tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 268726d1380756613-help-circumference-error-circumference-error
Sheet: Table 4 Inter %Region (FMO) 

Analyst 1
 | Relative Percentage
Animal | CD3+ | CD3+/TSF19+ | CD3-/CD20+ | CD3-/CD20+/TSF19+ | CD3-/CD20-/CD16+ | CD3-/CD20-/CD16+/TSF19+
MF38576F | 63.4 | 1.4 | 14.9 | 3.1 | 8.7 | 85.2
MF27755F | 80 | 5.9 | 9.1 | 3.7 | 5.1 | 95.7
MF38533F | 76.8 | 1.6 | 19.1 | 19.7 | 1.1 | 90.3
MF39736M | 63.3 | 0.6 | 21.9 | 9.8 | 10.4 | 89
MF39737M | 57.2 | 2.7 | 21.1 | 4.2 | 6.6 | 79.2
MF39754M | 74.8 | 0.9 | 12.8 | 37.9 | 2.8 | 71.4
Mean | 69.3 | 2.2 | 16.5 | 13.1 | 5.8 | 85.1
SD | 9.1 | 2 | 5.1 | 13.7 | 3.5 | 8.7
Analyst 2
 | Relative Percentage
Animal | CD3+ | CD3+/TSF19+ | CD3-/CD20+ | CD3-/CD20+/TSF19+ | CD3-/CD20-/CD16+ | CD3-/CD20-/CD16+/TSF19+
MF38576F | 63.5 | 1.6 | 13.5 | 3.8 | 9.4 | 83.9
MF27755F | 76.7 | 8.2 | 8.8 | 6.1 | 8.4 | 96.1
MF38533F | 75.9 | 2 | 19.4 | 21.5 | 1.6 | 96.3
MF39736M | 63.6 | 0.7 | 21.9 | 10.9 | 10 | 89.1
MF39737M | 57 | 3.3 | 21.2 | 5.6 | 7.3 | 73.6
MF39754M | 74.9 | 1 | 13.7 | 42.4 | 2.9 | 66.8
Mean | 68.6 | 2.8 | 16.4 | 15.1 | 6.6 | 84.3
SD | 8.3 | 2.8 | 5.2 | 14.9 | 3.5 | 12.1
[NAME]
Animal | Relative Percentage
MF38576F | CD3+ | CD3+/TSF19+ | CD3-/CD20+ | CD3-/CD20+/TSF19+ | CD3-/CD20-/CD16+ | CD3-/CD20-/CD16+/TSF19+
Mean | 63.5 | 1.5 | 14.2 | 3.5 | 9.1 | 84.6
%difference | 0.2 | 13.3 | 9.9 | 20.3 | 7.7 | 1.5
Pass/Fail | Pass | Pass | Pass | Fail | Pass | Pass
[NAME]
Animal | Relative Percentage
MF27755F | CD3+ | CD3+/TSF19+ | CD3-/CD20+ | CD3-/CD20+/TSF19+ | CD3-/CD20-/CD16+ | CD3-/CD20-/CD16+/TSF19+
Mean | 78.4 | 7.1 | 9 | 4.9 | 6.8 | 95.9
%difference | 4.2 | 32.6 | 3.4 | 49 | 48.9 | 0.4
Pass/Fail | Pass | Fail | Pass | Fail | Fail | Pass
[NAME]
Animal | Relative Percentage
MF38533F | CD3+ | CD3+/TSF19+ | CD3-/CD20+ | CD3-/CD20+/TSF19+ | CD3-/CD20-/CD16+ | CD3-/CD20-/CD16+/TSF19+
Mean | 76.4 | 1.8 | 19.3 | 20.6 | 1.4 | 93.3
%difference | 1.2 | 22.2 | 1.6 | 8.7 | 37 | 6.4
Pass/Fail | Pass | Fail | Pass | Pass | Fail | Pass
[NAME]
Animal | Mean Values (% in Region)
MF39736M | CD3+ | CD3+/TSF19+ | CD3-/CD20+ | CD3-/CD20+/TSF19+ | CD3-/CD20-/CD16+ | CD3-/CD20-/CD16+/TSF19+
Mean | 63.5 | 0.7 | 21.9 | 10.4 | 10.2 | 89.1
%difference | 0.5 | 15.4 | 0 | 10.6 | 3.9 | 0.1
Pass/Fail | Pass | Pass | Pass | Pass | Pass | Pass
[NAME]
Animal | Mean Values (% in Region)
MF39737M | CD3+ | CD3+/TSF19+ | CD3-/CD20+ | CD3-/CD20+/TSF19+ | CD3-/CD20-/CD16+ | CD3-/CD20-/CD16+/TSF19+
Mean | 57.1 | 3 | 21.2 | 4.9 | 7 | 76.4
%difference | 0.4 | 20 | 0.5 | 28.6 | 10.1 | 7.3
Pass/Fail | Pass | Pass | Pass | Fail | Pass | Pass
[NAME]
Animal | Mean Values (% in Region)
MF39754M | CD3+ | CD3+/TSF19+ | CD3-/CD20+ | CD3-/CD20+/TSF19+ | CD3-/CD20-/CD16+ | CD3-/CD20-/CD16+/TSF19+
Mean | 74.9 | 1 | 13.3 | 40.2 | 2.9 | 69.1
%difference | 0.1 | 10.5 | 6.8 | 11.2 | 3.5 | 6.7
Pass/Fail | Pass | Pass | Pass | Pass | Pass | Pass
Spreadsheet: 852704d1702652394-remove-repeated-results-from-sheet-keeping-one-copy-20231215-sample-aligw
Sheet: Incorrect Username

Current Username | Email address | Correct username
 | [EMAIL] | h74108973
[EMAIL] | [EMAIL] | h74111857
EXAD1234 | [EMAIL] | h74131547
Spreadsheet: 77820d1278326832-display-values-based-on-grouping-using-excel-functions-only-display-based-on-grouping
Sheet: Sheet1

Source |  |  |  | Expected Result
ID | Line Numbers | Source |  | ID | Details
ID1 | 1 | ST1 |  |  | Line: 1 Source: ST1  Line: 76 Source: ST4
ID2 | 2 | ST2 |  |  | Line: 2 Source: ST2  Line: 378 Source: ST5
ID1 | 76 | ST4
ID2 | 378 | ST5
Spreadsheet: 856568d1705769317-match-and-extract-lambda-map-filter-extract_2
Sheet: L3

 |  |  |  |  | Match |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | <<< Extract >> | <<< Extract >> |  |  | <<< Extract >>
 |  |  |  |  | B-1/2/3 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | x | x
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | x | x
 |  |  |  |  | Lon-2/5 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Apps,ND | Jasmine | x | x | 36.12
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | x | x
 |  |  |  |  | Lon-1 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Apps,ND | Lily | x | x | 44
 |  |  |  |  | S-4 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [NAME],Matrix | Rose | x | x | 101.36
 |  |  |  |  | x
 |  |  |  |  | ab
Spreadsheet: rick_buy__32915__Power Prices
Sheet: Jan 31

Jan 31 Power Prices
Mid-Columbia
 | First Trade |  | Last Trade
 | Time | Price | Time | Price
Next-Day Pk | 07:07:42 | 430 | 07:12:54 | 385
Next-Day OffPk | 07:15:29 | 335 | 07:31:04 | 315
Balance of Month Pk | 09:45:28 | 330 | 09:45:28 | 330
Mar 01 Pk | 09:45:40 | 365 | 09:45:40 | 365
May 01 Pk | 08:29:55 | 305 | 08:36:05 | 315
NP15
 | First Trade |  | Last Trade
 | Time | Price | Time | Price
Next-Day Pk | 07:10:08 | 280 | 07:10:08 | 280
Next-Day OffPk | 07:06:14 | 290 | 07:22:48 | 255
COB
 | First Trade |  | Last Trade
 | Time | Price | Time | Price
Next-Day Pk | 07:07:38 | 430 | 07:30:45 | 350
PALVE
 | First Trade |  | Last Trade
 | Time | Price | Time | Price
Next-Day Pk | 07:00:00 | 260 | 07:30:26 | 230
Next-Day OffPk | 07:02:19 | 150 | 07:30:25 | 160
Balance of Month Pk | 08:21:42 | 235 | 09:30:11 | 225
Mar 01 Pk | 08:57:26 | 230 | 08:57:26 | 230
Apr 01 Pk | 08:26:52 | 255 | 12:56:50 | 270
May 01 Pk | 08:28:06 | 255 | 12:56:54 | 274
SP15
 | First Trade |  | Last Trade
 | Time | Price | Time | Price
Next-Day Pk | 07:07:18 | 235 | 07:38:08 | 218
Next-Day OffPk | 07:04:27 | 175 | 07:29:48 | 172
Mar 01 Pk | 08:21:00 | 223 | 14:43:34 | 230
Spreadsheet: 803133d1667275189-sumif-each-month-but-ignore-year-end-totals-sumif-but-ignore-the-year-end-totals-sample-
Sheet: Sheet1

 |  | 2022-01-01 00:00:00 |  |  |  |  |  |  |  |  |  |  |  | 2022
 |  | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |  | 20 | 20 | 20 | 20
 |  | Problem:
 |  | I want to sum each individual month across my data set (C5:S5) but ignore the 2022 year total.
 |  | My desired result is 200 (C5:N5 + P5:S5).
Spreadsheet: 0a5f0643-297b-49e5-b675-1e7777c58ccf
Sheet: Reference

Reference | PI | Institution | Project | Value(£k)
BB/L018241/1 | Dr [NAME] | University College London | Automated identification of optimal data-specific organelle clusters using freely available protein annotations | 10
BB/L01825X/1 | Dr Carmen Domene | King's College London | [NAME] for probing cell membrane organisation using neutron sources | 135
BB/L018268/1 | Dr [NAME] | Imperial College London | [NAME] for Optogenetics | 146
BB/[NAME] | Dr [NAME] | The University of Manchester | Predicting Mammalian Gene Essentiality | 150
BB/L018330/1 | Professor [NAME] | University College London | Developing new methods to enable amino acid co-evolution algorithms to be applied to protein-protein interaction prediction | 143
BB/L018438/1 | Professor [NAME] | [NAME], University of London | Proteomics Goes Viral: Novel Resources for Identification and Quantification of Virus Proteins | 148
BB/L018454/1 | Dr [NAME] | The University of Manchester | ProteoFormer - a software toolkit for top-down proteomics | 38
BB/L018462/1 | Dr [NAME] | University of Liverpool | ProteoFormer - a software toolkit for top-down proteomics | 110
BB/L018497/1 | Dr Laurent Gatto | University of Cambridge | Automated identification of optimal data-specific organelle clusters using freely available protein annotations | 115
BB/L018535/1 | Professor [NAME] | University of East Anglia | A tool for haplotype reconstruction from polyploid genomes | 102
BB/L018543/1 | Professor [NAME] | University of Bristol | Tools for Ontology Annotation: dcGO | 81
BB/L018594/1 | Dr [NAME] | University of Reading | Next generation comparative methods | 144
BB/[NAME] | Dr [NAME] | The University of Manchester | Unifying metabolome and proteome informatics | 143
BB/L018632/1 | Dr [NAME] Frances Hodson-Tole | Manchester Metropolitan University | Imaging Motor Unit Recruitment Patterns | 150
BB/L018705/1 | Professor [NAME] | University of York | CellBranch : a toolset for exploring Stem Cell Differentiation and Pluripotency with Branching Process Theory | 149
BB/L018721/1 | Dr [NAME] [NAME] | EMBL - European Bioinformatics Institute | A comprehensive online spectra analysis and visualisation tool for the OMICS sciences | 47
BB/L018756/1 | Professor [NAME] | University of Bristol | Computational tools for enzyme engineering: bridging the gap between enzymologists and expert simulation | 149
Spreadsheet: 152306d1335338870-dynamic-vlookup-dynamic-vlookup
Sheet: Sheet1

Row to start looking in | Lookup value | Formulae
20 | 0.7493364372514635 |  |  |  |  | 0.658994568741222
15 | 0.9613838346612916 |  |  |  |  | 0.02892420709054433
4 | 0.7266501771852446 |  |  |  |  | 0.3383993783035534
2 | 0.1969769289981725 |  |  |  |  | 0.462672797953693
6 | 0.7261148837231859 |  |  |  |  | 0.9141233492999286
6 | 0.8191064156344108 |  |  |  |  | 0.5360121295594608
 |  |  |  |  |  | 0.0524995614974868
 |  |  |  |  |  | 0.1969769289981725
 |  |  |  |  |  | 0.7266501771852446
 |  |  |  |  |  | 0.3422350219153363
 |  |  |  |  |  | 0.7261148837231859
 |  |  |  |  |  | 0.8191064156344108
 |  |  |  |  |  | 0.9823962255168321
 |  |  |  |  |  | 0.1552810014652983
 |  |  |  |  |  | 0.5503417633851865
 |  |  |  |  |  | 0.4744468932307589
 |  |  |  |  |  | 0.8097495503253531
 |  |  |  |  |  | 0.9613838346612916
 |  |  |  |  |  | 0.5840138484936458
 |  |  |  |  |  | 0.4008239689686112
 |  |  |  |  |  | 0.7493364372514635
 |  |  |  |  |  | 0.2161051194020127
Spreadsheet: 272274d1382329732-working-schedule-test
Sheet: Nights

 | 2013-11-17 00:00:00 | 2013-11-18 00:00:00 | 2013-11-19 00:00:00 | 2013-11-20 00:00:00 | 2013-11-21 00:00:00 | 2013-11-22 00:00:00 | 2013-11-23 00:00:00 | 2013-11-24 00:00:00 | 2013-11-25 00:00:00 | 2013-11-26 00:00:00 | 2013-11-27 00:00:00 | 2013-11-28 00:00:00 | 2013-11-29 00:00:00 | 2013-11-30 00:00:00 | 2013-12-01 00:00:00 | 2013-12-02 00:00:00 | 2013-12-03 00:00:00 | 2013-12-04 00:00:00 | 2013-12-05 00:00:00 | 2013-12-06 00:00:00 | 2013-12-07 00:00:00 | 2013-12-08 00:00:00 | 2013-12-09 00:00:00 | 2013-12-10 00:00:00 | 2013-12-11 00:00:00 | 2013-12-12 00:00:00 | 2013-12-13 00:00:00 | 2013-12-14 00:00:00
 | Sun | Mon | Tue | Wed |  | Fri | Sat | Sun | Mon | Tue | Wed | Thu | Fri | Sat | Sun | Mon | Tue | Wed | Thu | Fri | Sat | Sun | Mon | Tue | Wed | Thu | Fri | Sat
[NAME]
Jessica
Jezz
[NAME]
[NAME]
Spreadsheet: 4b07c57d-2389-4545-a4e2-d8e75d4b355b
Sheet: Sheet1

Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4
 | Core Equity Fund
 | [NAME]
 | Inception Date | 2013-03-26 00:00:00
 |  | Class L | Class N | R1000
 | Date | QCELX | QCENX | Index
* | 2013-03-31 00:00:00 | 0.01 | 0.01 | 0.004172232174823209
 | 2013-04-30 00:00:00 | 0.01584158415841586 | 0.01584158415841586 | 0.01805859441151569
 | 2013-05-31 00:00:00 | 0.0341 | 0.0351 | 0.0222
 | 2013-06-30 00:00:00 | -0.01508011310084822 | -0.01600753295668544 | -0.01362827968381131
 | 2013-07-31 00:00:00 | 0.06507177033492839 | 0.06507177033492839 | 0.05350791407164568
 | 2013-08-31 00:00:00 | -0.03953279424977552 | -0.03953279424977552 | -0.02758241840840636
 | 2013-09-30 00:00:00 | 0.0374181478 | 0.0374181478 | 0.03486933
 | 2013-10-31 00:00:00 | 0.045987376 | 0.0450856628 | 0.044019845
 | 2013-11-30 00:00:00 | 0.04396551724137931 | 0.04314063848144944 | 0.02805706685098341
 | 2013-12-31 00:00:00 | 0.02513505 | 0.025426981 | 0.026977807
 | 2014-01-31 00:00:00 | -0.0357142857142857 | -0.03574329813160049 | -0.0319385965192972
 | * partial month return, inception date 3/26/2013
 | Month-end Returns as of | Class L | Class N | R1000
 | January 31, 2014 | QCELX | QCENX | Index
 | 1 Year
 | 3 Years
 | 5 Years
 | 10 Years
 | Since Inception Cumulative Return | 0.1971014541226037 | 0.1954287796957046 | 0.1661172336247165
 | Since Inception Annualized Return
 | Since Inception Annualized Monthly Volatility | 0.1185333538225256 | 0.1185696302818556 | 0.09825694831544347
 | Quarter-end Returns as of | Class L | Class N | R1000
 | December 31, 2013 | QCELX | QCENX | Index
 | 1 Year
 | 3 Years
 | 5 Years
 | 10 Years
 | Since Inception Cumulative Return | 0.2414385450160335 | 0.2397412197181235 | 0.2045901524757585
 | Since Inception Annualized Return
Spreadsheet: 791757d1660574376-pay-as-you-earn-calculation-paye-calculator-hd3
Sheet: Sheet1

Date of Birth | 1963-12-02 00:00:00 | 1942-04-08 00:00:00
Tax start period | 2021-07-01 00:00:00 | 2021-07-01 00:00:00
Tax end period | 2022-06-30 00:00:00 | 2022-06-30 00:00:00
Gross earnings | 123000 | 345000
Pension fund (Tax Deductible | 1755 | 1755
Taxable pay
Annual Tax rebate (not more than 60 years of ages | 8200 | 8200
Annual Tax rebate (more than 60 years of ages | 10900 | 10900
The rates of Income Tax applicable are as follows:
RATES OF NORMAL TAX FOR INDIVIDUALS
Exceeds | Does not exceed
Taxable Income |  |  |  | Rates
0 | 100000 | 0 | 0.2 | E0           +  20% of the excess of E0 00
100000 | 150000 | 20000 | 0.25 | E20 000 + 25% of the excess of E100 000
150000 | 200000 | 32500 | 0.3 | E32 500 + 30% of the excess of E150 000
200000 |  | 47500 | 0.33 | E47 500 + 33% of the excess of E200 000
Tac pro year
Taks pro month
Spreadsheet: sally_beck__33919__GlobalMgmtSum-Q101-0125
Sheet: Expense Weekly Change

ENRON GLOBAL MARKETS
1st QUARTER 2001 EXPENSES - WEEKLY CHANGE
 |  | Direct Expenses
Business Team |  | Forecast | Plan | [NAME] |  | [NAME]
Crude & Products
Coal
Emissions
Weather
Global Risk Markets
Financial Trading
Transportation
LNG
Middle East
Puerto Rico
Finance and Structuring
Office of the Chairman
Subtotal Commercial
Group
Other Interest Related Charges
Total
 |  | Operating Expenses
Business Team |  | Forecast | Plan | [NAME] |  | [NAME]
Spreadsheet: 134890d1325690717-percentages-percentage
Sheet: Sheet1

Total | Taken | Percentage
47 | 4 | 0.0851063829787234
60 | 17 | 0.2833333333333333
44 | 11 | 0.25
59 | 29 | 0.4915254237288136
65 | 13 | 0.2
32 | 8 | 0.25
80 | 16 | 0.2
72 | 71 | 0.9861111111111112
50 | 34 | 0.68
47 | 40 | 0.851063829787234
37 | 25 | 0.6756756756756757
50 | 7 | 0.14
50 | 42 | 0.84
48 | 14 | 0.2916666666666667
50 | 34 | 0.68
59 | 27 | 0.4576271186440678
50 | 24 | 0.48
48 | 29 | 0.6041666666666666
69 | 31 | 0.4492753623188406
47 | 12 | 0.2553191489361702
50 | 17 | 0.34
66 | 14 | 0.2121212121212121
79 | 26 | 0.3291139240506329
62 | 53 | 0.8548387096774194
50 | 16 | 0.32
44 | 37 | 0.8409090909090909
50 | 8 | 0.16
59 | 20 | 0.3389830508474576
50 | 30 | 0.6
47 | 37 | 0.7872340425531915
40 | 33 | 0.825
43 | 35 | 0.813953488372093
50 | 34 | 0.68
60 | 41 | 0.6833333333333333
50 | 43 | 0.86
60 | 22 | 0.3666666666666666
54 | 31 | 0.5740740740740741
49 | 22 | 0.4489795918367347
57 | 26 | 0.456140350877193
27 | 16 | 0.5925925925925926
47 | 38 | 0.8085106382978723
60 | 54 | 0.9
72 | 52 | 0.7222222222222222
79 | 15 | 0.189873417721519
60 | 38 | 0.6333333333333333
47 | 11 | 0.2340425531914894
55 | 16 | 0.2909090909090909
50 | 38 | 0.76
44 | 37 | 0.8409090909090909
67 | 36 | 0.5373134328358209
48 | 28 | 0.5833333333333334
67 | 42 | 0.6268656716417911
51 | 35 | 0.6862745098039216
58 | 35 | 0.603448275862069
50 | 47 | 0.94
50 | 34 | 0.68
72 | 14 | 0.1944444444444444
44 | 15 | 0.3409090909090909
46 | 20 | 0.4347826086956522
30 | 26 | 0.8666666666666667
45 | 43 | 0.9555555555555556
11 | 10 | 0.9090909090909091
49 | 48 | 0.9795918367346939
 |  | 0.08687943262411348 | Should be around 53% - 54%
Spreadsheet: 9dcdc561-b21b-4363-8b1d-b6162f1404a0
Sheet: IBM

IBM
Men | ATP | Tweets Mentioned | Tweets Exclusive | %pos | %neg
Federer | 1 | 487277 | 241728 | 29 | 6
Djokovic | 2 | 124386 | 23336 | 25 | 7
Nadal | 3 | 143864 | 67792 | 15 | 20
[NAME] | 4 | 496039 | 317650 | 42 | 9
Tsonga | 6 | 61594 | 21112 | 28 | 10
Rosol | 103 | 64149 | 19182 | 28 | 16
Women | WTA | Tweets Mentioned | Tweets Exclusive | %pos | %neg
Azarenka | 1 | 19004 | 8283 | 21 | 13
Radwanska | 2 | 55095 | 24666 | 36 | 11
Sharapova | 3 | 47307 | 24173 | 21 | 12
Serena | 4 | 98843 | 65200 | 55 | 3
Stosur | 5 | 8482 | 2881 | 24 | 16
Kvitova | 6 | 14842 | 6568 | 30 | 8
Total number of Tweets collected: 5012137
Total number of Tweets analysed: 2904271
Tweets Mentioned: A Tweet in which the player was mentioned
Tweets Exclusive: A Tweet in which only that player was mentioned
All Tweets filtered to ensure relevance to Wimbledon 2012
Spreadsheet: 856968d1706101687-distribution-of-closing-balance-to-open-pending-invoices-vouchers_-for-ageing-formula
Sheet: Sheet2

Ref No | Voucher No | Voucher Type | [NAME] | Ledger Group | Voucher Date | Voucher Value |  | Code | Modified On
1055 | 1055 | Purchase | Kothari Industries | Sundry Creditors | 2022-11-05 00:00:00 | 32903 |  | STLA9 | TP2
1058 | 1058 | [NAME] | Kothari Industries | Sundry Creditors | 2022-11-06 00:00:00 | 0 |  | STLA9 | TP2
1693 | 1693 | Purchase | Kothari Industries | Sundry Creditors | 2023-01-28 00:00:00 | 30454 |  | STLA9 | TP2
1699 | 1699 | [NAME] | Kothari Industries | Sundry Creditors | 2023-01-29 00:00:00 | 0 |  | STLA9 | TP2
1977 | 1977 | Purchase | Kothari Industries | Sundry Creditors | 2023-03-04 00:00:00 | 30160 |  | STLA9 | TP2
1982 | 1982 | [NAME] | Kothari Industries | Sundry Creditors | 2023-03-05 00:00:00 | 0 |  | STLA9 | TP2
2054 | 2054 | Payment | Kothari Industries | Sundry Creditors | 2023-03-16 00:00:00 |  | 32903 | STLA9 | TP2
 |  |  |  |  |  |  |  | Agae Days | Bucket
 |  |  |  |  |  |  |  |  | 0-90 days
 |  |  |  |  |  |  |  |  | 0-90 days
 |  |  |  |  |  |  |  |  | 0-90 days
 |  |  |  |  |  |  |  |  | 0-90 days
 |  |  |  |  |  |  |  |  | 90-180 days
Ref No | Voucher No | Voucher Type | [NAME] | Ledger Group | Voucher Date | Voucher Value |  | Code | Modified On
26 | 26 | Payment | Amerys Special Refractory | Sundry Creditors | 2022-04-04 00:00:00 | 0 | 91290 | STLA9 | TP2
30 | 30 | Receipt | Amerys Special Refractory | Sundry Creditors | 2022-04-05 00:00:00 | 91290 |  | STLA9 | TP2
31 | 31 | Payment | Amerys Special Refractory | Sundry Creditors | 2022-04-05 00:00:00 | 0 | 91290 | STLA9 | TP2
199 | 199 | Purchase | Amerys Special Refractory | Sundry Creditors | 2022-04-29 00:00:00 | 31583 |  | STLA9 | TP2
391 | 391 | Purchase | Amerys Special Refractory | Sundry Creditors | 2022-05-29 00:00:00 | 163412 |  | STLA9 | TP2
838 | 838 | Payment | Amerys Special Refractory | Sundry Creditors | 2022-09-21 00:00:00 | 0 | 550000 | STLA9 | TP2
933 | 933 | Payment | Amerys Special Refractory | Sundry Creditors | 2022-10-13 00:00:00 | 0 | 194995 | STLA9 | TP2
1159 | 1159 | Payment | Amerys Special Refractory | Sundry Creditors | 2022-11-21 00:00:00 | 0 | 600000 | STLA9 | TP2
1168 | 1168 | Purchase | Amerys Special Refractory | Sundry Creditors | 2022-11-22 00:00:00 | 1221427 |  | STLA9 | TP2
1243 | 1243 | Purchase | Amerys Special Refractory | Sundry Creditors | 2022-12-01 00:00:00 | 989355 |  | STLA9 | TP2
1336 | 1336 | Purchase | Amerys Special Refractory | Sundry Creditors | 2022-12-12 00:00:00 | 57313 |  | STLA9 | TP2
 |  |  |  |  |  |  |  | Outstanding
 |  |  |  |  |  |  |  | Agae Days | Bucket | 2023-03-31 00:00:00
 |  |  |  |  |  |  |  |  | 90-180 days
 |  |  |  |  |  |  |  |  | 90-180 days
Final Output
Party Name | 0-90 | 91-180 | 181-365 | 366-730 | 731-1095 | Above 1095 |  | Total
Amerys Special Refractory |  | 1026805
Kothari Industries
Spreadsheet: 686825d1594826598-index-match-with-merged-cell-client-hub_engagement_eh-1-
Sheet: Overview

Client | July | August | September | October | November | December | January | February | March
Spreadsheet: 758161d1638713653-get-data-from-one-table-to-another-with-any-method-match-index-task-6
Sheet: Hárok1

Get the data from blue table to the red table by using any method.
 |  | Category |  |  |  |  |  |  |  |  |  | Category
 |  | HDD | SPD | FFI | ADW | HDW | LAD | TWC |  |  |  | TWC | LAD | HDW | ADW | FFI | SPD | HDD
Brand | [NAME] |  |  | 2005 |  |  |  |  |  | Brand | [NAME]
 | Bref | 730 |  |  |  |  | 4575 |  |  |  | Silan
 | [NAME] | 3772 |  |  |  |  | 3867 | 3121 |  |  | Pur
 | Clin |  |  |  |  |  |  | 2269 |  |  | Bref
 | Coccolino |  |  | 678 |  |  |  |  |  |  | [NAME]
 | Domestos | 25 |  |  |  |  | 8416 |  |  |  | Clin
 | Finish |  |  |  | 7647
 | Jar |  |  |  | 5922 | 2903
 | Lenor |  |  | 7636
 | Pur |  |  |  |  | 8913
 | Silan |  |  | 5531
 | TS |  |  |  |  | 2502 | 7343
 | Dylon |  |  |  |  |  | 18217
 | [NAME] |  |  |  |  |  |  | 1230
Spreadsheet: benjamin_rogers__922__UBS Energy Prices
Sheet: 02-14-02

As of
2002-02-14 00:00:00
 |  | Power |  |  | Gas - Nymex
 |  | Bid | Ask
PJM |  |  |  |  |  | Close
Mar - April |  | 24.55 | 24.85 |  | Mar
May |  | 28.1 | 28.5 |  | April
June |  | 34.6 | 35 |  | May
July - Aug |  | 47.25 | 47.65 |  | June
 |  |  |  |  | July
ISO NE |  |  |  |  | August
Mar - April |  | 28.55 | 28.75 |  | September
May |  | 31.95 | 32.15 |  | October
June |  | 36.85 | 37.25 |  | November
July - Aug |  | 47.95 | 48.35 |  | December
NY Zone A
Mar - April |  | 24.6 | 24.9
May |  | 28.2 | 28.7
June |  | 34.4 | 34.9
July - Aug |  | 47.25 | 47.75
[NAME]
Mar - April
May
June
July - Aug |  | 60.85 | 61.85
CIN
March |  | 20.65 | 20.8
April |  | 21.45 | 21.7
May |  | 24.9 | 25.1
June |  | 29.8 | 30
July - Aug |  | 38.75 | 39
Spreadsheet: 871777d1718197734-teacher-markbook-letter-grade-to-numerical-value-average-excel-markbook-for-365-aligw
Sheet: Y5

 | Student 1 | Student 2 | Student 3 | Student 4 | Student 5 | Student 6 | Student 7 | Student 8 | Student 9 | Student 10 | Student 11 | Student 12 | Student 13 | Student 14
Achievement standard
students use place value to write and order decimals including decimals greater than one | C+ | B- | C+ | N | C | [NAME] | B- | [NAME] | B | A- | C- | B- | C+ | C+
express natural numbers as products of factors and identify multiples | A- | [NAME] | [NAME] | [NAME] | [NAME] | A- | N | A- | A- | A | [NAME] | [NAME] | C- | [NAME]
order and represent, add and subtract fractions with the same or related denominators | C+ | A- | C- | C- | C | A | A- | [NAME] | A | [NAME] | C+ | A | C | N
represent common percentages and connect them to their fraction and decimal equivalents
use their proficiency with multiplication facts and efficient calculation strategies to multiply large numbers by one- and two-digit numbers and divide by single-digit numbers | B | C | [NAME] | C | C+ | [NAME] | [NAME] | N | A | [NAME] | [NAME] | N | [NAME] | N
check the reasonableness of their calculations using estimation | N | A- | C+ | A | C | A | A- | C | A- | A | [NAME] | A- | C | N
use mathematical modelling to solve financial and other practical problems, formulating and solving problems, choosing arithmetic operations and interpreting results in terms of the situation
apply properties of numbers and operations to find unknown values in numerical equations involving multiplication and division | N | C | N | A- | C+ | [NAME] | B | D | A- | A- | N | [NAME] | [NAME] | N
create and use algorithms to identify and explain patterns in the factors and multiples of numbers | N | B | N | B | C+ | [NAME] | B- | C- | A- | [NAME] | N | N | C | N
choose and use appropriate metric units to measure the attributes of length, mass and capacity, and to solve problems involving perimeter and area | C+ | C+ | C+ | B | C- | [NAME] | B | D | B | B | C+ | B | C | B-
convert between 12- and 24-hour time
estimate, construct and measure angles in degrees | B- | A- | A+ | A | N | [NAME] | A | C | B | A | B- | A | C | N
use grid coordinates to locate and move positions
connect objects to their two-dimensional nets
perform and describe the results of transformations and identify any symmetries
plan and conduct statistical investigations that collect nominal and ordinal categorical and discrete numerical data using digital tools
identify the mode and interpret the shape of distributions of data in context
interpret and compare data represented in line graphs
conduct repeated chance experiments, list the possible outcomes, estimate likelihoods and make comparisons between those with and without equally likely outcomes
Average mark