tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 0d78d623-b4a8-4e51-a6c9-1a6df8aaf04a
Sheet: Reorganizations

2012-2013 Iowa School District Reorganizations
AEA | District | Name | AEA | District | Name | New AEA | New District | New Name
 | 0270 | Anthon-Oto |  | 4033 | Maple Valley |  | 4033 | Maple Valley-Anthon Oto | Move 0270 staff to 4033 0000
 | 5301 | Pomeroy-Palmer |  | 5283 | Pocahontas Area |  | 5283 | Pocahontas Area | Move 5301 staff to 5283 0000
 | 2367 | Fremont |  | 0657 | Eddyville-Blakesburg |  | 0657 | Eddyville-Blakesburg-Fremont | Move 2367 staff ti 0657 0000
Spreadsheet: 869366d1715937020-conditional-formatting-with-text-and-2-criterias-book-1-1-
Sheet: Sheet5

ALL |  |  |  | 4a
Species | Grade | Boxes |  | Species | Boxes |  | Species | Grade | Boxes |  | Species | Grade | Boxes
 | COD | 1 |  |  |  |  | COD | COD | 1 |  | COD | SPRAGG | 1
COD | SPRAGG | 1 |  | COLEY | 5 |  | COD | LARGE | 1 |  | COD | MEDIUM | 1
COD | LARGE | 1 |  | HADDOCK | 8 |  | COD | SELECTED | 1 |  | COD | SMALL | 1
COD | MEDIUM | 1 |  | HAKE | 6 |  | COD | SMALL | 1 |  | COD | ROE | 1
COD | SELECTED | 1 |  | LEMONS | 4 |  | COLEY | XLARGE | 1 |  | COLEY | LARGE | 1
COD | SMALL | 1 |  | LING | 3 |  | COLEY | MEDIUM | 1 |  | COLEY | SELECTED | 1
COD | ROE | 1 |  | LYTHE | 4 |  | COLEY | SMALL | 1 |  | HADDOCK | JUMBO | 1
COLEY | X LARGE | 1 |  | MEGRIM | 5
COLEY | LARGE | 1 |  | MONKS | 6
COLEY | MEDIUM | 1 |  | OTHERS | 8
COLEY | SELECTED | 1 |  | PLAICE | 4
COLEY | SMALL | 1 |  | SKATE & RAYS | 5
HADDOCK | JUMBO | 1 |  | SQUID | 4
HADDOCK | LARGE | 1 |  | WHITING | 4
HADDOCK | MEDIUM | 1 |  | Total
HADDOCK | SELECTED | 1
HADDOCK | SEED | 1 |  | Vessel + PLN | Boxes
HADDOCK | CHIPPERS | 1
HADDOCK | METRO | 1
HADDOCK | ROUND | 1
HAKE | LARGE | 1
HAKE | MEDIUM | 1
HAKE | SELECTED | 1 |  | Total
HAKE | SMALL | 1
HAKE | PINS | 1
HAKE | BYROS | 1
LEMONS | LARGE | 1
LEMONS | MEDIUM | 1
LEMONS | SELECTED | 1
LEMONS | SMALL | 1
LING | LARGE | 1
LING | MEDIUM | 1
LING | SELECTED | 1
LYTHE | LARGE | 1
LYTHE | MEDIUM | 1
LYTHE | SELECTED | 1
LYTHE | SMALL | 1
MEGRIM | LARGE | 1
MEGRIM | MEDIUM | 1
MEGRIM | SELECTED | 1
MEGRIM | SMALL | 1
MEGRIM | BRUISED | 1
MONKS | X LARGE | 1
MONKS | LARGE | 1
MONKS | MEDIUM | 1
MONKS | SELECTED | 1
MONKS | SMALL | 1
MONKS | FROGS | 1
OTHERS | DOVERSOLE | 1
OTHERS | TURBOT | 1
OTHERS | COD ROE | 1
OTHERS | GURNARD | 1
OTHERS | WITCH | 1
OTHERS | JOHN DORY | 1
OTHERS | BRILL | 1
OTHERS | OTHERS | 1
PLAICE | LARGE | 1
PLAICE | MEDIUM | 1
PLAICE | SELECTED | 1
PLAICE | SMALL | 1
SKATE & RAYS | ROKER | 1
SKATE & RAYS | ROKER | 1
SKATE & RAYS | THORNY | 1
SKATE & RAYS | 0 | 1
SKATE & RAYS | CUCKOO | 1
SQUID | LARGE | 1
SQUID | MEDIUM | 1
SQUID | SELECTED | 1
SQUID | SMALL | 1
WHITING | LARGE | 1
WHITING | MEDIUM | 1
WHITING | SMALL | 1
WHITING | ROUND | 1
Spreadsheet: ddff1c37-64da-4d3d-ac91-d78acb2670b8
Sheet: Sheet1

Test Referenced | Entry Type | Mayo ID | Description | Reporting Description | Method | Collection Container | Temperature | Stability | [NAME] | Unit of Measure | [NAME] | Minimum Volume | Reject Due To | Reference Values | Days/Times Performed | Analytic Time | Maximum Lab Time | CPT Units | CPT Code | LOINC® Code | LOINC® Attributes | Always Performed | Orderable Separately | Performing Location
LCCI | Test | LCCI | Ident Rapid PCR Coccidioides ([NAME] Only) | Ident Rapid [NAME] |  |  |  |  |  |  | This test is for billing purposes only.  This is not an orderable test. |  |  | This test is for billing purposes only.  This is not an orderable test. |  |  |  | 1 | 87150 |  |  |  |  | Rochester
LCCI | [NAME] | VARIES | Varies |  |  |  | See instructions
Spreadsheet: 826190d1681847264-index-match-min-max-testdataupdated
Sheet: User Payment Activity Details

Username | First Deposit Amount | First Deposit Card Issuer | Last Deposit Amount | Last Deposit Card Issuer | First Deposit GUID | Last Deposit GUID | First Deposit IP Address | Last Deposit IP Address | First Deposit Device | Last Deposit Device | First Deposit Version | Last Deposit Version | Total Deposit | Total Withdrawal
54givem9141 | 25 | Natwest | 30 | Natwest | 2a0e23ec-2f78-45ae-ac42-0dd83fc55ea8 | 5bdd6c9c-42f7-4601-946d-05032n9p9823 | 77.102.163.146 | 77.102.163.146 | N/A | N/A | N/A | N/A | 500 | 0
5shi72 | 25 | Barclays | 15 | Barclays | cb948961-aa1f-4fba-aa46-02aef7ff4b12 | 5bdd6c9c-42f7-4601-946d-05032n9p9823 | 51.183.123.211 | 77.102.163.146 | N/A | N/A | N/A | N/A | 500 | 300
kirst125 | 25 | RBS | 20 | RBS | 860ec7ac-e4fb-4ea9-99fd-72ca9b7e0418 | d82892ab-762a-462e-9ee4-6d517d6551ab | 77.102.163.146 | 82.32.94.112 | Apple | Apple | 15.6.1 | 15.6.1 | 155 | 350
Spreadsheet: c0c1b463-0703-4189-b273-e8854d23d2d6
Sheet: Sheet1

Unnamed: 0 | Unnamed: 1 | Railroad Quiet Zone AADT Request (Traffic Count Submittal Template) | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10
Municipality | Railroad | Crossing # | Street/Road Name | From (Street/Road) | To (Street/Road) | Total Raw 24 hour          Bi-directional count (machine)* | Total Raw 12 hr count (manual)** | Day of week & time of day count started | Month-Year-Day count was done | IDOT Use ONLY. AADT
Type of Equipment Used***:
To obtain this template electronically, see CMAP website's "Railroads and your Community" section (http://www.cmap.illinois.gov/railroads)
*  Traffic count machine output must also be submitted
** See AADT Guidance memo for instances when manual counts may be submitted
*** Indicate what type of equipment was used on machine counts
Spreadsheet: 220918d1363301639-i-need-to-find-the-average-of-separate-cells-greater-than-0-tempbook2
Sheet: Individual Courts

 | CT County | CT City | Judgments Sent | Average Time
 | Alameda | Alameda
 | Alameda | Berkeley
 | Alameda | Hayward
 | Alameda | Oakland
 | Alpine | Alpine
 | Amador | [NAME]
 | [NAME] | Chico
 | Calaveras | San Andreas
 | Colusa | Colusa
 | Contra Costa | Martinez
 | [NAME] | Crescent City
 | El Dorado | [NAME]
 | Fresno | Fresno
 | [NAME] | Orland
 | Humboldt | [NAME]
 | Imperial | Brawley
 | Imperial | Calexico
 | Imperial | El Centro
 | Imperial | Winterhaven
 | Inyo | Bishop
 | Inyo | Independence
 | Kern | Bakersfield
 | Kern | [NAME]
 | Kern | Lake Isabella
 | Kern | [NAME]
 | Kern | Mojave
 | Kern | Ridgcrest
 | Kern | Shafter
 | Kern | Taft
 | Kings | Hanford
 | Lake | Lakeport
 | [NAME] | Susanville
 | Los Angeles | Chatsworth
 | Los Angeles | Norwalk
 | Madera | Madera
 | Marin | San Rafael
 | Mariposa | Mariposa
 | Mendocino | Fort Bragg
 | Merced | Merced
 | Modoc | Alturas
 | Mono | Bridgeport
 | Mono | Mammoth [NAME]
 | [NAME] | [NAME]
 | Napa | Napa
 | Nevada | Nevada City
 | Orange | [NAME]
 | Orange | Laguna Hills
 | Orange | Newport Beach
 | Orange | Santa Ana
 | Placer | Roseville
 | Plumas | Quincy
 | Riverside | Banning
 | Riverside | Blythe
 | Riverside | Hemet
 | Riverside | Indio
 | Riverside | Riverside
 | Riverside | [NAME]
 | Sacramento | Sacramento
 | San Benito | Hollister
 | San Bernardino | Barstow
 | San Bernardino | Needles
 | San Bernardino | Rancho Cucamonga
 | San Bernardino | San Bernardino
 | San Bernardino | Victorville
 | San Diego | San Diego
 | San Diego | Vista
 | San Francisco | San Francisco
 | San Joaquin | [NAME]
 | [NAME] | Grover Beach
 | San Mateo | Redwood City
 | Santa Barbara | Santa Barbara
 | Santa Clara | San Jose
 | Santa Cruz | Santa Cruz
 | Santa Cruz | Watsonville
 | Shasta | Redding
 | Sierra | Downieville
 | Siskiyou | [NAME]
 | Solano | Fairfield
 | Solano | Vallejo
 | Sonoma | Santa Rosa
 | Stanislaus | Modesto
 | Sutter | [NAME]
 | [NAME] | Corning
 | Trinity | Weaverville
 | Tulare | Visalia
 | Tuolumne | Sonora
 | Ventura | Ventura
 | Yolo | Woodland
 | [NAME] | Marysville
Spreadsheet: 818591d1676936722-formula-for-reviewing-multiple-rows-and-pulling-cell-info-if-certain-criteria-is-met-table-example-etaf
Sheet: Sheet1

Table |  |  |  |  |  |  |  | Desired results
Customer | PO | Ship from | Date | Qty sold | Account Name | Value |  | Customer | PO | Ship from | Date | Qty sold | Freight
JB&Stuff | 123456 | Dallas | 2023-01-01 00:00:00 | 18000 | Material | 9000 |  | JB&Stuff | 123456 | Dallas | 2023-01-01 00:00:00 | 18000 | 6000
JB&Stuff | 123456 | Dallas | 2023-01-01 00:00:00 | 6 | Pallet | 60 |  | JB&Stuff | 456789 | Dallas | 2023-02-02 00:00:00 | 14000 | 2000
JB&Stuff | 123456 | Dallas | 2023-01-01 00:00:00 | 1 | Freight | 6000
nonamesrus | 654321 | Chicago | 2023-01-01 00:00:00 | 40000 | Material | 20000
makerofstuff | 123123 | Fresno | 2023-01-08 00:00:00 | 25000 | Material | 10000 |  |  | 123456 | Dallas | 2023-01-01 00:00:00 | 18000 | 9000
makerofstuff | 123123 | Fresno | 2023-01-08 00:00:00 | 1 | Freight 2 | 800 |  | JB&Stuff | 456789 | Dallas | 2023-02-02 00:00:00 | 14000 | 9000
JB&Stuff | 456789 | Dallas | 2023-02-02 00:00:00 | 14000 | Material | 9000
JB&Stuff | 456789 | Dallas | 2023-02-02 00:00:00 | 4 | Pallet | 40
JB&Stuff | 456789 | Dallas | 2023-02-02 00:00:00 | 1 | Freight 2 | 2000
 |  |  |  |  | What I need: Sample attached. If column F contains any variation of the word "Freight" I need to populate column A, B, C, D, E if F = "Material" and E is less than 20,000, G for any variation of "Freight"
 |  |  |  | =SORT(UNIQUE(FILTER(FILTER(I23:M30,I23:I30<>""),{1,0,0,1,0})))
Spreadsheet: 874689d1721227890-array-with-a-delayed-sequence-array-count
Sheet: Logistics (2)

 |  |  | FORMULA TO BE ADJUSTED |  |  |  |  |  |  | REQUIRED
 |  | 0 |  | 4 | 7 | 3 | 6 |  |  | 0 | 1 | 4 | 7 | 2 | 5
 |  |  | 1 | 4 | 8 | 3 | 6 |  |  |  | 1 | 4 | 7 | 2 | 5
 |  |  | 1 | 4 | 8 | 3 | 7 |  |  |  | 1 | 4 | 7 | 2 | 5
 |  |  | 1 | 4 | 8 | 3 | 7 |  |  |  | 1 | 4 | 7 | 2 | 5
 |  |  | 1 | 5 | 8 | 3 | 7 |  |  |  | 1 | 5 | 7 | 2 | 6
 |  |  | 1 | 5 | 8 | 4 | 7 |  |  |  | 1 | 5 | 7 | 2 | 6
 |  |  | 1 | 5 | 8 | 4 | 7 |  |  |  | 1 | 5 | 7 | 2 | 6
 |  |  | 2 | 5 | 8 | 4 | 7 |  |  |  | 2 | 5 | 8 | 3 | 6
 |  |  | 2 | 5 | 1 | 4 | 7 |  |  |  | 2 | 5 | 8 | 3 | 6
 |  |  | 2 | 5 | 1 | 4 | 8 |  |  |  | 2 | 5 | 8 | 3 | 6
 |  |  | 2 | 5 | 1 | 4 | 8 |  |  |  | 2 | 5 | 8 | 3 | 6
 |  |  | 2 | 6 | 1 | 4 | 8 |  |  |  | 2 | 6 | 8 | 3 | 7
 |  |  | 2 | 6 | 1 | 5 | 8 |  |  |  | 2 | 6 | 8 | 3 | 7
 |  |  | 2 | 6 | 1 | 5 | 8 |  |  |  | 2 | 6 | 8 | 3 | 7
 |  |  | 3 | 6 | 1 | 5 | 8 |  |  |  | 3 | 6 | 1 | 4 | 7
 |  |  | 3 | 6 | 2 | 5 | 8 |  |  |  | 3 | 6 | 1 | 4 | 7
 |  |  | 3 | 6 | 2 | 5 | 1 |  |  |  | 3 | 6 | 1 | 4 | 7
 |  |  | 3 | 6 | 2 | 5 | 1 |  |  |  | 3 | 6 | 1 | 4 | 7
 |  |  | 3 | 7 | 2 | 5 | 1 |  |  |  | 3 | 0 | 1 | 4 | 0
 |  |  | 3 | 7 | 2 | 6 | 1 |  |  |  | 3 | 0 | 1 | 4 | 0
 |  |  | 3 | 7 | 2 | 6 | 1 |  |  |  | 3 | 0 | 1 | 4 | 0
 |  |  | 4 | 7 | 2 | 6 | 1 |  |  |  | 4 | 0 | 0 | 5 | 0
 |  |  | 4 | 7 | 3 | 6 | 1 |  |  |  | 4 | 0 | 0 | 5 | 0
 |  |  | 4 | 7 | 3 | 6 | 2 |  |  |  | 4 | 0 | 0 | 5 | 0
 |  | Formual in D6 to check against range>> | 4 | 3 | 3 | 4 | 3 |  |  | Number of different integers>> | 4 | 3 | 3 | 4 | 3
Spreadsheet: 813070d1673535267-countifs-with-a-complex-criteria-countifs-ans
Sheet: Sheet1

DATE | REF | STATUS | SUBREFS | Subs? | No. Subs |  | Subs? | No. Subs
2022-01-09 00:00:00 | EGTRI01 | CANCELLED | EGTRI010102 |  |  |  | Yes | 2
2022-01-10 00:00:00 | DONY23 | CANCELLED | DONY2301 |  |  |  | Yes | 2
2022-01-11 00:00:00 | FANDU7 | CANCELLED |  |  |  |  | No | 0
2022-01-12 00:00:00 | EGTRI01 | CANCELLED | EGTRI0101 |  |  |  | Yes | 2
2022-01-13 00:00:00 | DONY23 | CANCELLED | DONY230203 |  |  |  | Yes | 2
Spreadsheet: 197705d1354540231-linking-a-worksheet-to-a-pivot-table-example-1-
Sheet: Pivot

Month | October
 | Values
Catagory | Sum of Site 1 | Sum of Site 2 | Sum of Site 3
Advertising |  |  | 2
Bad Debts | 1 | 1.5 | 2
Bank Charges and Interest | 3 | 4.5 | 6
Branch staff salaries and ERS NIC | 30 | 45 | 60
Class 1A NIC | 2 | 3 | 4
Clock Card Costs | 3 | 4.5 | 6
Computer maintenance and support | 3 | 4.5 | 6
Depreciation | 2 | 3 | 4
Direct Wages ERS NIC | 100 | 150 | 200
Direct Wages Gross | 800 | 1200 | 1600
[NAME] Claims | 2 | 3 | 4
General Expenses | 2 | 3 | 4
Heat, Light and Power | 20 | 30 | 40
Hotels | 2 | 3 | 4
Insurances | 2 | 3 | 4
[NAME] | 2 | 3 | 4
Other Pensions | 1 | 1.5 | 2
Parking Fine Contributions | 1 | 1.5 | 2
Printing and Stationery | 2 | 3 | 4
Professional Fees | 5 | 7.5 | 10
Rent and Rates | 100 | 150 | 200
Repairs and renewals | 2 | 3 | 4
Sales | 1000 | 1500 | 2000
Staff Entertaining | 3 | 4.5 | 6
Staff Training | 2 | 3 | 4
Telephone | 5 | 7.5 | 10
Travelling and Entertainment | 11 | 16.5 | 22
Vehicle Leasing | 2 | 3 | 4
Work wear | 2 | 3 | 4
Grand Total | 2110 | 3165 | 4222
Spreadsheet: 837483d1690203042-formula-to-pick-up-max-value-and-apply-that-max-record-values-to-other-recordswith-same-id-test
Sheet: Sheet1

Base | Attribute1 | Attribute2 | Attribute3 | Output1 | Output2 | Output3 | Reason
766687 | 1 | 3 | 70 | 4 | 6 | 70 | 1. Data is different across all the 766687 records  2. Check from Attribute 1 column 3. [NAME] from Attribute 1 column for 766687 record is 4 4. Assign the [NAME] record values to the other attributes
766687 | 2 | 4 | 70 | 4 | 6 | 70
766687 | 3 | 5 | 70 | 4 | 6 | 70
766687 | 4 | 6 | 70 | 4 | 6 | 70
632165 | 2 | 3 | 60 | 4 | 1 | 60 | Similar to above
632165 | 3 | 2 | 60 | 4 | 1 | 60
632165 | 4 | 1 | 60 | 4 | 1 | 60
123456 | 2 | 3 | 60 | 2 | 5 | 70 | 1. Data is different across all the 123456 records 2. Check from Attribute 1 column, it’s the same so move on to Attribute 2 column 3. [NAME] from Attribute 2 column for 123456 record is 5 4. Assign the [NAME] record values to the other attributes
123456 | 2 | 5 | 70 | 2 | 5 | 70
123456 | 2 | 4 | 80 | 2 | 5 | 70
Spreadsheet: 739073d1625493403-creating-a-variable-based-on-a-sumif-dynamicsumifs_issue
Sheet: Sheet1

 |  |  | Net Hours |  | What I do |  |  |  | Net Hours | How do I work this out |  | What I need it to do
 |  |  | -12243.3871901752 |  |  |  |  |  | -12243.3871901752 | 1
 |  |  | -10919.47736188488 |  |  |  |  |  | -10919.47736188488 | 1
 |  |  | -24489.9809667084 |  |  |  |  |  | -24489.9809667084 | 1
 |  |  | 33383.67821256899 |  |  |  |  |  | 33383.67821256899 | 1
 |  |  | 37051.67957269229 |  |  |  |  |  | 37051.67957269229 | 1
 |  |  | 38009.2804655081 |  |  |  |  |  | 38009.2804655081 | 1
 |  |  | 35177.15755781643 |  |  |  |  |  | 35177.15755781643 | 1
 |  |  | 35040.35637452849 |  |  |  |  |  | 35040.35637452849 | 1
 |  |  | -10738.83336613055 |  |  |  |  |  | -10738.83336613055 | 2
 |  |  | -6787.624966770789 |  |  |  |  |  | -6787.624966770789 | 2
 |  |  | -6534.299342687213 |  |  |  |  |  | -6534.299342687213 | 2
 |  |  | -5038.789598735922 |  |  |  |  |  | -5038.789598735922 | 2
 |  |  | 33784.29883852464 |  |  |  |  |  | 33784.29883852464 | 2
 |  |  | 26075.09444824747 |  |  |  |  |  | 26075.09444824747 | 2
 |  |  | 7071.966226800316 |  |  |  |  |  | 7071.966226800316 | 2
 |  |  | 3743.782388603111 |  |  |  |  |  | 3743.782388603111 | 2
 |  |  | 3439.805171921413 |  |  |  |  |  | 3439.805171921413 | 2
 |  |  | 2137.042164688639 |  |  |  |  |  | 2137.042164688639 | 2
 |  |  | -10738.83336613055 |  |  |  |  |  | -10738.83336613055 | 3
 |  |  | -6787.624966770789 |  |  |  |  |  | -6787.624966770789 | 3
 |  |  | -6534.299342687213 |  |  |  |  |  | -6534.299342687213 | 3
 |  |  | -5038.789598735922 |  |  |  |  |  | -5038.789598735922 | 3
 |  |  | -7990.4880443086 |  |  |  |  |  | -7990.4880443086 | 3
 |  |  | -11401.02441597554 |  |  |  |  |  | -11401.02441597554 | 3
 |  |  | 35040.35637452849 |  |  |  |  |  | 35040.35637452849 | 3
 |  |  | 33784.29883852464 |  |  |  |  |  | 33784.29883852464 | 3
Spreadsheet: 727553d1617958993-formula-with-to-select-a-percentage-from-a-table-help
Sheet: Sheet1

Region % | Managers %
 | -0.01 | -0.0005 | 0 | 0.06
-0.01 | 0 | -0.1 | 0 | 0
-0.0005 | -0.15 | -0.05 | 0.05 | 0.025
0 | -0.1 | 0 | 0.075 | 0.1
0.06 | -0.05 | 0 | 0.1 | 0.2
Name | % Region | % Managers | [NAME]
Name 1 | 0.05 | 0.01
Name 2 | 0.1 | 0.05
Name 3 | -0.1 | 0.11
Spreadsheet: 715906d1611914489-error-with-countif-why-sum-with-criteria
Sheet: Sheet1

CalendarWeek | Year | Value | helper
1 | 2020 | 18695.92 |  |  | CRITERIA
2 | 2020 | 25778.31 |  |  | 52021
3 | 2020 | 10174.06
4 | 2020 | 13163.79 |  |  | number of rows
5 | 2020 | 13402.45
6 | 2020 | 17256.78
7 | 2020 | 15241.13
8 | 2020 | 23650.76
9 | 2020 | 7774.820000000001
10 | 2020 | 12469.86
11 | 2020 | 15347.92
12 | 2020 | 10576.4
13 | 2020 | 14839.47
14 | 2020 | 20338.5
15 | 2020 | 20647.78
16 | 2020 | 7136.88
17 | 2020 | 20551.89
18 | 2020 | 8803.33
19 | 2020 | 16665
20 | 2020 | 9539.5
21 | 2020 | 26539.34
22 | 2020 | 30439.33
23 | 2020 | 31021.46
24 | 2020 | 11590.66
25 | 2020 | 17167.82
26 | 2020 | 14020.98
27 | 2020 | 34754.38
28 | 2020 | 19736.92
29 | 2020 | 19069.58
30 | 2020 | 9606.36
31 | 2020 | 26037.5
32 | 2020 | 30417.69
33 | 2020 | 19399.86
34 | 2020 | 12063.99
35 | 2020 | 23507.9
36 | 2020 | 24940.7
37 | 2020 | 16071.47
38 | 2020 | 24550.17
39 | 2020 | 17508.69
40 | 2020 | 18167.2
41 | 2020 | 7369.59
42 | 2020 | 13055.22
43 | 2020 | 16689.03
44 | 2020 | 11733.18
45 | 2020 | 18712.8
46 | 2020 | 14536.38
47 | 2020 | 14229.84
48 | 2020 | 14075.66
49 | 2020 | 32626.99
50 | 2020 | 34174.36
51 | 2020 | 11078.88
52 | 2020 | 21485.36
2 | 2021 | 9374.880000000001
3 | 2021 | 10807.27
4 | 2021 | 19113.33
5 | 2021 | 9645.42
Spreadsheet: 39f4504e-57f8-4e76-b00a-5cff56dfb30b
Sheet: Sheet1

Table 10--Global rice exporters, calendar years; monthly revisions and annual changes | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | Unnamed: 12 | Unnamed: 13
 | 2011 |  |  |  | 2012 |  |  |  |  |  | 2013 1/
 | March |  | February | March |  | Monthly | Annual |  | February | March |  | Monthly | Annual
 | 2013 |  | 2013 | 2013 |  | revisions | changes |  | 2013 | 2013 |  | revisions | changes
 |  |  |  |  |  |  | 1,000 metric tons (milled basis)
Argentina | 732 |  | 608 | 608 |  | 0 | -124 |  | 625 | 625 |  | 0 | 17
Australia | 311 |  | 450 | 450 |  | 0 | 139 |  | 500 | 500 |  | 0 | 50
Brazil | 1296 |  | 1105 | 1105 |  | 0 | -191 |  | 800 | 800 |  | 0 | -305
Burma | 778 |  | 700 | 690 |  | -10 | -88 |  | 600 | 600 |  | 0 | -90
Cambodia | 860 |  | 800 | 800 |  | 0 | -60 |  | 825 | 975 |  | 150 | 175
China | 487 |  | 267 | 267 |  | 0 | -220 |  | 300 | 300 |  | 0 | 33
Ecuador | 70 |  | 30 | 30 |  | 0 | -40 |  | 15 | 15 |  | 0 | -15
Egypt | 320 |  | 600 | 600 |  | 0 | 280 |  | 850 | 850 |  | 0 | 250
European Union-27 | 241 |  | 210 | 210 |  | 0 | -31 |  | 235 | 235 |  | 0 | 25
Guinea | 80 |  | 80 | 80 |  | 0 | 0 |  | 80 | 80 |  | 0 | 0
Guyana | 257 |  | 285 | 285 |  | 0 | 28 |  | 300 | 300 |  | 0 | 15
India | 4637 |  | 10250 | 10250 |  | 0 | 5613 |  | 7500 | 7600 |  | 100 | -2650
Japan | 200 |  | 200 | 200 |  | 0 | 0 |  | 200 | 200 |  | 0 | 0
Korea, South | 4 |  | 2 | 2 |  | 0 | -2 |  | 5 | 5 |  | 0 | 3
Pakistan | 3414 |  | 3500 | 3500 |  | 0 | 86 |  | 3800 | 3800 |  | 0 | 300
Paraguay | 208 |  | 250 | 250 |  | 0 | 42 |  | 200 | 200 |  | 0 | -50
Peru | 50 |  | 30 | 50 |  | 20 | 0 |  | 60 | 50 |  | -10 | 0
Russia | 142 |  | 282 | 282 |  | 0 | 140 |  | 160 | 160 |  | 0 | -122
Thailand | 10647 |  | 6945 | 6945 |  | 0 | -3702 |  | 8000 | 8000 |  | 0 | 1055
Turkey | 90 |  | 75 | 75 |  | 0 | -15 |  | 80 | 80 |  | 0 | 5
Uganda | 40 |  | 35 | 35 |  | 0 | -5 |  | 35 | 35 |  | 0 | 0
United States | 3247 |  | 3300 | 3300 |  | 0 | 53 |  | 3450 | 3500 |  | 50 | 200
Uruguay | 841 |  | 1056 | 1056 |  | 0 | 215 |  | 850 | 850 |  | 0 | -206
[NAME] | 50 |  | 50 | 50 |  | 0 | 0 |  | 50 | 50 |  | 0 | 0
Vietnam | 7000 |  | 7717 | 7717 |  | 0 | 717 |  | 7400 | 7400 |  | 0 | -317
Subtotal | 36002 |  | 38827 | 38837 |  | 10 | 2835 |  | 36920 | 37210 |  | 290 | -1627
Other | 219 |  | 200 | 223 |  | 23 | 3 |  | 210 | 210 |  | 0 | -13
 |  |  |  |  |  |  |  |  |  |  |  | 0
World total | 36221 |  | 39027 | 39060 |  | 33 | 2839 |  | 37130 | 37420 |  | 290 | -1640
U.S. Share | 0.08964412909638056 |  | 0.08455684526097318 | 0.08448540706605223 |  | -- | -- |  | 0.09291677888499865 | 0.09353287012292892 |  | -- | --
-- Not available.  Note: All trade data are reported on a calendar-year basis.
1/ Projected.
Source: Production, Supply, & Distribution Online Data Base, FAS/USDA, http://www.fas.usda.gov/psdonline/psdHome.aspx.
Last updated March 8, 2013.
Spreadsheet: 79442d1279711999-conditional-formatting-conditional-formatting
Sheet: Sheet1

 |  | Q | Name | Value |  | From DB (Sheet2)
 |  | Q1 | Name1 | 2
 |  | Q1 | [NAME] | 2
 |  | Q2 | Name3 | 4
 |  | Q1 | Name4 | 3
 |  | Q2 | Name5 | 1
Spreadsheet: 858512d1707167740-index-column-with-multiple-repeating-ids-but-unique-quantities-repeating_values_distinct_quantities_index
Sheet: Sheet1

ID | QTY | Material ID |  | BOM ID | BOM QTY
1065600-CH-B | 9.67 |  |  | 1065600-CH-B | 9.67
[NAME] | 1 |  |  | [NAME] | 1
PHTM-00006446-CN-A | 2 |  |  | PHTM-00006446-CN-A | 2
1110880-C1-A | 4.9 |  |  | 1110880-C1-A | 4.9
[NAME] | 14 |  |  | [NAME] | 14
MOD-00009588-CN-A | 1 |  |  | MOD-00009588-CN-A | 1
1714884-CN-D | 1 |  |  | 1714884-CN-D | 1
1732896-00-E | 1 | SWRCH18A |  | 1732896-00-E | 1
 |  | CR5-110-260-S |  | 1732896-00-E | 9.67
 |  | GI60/60
1732896-00-E | 9.67
Spreadsheet: 594447d1539775690-index-match-question-test1
Sheet: Sheet1

Date | Item |  | High | Low | Low1 |  | TEST1 |  |  |  |  | TEST2
2013-01-02 00:00:00 | TEST1 |  | 5 | 4 | 3 |  | 2013-01-02 00:00:00 | 5 | 4 | 3 |  | 2013-01-17 00:00:00 | 5 | 4 | 3
2013-01-03 00:00:00 | TEST3 |  | 100 | 99 | 98 |  | 2013-01-04 00:00:00 | 10 | 9 | 8 |  | 2013-01-24 00:00:00 | 90 | 89 | 88
2013-01-04 00:00:00 | TEST8 |  | 50 | 49 | 48 |  | 2013-01-04 00:00:00 | 10 | 9 | 8 |  | 2013-01-30 00:00:00 | 40 | 39 | 38
2013-01-04 00:00:00 | TEST1 |  | 10 | 9 | 8 |  | 2013-01-15 00:00:00 | 30 | 29 | 28
2013-01-04 00:00:00 | TEST4 |  | 20 | 19 | 18 |  | 2013-01-16 00:00:00 | 50 | 49 | 48
2013-01-04 00:00:00 | TEST1 |  | 10 | 9 | 8 |  | 2013-01-22 00:00:00 | 100 | 99 | 98
2013-01-10 00:00:00 | TEST8 |  | 50 | 49 | 48 |  | 2013-01-30 00:00:00 | 30 | 29 | 28
2013-01-11 00:00:00 | TEST8 |  | 10 | 9 | 8
2013-01-15 00:00:00 | TEST3 |  | 20 | 19 | 18
2013-01-15 00:00:00 | TEST1 |  | 30 | 29 | 28
2013-01-15 00:00:00 | TEST9 |  | 40 | 39 | 38
2013-01-16 00:00:00 | TEST1 |  | 50 | 49 | 48
2013-01-17 00:00:00 | TEST2 |  | 5 | 4 | 3
2013-01-17 00:00:00 | TEST7 |  | 6 | 5 | 4
2013-01-17 00:00:00 | TEST3 |  | 8 | 7 | 6
2013-01-18 00:00:00 | TEST8 |  | 10 | 9 | 8
2013-01-22 00:00:00 | TEST1 |  | 100 | 99 | 98
2013-01-24 00:00:00 | TEST2 |  | 90 | 89 | 88
2013-01-25 00:00:00 | TEST3 |  | 80 | 79 | 78
2013-01-28 00:00:00 | TEST8 |  | 70 | 69 | 68
2013-01-28 00:00:00 | TEST7 |  | 60 | 59 | 58
2013-01-29 00:00:00 | TEST4 |  | 50 | 49 | 48
2013-01-30 00:00:00 | TEST2 |  | 40 | 39 | 38
2013-01-30 00:00:00 | TEST1 |  | 30 | 29 | 28
2013-01-31 00:00:00 | TEST3 |  | 20 | 19 | 18