tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: tracy_geaccone__40395__NNG3rdCEEM
Sheet: Sales&Liq-COS

PRINT: PRT.1
 |  | JAN | FEB | MAR | APR | MAY | JUN | JUL | AUG | SEP | OCT | NOV | DEC
Merchant Service ($000's)
Gas Sales - Miscellaneous
- Item 2
- Other |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
- Base Gas Sale ??? / Monetization ???
- Other |  | 0
Hyperion Entry / Reversal |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Total Merchant Service Revenue
Liquids Revenue ($000's)
Condensate
Other
Other
Other |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Hyperion Entry / Reversal |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Total Liquids Revenue
Sales & Liquids Rev. Adjust.   (NON-CASH ITEMS ONLY)
Suspense Gas Adjustment |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Penalty - Misc. |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Other |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Other |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Other |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Hyperion Entry / Reversal |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Total Sales & Liquids Revenue Adjustments
TOTAL GAS & LIQUID SALES REVENUE
COST OF SALES ($000's)
Gas Purchases (Item 1)
Item 2
Other |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Other |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
Hyperion Entry / Reversal |  | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
TOTAL COST OF SALES
Spreadsheet: 63964d1264801804-vlookup-with-multiple-results-gss_survey_data_sample
Sheet: Overall_Results

Course code | Facilitator | n | # in perf rec | Enrolled | Completed | Distinction | Call Total | Deliv Total | Cadence Call (Team) | Client Meeting Round 1 | Non-Challenge Call | Client Meeting Round 2 | [NAME] | Exec Sum | [NAME] | Follow Up Ltr | Terr Plan | Whole Foods CIM | Whole Foods Call Plan | [NAME] | Whole Foods Call Rnd 1 | [NAME] 1 | [NAME] 2 | Whole Foods Call Rnd 2 | Soln Frame work | Unique Value | Value Prop | Presentation | Negotiation  Call 1 | [NAME] Call | Negotiation  Call 2 | GEO | Market | IOT / IMT / GMT | Final Survey Offering Code | Response % | [NAME] % (Perf. Record) | Overall NEI Compl | Overall NEI Resp | Ave. of All NEIs > | Able to perform sales activities | Confident Increase | Content Organized Structured | increase Job Perform | Facilitator | Intent to Apply | Learning Activities Effective | Learning Objective Met | Organiztn will help apply | Overall | [NAME] over Traditnal | Call taker | MGR | Expert Mentor | Challenge | Content Relevant | Format | PARRs | Understnd After | Understnd Before | Practical Benefit
US-E901 | X |  | 13 | 13 | 12 | 3 | 1.769230769230769 | 1.389423076923077 | 2.230769230769231 | 1.884615384615385 | 1.884615384615385 | 1.538461538461539 |  |  |  |  | 1 | 1.538461538461539 | 1.269230769230769 | 1.538461538461539 |  |  |  |  | 1.538461538461539 | 1 | 1 |  |  |  |  |  | Major | North America IOT
US-E902 | X |  | 15 | 14 | 13 | 0 | 2.066666666666667 | 1.416666666666667 | 2.033333333333333 | 2.333333333333333 | 1.933333333333333 | 1.933333333333333 |  |  |  |  | 0.9333333333333333 | 1.7 | 1.533333333333333 | 1.7 |  |  |  |  | 0.9333333333333333 | 1.1 | 1.4 |  |  |  |  |  | Major | North America IOT
US-E903 | X |  | 22 | 22 | 22 | 1 | 1.863636363636364 | 1.630681818181818 | 1.818181818181818 | 2.113636363636364 | 1.840909090909091 | 1.636363636363636 |  |  |  |  | 1 | 1.863636363636364 | 1.636363636363636 | 1.772727272727273 |  |  |  |  | 1.863636363636364 | 1.477272727272727 | 1.613636363636364 |  |  |  |  |  | Major | North America IOT
 |  | 0 | 50 | 49 | 47 | 4 | 1.8998445998446 | 1.478923853923854 | 2.027428127428128 | 2.110528360528361 | 1.886285936285936 | 1.702719502719503 | #N/A | #N/A | #N/A | #N/A | 0.9777777777777779 | 1.700699300699301 | 1.47964257964258 | 1.67039627039627 | #N/A | #N/A | #N/A | #N/A | 1.445143745143745 | 1.192424242424242 | 1.337878787878788 | #N/A | #N/A | #N/A | #N/A |  |  |  |  |  |  |  |  | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A | #N/A
 |  |  |  | 3 |  |  | 3
Spreadsheet: 112143d1308285303-report-by-multiple-validation-list-multiple-validation-list-report
Sheet: Report

 | Zone | BM | Branch |  |  |  |  |  |  |  |  |  |  | East | x | xya
 | East | y | [NAME] |  |  | There are three validation list(Zonewise, AVP wise and [NAME]) |  |  |  |  |  |  |  | West | y | [NAME]
 |  |  |  |  |  |  |  |  |  |  |  |  |  | North | z | rni
Month | Total Cases | Collected | Not Collected | % of Collection |  |  |  |  |  |  |  |  |  | South |  | rhii
2011-04-01 00:00:00 | 2 | 1 | 1 | 0.5
2011-05-01 00:00:00 | 1 | 1 | 0 | 1
2011-06-01 00:00:00 | 2 | 1 | 1 | 0.5
My manager wants a report should be in three different different form
1 | [NAME] to view report below, then select blank in zone validation list
2 | then view report by selecting BM Name, then select blank in BM list
3 | then view report by branch wise by selecting branch name
 | Means he wants to view report zone wise then BM Wise and then Branch Wise one by one using above three validation list
 | The present report is only on the baisi of Zone Wise Now I need a formula to show report BM and Branch Wise as well
 | Please Note: This is only sample sheet I have very huge data sheet to make report
Spreadsheet: a2c60b26-5e82-4ab7-83d8-f9b8aef8cd64
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
FNEOP | Test | FNEOP | Neopterin | Neopterin, Serum | Radioimmunoassay (RIA) | Plain, Red-Top Tube: Red |  |  | 1 | mL | Draw blood in a plain, red-top tube(s). (Serum gel tube is also acceptable.) Spin down and send 1 mL serum frozen in amber vial (Supply T192) to protect from light, freeze immediately. | 0.2 mL | Hemolysis	NA Thawing	NA Lipemia	NA Icterus	NA Other	Not light protected | Reference Range:  <15 nmol/L   Test Performed By:   Quest Diagnostics Nichols Institute                                 33608 [NAME]                                 San Juan Capistrano, CA 92690-6130 | Wednesday | 1 day | 10 days | 1 | 83519 |  |  |  |  | Quest Diagnostics/Nichols Institute
FNEOP | [NAME] | SERUM | Serum |  |  |  | Frozen | 30 days
FNEOP | Result | Z1568 | Neopterin, Serum | Neopterin, Serum | Alphanumeric |  |  |  |  | nmol/L |  |  |  |  |  |  |  |  |  | In Process
Spreadsheet: 792174d1660760698-use-the-results-from-one-formula-to-find-the-answer-on-a-different-sheet-examplefile-08-17
Sheet: Del

VIN | Account Number | Customer Equipment ID | Device | Equipment Travel Type | Vehicle Reference Number | Declared Weight | IFTA | IRP | Expiration Date | Make Year | Make | Plate Number | State | Country
Spreadsheet: 266167d1379517655-almost-there-deleting-blank-spaces-and-eliminating-indirect-statements-daily
Sheet: 15 Sep

ADA/OFF | LEAVE | DNIF | OTHER | REASON
Should look like (no blanks between names)
ADA/OFF | LEAVE | DNIF | OTHER | REASON
DOG | SNAKE | COW | MONKEY | MEETING
 |  |  | SKUNK | BRIEFING
Spreadsheet: 275892d1383675219-age-range-formula-counting-blank-cells-copy-of-age-range
Sheet: Sheet1

 | DOB
 | 1956-08-19 00:00:00 |  | Count |  | Count
 | 1956-08-20 00:00:00 |  |  | From 0 to 17 Years
 | 1956-08-21 00:00:00 |  |  | From 18 to 24 Years
 | 1990-07-20 00:00:00 |  |  | From 25 to 29 Years
 | 1970-06-06 00:00:00 |  |  | From 30 to 34 Years
 | 1934-05-05 00:00:00 |  |  | From 35 to 39 Years
 | 1980-01-06 00:00:00 |  |  | From 40 to 44 Years
 | 1979-10-01 00:00:00 |  |  | From 45 to 49 Years
 | 1956-12-01 00:00:00 |  |  | From 50 to 59 Years
 | 1948-03-14 00:00:00 |  |  | From 60 to 64 Years
 | 1946-09-13 00:00:00 |  |  | 65 Years and over
 | 1964-11-17 00:00:00
 | 1986-12-15 00:00:00
Spreadsheet: 140142d1328633520-vlookup-or-embedded-ifthen-ifthen
Sheet: ifthen

RegisteredCompanies | Type1-or-Type2-or-IfBoth_Here | CompanyName | Type1 | Type2 | IfBoth
A | FORMULA? | 1 | one | two | both
B | FORMULA? | 2 | one
C | FORMULA? | 3 | one
D | FORMULA? | 4 | one
E | FORMULA? | 5 |  | two
F | FORMULA? | 6 |  | two
G | FORMULA? | 7 |  | two
H | FORMULA? | 8 | one | two | both
I | FORMULA? | 9 | one | two | both
Spreadsheet: 59045d1259401028-counting-and-averaging-information-example
Sheet: Sheet2

 | Company | Open | Closed | % <14 | Average
2009-11-01 00:00:00 | A
 | B
 | C
2009-12-01 00:00:00 | A
 | B
 | C
2010-01-01 00:00:00 | A
 | B
 | C
2010-02-01 00:00:00 | A
 | B
 | C
2010-03-01 00:00:00 | A
 | B
 | C
Spreadsheet: 854555d1704382964-assist-in-vlookup-match-index-formula-budget
Sheet: Mannin

DEPARTMENT | DEPARTMENT | POSITION
 |  |  | Salary | Food Allowance | Housing  Allowance | Transportation | Phone Allowance | MISCL
F&B | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
 | F&B
Engineering | Engineering
 | Engineering
 | Engineering
 | Engineering
 | Engineering
 | Engineering
 | Engineering
 | Engineering
 | Engineering
 | Engineering
 | Engineering
Spreadsheet: 131368d1322928309-matrix-2-way-lookup-site-distance-matrix_2
Sheet: AGGREGATE

Sites | 01 | 02 | 18 | 08 | 04 | 11 | 12 | 34 | 06 | 42 | Occurrences | Distance | Coordinates
01 |  |  |  |  |  |  |  |  |  |  |  | 458.34
02 | 0 |  |  |  |  |  |  |  |  |  |  | 388.42
18 | 0 | 0 |  |  |  |  |  |  |  |  |  | 388.42
08 | 0 | 0 | 7.439999999999998 |  |  |  |  |  |  |  |  | 327.09
04 | 0 | 0 | 10.59999999999999 | 181.68 |  |  |  |  |  |  |  | 278.32
11 | 0 | 0 | 12.12 | 137.23 | 232.88 |  |  |  |  |  |  | 278.32
12 | 0 | 0 | 9.949999999999989 | 214.33 | 278.32 | 261.99 |  |  |  |  |  | 261.99
34 | 0 | 0 | 9.949999999999989 | 214.33 | 278.32 | 261.99 | 458.34 |  |  |  |  | 261.99
06 | 0 | 0 | 4.720000000000027 | 0.009999999999990905 | 16.49000000000001 | 58.79999999999995 | 13.01999999999998 | 13.01999999999998 |  |  |  | 258.4400000000001
42 | 0 | 0 | 12.25 | 161.6999999999999 | 258.4400000000001 | 327.09 | 388.42 | 388.42 | 146.9500000000002 |  |  | 232.88
 |  |  |  |  |  |  |  |  |  |  |  | 214.33
 |  |  |  |  |  |  |  |  |  |  |  | 214.33
 |  |  |  |  |  |  |  |  |  |  |  | 181.68
 |  |  |  |  |  |  |  |  |  |  |  | 161.6999999999999
 |  |  |  |  |  |  |  |  |  |  |  | 146.9500000000002
 |  |  |  |  |  |  |  |  |  |  |  | 137.23
 |  |  |  |  |  |  |  |  |  |  |  | 58.79999999999995
 |  |  |  |  |  |  |  |  |  |  |  | 16.49000000000001
 |  |  |  |  |  |  |  |  |  |  |  | 13.01999999999998
 |  |  |  |  |  |  |  |  |  |  |  | 13.01999999999998
 |  |  |  |  |  |  |  |  |  |  |  | 12.25
 |  |  |  |  |  |  |  |  |  |  |  | 12.12
 |  |  |  |  |  |  |  |  |  |  |  | 10.59999999999999
 |  |  |  |  |  |  |  |  |  |  |  | 9.949999999999989
 |  |  |  |  |  |  |  |  |  |  |  | 9.949999999999989
 |  |  |  |  |  |  |  |  |  |  |  | 7.439999999999998
 |  |  |  |  |  |  |  |  |  |  |  | 4.720000000000027
 |  |  |  |  |  |  |  |  |  |  |  | 0.009999999999990905
 |  |  |  |  |  |  |  |  |  |  |  | 0
Spreadsheet: 728219d1618373326-how-to-automatically-show-value-from-a-table-row-every-time-a-new-entry-is-made-using-index-ans
Sheet: Sales

Date | SalesTeam | Q1 | Q2 | Q3 | Q4
2021-04-01 00:00:00 | A | 100 | 200 | 200 | 400
2021-04-02 00:00:00 | B | 50 | 60 | 60 | 70
2021-04-03 00:00:00 | A | 100 | 100 | 200 | 450
2021-04-04 00:00:00 | B | 250 | 350 | 150 | 100
2021-04-05 00:00:00 | A | 58 | 90 | 100 | 100
2021-04-06 00:00:00 | B | 100 | 200 | 250 | 300
2021-04-07 00:00:00 | A | 100 | 200 | 300 | 400
Spreadsheet: 754006d1635880603-monthly-and-annual-totals-in-two-columns-monthly-and-annual-total-example-sheet
Sheet: Sheet1

 | Item | Date | Dollars | ID # | Monthly total | Annual total
 | Contacts | 2020-10-05 00:00:00 | 400
 | Contacts | 2020-10-06 00:00:00 | 350
 | Contacts | 2020-10-07 00:00:00 | 410
 | Contacts | 2020-10-08 00:00:00 | 370 |  | 1530 |  | (no value here, because contacts purchased again in November)
 | Glasses | 2020-10-01 00:00:00 | 1000 |  | 1000 |  | (total here, because glasses only purchased in October)
 | Lens wipes | 2020-10-01 00:00:00 | 2
 | Lens wipes | 2020-10-20 00:00:00 | 3 |  | 5
 | Screwdriver | 2020-10-02 00:00:00 | 15 |  | 15
 | Case for glasses | 2020-11-05 00:00:00 | 20 |  | 20
 | Contacts | 2020-11-20 00:00:00 | 200
 | Contacts | 2020-11-21 00:00:00 | 200 |  | 400
 | Lens wipes | 2020-11-07 00:00:00 | 2
 | Lens wipes | 2020-11-14 00:00:00 | 2 |  | 4
 | Case for glasses | 2020-12-25 00:00:00 | 45 |  | 45
 | Contact solution | 2020-12-15 00:00:00 | 128 |  | 128
 | Lens wipes | 2020-12-31 00:00:00 | 50 |  | 7
Spreadsheet: 873902d1720499929-named-range-data-validation-error-demo
Sheet: Data

Item | Type | Colour
Cabbage - red | Vegetable | Red
Tomato | Vegetable | Red
Chicory (radicchio) - red | Vegetable | Red
Potato - red | Vegetable | Red
Chard - red | Vegetable | Red
Bell pepper - red | Vegetable | Red
Onion - red | Vegetable | Red
Jalapeno pepper - red | Vegetable | Red
Apple - red | Fruit | Red
Cranberries | Fruit | Red
Grapefruit - pink | Fruit | Red
Currants - red | Fruit | Red
Strawberries | Fruit | Red
Orange - blood | Fruit | Red
Lingonberries | Fruit | Red
Pomegranate | Fruit | Red
Pear - red | Fruit | Red
Watermelon | Fruit | Red
Cherries | Fruit | Red
Nectarine | Fruit | Red
Raspberries | Fruit | Red
Plum - red | Fruit | Red
Carrot | Vegetable | Orange
[NAME] | Vegetable | Orange
Bell pepper - orange | Vegetable | Orange
Sweet potato | Vegetable | Orange
Pumpkin | Vegetable | Orange
Yams | Vegetable | Orange
[NAME] | Fruit | Orange
Kumquat | Fruit | Orange
Nectarine | Fruit | Orange
Passionfruit | Fruit | Orange
Mandarine | Fruit | Orange
Orange | Fruit | Orange
Peach | Fruit | Orange
Cantaloupe | Fruit | Orange
Mango | Fruit | Orange
Papaya | Fruit | Orange
Persimmon | Fruit | Orange
Corn | Vegetable | Yellow
Potatoes (Yukon) | Vegetable | Yellow
Onion - yellow) | Vegetable | Yellow
Ginger | Vegetable | Yellow
Bell pepper - yellow | Vegetable | Yellow
Squash (acorn, butternut, summer, winter) | Vegetable | Yellow
Apple (Golden Delicious) | Fruit | Yellow
Lemon | Fruit | Yellow
Pear - Asian | Fruit | Yellow
Pineapple | Fruit | Yellow
[NAME] | Fruit | Yellow
Starfruit | Fruit | Yellow
Artichoke | Vegetable | Green
Bell pepper - green | Vegetable | Green
Celery | Vegetable | Green
Peas | Vegetable | Green
Chard - green | Vegetable | Green
Mustard greens | Vegetable | Green
Avocado | Vegetable | Green
Broccoli | Vegetable | Green
Edamame | Vegetable | Green
Beet greens | Vegetable | Green
Mustard greens | Vegetable | Green
Okra | Vegetable | Green
Bean sprouts | Vegetable | Green
Cabbage | Vegetable | Green
Green beans | Vegetable | Green
[NAME] | Vegetable | Green
Spinach | Vegetable | Green
Watercress | Vegetable | Green
Green tea | Fruit | Green
Limes | Fruit | Green
Apple - green | Fruit | Green
Olives | Fruit | Green
Kiwi | Fruit | Green
Pears | Fruit | Green
[NAME] | Vegetable | Purple
Kale - purple | Vegetable | Purple
Potato - purple | Vegetable | Purple
Beetroot | Vegetable | Purple
Carrot - purple | Vegetable | Purple
Turnip | Vegetable | Purple
Bell pepper - purple | Vegetable | Purple
Cauliflower - purple | Vegetable | Purple
Blackberries | Fruit | Purple
Plum | Fruit | Purple
Prunes | Fruit | Purple
Blueberries | Fruit | Purple
Grapes - purple | Fruit | Purple
[NAME] | Fruit | Purple
Raisins | Fruit | Purple
Spreadsheet: 75843d1276453534-numbering-a-mixed-range-sequentialy-sequential-number
Sheet: Sheet1

 | Iron | 1
 | Iron | 2
 | Driver | 1
 | Driver | 2
 | Driver | 3
 | Driver | 4
 | Iron | 3
 | Iron | 4
 | Iron | 5
 | Putter | 1
Spreadsheet: 731538d1620395572-help-formula-to-find-a-match-for-cell-and-return-value-of-the-title-of-that-column-league
Sheet: NEO

S.N. | Particulars | Bought  Units | Unit or NEO Price | Bought/ Sold Price  ($US) | Bought/ Sold Price (BTC) | Token Unit Price During Txn | Activity  Date | Received Date | Fee  ($US) | Fee (%) | Total Price ($US) | Estimated  Price ($US) | Status | Notes
1 | NEO @ Bittrex - Purchase 1 | 15.61775868 | 0.00638695 |  | -0.09999936 | 4613.5 | 2017-09-07 00:00:00 | 2017-09-07 00:00:00 |  |  |  |  | SOLD
2 | NEO @ Bittrex - Purchase 2 | 4.85253415 | 0.00519998 |  | -0.02529616 | 4142.9 | 2017-09-12 00:00:00 | 2017-09-12 00:00:00 |  |  |  |  | SOLD
3 | NEO & Ripple @ Gemini - Sale | -20.47029283 | 0.00779699 |  | 0.15920786 | 4436 | 2017-10-07 00:00:00
4 | NEO @ Bittrex - Purchase 3 | 120.2657297 | 0.0042857 |  | -0.5167126 | 8097.3 | 2017-11-21 00:00:00 | 2017-11-21 00:00:00 |  |  |  |  | SOLD
5 | NEO @ Bittrex - Sale 2 | -20.2657297 | 0.00450774 |  | 0.09112439 | 8766.2 | 2017-11-25 00:00:00
6 | Fees for transferring 50 NEO from Bittrex to Ledger | -0.025
7 | NEO @ Bittrex - Sale 3 | -49.975 | 0.00210707 |  | 0.10503806 | 16754 | 2017-12-11 00:00:00
8 | NEO@ Binance - Sale 1 | -33 | 0.008763 |  |  | 14477.5 | 2018-01-09 00:00:00
9 | Fees for transferring 17 NEO from Ledger to Bittrex | 0 |  |  |  |  | 2018-01-15 00:00:00
10 | NEO @ Binance- Purchase | 1.53 | 0.013629 |  | -0.020853 | 13597.6 | 2018-01-15 00:00:00 | 2018-01-15 00:00:00
11 | NEO @ Binance - Purchase | 0.18 | 0.013652 |  | -0.002458 | 13597.6 | 2018-01-15 00:00:00 | 2018-01-15 00:00:00
12 | NEO @ Binance - Purchase | 170.80731 | 0.01395 |  | -2.385171 | 13597.6 | 2018-01-15 00:00:00 | 2018-01-15 00:00:00
13 | NEO@ Binance - Sale | -17 | 0.011623 |  | 0.197591 | 12745 | 2018-01-20 00:00:00
14 | NEO@ Binance - Sale | -107.51 | 0.014957 |  | 1.60805758 | 11135.3 | 2018-01-29 00:00:00
15 | [NAME] | -1 | 0.00979992 |  | 0.00977543 | 10878.81 | 2018-03-06 00:00:00
16 | [NAME] |  |  |  |  | 10000 | 2018-03-07 00:00:00
17 | NEO@ Binance - Sale | -5 | 0.010093 |  |  | 10000 | 2018-03-07 00:00:00
18 | NEO @ Binance - Purchase |  | 0.007764 |  |  | 6957 | 2018-04-11 00:00:00 | 2018-04-11 00:00:00
19 | NEO@ Binance - Sale | -97.04018
20
21
22
23
24
25
26
 | Next Buy
 | NEO in [NAME] | 0
 | NEO in Bittrex | 0
 | NEO in Binance | 0
 | Total Investment Loss/Profit ($US)
 | Website: https://
 | Contract address:
 | Symbol:
 | Decimals:
 | About/Idea
 | NEO smart contracts are known as NEO contracts. NEO is looking to do what Ethereum is looking to do. NEO can handle 1000 txns/sec, with a goal to optimize it to 10000 txns/sec. Ethereum currently processes 15 txns/sec.
 | Team Members (reputation, prior projects, LinkedIn, social presence)
 | Website Design
 | White Paper
 | ICO Rating
 | www.icoranker.com
 | www.icobench.com
 | www.tokenmarket.net
 | Marketing
 | Twitter
 | Instagram
 | Facebook
 | CEO Presence in the market
 | Roadmap & timeline
 | When are they hitting the exchanges?
Spreadsheet: 144433d1330977358-lookup-and-find-formula-modified-by-tigeravatar-
Sheet: Sheet1

CODE | Insurance Companies name with Narration
 | BUPA_NATIONAL INFORMATION CENTER (AL ELM I S)
 | ASICO_IBN KHALDOUN EDUCATIONAL_1060213
 | AXA INSURANCE COMP.(SPECIAL REFERRAL)
 | MEDGULF_GCC-GSO_5808001
 | NCCI_NADEC PROJECTS_9192
 | SAICO_M/s ZIAD SAEED ALQASAS EST. FOR CONT'G_2713
 | SANAD_AL JASRIAH CO. LTD._MG201001426
 | CARS_MANNING SALVAGE & LEE_1/51/0173/2010
 |  | Standard Code for Ins.Coys
 |  | Name | CODE
 |  | BUPA | 1
 |  | ASICO | 2
 |  | AXA | 3
 |  | Medgulf | 4
 |  | NCCI | 5
 |  | SAICO | 6
 |  | SANAD | 7
 |  | CARS | 8
Spreadsheet: 70741d1271431461-conditional-formatting-test3
Sheet: Sheet1

January | Feb | March | April | Average
10 | 20 | 50 | 80 | 40
20 | 30 | 100 | 50 | 50
30 | 40 | 30 | 20 | 30
40 | 50 | 20 | -10 | 25
50 | 60 | 10 | -40 | 20
60 | 70 | 0 | -70 | 15
70 | 80 | -10 | -100 | 10
Spreadsheet: 152575d1335444086-pivot-and-fixed-values-fish-processing-systems2
Sheet: Segment

Market Segment | (Alle)
Radetiketter | Sales 2010 (NOK) | Profit after tax | Equity | Employees
[NAME] | 68842000 | 3268000 | 3941000 | 13
Bjørdal Industrier AS | 146000000 | 208000 | 20460000 | 104
First Process AS | 195411000 | 10692000 | 12027000 | 24
[NAME] | 190567000 | 10238000 | 26669000 | 95
[NAME] | 125475000 | 8547000 | 12435000 | 75
Prosess Industri AS | 23687000 | 1172000 | 6837000 | 17
Steel Tech AS | 24654000 | 1888000 | 3152000 | 6
[NAME] | 23982000 | 118000 | 1980000 | 22
(tom)
Totalsum | 798618000 | 36131000 | 87501000 | 356
Spreadsheet: 117112d1312457646-matching-data-form-multiple-columns-multiple-match-3-
Sheet: eg1

Model | Capacity | Fuel | Conversion
RXA | 10 | Husk | 15
SGI | 15 | Coal | 12
HPBB | 20 | Wood | 11
IFB | 10 | Coal | 10.5
CPD | 15 | LDO | 14.5
CPFD | 30 | HSD | 13
SMDH | 40 | FO | 16
TPD | 20 | Gas | 18
VTB | 20 | Briquette | 11
Example 1:
RXA | 10 | Husk | 15
Spreadsheet: 194433d1353169807-display-the-value-of-a-row-with-the-higher-corresponding-bottom-value-example
Sheet: Foglio1

100 | 0 | -100 | -100 | -100 |  | -100
50 | 0 | 0 | -50 | 50 |  | 50
25 | 25 | -25 | 0 | 0 |  | 25
0 | 0 | 0 | 0 | 0 |  | 0
40 | -40 | 40 | -40 | 0 |  | -40
10 | 50 | 40 | 20 | 30
Spreadsheet: 870302d1716841036-can-anyone-tell-what-mistake-i-am-doing-in-my-formulas-given-in-the-sample-file-sample-file
Sheet: Table 38

कार्यक्रम PROGRAM |  |  | कृ तष ऋण के  बुतनर्ादी ित्व (कृ तष तर्स्तारण अतधकाररर्ोिं के  अतिररक्त) BASICS OF AGRI CREDIT (NON-AEOs) | PROG CODE
 |  |  |  | IT 1701
तर्षर् समूह/JOB FAMILY |  |  | CREDIT
उद्देश्य/OBJECTIVE |  |  | TO BRING AWARENESS ABOUT THE IMPORTANCE OF CREDIT RELATED  MATTERS,  AND  TO  IMPART  JOB  KNOWLEDGE  TO ENABLE     PARTICIPANTS     TO     PERFORM     THE     DUTIES EFFECTIVELY
लतक्षि समूह/TARGET GROUP |  |  | [NAME]
अर्तध/DURATION |  |  | 3 DAYS
DAY | SESSION | CONTENTS
Day 1 | I | Guidelines      on      Agricultural      Lending:      Priority      Sector Guidelines(pertaining only to agriculture), Agriculture lending norms in Credit policy & Delegation of Powers of branches in agriculture
 | II | Practical Aspects of Agriculture Financing: Identification of Borrower (KYC & due Diligence),Pre-sanction visit and assessing credit need of farmer, Do & Don’ts for officials handling agri-advance for the first time
 | III | KCCS –Assessment and monitoring of short term loans with case study
 | IV | Agricultural Term Loans – Investment & development schemes
Day 2 | I | Legal Aspects and documentation: Scrutiny of land records, LSR and valuation   reports   of   agriculture   landed   properties   offered   as security, Documentation and follow-up of agricultural loan accounts.
 | II | Interaction with Agriculture Finance section CO/RO: Role of Panel Agricultural  Consultants  &  AIC  &  Detailed  procedural  aspects  of various  Agri-loans  &  general  discussion  on  review  observations  of branches (WITHOUT DISCLOSING NAME OF THE BRANCH)
 | III | Interaction with LDM
 | IV | Discussion on Technical problems faced by branches handling loans pertaining  to  agriculture  Credit  monitoring  &  recovery  methods involved in Agri. loans
Day 3 | I | [NAME]  Schemes  especially  AIF,  AHIDF,  PM-FME,  PM KUSUM, CBG, EBP, Overview on LAPS
 | II | Motivation & Effective Communication Skills
 | III | Business Ethics and Value System
 | IV | Implementation   of   OL,   Prevention   of   Sexual   Harassment   at workplace and Valediction
Spreadsheet: 39003d1237225597-average-for-specific-dates-of-gap-time-676002_rvancura
Sheet: Sheet4

Row Labels | Average of Gap Time (Hours/Min/Sec)
2009
Jan
02-Jan | 0:05:50.500000
05-Jan | 1 day, 9:31:49
06-Jan | 3:55:11
07-Jan | 3:00:54
09-Jan | 5:02:23
12-Jan | 8:15:05.500000
13-Jan | 1 day, 2:28:35
14-Jan | 0:07:05.667000
15-Jan | 5:15:09.500000
16-Jan | 2:09:12.182000
20-Jan | 7:45:39
21-Jan | 3:58:06.800000
22-Jan | 0:09:27.312000
23-Jan | 8:32:53.500000
26-Jan | 7:12:21.900000
27-Jan | 1:46:08.857000
28-Jan | 1:33:30.444000
29-Jan | 1:15:50
30-Jan | 1:30:41.545000
Mar
10-Mar | 38 days, 18:49:15
Grand Total | 11:01:29.053000