tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 690926d1597374513-use-dropdown-to-get-from-a-pivot-table-dynamic-range-excel
Sheet: Merge_All (2)

Pay Item | Rate |  | Row Labels | Average
G38P2.A | 666 |  | G10P1 | 813.0833333333334 |  | G36P2.3
G38P2.A | 1324 |  | G2C2P1 | 932
G4P2 | 1260 |  | G36P1 | 795.44
G7P9.1.1 | 319 |  | G36P2.1 | 841.6666666666666
G7P9.1.1 | 1411 |  | G36P2.3 | 979.5625
G36P1 | 734 |  | G38P2.A | 826.675
G36P1 | 216 |  | G4P1 | 794.7692307692307
G2C2P1 | 670 |  | G4P2 | 790.4
G4P4 | 871 |  | G4P4 | 726.3333333333334
G36P2.3 | 1076 |  | G7P9.1.1 | 921.2857142857143
G10P1 | 407 |  | Grand Total | 845.19
G38P2.A | 1407
G38P2.A | 600
G7P9.1.1 | 878
G4P4 | 602
G36P1 | 1423
G2C2P1 | 868
G7P9.1.1 | 976
G7P9.1.1 | 273
G36P2.3 | 1260
G38P2.A | 266
G7P9.1.1 | 588
G38P2.A | 344
G2C2P1 | 686
G7P9.1.1 | 759
G7P9.1.1 | 1143
G38P2.A | 1133
G7P9.1.1 | 1252
G36P2.1 | 871
G7P9.1.1 | 697
G4P2 | 805
G7P9.1.1 | 1411
G36P2.3 | 1271
G7P9.1.1 | 1052
G4P1 | 460
G38P2.A | 1479
G36P1 | 551
G38P2.A | 639
G38P2.A | 385
G4P4 | 557
G4P1 | 771
G38P2.A | 534
G4P4 | 1208
G36P2.3 | 1445
G4P4 | 271
G36P2.1 | 772
G2C2P1 | 1330
G7P9.1.1 | 1220
G7P9.1.1 | 281
G36P2.1 | 1165
G38P2.A | 562
G4P1 | 586
G36P2.1 | 846
G38P2.A | 205
G38P2.A | 1140
G10P1 | 1268
G38P2.A | 1481
G10P1 | 802
G7P9.1.1 | 644
G4P1 | 962
G4P2 | 302
G38P2.A | 470
G36P1 | 824
G36P2.1 | 860
G4P2 | 1065
G36P2.1 | 279
G36P2.3 | 1197
G2C2P1 | 1304
G36P2.3 | 1461
G7P9.1.1 | 644
G4P1 | 697
G7P9.1.1 | 774
G7P9.1.1 | 461
G2C2P1 | 764
G36P2.3 | 935
G4P1 | 1106
G36P2.3 | 1059
G38P2.A | 897
G38P2.A | 1315
G36P1 | 586
G10P1 | 446
G7P9.1.1 | 1184
G38P2.A | 1441
G7P9.1.1 | 1394
G36P1 | 270
G38P2.A | 351
G36P1 | 943
G38P2.A | 838
G36P1 | 977
G10P1 | 328
G2C2P1 | 676
G4P1 | 836
G36P2.3 | 768
G7P9.1.1 | 1171
G10P1 | 1368
G36P1 | 804
G38P2.A | 668
G36P2.3 | 466
G36P1 | 851
G4P1 | 745
G7P9.1.1 | 815
G36P2.1 | 451
G36P1 | 804
G2C2P1 | 1261
G36P1 | 954
G36P2.3 | 966
G36P2.1 | 1068
G2C2P1 | 343
G4P4 | 522
G38P2.A | 899
G4P4 | 238
G36P1 | 647
G4P4 | 779
G38P2.A | 691
G36P2.3 | 881
G10P1 | 1333
G36P2.1 | 754
G38P2.A | 416
G7P9.1.1 | 768
G4P4 | 766
G7P9.1.1 | 1361
G4P4 | 718
G4P2 | 294
G4P4 | 373
G38P2.A | 785
G4P2 | 1183
G4P2 | 677
G7P9.1.1 | 1476
G4P4 | 340
G36P1 | 754
G38P2.A | 706
G4P2 | 236
G4P4 | 241
G4P4 | 1171
G36P2.1 | 1225
G38P2.A | 509
G38P2.A | 1360
G36P2.3 | 319
G38P2.A | 533
G10P1 | 746
G7P9.1.1 | 358
G4P1 | 1398
G36P1 | 582
G4P4 | 645
G4P4 | 1238
G38P2.A | 1360
G2C2P1 | 1423
G36P2.1 | 547
G7P9.1.1 | 904
G36P1 | 532
G4P1 | 485
G38P2.A | 1319
G4P2 | 1313
G38P2.A | 909
G7P9.1.1 | 202
G4P2 | 624
G7P9.1.1 | 1339
G2C2P1 | 952
G38P2.A | 763
G36P2.1 | 1262
G4P4 | 1253
G38P2.A | 1283
G7P9.1.1 | 1348
G10P1 | 454
G4P2 | 801
G36P1 | 799
G7P9.1.1 | 1456
G4P1 | 424
G4P2 | 874
G38P2.A | 658
G36P1 | 798
G4P2 | 1423
G2C2P1 | 535
G7P9.1.1 | 650
G36P1 | 1301
G38P2.A | 251
G4P4 | 1281
G10P1 | 581
G36P1 | 1373
G38P2.A | 787
G36P1 | 651
G4P2 | 302
G36P2.3 | 984
G10P1 | 762
G36P2.3 | 393
G7P9.1.1 | 1321
G38P2.A | 546
G7P9.1.1 | 1058
G7P9.1.1 | 657
G36P1 | 564
G4P2 | 697
G2C2P1 | 1310
G10P1 | 1262
G36P2.3 | 1192
G4P1 | 835
G4P1 | 1027
G2C2P1 | 926
G38P2.A | 1147
G36P1 | 1318
G36P1 | 630
Spreadsheet: 808534d1670510011-2-formulas-subtract-from-specific-columns-formulatest
Sheet: Data

Department | Name | Target | 2022-06-01 00:00:00 | 2022-07-01 00:00:00 | 2022-08-01 00:00:00 | 2022-09-01 00:00:00 | 2022-10-01 00:00:00 | 2022-11-01 00:00:00 | 2022-12-01 00:00:00
DeptA | Person1 | 1750
DeptB | Person2 | 1300
DeptA | Person1 | 1750
DeptB | Person1 | 1750
DeptA | Person1 | 1750
DeptB | Person6 | 1250
DeptA | Person7 | 2485
DeptB | Person8 | 2552
DeptA | Person9 | 251
DeptB | Person10 | 25455
DeptA | Person11 | 1255
DeptB | Person12 | 255
DeptA | [NAME] | 654
DeptB | Person14 | 654
DeptA | Person15 | 651
DeptB | Person16 | 651
DeptA | Person17 | 651
DeptB | Person18 | 651
DepC | Person19 | 68
[NAME] | [NAME] | 897
DepC | Person21 | 9
[NAME] | Person22 | 598
DepC | Person23 | 4987
[NAME] | Person24 | 98
DepC | Person25 | 9848
[NAME] | Person26 | 99
DepC | Person27 | 18
[NAME] | Person28 | 9
Spreadsheet: 824772d1680811599-looking-for-formula-to-return-array-of-data-excluding-blank-cells-test-deliverables
Sheet: Input Data

 | Instructions: Enter in all of the deliverable items along with the first due date into each section dependig on the frequency.  Enter in the Client Name and the Year.  Once you are done with entering the dates, click on the "Done" button - this will save the client fact sheet and client calendar to your desktop in a pdf version.
 |  |  |  | Annual [NAME] |  |  | Semi-Annual Deliverables
 |  |  |  | Deliverable Item | Due Date |  | Deliverable Item | First Due Date
 |  |  |  | Reporting 1 | 2023-01-01 00:00:00 |  | Semi Reporting | 2023-05-01 00:00:00
 |  |  |  | Reporting 2 | 2023-01-02 00:00:00
 |  |  |  | Reporting 3 | 2023-01-03 00:00:00
 | Client Name | Bakery Bees |  | Reporting 4 | 2023-01-04 00:00:00
 | Year | 2023 |  | Reporting 5 | 2023-01-05 00:00:00
 |  |  |  | Reporting 6 | 2023-01-06 00:00:00
 |  |  |  | Reporting 7 | 2023-01-07 00:00:00
 |  |  |  | Reporting 8 | 2023-01-08 00:00:00
 |  |  |  | Reporting 9 | 2023-01-09 00:00:00
 |  |  |  | Reporting 10 | 2023-01-10 00:00:00
 |  |  |  | Reporting 11 | 2023-01-11 00:00:00
 |  |  |  | Reporting 12 | 2023-01-12 00:00:00
 |  |  |  | Reporting 13 | 2023-01-13 00:00:00
 |  |  |  | Reporting 14 | 2023-01-14 00:00:00
 |  |  |  | Quarterly Deliverables |  |  | Monthly Deliverables
 |  |  |  | Deliverable Item | First Due Date |  | Deliverable Item | First Due Date
 |  |  |  | Quarterly 1 | 2023-04-01 00:00:00 |  | Monthly 1 | 2023-02-01 00:00:00
 |  |  |  | Quarterly 2 | 2023-04-02 00:00:00
 |  |  |  | Quarterly 3 | 2023-04-03 00:00:00
 |  |  |  | Quarterly 4 | 2023-04-04 00:00:00
 |  |  |  | Quarterly 5 | 2023-04-05 00:00:00
 |  |  |  | Quarterly 6 | 2023-04-06 00:00:00
 |  |  |  | Quarterly 7 | 2023-04-07 00:00:00
Spreadsheet: 764445d1642748750-data-from-lw-compared-to-tw-formula-help
Sheet: Sheet1

PRESENT (W2)
Name | Employee # | Scans WTD | LW CHANGE | Scan PW |  | Last Week | LW Change
[NAME] | 349587 | 34.2 |  | 12.4
Morgan | 948357 | 56.5 |  | 42.3
[NAME] | 893455 | 76.7 |  | 98.7
[NAME] | 348094 | 85.3 |  | 89.5
[NAME] | 895435 | 87.6 |  | 92.3
Fredrick | 243978 | 89.2 |  | 34.5
[NAME] | 458095 | 93.4 |  | 42.3
Rileh | 120984 | 98.5 |  | 96.4
DATA SHOWN SHEET2
 |  |  |  |  |  |  |  | -1.2
 |  |  |  |  |  |  |  | -4.9
 |  |  |  |  |  |  |  | 3.3
Spreadsheet: 46839d1245771759-rows-to-column-for-specific-data-sample-spreadsheet
Sheet: Worksheet A

Dealer # | Dealer Name | Zip Code
A30352 | [NAME] | 48152
A30352 | [NAME] | 48335
B02194 | [NAME]'s Refrig Htg & A/C Inc | 48038
B02194 | [NAME]'s Refrig Htg & A/C Inc | 48044
B02194 | [NAME]'s Refrig Htg & A/C Inc | 48036
B06674 | Hopkins Air Conditioning | 50266
B06674 | Hopkins Air Conditioning | 53135
B07618 | Aiello Home Services | 06096,06095,06082,06088,06035,06033,06117,06119
B07838 | Parrish Services | 20147,20165
B10342 | [NAME] | 19958,19971,19968,19969,19930,19970
B12086 | Patriot Air Comfort Systems LLC | 43221,43054,43071,43147,43209
Spreadsheet: cdafa788-bffa-4a16-a90a-7aa5f956a816
Sheet: Sheet1

COUNTY SUMMARY/BALLOTS CAST | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6
 | Republican |  |  | Democratic
 | Regular | Absentee | Total | Regular | Absentee | Total
Belknap County | 3880 | 176 | 4056 | 1738 | 81 | 1819
Carroll County | 5410 | 257 | 5667 | 2146 | 132 | 2278
Cheshire County | 3992 | 111 | 4103 | 3989 | 152 | 4141
Coos County | 1511 | 77 | 1588 | 1342 | 69 | 1411
Grafton County | 3550 | 159 | 3709 | 3037 | 184 | 3221
Hillsborough County | 20639 | 807 | 21446 | 13818 | 1030 | 14848
Merrimack County | 8289 | 272 | 8561 | 6315 | 262 | 6577
Rockingham County | 15663 | 543 | 16206 | 8885 | 399 | 9284
Strafford County | 4641 | 143 | 4784 | 4837 | 169 | 5006
Sullivan County | 1807 | 36 | 1843 | 1651 | 44 | 1695
Totals | 69382 | 2581 | 71963 | 47758 | 2522 | 50280
Spreadsheet: 790829d1660020511-the-number-of-unique-values-in-a-matrix-number-of-unique-values-hd
Sheet: Sheet1

emphasis | garbage | mud | feedback | accident | method | lake | gene | addition | cheek | library | addition
suggestion | worker | depth | teaching | meat | relation | birthday | pie | tradition | lake | addition | setting
audience | depth | income | physics | wife | wife | guest | community | birthday | physics | medicine | intention
community | wife | variation | historian | chest | meat | wife | article |  | meat | guest | photo
outcome | method | birthday | garbage | mud | variation | scene | equipment | garbage | article | accident | feedback
coffee | depth | gene | birthday | depth | language | river | language | medicine | photo | intention | garbage
pie | depth | user | garbage | garbage | accident | tradition | teaching | bonus | photo | scene | variation
pie | scene | relation |  | outcome | feedback | historian | feedback | physics | mud | gene | sympathy
article | intention | meat | language | addition | article | article | worker | equipment | user | marketing | wife
accident | marketing | tradition | chest | river | method | scene | chest | pie | sympathy | series | outcome
tradition | pie | bonus | birthday | teaching | disease | coffee | variation | photo | cheek | intention | guest
variation | sympathy | scene | feedback | worker | series | gene | lake | coffee | equipment | feedback | pie
#[NAME] values in A1:L12
 | please add other solutions, preferably simpler ones
Spreadsheet: 126712d1319831074-multiple-regression-solver-solver-2-var-indep
Sheet: caso precio y publi con solver

 |  |  |  | error | regresión | total
TARTAS | PRECIO | Publicidad ($[NAME]) | Tartas estimadas ([NAME] con [NAME]) | SSE | SSR | SST
350 | 5.5 | 3.3 | 386.3175508336296 | 1318.964498553273 | 169.4105940795925 | 2433.777777777776
460 | 7.5 | 3.3 | 386.3175508336296 | 5429.103315154753 | 169.4105940795925 | 3680.444444444447
350 | 8 | 3 | 364.6249755977526 | 213.8899112348593 | 1204.670096701045 | 2433.777777777776 |  |  | r2 | 0.3094917834645479
430 | 8 | 4.5 | 473.0878517771378 | 1856.562970768593 | 5439.72899087749 | 940.4444444444456 |  |  | r2 adjust | 0.1944070807086392
350 | 6.8 | 3 | 364.6249755977526 | 213.8899112348593 | 1204.670096701045 | 2433.777777777776
380 | 7.5 | 4 | 436.9335597173427 | 3241.430222088233 | 1413.777024128759 | 373.777777777777
430 | 4.5 | 3 | 364.6249755977526 | 4273.893815594442 | 1204.670096701045 | 940.4444444444456
470 | 6.4 | 3.7 | 415.2409844814657 | 2998.549780559078 | 253.0533650506781 | 4993.77777777778
450 | 7 | 3.5 | 400.7792676575476 | 2422.680492327338 | 2.090726069941083 | 2567.111111111113
490 | 5 | 4 | 436.9335597173427 | 2816.047084272829 | 1413.777024128759 | 8220.444444444447
340 | 7.2 | 3.5 | 400.7792676575476 | 3694.119376987814 | 2.090726069941083 | 3520.444444444442
300 | 7.9 | 3.2 | 379.0866924216706 | 6254.704918199928 | 409.9264682058147 | 9867.111111111108
440 | 5.9 | 4 | 436.9335597173427 | 9.4030560071031 | 1413.777024128759 | 1653.777777777779
450 | 5 | 3.5 | 400.7792676575476 | 2422.680492327338 | 2.090726069941083 | 2567.111111111113 |  |  | Coeficientes
300 | 7 | 2.7 | 342.9324003618755 | 1843.19100083237 | 3181.065240050875 | 9867.111111111108 |  |  | 147.6992232389821
399.3333333333333 |  |  |  | 39009.11084614281 | 17484.20879304328 | 56493.33333333333 |  |  | 0
 |  |  |  | 57.01542981666075 |  |  |  |  | 72.30858411959015
Resumen
[NAME]
Coeficiente de correlación múltiple | 0.722134292240182
Coeficiente de determinación R^2 | 0.5214779360292287
R^2  ajustado | 0.4417242587007668
Error típico | 47.46341263476482
Observaciones | 15
ANÁLISIS DE VARIANZA
 | Grados de libertad | Suma de cuadrados | Promedio de los cuadrados | F | Valor crítico de F
Regresión | 2 | 29460.02686607789 | 14730.01343303894 | 6.538606789020468 | 0.0120063722331864
Residuos | 12 | 27033.30646725544 | 2252.775538937953
Total | 14 | 56493.33333333333
 | Coeficientes | Error típico | Estadístico t | Probabilidad | Inferior 95% | Superior 95%
Intercepción | 306.5261932837437 | 114.2538935104915 | 2.682851182271496 | 0.01993159098536969 | 57.58834426365158 | 555.4640423038358
PRECIO | -24.97508951782694 | 10.83212511941165 | -2.305650021810631 | 0.03978846117866518 | -48.5762627005617 | -1.373916335092186
Publicidad ($[NAME]) | 74.13095748874096 | 25.96731791793909 | 2.854779139031868 | 0.01449362679645921 | 17.55303205708756 | 130.7088829203944
Spreadsheet: 858643d1707254525-create-spread-per-workweek-based-on-dataset-with-start-and-finish-dates-weekly-table-based-on-dates-excelforum
Sheet: Sheet1

Activity A | 2024-02-05 00:00:00 | 2024-02-12 00:00:00 |  | 2024-01-15 00:00:00 | 2024-01-22 00:00:00 | 2024-01-29 00:00:00 | 2024-02-05 00:00:00 | 2024-02-12 00:00:00 | 2024-02-19 00:00:00
Activity B | 2024-01-29 00:00:00 | 2024-02-07 00:00:00
Activity C | 2024-01-18 00:00:00 | 2024-02-20 00:00:00
Activity D | 2024-01-31 00:00:00 | 2024-02-10 00:00:00
Spreadsheet: 880378d1728044653-filter-functions-with-combobox-example-2-
Sheet: Raw

Company | Month | Level | Partcipant | Training Topic | Department | Amount | Result
A | Jan | Manager | [NAME] | Excel | GA | 50 | Passed |  |  |  |  |  |  |  | Topics | Dept | Company
A | Jan | Staff | [NAME] | Excel | HR | 50 | Passed |  |  |  |  |  |  |  | Excel | GA | A
A | Feb | Staff | Marlyn | Excel | HR | 50 | Passed |  | Cell link Topics | 4 |  |  |  |  | Powerpoint | HR | B
B | Feb | Manager | [NAME] | Excel | Sales | 55 | Passed |  | Cell link Dept | 6 |  |  |  |  | Power BI | Sales | C
B | Mar | Manager | [NAME] | Powerpoint | Sales | 30 | Not Passed |  | Cell link Company | 4 |  |  |  |  | ALL | Finance | ALL
B | Mar | Staff | Cathy | Powerpoint | Finance | 30 | Passed |  |  |  |  |  |  |  |  | Logistics
C | Mar | Staff | [NAME] | Power BI | Finance | 60 | Not Passed |  |  | Topics |  |  |  |  |  | ALL
C | Mar | Staff | Michelle | Powerpoint | Logistics | 50 | Passed |  |  |  |  |  | if we would like to know how many topics followed based on conditions below, how to put in in formula?
C | Apr | Staff | Michelle | Excel | Logistics | 60 | Passed
 |  |  |  |  |  |  |  |  |  |  |  |  | Combobox Option | Choose | [NAME] | Expected Result
 |  |  |  |  |  |  |  |  |  | Dept |  |  | Company & Dept & Topics | ALL |  | 3
 |  |  |  |  |  |  |  |  |  |  |  |  | Company & Dept | ALL |  | 1
 |  |  |  |  |  |  |  |  |  |  |  |  | Company & Topics | ALL |  | if dept=sales, then topics =2;if dept = HR , then topics=1
 |  |  |  |  |  |  |  |  |  |  |  |  | Dept & Topics | ALL |  | if company=A, then topics=1, if company=B then topics=2
 |  |  |  |  |  |  |  |  |  | Company |  |  | Company | ALL |  | if dept=Sales and topics=Excel, then topics=1
 |  |  |  |  |  |  |  |  |  |  |  |  | Dept | ALL |  | if company=A and topics =Excel, then topics=1
 |  |  |  |  |  |  |  |  |  |  |  |  | Topics | ALL |  | if company=B and dept=finance, then topcis=2
Spreadsheet: 844642d1696396814-hi-i-need-a-few-things-adding-or-changing-on-this-fishing-results-workbook-please-golborne-results-master-copy-aligw
Sheet: 13

Name | lbs | oz | Oz (total) | Points
Spreadsheet: 231115d1366952791-golf-leaders-table-golf-leaders-v1
Sheet: Scores

 |  |  |  |  | HIDE COLUMNS IF DESIRED |  |  |  |  |  |  |  |  |  | FINISH
 |  | Played | Totals | Wins | Win | 2nd | 3rd | 4th | 5th | 6th | 7th | 8th | 9th | 10th | RD1 | RD2 | RD3 | RD4 | RD5 | RD6 | RD7 | RD8 | RD9 | RD10 | RD11 | RD12 | [NAME]
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 1
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 2
 | Glynn Cooper |  |  |  |  |  |  |  |  |  |  |  |  |  | 3
 | Kev Pine |  |  |  |  |  |  |  |  |  |  |  |  |  | 4
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 5
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 6
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 7
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 8
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 9
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 10
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 11
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 12
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 13
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 14
 | [NAME] |  |  |  |  |  |  |  |  |  |  |  |  |  | 15
 | [NAME] horbury |  |  |  |  |  |  |  |  |  |  |  |  |  | 16
Spreadsheet: 775870d1649355450-drop-down-menu-with-no-blanks-tendina
Sheet: fonte

NOME | ALIMENTO
Pippo | mela | Pluto
Pippo | pera | Topolino
Pippo | banana | Gastone
Pluto | fragola
Topolino | pera
Pluto | ribes
Topolino | ananas
Gastone | ciliegia
Spreadsheet: 861784d1709627267-help-with-creating-a-formula-to-compare-data-between-sheets-zeszyt1-4-aligw
Sheet: Arkusz2

NAME | SURNAME | 2024-03-04 00:00:00 | 2024-04-05 00:00:00 | 2024-10-10 00:00:00
[NAME] | Figo
[NAME] | [NAME]
Zinedine | [NAME]
[NAME] | Beckham
Iker | Casillas
Spreadsheet: 2221c500-1a35-40aa-b8bc-d710e0f6acb9
Sheet: 11c

Product | AUD** | CAD | CHF | DKK | KRW | NOK | NZD | SEK | ZAR | TOTAL
Basis* | <100 | <100 | <100 |  |  |  |  |  |  | <100
FRA |  |  |  | <100 |  | <100 |  | <100 |  | <100
IRS- [NAME] | 1041 | 715 | 253 | <100 |  | <100 | 248 | 329 | 156 | 2742
OIS |  | <100 | <100 |  |  |  |  |  |  | <100
VNS
Zero Coupon
TOTAL | 1041 | 715 | 253 | <100 |  | <100 | 248 | 329 | 156 | 2742
Estimated swap transaction volumes, week ending December 21, by product type and major currency (millions of USD), all participant types, cleared and uncleared.
Total(s) may be less than total shown for this asset class in the All Swaps reports and/or other CFTC Swaps Report tables if products were excluded from this table that (a) presented disclosure risk or (b) did not belong in the categories shown.
*See Data Dictionary for product descriptions.
**See Data Dictionary for major currency descriptions.
***N/A indicates that data are not currently available.
Note: Transaction volumes currently include house and client trades by FCMs in the interest rate asset class.
The Commission requests feedback on the format and content of this CFTC Swaps Report table. Submit comments to [EMAIL].
Spreadsheet: 848450d1699193354-how-to-filter-select-specific-strings-from-a-big-database-if-and-or-search-left-example-for-forum-3-nov-2023-jacc-1-
Sheet: Forum (3)

 |  |  |  |  |  |  |  |  |  | How can I adapt the formula to only exclude strings that start with "Method", "A method", "The Method"?
Some examples for strings that occur in the big data sheet (80 columns; 900 rows) |  |  |  | Two attemps from myself (but not succesful yet) |  |  |  |  |  | Method
 |  |  |  |  |  |  |  |  |  | A method
Attempt 1 |  |  |  |  |  |  |  |  |  | The method
Apparatus for controlling a flow of molten source material | Apparatus for controlling something different | A method for controlling a flow
A method of improving a measurement of a parameter of interest | A method of improving a measurement of a parameter of interest | A method as claimed in claim 1
A lithographic apparatus | A lithographic apparatus comprising: [NAME] and the processing arrangement of claim XXX. | Exposure apparatus
An apparatus to perform the method of any of claims 1 to 14. | An apparatus | An apparatus, comprising a component
Method for control a flow | Method for controlling a flow according to claim 1 | The method for controlling a flow according to claim 1 or 2
 |  |  |  |  | Apparatus for controlling something different | A method for controlling a flow |  |  |  |  |  | Apparatus for controlling something different
 |  |  |  | A method of improving a measurement of a parameter of interest | A method of improving a measurement of a parameter of interest
 |  |  |  | A lithographic apparatus | A lithographic apparatus comprising: [NAME] and the processing arrangement of claim XXX. | Exposure apparatus |  |  |  |  | A lithographic apparatus | A lithographic apparatus comprising: [NAME] and the processing arrangement of claim XXX. | Exposure apparatus
 |  |  |  | An apparatus to perform the method of any of claims 1 to 14. | An apparatus | An apparatus, comprising a component |  |  |  |  | An apparatus to perform the method of any of claims 1 to 14. | An apparatus | An apparatus, comprising a component
 |  |  |  | Method for control a flow
Spreadsheet: 346c3b30-51fb-4be2-82d6-37e462978879
Sheet: FHA - VA Rates

 |  | TIB-The Independent BankersBank
 |  | 350 [NAME]., [NAME]
 |  | [PHONE] Toll Free//[PHONE] fax |  |  |  | Loan Level Price Adjustment
 |  | Rate Sheet Effective: |  |  | 2014-06-20 09:49:30.780000
Product: FHA / VA Fixed Rate 30 Year |  |  |  |  |  | Price adjustments are cumulative
(Prices DO NOT include SRP)
Rate |  | 30 Day | 45 Day | 60 Day | 75 Day | and are added to price quote
4.25 |  | 100.95 | 100.7 | 100.575 |  | Credit Score Adjustment
4.125 |  | 100.95 | 100.7 | 100.575
4 |  | 100.875 | 100.625 | 100.5 |  | 640 - 659 | -0.375
3.875 |  | 100.375 | 100.125 | 100 |  | 660 - 719 | 0
3.75 |  | 99.75 | 99.5 | 99.125 |  | 720 + | 0.125
 |  |  |  |  |  | VA Adjustment | -0.25
Product: FHA / VA Fixed Rate 15 Year |  |  |  |  |  | FHA Streamline [NAME] | -0.5
(Prices DO NOT include SRP)
Rate |  | 30 Day | 45 Day | 60 Day | 75 Day | SRPs
4 |  | 103 | 102.75 | 102.375 |  | See Government [NAME]
3.5 |  | 102.875 | 102.625 |  |  | - 240 Terms Priced with 30 Yr base and 15 Yr SRP
 |  |  |  |  |  | Fees:
 |  |  |  |  |  | Fees for Loans TIB Underwrites
 |  | TIB FHA Fulfillment Loans |  |  |  | Underwriting | 450
Product: FHA Fixed Rate 30 Year |  |  |  |  |  | Insuring | 100
(Prices include SRP) |  |  |  |  |  | Fees for Loans Not Underwritten by TIB
Tier 1 : Rate | Rate | 30 Day | 45 Day |  |  | Administration | 150
 | 4.25 | 104 | 103.875
 | 3.875 | 103.175 | 103.05 |  |  | TIB Fulfillment Loans
[NAME] : Rate
 | 4.25 | 103.9 | 103.775 |  |  | - Credit Score Adjustments Still Apply
 | 3.875 | 103.075 | 102.95
Tier 3 : Rate |  |  |  |  |  | -SRPs are Included
 | 4.25 | 103.8 | 103.675
 | 3.875 | 102.975 | 102.85 |  |  | [NAME]
 |  |  |  |  |  | 30 Year | 0.025
Product: FHA Fixed Rate 15 Year
(Prices include SRP) |  |  |  |  |  | 15 Year | 0.025
Tier 1 : Rate |  | 30 Day | 45 Day
 | 3.5 | 103.75 | 103.625 |  |  | State Tiers
 | 3.25 | 103.75 | 103.625 |  |  | Tier 1  -  AL, AR, FL, GA, IN, KS, KY, LA,
[NAME] : Rate |  |  |  |  |  | MO, MS, ND, NE, OH, OK, PA, TX, WI
 | 3.5 | 103.65 | 103.525
 | 3.25 | 103.65 | 103.525
Tier 3 : Rate |  |  |  |  |  | [NAME], CT, DE, IA, MA, ME, MI, NC,
 | 3.5 | 103.55 | 103.425 |  |  | NH, NY, RI, SC, TN, UT, VT, WY
 | 3.25 | 103.55 | 103.425
 |  |  |  |  |  | Tier 3  -  AZ, CA, CO, DC, HI, ID, IL, MD,
Foot Notes: |  |  |  |  |  | NM, MT, NJ, NM, NV, OR, VA, WA, WV
Loans must be AUS Approved / Eligible from FHA Scorecard
 |  |  |  |  |  | Expiration Dates
Seasoning - No loans older than 60 days from Note Date to TIB |  |  |  |  |  | 15 Day | 2014-07-05 09:49:30.780000
 |  |  |  |  |  | 30 Day | 2014-07-20 09:49:30.780000
 | Call: [NAME], [PHONE] |  |  |  |  | 45 Day | 2014-08-04 09:49:30.780000
 | E-Mail: [EMAIL] |  |  |  |  | 60 Day | 2014-08-19 09:49:30.780000
Spreadsheet: 737093d1624120224-need-age-bucket-using-vlookup-and-if-function-book1-1-
Sheet: Sheet1

 |  |  |  |  |  | Date | Today | Ageing | [NAME] answer here
 |  |  |  |  |  | 2021-06-05 00:00:00 |  |  |  |  |  |  | 15 | 0-15
 |  |  |  |  |  | 2021-06-01 00:00:00 |  |  |  |  |  |  | 16-30 | No calculation needed
 |  |  |  |  |  | 2021-05-16 00:00:00 |  |  |  |  |  |  | 40 | 31-40
 |  |  |  |  |  | 2021-05-19 00:00:00 |  |  |  |  |  |  | 50 | 41-50
 |  |  |  |  |  | 2021-05-21 00:00:00 |  |  |  |  |  |  | 60 | 51-60
 |  |  |  |  |  | 2021-05-17 00:00:00 |  | 41
 |  |  |  |  |  | 2021-05-24 00:00:00
 |  |  |  |  |  | 2021-05-23 00:00:00
 |  |  |  |  |  | 2021-05-29 00:00:00
 |  |  |  |  |  | 2021-06-01 00:00:00 |  | 30
 |  |  |  |  |  | 2021-05-15 00:00:00
 |  |  |  |  |  | 2021-05-23 00:00:00
 |  |  |  |  |  | 2021-06-03 00:00:00
 |  |  |  |  |  | 2021-06-18 00:00:00
 |  |  |  |  |  | 2021-06-16 00:00:00
 |  |  |  |  |  | 2021-06-03 00:00:00
 |  |  |  |  |  | 2021-05-15 00:00:00
 |  |  |  |  |  | 2021-06-03 00:00:00
 |  |  |  |  |  | 2021-05-15 00:00:00
 |  |  |  |  |  | 2021-06-14 00:00:00
 |  |  |  |  |  | 2021-05-29 00:00:00
 |  |  |  |  |  | 2021-06-08 00:00:00
 |  |  |  |  |  | 2021-05-17 00:00:00
 |  |  |  |  |  | 2021-05-18 00:00:00
 |  |  |  |  |  | 2021-06-10 00:00:00
 |  |  |  |  |  | 2021-05-25 00:00:00