1 day, 11:00:00
 | Result |  |  |  |  | Result
 | Final result: |  |  |  |  | Final result:
 | W1-W4 35:00 |  |  |  |  | W1-W4 35:00 W5 30:00 W6 35:00
Spreadsheet: kate_symes__18432__MeadMktplace (2)
Sheet: MeadMktplace

 |  |  | Firm | Volume | Firm | Volume | Non Firm | Volume | Non Firm | Volume
 | DATE |  | On [NAME] | [NAME] | [NAME] | [NAME] | On [NAME] | [NAME] | [NAME] | [NAME]
 | 2001-01-08 00:00:00 |  | 132.55 | 1024 | 76.1 | 912
 | 2001-01-07 00:00:00 |  | 115 | 0 | 62.5 | 0 | 63.75 | 2237 | 26.33 | 2562
 | 2001-01-06 00:00:00 |  | 124.17 | 384 | 93.8 | 432 | 102.76 | 521 | 66.24 | 647
 | 2001-01-05 00:00:00 |  | 136.45 | 1216 | 93.11 | 528 | 129.45 | 1527 | 123.46 | 455
 | 2001-01-04 00:00:00 |  | 146.87 | 2016 | 88.03 | 912 | 125.05 | 1851 | 107.45 | 778
 | 2001-01-03 00:00:00 |  | 145.65 | 544 | 75.2 | 32 | 125.71 | 1965 | 99.79 | 652
 | 2001-01-02 00:00:00 |  | 151.43 | 224 | 80 | 32 | 120.29 | 1597 | 76.99 | 944
 | 2001-01-01 00:00:00 |  |  |  |  |  | 102.04 | 432 | 64.43 | 628
 | 2000-12-31 00:00:00 |  |  |  |  |  | 73.65 | 2126 | 66.14 | 1101
 | 2000-12-30 00:00:00 |  | 118.57 | 224 | 75 | 32 | 103.71 | 1824 | 100.69 | 360
 | 2000-12-29 00:00:00 |  | 140 | 64 | 103.1 | 232 | 139.65 | 2163 | 106.37 | 925
 | 2000-12-28 00:00:00 |  | 140 | 64 | 103.1 | 232 | 130.72 | 830 | 120.35 | 1115
 | 2000-12-27 00:00:00 |  | 200 | 64 | 160 | 200 | 121.92 | 1931 | 94.63 | 1065
 | 2000-12-26 00:00:00 |  | 275 | 64 | 203.1 | 232 | 124.96 | 2250 | 71.81 | 666
 | 2000-12-25 00:00:00 |  | 265 | 0 | 210 | 200 | 99.62 | 1532 | 62.74 | 155
 | 2000-12-24 00:00:00 |  | 265 | 0 | 210 | 200 | 131.21 | 1155 | 50.53 | 582
 | 2000-12-23 00:00:00 |  | 291.55 | 464 | 217.19 | 512 | 285 | 0 | 215 | 0
 | 2000-12-22 00:00:00 |  | 291.55 | 464 | 217.19 | 512 | 299 | 25 | 215 | 0
 | 2000-12-21 00:00:00 |  | 291.55 | 464 | 216.99 | 512 | 189.32 | 2060 | 171.06 | 292
 | 2000-12-20 00:00:00 |  | 275 | 0 | 212.86 | 280 | 214.57 | 1110 | 161.24 | 218
 | 2000-12-19 00:00:00 |  | 265.68 | 1180 | 201.43 | 280 | 194.39 | 1310 | 178.74 | 855
 | 2000-12-18 00:00:00 |  | 236.9 | 1680 | 190.62 | 960 | 223.03 | 852 | 172.62 | 525
 | 2000-12-17 00:00:00 |  | 215 | 800 | 200 | 200 | 173.03 | 1450 | 148.06 | 1255
 | 2000-12-16 00:00:00 |  | 275 | 480 | 210 | 400 | 183.11 | 2287 | 181.95 | 535
 | 2000-12-15 00:00:00 |  | 275 | 480 | 210 | 400 | 187.75 | 2655 | 153.05 | 1714
 | 2000-12-14 00:00:00 |  | 327.14 | 560 | 226 | 600 | 186.36 | 2035 | 170.6 | 1669
 | 2000-12-13 00:00:00 |  | 497.86 | 448 | 237.79 | 704 | 287.91 | 1278 | 165.71 | 1791
 | 2000-12-12 00:00:00 |  | 342.11 | 1328 | 239.28 | 1064 | 334.23 | 1940 | 165.94 | 1277
 | 2000-12-11 00:00:00 |  | 447.65 | 544 | 245.19 | 832 | 244.01 | 1470 | 147.46 | 1240
 | 2000-12-10 00:00:00 |  | 297.1 | 992 | 244.42 | 1504 | 200.84 | 712 | 127.41 | 600
 | 2000-12-09 00:00:00 |  | 315.42 | 2304 | 213.76 | 1976 | 187.04 | 810 | 148.98 | 1225
Spreadsheet: 168360d1342426491-countif-unique-records-in-list-match-criteria-book1
Sheet: Sheet1

Associate | Date | Helper |  | Associate | Worked Days
[NAME] | 2012-06-16 00:00:00 | 1 |  | [NAME] | 4
[NAME] | 2012-06-16 00:00:00 | 2 |  | [NAME] | 1
[NAME] | 2012-06-16 00:00:00 | 3 |  | Doug | 3
[NAME] | 2012-06-17 00:00:00 | 1
[NAME] | 2012-06-17 00:00:00 | 2
[NAME] | 2012-06-18 00:00:00 | 1
[NAME] | 2012-06-19 00:00:00 | 1
[NAME] | 2012-06-12 00:00:00 | 1
[NAME] | 2012-06-12 00:00:00 | 2
[NAME] | 2012-06-12 00:00:00 | 3
Doug | 2012-06-27 00:00:00 | 1
Doug | 2012-06-28 00:00:00 | 1
Doug | 2012-06-28 00:00:00 | 2
Doug | 2012-06-29 00:00:00 | 1
Spreadsheet: 121035d1315785709-converting-column-info-to-row-info-test1
Sheet: Sheet1

Customer # | Equipment 1 | Equipment 2 | Equipment 3 | Equipment 4 |  |  | Customer Number | Equipment Type
AM100 |  |  |  |  |  |  | AM100 | Computer
[NAME] |  |  |  |  |  |  | AM100 | Cell Phone
AM102 |  |  |  |  |  |  | AM100 | [NAME]
AM103 |  |  |  |  |  |  | [NAME] | Computer
AM104 |  |  |  |  |  |  | AM102 | [NAME]
 |  |  |  |  |  |  | AM102 | Cup
 |  |  |  |  |  |  | AM102 | Computer
 |  |  |  |  |  |  | AM102 | Remote
 |  |  |  |  |  |  | AM103 | [NAME]
 |  |  |  |  |  |  | AM103 | Cell Phone
 |  |  |  |  |  |  | AM104 | Cup
Result I need |  |  |  |  |  | Basically I have a bunch of equipment in one column and I need to match it with a customer number and cascade it in a row. Is this possible?
Customer # | Equipment 1 | Equipment 2 | Equipment 3 | Equipment 4
AM100 | Computer | Cell Phone | [NAME]
[NAME] | Computer
AM102 | [NAME] | Cup | Computer | Remote
AM103 | [NAME] | Cell Phone
AM104 | Cup
Spreadsheet: 769315d1645464702-get-cell-by-search-test
Sheet: Plan

 | 3 | 4 | 5 | 6 | 7
Mon | 1 |  |  |  | 1
Tue |  | 1 |  |  | 1
Wed | 1 |  | 1
DESIRED OUTPUT
3 | Mon
 | Wed
4 | Tue
 | -
5 | Wed
 | -
6 | -
 | -
7 | Mon
 | Tue
Spreadsheet: 07b7d9cc-1da0-41c7-807f-7bc1bf5e7998
Sheet: Sheet1

PROG ID | ATHLETE ID | ATHLETE FIRST | ATHLETE LAST | NATIONALITY | START NUMBER | SWIM | T1 | BIKE | T2 | RUN | POSITION | TOTAL TIME
5781 | 21486 | Pamella | Oliveira | BRA | 51 | 00:11:03 | 00:00:00 | 00:32:26 | 00:00:00 | 00:20:24 | 1 | 01:03:55
5781 | 12798 | Carolina | [NAME] | BRA | 0 | 00:12:07 | 00:00:00 | 00:34:25 | 00:00:00 | 00:20:25 | 2 | [NAME]
5781 | 19208 | Suely | Lima | BRA | 53 | 00:12:11 | 00:00:00 | 00:35:30 | 00:00:00 | 00:21:06 | 3 | 01:08:48
5781 | 17849 | Ana [NAME] | Aguirre | ARG | 0 | 00:13:07 | 00:00:00 | 00:35:06 | 00:00:00 | 00:20:36 | 4 | 01:08:49
5781 | 44079 | [NAME] | Marcal Dias | BRA | 0 | 00:11:23 | 00:00:00 | 00:38:06 | 00:00:00 | 00:26:21 | 5 | 01:15:50
5781 | 39353 | Sandra | Lazo | PER | 52 | 00:13:09 | 00:00:00 | 00:40:41 | 00:00:00 | 00:25:07 | 6 | 01:18:58
Spreadsheet: 167100d1341901330-filtering-one-specific-name-in-excel-2010-copy-of-morning-attendance-2-
Sheet: 11 Jan 2012

Daily Morning Staff |  |  |  |  | Wednesday
 | 07:30-14:30 |  | 2012-01-11 00:00:00
Name | Staff No. | Title | Time In | Out | Locations | Remarks
Naima Mohamed | 2166 | Client Ralation Supervisor | 07:30:00 | 22:00:00 | EFS | (countiued working in [NAME] to finish the PMS)
Sheikha [NAME] | 2120 | Reservation Officer | 07:30:00 | 14:30:00 | TOPS
Syed Sultan | 2888 | Supervisor | 07:30:00 | 22:00:00 | EFS | (countiued working in [NAME] to finish the PMS)
[NAME] | 4143 | Aircraft Team Leader | 07:00:00 | 14:30:00 | EFS
Jalil Nushadi | 1889 | Driver | 05:45:00 | 14:30:00 | EFS
[NAME] | 624 | Driver | 07:30:00 | 14:30:00 | EFS
Soud Sharar | 2097 | [NAME] Officer | Sick leave | Sick leave | [NAME]
[NAME] | 1897 | Manager | 07:30:00 | 14:30:00 | EFS
Spreadsheet: 887656d1737949422-index-sumproduct-spill-issue-afl-supercoach-2025-test
Sheet: CAR

PLAYER | TOT | AVG |  |  |  | 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
 |  | CBA | CBA 1H | CBA2H | CBA L5
[NAME] | 561 |  |  |  |  | 69 | 81 |  | 70 | 73 | 82 | 80 | 73 | 82 | 67 | 73 | 77 | 79 | 75 |  | 77 | 79 | 76 | 80 | 88 | 92 | 81 | 88 | 90 | 88 | 81
[NAME] | 297 |  |  |  |  |  |  |  |  |  | 52 | 57 | 73 | 75 | 78 | 83 |  |  |  |  |  | 85 |  | 63 |  | 79 | 81 | 88 | 86 | 88 | 56
[NAME] | 415 |  |  |  |  |  |  |  |  |  | 70 | 83 | 70 | 64 | 63 | 50 | 63 | 79 | 68 |  | 71 | 76 | 74 | 63 | 70 | 75 | 62 | 72 | 67 | 65 | 81
[NAME] | 439 |  |  |  |  | 69 | 70 |  | 81 | 73 | 76 | 74 | 78 | 61 | 26 | 67 | 63 | 59 | 71 |  | 57 | 21 |  |  | 79 | 67 | 88 | 80 | 86 | 77 | 89
[NAME] | 336 |  |  |  |  | 90 | 85 |  | 81 | 77 | 48 | 43 | 27 | 25 | 22 | 17 | 83 | 86 | 86 |  | 80 |  | 82 | 37 | 79 |  |  |  |  |  | 41
[NAME] | 25 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 38 | 65
[NAME] | 189 |  |  |  |  | 76 | 74 |  | 54 | 68 |  |  |  | 61 | 59 |  |  |  |  |  |  | 38 | 63 | 77 | 0 | 13 | 15 |  |  |  | 44
[NAME] | 236 |  |  |  |  | 21 | 11 |  | 35 | 14 | 27 | 34 | 70 | 21 | 70 | 67 | 27 | 17 | 29 |  | 77 | 38 | 29 | 50 | 9 | 8 | 42 | 56 | 5 | 4 | 0
[NAME] | 99 |  |  |  |  | 34 | 52 |  | 46 | 55 | 27 | 14 | 0 |  |  | 23 | 10 | 10 | 0 |  |  |  |  |  | 45 | 13 |  |  | 5 | 0
Elijah Hollands | 120 |  |  |  |  |  |  |  | 5 | 5 | 15 | 14 | 8 | 11 | 15 | 13 | 53 | 55 | 46 |  | 11 | 26 | 45 | 20 | 9 | 4 | 8 | 4 | 14 | 4 | 4
[NAME] | 66 |  |  |  |  | 10 | 15 |  | 16 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 14 | 14 |  | 20 | 15 | 18 | 0 | 21 |  | 19 | 12 |  |  | 4
[NAME] | 30 |  |  |  |  | 0 | 11 |  | 11 | 14 |  |  |  |  |  |  | 0 | 0 | 11 |  | 3 | 21 | 13 | 10 | 0 | 0 | 4 | 0
[NAME] | 3 |  |  |  |  | 10 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 0
[NAME] | 9 |  |  |  |  | 0 | 0 |  |  |  |  | 0 | 0 | 0 |  |  |  |  |  |  |  |  |  |  |  | 21 |  |  | 10 | 8 | 0
[NAME] | 7 |  |  |  |  |  |  |  |  |  |  |  |  | 0 | 0 | 0 | 0 | 0 | 0 |  | 0 | 0 | 0 | 0 | 0 | 29 | 0 | 0 | 0 | 0
[NAME] | 2 |  |  |  |  | 7 | 0 |  |  |  |  |  |  | 0 |  |  |  |  |  |  |  |  |  |  | 0 | 0
[NAME] | 3 |  |  |  |  |  | 0 |  | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 7 |  |  | 0 |  | 0 |  |  |  |  |  |  |  | 0 | 0 | 0
[NAME] | 4 |  |  |  |  | 14 | 0 |  | 0 | 0 | 0 | 0 | 0 |  | 0 | 0 | 0 | 0 | 0 |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
[NAME] | 3 |  |  |  |  | 0 | 0 |  | 0 | 0 | 0 | 0 | 0 | 0 |  | 0 | 7 | 0 | 0 |  | 3 | 0 | 0 | 0 |  |  |  | 0 | 0 |  | 0
Total Team CBAs |  |  |  |  |  | 29 | 27 |  | 37 | 22 | 33 | 35 | 37 | 28 | 27 | 30 | 30 | 29 | 28 |  | 35 | 34 | 38 | 30 | 33 | 24 | 26 | 25 | 21 | 26 | 27
Spreadsheet: 44631d1243411171-from-a-value-select-a-range-then-find-the-specify-the-highest-figure-book1
Sheet: Sheet1

Reading | Low | Entry | Stop | This gives location (Reading number) where range needs to begin selection. | [NAME] needed to find where stop point is reached, or breached in the [NAME], starting from the entry point. | With the start point and the stop point to select a range, the highest figure then needs to be found within.
 |  |  |  |  | Figures are manually inputted for explanation
1 | 98.27 | 99.15 | 98.11999999999999 | 8 | 13 | 101.57
2 | 99.06 | 99.59 | 98.91 | 8 | 13 | 101.57
3 | 98.96 | 99.29 | 98.80999999999999 | 8 | 13 | 101.57
4 | 98.95 | 99.31 | 98.8 | 8 | 13 | 101.57
5 | 98.8322 | 99.25 | 98.6822 | 8 | 13 | 101.57
6 | 97.79 | 98.04 | 97.64 | 1 | 7 | 99.06
7 | 96.44 | 97.12 | 96.28999999999999 | 1 | 15 | 101.57
8 | 99.69 | 100.31 | 99.53999999999999 | 9 | 12 | 101.57
9 | 101.04 | 101.32 | 100.89 | 10 | N/A | N/A
10 | 101.57 | 101.52 | 100.7 | 10 | N/A | N/A
11 | 100.22
12 | 99.1
13 | 98.09
14 | 99.01
15 | 96.24
Spreadsheet: 685810d1594210446-matching-cash-received-against-corresponding-invoice-matching-excel-sheet
Sheet: Sheet1

 | INCOME LEDGER
Date | Code | Name | Invoice | Amount Paid
2020-02-01 00:00:00 | US001 | [NAME] | A0001 | 4000
2020-02-02 00:00:00 | US-0002 | [NAME] | A0002 | 2000
2020-02-03 00:00:00 | US-0003 | [NAME] | A0003 | 5000
2020-02-10 00:00:00 | US-0004 | Omega Jonnes | A0004 | 1000
11/02/202 | US-0005 | [NAME] | A0005 | 3000
I woul like to match all cash paid to the ledger below for example by [NAME] $9000 ($4000 and $5000)
and to the the accounts receivable ledger
 | ACCOUNTS RECEIVEABLE LEDGER
Date | Code | Name | Invoice | Amount
1/0/20 | US001 | [NAME] | A0001 | 9000
2020-02-02 00:00:00 | US-0002 | [NAME] | A0002 | ?
2020-02-10 00:00:00 | US-0004 | Omega Jonnes | A0004 | ?
11/02/202 | US-0005 | [NAME] | A0005 | ?
Spreadsheet: 1f2817c7-72f9-4e14-9782-6a140362f53f
Sheet: Sheet1

Battle ID | Date & Time | Side | Victory | Score | Opp. Score | Opp. ID | Opp. Name | Scenario ID | Front | Start Date | End Date | Name EN | Name FR
38723126 | 2013-11-27 00:27:49 | ALLIES | 0 | 7 | 10 | 1256668 | Major [NAME] | 7662 | eastern | 1942-10-22 | 1942-10-22 | Red Barricades Factory | Les Barricades Rouges
38724807 | 2013-11-27 02:59:14 | AXIS | 1 | 6 | 5 | 830911 | Captain1 | 12505 | western | 1945-02-17 | 1945-02-20 | Moyland Wood | [NAME]
38724996 | 2013-11-27 03:08:52 | ALLIES | 1 | 6 | 0 | 830911 | Captain1 | 12505 | western | 1945-02-17 | 1945-02-20 | Moyland Wood | [NAME]
38734662 | 2013-11-27 21:10:21 | ALLIES | 1 | 6 | 4 | 192839 | Bernie_be | 8043 | eastern | 1939-09-08 | 1939-09-26 | Battle of Warsaw | Bataille de Varsovie
38734884 | 2013-11-27 21:29:03 | AXIS | 1 | 6 | 3 | 192839 | Bernie_be | 8043 | eastern | 1939-09-08 | 1939-09-26 | Battle of Warsaw | Bataille de Varsovie
Spreadsheet: 144287d1330889524-sum-values-by-month-from-dates-sum-values-by-month-from-dates
Sheet: Sheet2

Enter a Year | 2011
Select a Month | Oct
Products | Total
Product 1
Product 2
Product 3
Product 4
Product 5
Product 6
Product 7
Product 8
Product 9
Product 10
Product 11
Product 12
Product 13
Product 14
Product 15
Product 16
Product 17
Product 18
Product 19
Product 20
Product 21
Product 22
Product 23
Product 24
Product 25
Product 26
Product 27
Product 28
Product 29
Product 30
Product 31
Product 32
Product 33
Product 34
Product 35
Product 36
Product 37
Product 38
Product 39
Product 40
Product 41
Product 42
Product 43
Product 44
Product 45
Product 46
Product 47
Product 48
Product 49
Product 50
Product 51
Product 52
Product 53
Product 54
Product 55
Product 56
Product 57
Product 58
Product 59
Product 60
Product 61
Product 62
Product 63
Product 64
Product 65
Product 66
Product 67
Product 68
Product 69
Product 70
Product 71
Product 72
Product 73
Product 74
Product 75
Product 76
Product 77
Product 78
Product 79
Product 80
Spreadsheet: 044aa192-1a62-484e-96c0-6ec346c93213
Sheet: PAG 15 - table 1

Other operating income/(expenses) | 3Q10 | 3Q11 | YTD10 | YTD11
R$ million
Government grants | 112.242 | 98.58368538 | 283.97 | 289.90217238
Tax recovery | 31.69955907 | 17 | 40.75601382 | 19.86837807
(Additions to)/reversals of provisions | -1.179 | 2.975 | -10.268 | 19.239
Net gain on disposal of property, plant and equipment and intangible assets | 1.148 | 2.323 | 4.691 | 7.513999999999999
Net other operating income | 16.73044092999999 | 46.23131462000003 | 52.58198618 | 127.39144955
 | 160.641 | 167.113 | 371.731 | 463.915
Spreadsheet: 272244d1382299315-splitting-a-number-split
Sheet: Sheet1

n1 | n2 | n3 | n4 | n5 | n6 |  | n1 | n1 | n2 | n2 | n3 | n3 | n4 | n4 | n5 | n5 | n6 | n6 |  |  |  | n1 |  | n1 | n1
how i split large number"00" to "0","0" from H3 to S3 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | how i split column W to other 2 columns(as in the following example)
01 | 11 | 21 | 31 | 41 | 51 |  | 0 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 4 | 1 | 5 | 1 |  |  |  | 0 |  | 0 | 1 | or | 0 | 1
02 | 12 | 22 | 32 | 42 | 52 |  | 0 | 2 | 1 | 2 | 2 | 2 | 3 | 2 | 4 | 2 | 5 | 2 |  |  |  | 1 |  |  |  |  | 2 | 3
03 | 13 | 23 | 33 | 43 | 53 |  | 0 | 3 | 1 | 3 | 2 | 3 | 3 | 3 | 4 | 3 | 5 | 3 |  |  |  | 2 |  | 2 |  |  | 4 | 5
04 | 14 | 24 | 34 | 44 | 54 |  | 0 | 4 | 1 | 4 | 2 | 4 | 3 | 4 | 4 | 4 | 5 | 4 |  |  |  | 3 |  |  | 3 |  | 6 | 7
05 | 15 | 25 | 35 | 45 | 55 |  | 0 | 5 | 1 | 5 | 2 | 5 | 3 | 5 | 4 | 5 | 5 | 5 |  |  |  | 4 |  | 4 |  |  | 8 | 9
06 | 16 | 26 | 36 | 46 | 56 |  | 0 | 6 | 1 | 6 | 2 | 6 | 3 | 6 | 4 | 6 | 5 | 6 |  |  |  | 5 |  |  | 5
07 | 17 | 27 | 37 | 47 | 57 |  | 0 | 7 | 1 | 7 | 2 | 7 | 3 | 7 | 4 | 7 | 5 | 7 |  |  |  | 6 |  | 6
08 | 18 | 28 | 38 | 48 | 58 |  | 0 | 8 | 1 | 8 | 2 | 8 | 3 | 8 | 4 | 8 | 5 | 8 |  |  |  | 7 |  |  | 7
09 | 19 | 29 | 39 | 49 | 59 |  | 0 | 9 | 1 | 9 | 2 | 9 | 3 | 9 | 4 | 9 | 5 | 9 |  |  |  | 8 |  | 8
10 | 20 | 30 | 40 | 50 | 60 |  | 1 | 0 | 2 | 0 | 3 | 0 | 4 | 0 | 5 | 0 | 6 | 0 |  |  |  | 9 |  |  | 9
 |  | 60
Spreadsheet: 15eeee22-a2d2-49c5-a6e3-95cf22d74731
Sheet: Summary Valle del Cauca

PRESUPUESTO ENTIDADES EJECUTORAS
VALLE DEL CAUCA
Nombre Entidad Ejecutora:
Detalle | Presupuesto  Total | Tope Presupuestal
[NAME]
Monitoreo y Evaluación
[NAME] y [NAME]
Total Rubros de Funcionamiento
[NAME]
Total Destinación Específica
PRESUPUESTO TOTAL
XXXXXX
REPRESENTANTE LEGAL
Spreadsheet: sally_beck__35195__Global Hot List 1019
Sheet: Hotlist - Identified 

E N R O N   G L O B A L   M A R K E T S  -  H O T  L I S T |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | DEALS IDENTIFIED
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Results based on activity through October 19, 2000
 |  | Fourth Quarter 2000 |  |  | First Quarter 2001 |  |  | Second Quarter 2001 |  |  | Third Quarter 2001 |  |  | TOTAL
 | Liquids | Deal | Value |  | Deal | Value |  | Deal | Value |  | Deal | Value
 |  | Talisman Crude | 3000 |  | Dow / Tosco | 2000 |  |  |  |  | [NAME] | 2800
 |  |  |  |  | Enron Global Risk Markets | 900
 |  |  |  |  | Peerless | 500
 |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | TOTAL
Coal | Origination & Finance | Deal | Value |  | Deal | Value |  | Deal | Value |  | Deal | Value
 |  | SynFuel - AIG | 25000 |  | SynFuel - Pacificorp | 50000
 |  | Cline-Panther | 5000 |  | CMS - Noxtech | 30000
 |  | British Energy - UK | 5000 |  | DPR | 5000
 |  | Jupiter / Eagle Energy | 5000 |  | AES - UK | 5000
 |  | BEWAG - Germany | 1000 |  | M&G UK | 1000
 |  | Coellerici | 500 |  | RR Marketing JV - USA | 0
 |  | [NAME] | 500
 |  | DRS | 500
 |  | ETOL-UK | 500
 |  | ENV - Germany | 300
 |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | TOTAL
 | Emissions | Deal | Value |  | Deal | Value |  | Deal | Value |  | Deal | Value
 |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | TOTAL
 | Weather | Deal | Value |  | Deal | Value |  | Deal | Value |  | Deal | Value
 |  | SMUD | 500 |  | Harvard Fund | 250
 |  | IPC | 40 |  | Conoco Propane Hedge | 100
 |  |  |  |  | Conoco Propane Products | 100
 |  |  |  |  | Griffin | 25
 |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | TOTAL
Global | Risk Markets | Deal | Value |  | Deal | Value |  | Deal | Value |  | Deal | Value
 |  | Project Mensa | 9000 |  |  |  |  |  |  |  | Project MI - 3 | 15000
 |  |  |  |  |  |  |  |  |  |  | Project Taft | 10000
 |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | TOTAL
 | Transportation | Deal | Value |  | Deal | Value |  | Deal | Value |  | Deal | Value
 |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | TOTAL
 | LNG | Deal | Value |  | Deal | Value |  | Deal | Value |  | Deal | Value
 |  |  |  |  | [NAME] | 1538
 |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | TOTAL
 | Middle East | Deal | Value |  | Deal | Value |  | Deal | Value |  | Deal | Value
 |  |  |  |  | Oman sell down | 2900
 |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | TOTAL
 | Puerto Rico | Deal | Value |  | Deal | Value |  | Deal | Value |  | Deal | Value
 |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | SUBTOTAL |  |  | TOTAL
 |  | TOTALS |  |  | TOTALS |  |  | TOTALS |  |  | TOTALS |  |  | TOTALS
Spreadsheet: andy_zipper__177__DYNEGY-ICE VOL Apr25
Sheet: Enron Activity - Dynegy Direct

DYNEGY DIRECT
Transactions and Notional Volume
By Enron Entity, Commodity, and [NAME]
Note: Total Activity for Dynegy Direct is not available, therefore Enron's percentage can not be calculated
Enron North America - Dynegy Direct |  |  |  |  | Enron Power Marketing - Dynegy Direct |  |  |  |  | Enron Global Liquids - Dynegy Direct
 |  | Data |  |  |  |  | Data |  |  |  |  | Data
Major Commodity | Enron Trader | Transactions | Notional Volume |  | Major Commodity | Enron Trader | Transactions | Notional Volume |  | Major Commodity | Enron Trader | Transactions | Notional Volume
US Natural Gas | [NAME] | 2 | 20000 |  | Power | [NAME] | 3 | 5600 |  | (blank) | No Activity | 1 | 0
 | [NAME] | 2 | 10000 |  |  | [NAME] | 5 | 13600 |  | (blank) Total |  | 1 | 0
US Natural Gas Total |  | 4 | 30000 |  | Power Total |  | 8 | 19200 |  | Grand Total |  | 1 | 0
Grand Total |  | 4 | 30000 |  | Grand Total |  | 8 | 19200
Spreadsheet: 150223d1334337273-lookup-part-description-and-price-from-different-tabs-beerbowerr_toolandequipment-test_13apr12_01
Sheet: Tool Sale Order

Line | Part # | Desc | Gold | Tool Day Price
AIT | 10004
APX | 122090GR
BK | 8156229
Spreadsheet: 740568d1626382009-counting-winner-across-multiple-columns-most-high-scores
Sheet: Sheet1

 | Game 1 | Game 2 | Game 3 | Game 4 | Game 5 | Game 6 | # of times scored highest
[NAME] | 433 | 107 | 345 | 223 | 109 | 306 | 0
[NAME] | 512 | 483 | 209 | 377 | 455 | 118 | 2
[NAME] | 202 | 306 | 402 | 187 | 502 | 409 | 3
[NAME] | 388 | 154 | 398 | 209 | 318 | 227 | 0
[NAME] | 129 | 411 | 271 | 329 | 198 | 212 | 0
[NAME] | 88 | 179 | 209 | 401 | 333 | 343 | 1
[NAME] | 309 | 238 | 298 | 128 | 254 | 199 | 0
Spreadsheet: 814363d1674313310-attendance-tracker-ref-errors-employee-attendance-test-2
Sheet: Company Holidays

 | Company holidays
 | Company holidays | Description
 |  | New Year's Day
 |  | MLK Day
 |  | Good Friday
 |  | Memorial Day
 |  | Independence Day
 |  | Labor Day
 |  | Election Day
 |  | Veteran's Day
 |  | Thanksgiving Day
 |  | Christmas Day