tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 8171d94e-be90-485b-aa9f-0ce0658eca84
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/
 | April |  | March | April |  | Monthly | Annual |  | March | April |  | Monthly | Annual
 | 2013 |  | 2013 | 2013 |  | revisions | changes |  | 2013 | 2013 |  | revisions | changes
 |  |  |  |  |  |  | 1,000 metric tons (milled basis)
Argentina | 732 |  | 608 | 608 |  | 0 | -124 |  | 625 | 600 |  | -25 | -8
Australia | 311 |  | 450 | 450 |  | 0 | 139 |  | 500 | 500 |  | 0 | 50
Brazil | 1296 |  | 1105 | 1105 |  | 0 | -191 |  | 800 | 850 |  | 50 | -255
Burma | 778 |  | 690 | 690 |  | 0 | -88 |  | 600 | 750 |  | 150 | 60
Cambodia | 860 |  | 800 | 800 |  | 0 | -60 |  | 975 | 975 |  | 0 | 175
China | 487 |  | 267 | 267 |  | 0 | -220 |  | 300 | 300 |  | 0 | 33
Ecuador | 70 |  | 30 | 63 |  | 33 | -7 |  | 15 | 50 |  | 35 | -13
Egypt | 320 |  | 600 | 600 |  | 0 | 280 |  | 850 | 850 |  | 0 | 250
European Union-27 | 241 |  | 210 | 197 |  | -13 | -44 |  | 235 | 235 |  | 0 | 38
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 |  | 7600 | 7600 |  | 0 | -2650
Japan | 200 |  | 200 | 200 |  | 0 | 0 |  | 200 | 200 |  | 0 | 0
Korea, South | 4 |  | 2 | 2 |  | 0 | -2 |  | 5 | 2 |  | -3 | 0
Pakistan | 3414 |  | 3500 | 3500 |  | 0 | 86 |  | 3800 | 3300 |  | -500 | -200
Paraguay | 208 |  | 250 | 250 |  | 0 | 42 |  | 200 | 200 |  | 0 | -50
Peru | 50 |  | 50 | 50 |  | 0 | 0 |  | 50 | 100 |  | 50 | 50
Russia | 142 |  | 282 | 282 |  | 0 | 140 |  | 160 | 210 |  | 50 | -72
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 |  | 3500 | 3500 |  | 0 | 200
Uruguay | 841 |  | 1056 | 1056 |  | 0 | 215 |  | 850 | 900 |  | 50 | -156
[NAME] | 50 |  | 50 | 150 |  | 100 | 100 |  | 50 | 200 |  | 150 | 50
Vietnam | 7000 |  | 7717 | 7717 |  | 0 | 717 |  | 7400 | 7400 |  | 0 | -317
Subtotal | 36002 |  | 38837 | 38957 |  | 120 | 2955 |  | 37210 | 37217 |  | 7 | -1740
Other | 244 |  | 223 | 259 |  | 36 | 14 |  | 210 | 215 |  | 5 | -44
World total | 36246 |  | 39060 | 39216 |  | 156 | 2970 |  | 37420 | 37432 |  | 12 | -1784
U.S. Share | 0.0895822987364123 |  | 0.08448540706605223 | 0.08414932680538556 |  | -- | -- |  | 0.09353287012292892 | 0.09350288523188716 |  | -- | --
-- 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 April 11, 2013.
Spreadsheet: 786538d1657251138-if-1-row-a-is-missing-then-the-2-3-row-in-b-will-not-display-samples
Sheet: Sheet1

A |  |  |  | B |  |  | Output
01/04/2022 46.00 |  |  |  | DR |  |  | 01/04/2022 46.00 |  |  |  | DR
 |  |  |  | 250IF090763 |  |  |  |  |  |  | 250IF090763
 |  |  |  | Trade [NAME] |  |  |  |  |  |  | Trade [NAME]
 |  |  |  | 250IF090763 250 |  |  |  |  |  |  | 250IF090763 250
*Date is blank* |  |  |  | [NAME] |  |  | *If date A mising , then B not display until next A value*
 |  |  |  | NONE
 |  |  |  | [NAME]
 |  |  |  | EVANT YHUSOEF
01/04/2022 15.05 |  |  |  | [NAME] |  |  | 01/04/2022 15.05 |  |  |  | [NAME]
 |  |  |  | NONE |  |  |  |  |  |  | NONE
 |  |  |  | [NAME] |  |  |  |  |  |  | [NAME]
 |  |  |  | EVANT YHUSOEF |  |  |  |  |  |  | EVANT YHUSOEF
04/04/2022 0.00 |  |  |  | [NAME] |  |  | 04/04/2022 0.00 |  |  |  | [NAME]
 |  |  |  | TRF FUND FRM |  |  |  |  |  |  | TRF FUND FRM
 |  |  |  | [PHONE] |  |  |  |  |  |  | [PHONE]
*For example A6 date is missing, then the B cells is blank until next value in A is display
Spreadsheet: 714016d1611062239-sum-with-multiple-criteria-sumifs-query
Sheet: Sheet1

 |  | TABLE A
 |  |  |  |  |  |  | 2020/21 | 2020/21 | 2020/21
 |  | CostCtre | DeptCode | Job Number | Project Number i.e. this is the NOC, NPFC or AT number | Project Header | Period Number | Period Number | Period Number
 |  |  |  |  |  |  | 10 | 11 | 12
 |  | ABC | DEP | 123 |  | Labour_Solutions | 1644.522530953505 | 1644.522530953505 | 1644.522530953505
 |  | DEF | DEP | 123 |  | Labour_Solutions | 27359.39386884803 | 27359.39386884803 | 27359.39386884803
 |  | GHI | DEP | 123 |  | Labour_Solutions | 48410.2402067556 | 48410.2402067556 | 48410.2402067556
 |  | JKL | DEP | 123 |  | Labour_Solutions | 3979.475667812074 | 3979.475667812074 | 3979.475667812074
 |  | MNO | DEP | 123 |  | Labour_Quality | 16018.07660019648 | 16018.07660019648 | 16018.07660019648
 |  | PQR | DEP | 123 |  | Labour_Quality | 8009.038300098238 | 8009.038300098238 | 8009.038300098238
 |  | TABLE B
 |  | Project | Project Header | Phase | Stage | Activity | Period Number | Period Number | Period Budget
 |  | 123 | Labour_Solutions |  |  |  | 10 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 10 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 10 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 10 | 2020
 |  | 123 | Labour_Quality |  |  |  | 10 | 2020
 |  | 123 | Labour_Quality |  |  |  | 10 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 11 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 11 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 11 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 11 | 2020
 |  | 123 | Labour_Quality |  |  |  | 11 | 2020
 |  | 123 | Labour_Quality |  |  |  | 11 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 12 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 12 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 12 | 2020
 |  | 123 | Labour_Solutions |  |  |  | 12 | 2020
 |  | 123 | Labour_Quality |  |  |  | 12 | 2020
 |  | 123 | Labour_Quality |  |  |  | 12 | 2020
 |  |  |  |  | Hi, How do I get the values in col H, I & J from table A into the table below by Project header and Period Number please?  I think it is some form of sumifs statement, but nothing I do is working.  Hoping someone can advise please.  Thank you so much in advance!  [NAME]
Spreadsheet: 145522d1331634186-about-column-chart-818613c
Sheet: 1

data | [NAME] | frquency | %
0.4747765192177438 | 0.1
0.2368296949413358 | 0.2 | 3
0.8029807100895727 | 0.3 | 1
0.4586084257227299 | 0.4 | 0
0.5260953931122121 | 0.5 | 7
0.122167220516036 | 0.6 | 2
0.4701451170543325 | 0.7 | 1
0.940842112000909 | 0.8 | 2
0.4702137045887462 | 0.9 | 3
0.4752698020103026 | 1 | 1
0.1661522285227299
0.728783673306717
0.4087543367812358
0.8115954071871174
0.8594223761163091
0.6519699014015607
0.4002621682814311
0.7215366767367675
0.5939157954989271
0.1741168973511673
Spreadsheet: 186373d1350012262-vlookup-cell-offset-in-a-formula-sample
Sheet: Data

Revenue Department 1 | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug
2012 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000
2011 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000
Revenue Department 2
2012 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000
2011 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000
Revenue Department 3
2012 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000
2011 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000
Spreadsheet: 694648d1599735420-specific-due-dates-formula-test
Sheet: Sheet1

 |  | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
Payment | Due date | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12
Quarterly | 2020-08-17 00:00:00
Annual | 2020-11-07 00:00:00
Spreadsheet: 835491d1688632170-sumproduct-with-dynamic-arrays-query
Sheet: Source

 | Table 1 |  |  |  |  |  |  | Table 2
 | Students | English | Maths | Commerce | Economics | Physics |  | Students | Multipler
 | Joel | 10 | 12 | 14 | 16 | 18 |  |  | 14
 | [NAME] | 15 | 17 | 19 | 21 | 23 |  |  | 19
 | [NAME] | 20 | 22 | 24 | 26 | 28 |  |  | 15
 | Joel | 25 | 27 | 29 | 31 | 33 |  |  | 11
 | Prasanth | 30 | 32 | 34 | 36 | 38 |  |  | 10
 | Joel | 35 | 37 | 39 | 41 | 43 |  |  | 6
 | [NAME] | 40 | 42 | 44 | 46 | 48 |  |  | 9
 | [NAME] | 45 | 47 | 49 | 51 | 53 |  |  | 23
 | [NAME] | 20 |  |  |  |  |  |  | 11
 | [NAME] | 50 | 52 | 54 | 56 | 58 |  |  | 12
Spreadsheet: 82175d1282197691-conditional-formatting-of-cells-requiring-more-than-3-criteria-and-different-column-conditional-format-more-than-3-criteria
Sheet: Sheet1

GEMS / PRODUCTIVITY MEASURES | Jul | Aug | Sep | Oct | Nov | Dec | Jan | Feb | Mar | Apr | May
GEM's
OTP
ACGC (secs)
Data Quality
Sales Conversion %
Sales Value $
Sales #PNR's Created
AHT (mins)
#[NAME]
[NAME] @ 8% Target
Compliments
Complaints
Team Highlights / Impacts (refer to comments box under each month)
HD - Highly Developed
D - Developed
DP - Developing
RD - Requires Development
Spreadsheet: a01e4f7c-8519-4901-9548-2ad5b8f23034
Sheet: About

 | Definition and explanations
 | Indicator name | Alcohol consumption per adult (15+), litres
 | Definition of indicator | Recorded and estimated average alcohol consumption, adult (15+) per capita consumption in litres pure alcohol
 | Unit of measurement
 | Data source
 | Source organization(s) | WHO
 | Link to source organization | www.who.int
 | Complete reference | WHO:s "GISAH"-database for the years 2005 and 2008; WHO:s "Global Status Report On Alcohol 1999", Table 4, for the earlier years - except Russia and Sweden. For Sweden, all data is from Swedish Public Health Institute (http://app.fhi.se/PXwebFHI/database/folkhalsodata/databasetree.asp) from For Russia, data from 1985-1994 is from Nemtsov AV (2000) Estimates of total alcohol consumption in Russia, 1980-1994. Drug and Alcohol Dependence 58(1-2): 133–142
 | [NAME] | Estimating total average alcohol consumtion is very complicated. Much of the alcohol consumed is unregistered (home brewed, illegal etc). In general, in countries with high GDP per [NAME], the registered alcohol sales make up a larger part of total consumption than in countries with low GDP per Capita. It is thus especially difficult to estimate total consumption for low-income countries. The data here is based on estimations made by a team of experts at WHO. Since the data come from two different studies from different years, we advice caution when looking at change over time. Note also that the average consumption does not inform about how many in the population have alcohol problems - a country with a low average can have a large part of the population who abstain from alcohol completely, while there are other groups with high-risk consumption; and a country with high average can have a large part of the population who drink regularly but not at risk levels.
 | Link to references | Link to GISAH (data for 2005 and 2008): http://apps.who.int/ghodata/?theme=GISAH
 |  | Link to WHO:s alcohol publications, including the Global Status Report On Alcohol 1999 (data for earlier years, except Russia and Sweden): http://www.who.int/substance_abuse/publications/alcohol/en/index.html
 | Specific information about this indicator
 | Uploader | [NAME]
 | [Add other fields as required]
 |  | 2012-03-05 00:00:00
Spreadsheet: 65571d1266301366-automatic-filling-karratha-baby-and-toddler-equipment-hire
Sheet: Main

Karratha Baby & Toddler Equipment Hire
Item # | Item | Customers Name | Customers Contact | Date of Hire | Est Return Date | Actual Return Date | Rate | Weeks | TOTAL | Invoice #
 |  |  |  |  |  |  | 20 | 2
Item # | Item | Rate$/Week
1A | Car Seat- 5 Point Harness | 40
1B | Car Seat- 5 Point Harness | 40
1C | Car Seat- 5 Point Harness | 40
1D | Car Seat- 5 Point Harness | 40
1E | Car Seat- 5 Point Harness | 40
1F | Car Seat- 5 Point Harness | 40
1G | Car Seat- 5 Point Harness | 40
1H | Car Seat- 5 Point Harness | 40
1I | Car Seat- 5 Point Harness | 40
1J | Car Seat- 5 Point Harness | 40
2A | Portacot | 30
2B | Portacot | 30
2C | Portacot | 30
2D | Portacot | 30
2E | Portacot | 30
7A | Capsule (0 - 6 months) | 40
7B | Capsule (0 - 6 months) | 40
7C | Capsule (0 - 6 months) | 40
9A | Car Seats- Maxi Riders (1 - 7 year olds) | 40
5A | [NAME] | 25
5B | [NAME] | 25
5C | [NAME] | 25
5D | [NAME] | 25
8A | Stroller | 25
8B | Stroller | 25
8C | Stroller | 25
10A | [NAME] | 15
10B | [NAME] | 15
10C | [NAME] | 15
10D | [NAME] | 15
10E | [NAME] | 15
10F | [NAME] | 15
10G | [NAME] | 15
11A | [NAME] | 30
12A | Safe n Secure Infant Bed | 20
13A | Adjustable Bedrails | 10
14A | Bumbo | 15
14B | Bumbo | 15
15A | Highchair | 30
15B | Highchair | 30
15C | Highchair | 30
15D | Highchair | 30
16A | Portable High Chair | 20
16B | Portable High Chair | 20
16C | Portable High Chair | 20
17A | Barrier Gate | 10
18A | [NAME] | 25
19A | [NAME] | 20
20A | Rockers | 15
21A | [NAME] | 10
22A | Playmat (motorised animals) | 10
23A | [NAME] | 10
24A | Learning to sit up Pillow | 10
25A | Vaporiser | 15
25B | Vaporiser | 15
25C | Vaporiser | 15
26A | Step | 5
27A | Walker | 25
27B | Walker | 25
27C | Walker | 25
28A | Outdoor Cantdy Stripe Swing | 15
28B | Outdoor Cantdy Stripe Swing | 15
28C | Outdoor Cantdy Stripe Swing | 15
29A | Jolly Jumper | 10
29B | Jolly Jumper | 10
30A | Jolly [NAME] | 10
30B | Jolly [NAME] | 10
31A | Toys | 10
Spreadsheet: 232240d1367359890-component-life-cycle-management-how-to-extract-correct-data-component-lifecycle-example
Sheet: Sheet1

Date | Set No. | Machine | Image Ref.
2013-04-01 00:00:00 | 2 | 9oz | Pink
2013-04-01 00:00:00 | 1 | 4oz | Yellow
2013-04-01 00:00:00
2013-04-04 00:00:00
2013-04-05 00:00:00
2013-04-05 00:00:00
2013-04-09 00:00:00
2013-04-11 00:00:00
2013-04-12 00:00:00
2013-04-18 00:00:00
2013-04-21 00:00:00
2013-04-21 00:00:00
Spreadsheet: 117932d1313262726-excel-2007-sum-of-certain-cells-from-one-column-to-another-worksheet-book2
Sheet: States

StAbb | Name | Count
AK | Alaska | 1 |  |  | w
AL | Alabama | 25 |  |  | s
AR | Arkansas |  |  |  | s
AZ | Arizona | 6 |  |  | w
CA | California | 12 |  |  | w
CO | Colorado | 11 |  |  | w
CT | Connecticut | 39 |  |  | n
DC | Washington D.C. | 1 |  |  | s
DE | Delaware | 7 |  |  | n
FL | Florida | 96 |  |  | s
GA | Georgia | 70 |  |  | s
HI | Hawaii | 2 |  |  | w
IA | Iowa | 3 |  |  | c
ID | Idaho | 1 |  |  | w
IL | Illinois | 10 |  |  | c
IN | Indiana | 12 |  |  | c
KS | Kansas | 6 |  |  | c
KY | Kentucky | 21 |  |  | c
LA | Louisiana | 3 |  |  | s
MA | Massachusetts | 66 |  |  | n
MD | Maryland | 55 |  |  | n
ME | Maine | 44 |  |  | n
MI | Michigan | 23 |  |  | c
MN | Minnesota | 7 |  |  | c
MO | Missouri | 8 |  |  | c
MS | Mississippi | 2 |  |  | s
MT | Montana | 3 |  |  | w
NC | North Carolina | 90 |  |  | s
ND | North Dakota |  |  |  | c
NE | Nebraska | 2 |  |  | c
NH | New Hampshire | 45 |  |  | n
NJ | New Jersey | 24 |  |  | n
NM | New Mexico | 2 |  |  | w
NV | Nevada | 3 |  |  | w
NY | New York | 69 |  |  | n
OH | Ohio | 57 |  |  | c
OK | Oklahoma | 7 |  |  | c
OR | Oregon | 2 |  |  | w
PA | Pennsylvania | 107 |  |  | n
RI | Rhode Island | 11 |  |  | n
SC | South Carolina | 31 |  |  | s
SD | South Dakota | 4 |  |  | c
TN | Tennessee | 61 |  |  | s
TX | Texas | 25 |  |  | c
UT | Utah | 2 |  |  | w
VA | Virginia | 126 |  |  | s
VT | Vermont | 25 |  |  | n
WA | Washington | 6 |  |  | w
WI | Wisconsin | 11 |  |  | c
WV | West Virginia | 21 |  |  | s
WY | Wyoming | 3 |  |  | w
AB | Alberta
BC | British Columbia
MB | Manitoba
NB | New Brunswick
NL | Newfoundland & Labrador
NS | Nova Scotia
NT | Northwest Territories
ON | Ontario | 2
NU | Nunavut
PE | Prince Edward Island
QC | Quebec | 2
SK | Saskatchewan
YK | Yukon
 | Sum (US & Canada) |  |  |  | Sum of US & Canada is OFF, should be 11 less than the total aggregate which includes 9 internationals, one apo, and WC.
 | North:
 | South:
 | Central:
 | West:
Spreadsheet: 770077d1645801231-naming-manager-duplication-causing-issues-help-version-25th-feb
Sheet: Bretonnia Data

ROLE | TROOP TYPE | UNIT SIZE Min | [NAME] | POINTS | NOTES | Default Equipment | M | WS | BS | S | T | W | I | A | LD | Additional Equipment
Leader | General | 1 | 1 | 100 |  | Hand Weapon | 4 | 6 | 6 | 4 | 4 | 3 | 6 | 4 | 9
Leader | [NAME] | 1 | 1 | 80 |  | Hand Weapon & Battle Standard | 4 | 4 | 4 | 4 | 3 | 1 | 4 | 2 | 7
GENERAL WEAPON LIST | GEAR COST LIST |  |  | BATTLE STANDARD LIST |  |  |  |  |  |  |  |  |  |  |  | ARMOUR GEAR LIST | COST
Yadda | Double-handed weapon | 2 |  |  |  |  |  |  |  |  |  |  |  |  |  | [NAME] | 0.5 |  | BATTLE STANDARD ARMOUR
[NAME] | Halberd | 2 |  | Double-handed weapon |  |  |  |  |  |  |  |  |  |  |  | Shield | 1 |  | Shield
 | Spear | 1 |  | Lance |  |  |  |  |  |  |  |  |  |  |  | Light Armour | 2 |  | Light Armour
 | Lance | 2 |  |  |  |  |  |  |  |  |  |  |  |  |  | [NAME] | 3 |  | [NAME]
 | Bow | 2 |  |  |  |  |  |  |  |  |  |  |  |  |  | N/A
 | Long Bow | 3 |  |  |  |  |  |  |  |  |  |  |  |  |  | N/A
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | N/A
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | N/A
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | MOUNT LIST
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | GENERAL |  |  | BATTLE STANDARD
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [NAME] | 3 |  | [NAME]
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Barded Warhorse | 7 |  | Barded Warhorse
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Chimera | 250 |  | Chimera
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Wyvern | 180 |  | Wyvern
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Cockatrice | 150 |  | Cockatrice
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [NAME] | 50 |  | [NAME]
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Pegasus | 50 |  | Pegasus
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Dragon | 450 |  | Dragon
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Great Dragon | 600 |  | Great Dragon
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Emperor [NAME] | 750 |  | Emperor [NAME]
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Griffon | 150 |  | Griffon
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Hippogriff | 145 |  | Hippogriff
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [NAME] | 225 |  | [NAME]
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Gigantic Spider | 50 |  | Gigantic Spider
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | Manticore | 200 |  | Manticore
Spreadsheet: 138e8047-7a9e-48a7-90ea-89f3f8110f46
Sheet: Sheet1

Bevölkerung des Freistaates Sachsen nach ausgewählten Altersgruppen | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4
Leipzig, Stadt - Variante 2
Alter von ... bis                     unter ... Jahren | 2011 | 2015 | 2020 | 2025
1 000
unter 20 | 77186 | 82272 | 86550 | 85315
20 - 40 | 164135 | 147921 | 134081 | 121178
40 - 60 | 145188 | 140598 | 140861 | 137685
60 - 80 | 115895 | 117722 | 113840 | 119351
80 und mehr | 29405 | 34449 | 44544 | 48432
Insgesamt | 531809 | 522962 | 519876 | 511961
%
unter 20 | 14.51385741873492 | 15.73192698513468 | 16.64820072478822 | 16.66435529268831
20 - 40 | 30.86352431041972 | 28.28522913710747 | 25.79095784379352 | 23.6693810661359
40 - 60 | 27.3007790390911 | 26.88493619039242 | 27.09511498895891 | 26.89365010225388
60 - 80 | 21.79259847050351 | 22.51062218669808 | 21.89752941085951 | 23.3125179457029
80 und mehr | 5.52924076125075 | 6.587285500667353 | 8.568197031599842 | 9.460095593219014
Insgesamt | 100 | 100 | 100 | 100
Gebietsstand: | 1. Januar 2012
Quelle: | 2011 Bevölkerungsfortschreibung auf Basis der [NAME] vom 03. Oktober 1990
 | 2012 - 2025 Bevölkerungsprognose
Bevölkerung jeweils am 31. Dezember
Prognoseergebnisse werden bei der Veröffentlichung immer auf volle [NAME] gerundet. [NAME] basieren [NAME] ungerundeten Werten. Beim [NAME] der [NAME] werden Ihnen die ungerundeten [NAME] zur Verfügung gestellt, damit [NAME] eigenen Berechnungen genauer durchführen können. Die Darstellung der Ergebnisse sollte dann aber wieder gerundet erfolgen.
Spreadsheet: 600330d1543601407-diasplay-values-based-on-date-end-of-shift-example-revised
Sheet: Form Submissions

Date | Completion time | [NAME] | Name | Shift | Off-Going Charge Name | On-Coming Charge Name | 0000 | 0700 | 1900
2018-11-26 00:00:00 | 2018-11-26 08:34:45 |  |  | Night |  |  | 23 | 24
2018-11-26 20:26:54 |  |  |  | Day |  |  |  |  | 25
2018-11-27 00:00:00 | 2018-11-27 08:29:49 |  |  | Night |  |  | 28 | 29
2018-11-27 00:00:00 |  |  |  | Day |  |  |  |  | 31
2018-11-28 08:13:54 | 2018-11-28 08:20:15 |  |  | Night |  |  | 34 | 35
2018-11-28 20:37:42 | 2018-11-28 20:52:27 |  |  | Day |  |  |  |  | 40
2018-11-29 06:41:07 | 2018-11-29 07:07:22 |  |  | Night |  |  | 40 | 40
2018-11-29 17:06:11 | 2018-11-29 19:58:57 |  |  | Day |  |  |  |  | 38
2018-11-30 08:25:05 | 2018-11-30 08:33:30 |  |  | Night |  |  | 38 | 38
Spreadsheet: 848842d1699467779-calculating-an-average-smoothed-monthly-number-from-varied-length-actuals-example-workbook
Sheet: Sheet1

 |  | 2024-11-01 00:00:00
 | Revenue | 100000 | 105000 | 110000 | 115000 | 120000 | 125000 | 130000 | 135000 | 140000 | 145000 | 150000 | 155000 | 0 | 155000 | 160000 | 165000 | 170000 | 175000 | 180000 | 185000 | 190000 | 195000 | 200000 | 205000 | 210000 | 215000 | 220000 | 225000 | 230000 | 0 | 230000 | 235000 | 240000 | 245000 | 250000 | 255000 | 260000 | 265000 | 270000 | 0 | 275000 | 280000 | 290000 | 296666.666666667 | 304166.666666667 | 311666.666666667 | 319166.666666667 | 326666.666666667
 | Smoothed |  |  |  |  |  |  |  |  |  |  |  |  | 0 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 0 |  |  |  |  |  |  |  |  |  | 0
Spreadsheet: 526103d1499116557-returning-most-recent-entries-from-a-dataset-return-most-recent-entries
Sheet: Sheet1

How to return the 3 most recent entries of data from TODAY
dataset: |  |  |  |  |  |  | Type name here:
Date | Name | Fruit |  |  |  |  | Name: | [NAME]
2017-01-12 00:00:00 | [NAME] | Apple
2017-01-14 00:00:00 | Carol | Pear |  |  |  |  | Forumla will then lookup and return the 3 most recent entries:
2017-01-16 00:00:00 | [NAME] | [NAME] |  |  |  |  | Date | Fruit
2017-01-18 00:00:00 | [NAME] | Mango |  |  |  |  | 2017-01-26 00:00:00 | Lime
2017-01-22 00:00:00 | Carol | Orange |  |  |  |  | 2017-01-25 00:00:00 | Lemon
2017-01-25 00:00:00 | [NAME] | Lemon |  |  |  |  | 2017-01-18 00:00:00 | Mango
2017-01-26 00:00:00 | [NAME] | Lime
2017-01-31 00:00:00 | Carol | Pear |  |  |  |  | NB IF only 2 entries exist then only 2 are returned.
Spreadsheet: 3e7d8c7d-0de7-4135-a2a7-50173407a2fc
Sheet: San Francisco

REGIONAL DIRECTORY 2014/2015
SAN FRANCISCO UNIFIED SCHOOL DISTRICT
[NAME], Superintendent | [NAME], Supervisor, GATE, Migrant Education Program, Innovative Program and Neglected Student Program | [NAME], Fiscal Officer
555 Franklin Street | 750 25th Avenue | 135 Van Ness Ave. Suite 300
San Francisco, CA, 94102 | San Francisco, CA 94121 | San Francisco, CA, 94102
O: [PHONE] | O: [PHONE] | O: [PHONE] ext. 1622
F: [PHONE] | F: [PHONE] | F: [PHONE]
[EMAIL] | [EMAIL] | [EMAIL]
Shamin [NAME] | Blanca Gonzalez, Community Liaision | Gina Padilla- Quintanar, [NAME]
750 25th Avenue | 3750 18th Street | 3750 18th Street
San Francisco, CA 94121 | San Francisco, CA 94114 | San Francisco, CA 94114
O: [PHONE] | O: [PHONE] | O: [PHONE]
F: [PHONE] | C: [PHONE] | C: [PHONE]
[EMAIL] | [EMAIL] | [EMAIL]
Spreadsheet: 581424d1531297893-compare-2-sheets-and-show-balance-bbsn
Sheet: QUARTER

 | Yum Nak
Spreadsheet: 119721d1314693660-if-function-dummy-1-
Sheet: Sheet1

 | Company | Selected | Fee |  |  |  |  |  | yes
 | FIRE ENGINEER |  |  |  |  |  |  |  | no
 | abc | yes | 10000
 | def | yes | 5000 |  |  | I want this
 | hij |  | 6000
 | CIVIL ENGINEER
 | lkj | yes | 4000 |  |  | and this added together
 | hgf |  | 6000
 | dsa |  | 4500
TOTAL |  |  |  | and for the total to be in here
 | Also is the following possible
Number of weeks: |  |  |  | leading to |  | Week |  | Month
Start: |  | 2011-08-29 00:00:00
End: |  | 2011-10-03 00:00:00 |  |  |  |  |  |  |  |  |  |  | 1 | Jan
 |  |  |  |  |  |  |  |  |  |  |  |  | 2 | Feb
 |  |  |  |  |  |  |  |  |  |  |  |  | 3 | Mar
 |  |  |  |  |  |  |  |  |  |  |  |  | 4 | Apr
 |  |  |  |  |  |  |  |  |  |  |  |  | 5 | May
 |  |  |  |  |  |  |  |  |  |  |  |  | 6 | Jun
 |  |  |  |  |  |  |  |  |  |  |  |  | 7 | Jul
 |  |  |  |  |  |  |  |  |  |  |  |  | 8 | Aug
 |  |  |  |  |  |  |  |  |  |  |  |  | 9 | Sep
 |  |  |  |  |  |  |  |  |  |  |  |  | 10 | Oct
 |  |  |  |  |  |  |  |  |  |  |  |  | 11 | Nov
 |  |  |  |  |  |  |  |  |  |  |  |  | 12 | Dec