Excel formulas are essential for analyzing, visualizing, and extracting insights from data. With over 400 built-in functions, Excel formulas enable you to perform calculations, manipulate text, look up values, and much more. However, learning the syntax and using Excel formulas can have a steep learning curve. This is where ChatGPT can help!
ChatGPT is an artificial intelligence system developed by Anthropic that can understand natural language prompts and provide helpful responses. This guide will explore how ChatGPT can be leveraged to write Excel formulas more efficiently.
Getting Started with ChatGPT
ChatGPT is available as a free web application at chat.anthropic.com. No sign-up is required to start chatting.
When you open the ChatGPT web interface, you will see a chatbox where you can start typing prompts. ChatGPT will then provide contextual responses in natural language.
It is important to frame the prompts properly to get relevant and accurate responses from ChatGPT for Excel formulas. Here are some tips:
- Provide enough context in the prompt – Specify that you need an Excel formula and explain what you are trying to accomplish.
- Ask specific questions – Rather than an open-ended request like “How do I calculate x in Excel?”, provide details of what you want to calculate and any sample data if possible.
- Use Excel terminology correctly – For example, refer to cell references like A1 instead of “first cell”.
- Request examples – Ask ChatGPT to provide examples of formulas for your use case using dummy data. This will help you understand how to apply the formulas better.
- Request references – If needed, ask ChatGPT to cite references or provide links to documentation for the suggested formulas.
Let’s review examples of prompts for common Excel formula needs and see ChatGPT’s responses.
Math Formulas
Math formulas involve arithmetic operations like addition, subtraction, multiplication, and division.
Prompt: I want to add the values in cells A1 and B1 and display the result in C1. Can you provide the Excel formula for this with an example?
ChatGPT: Here is the Excel formula to add the values in cells A1 and B1 and display the result in C1:
=A1+B1For example, if A1 contains the value 5 and B1 contains the value 10, the formula =A1+B1 in cell C1 will display the result 15.
The + operator performs addition in Excel formulas. To add values from cells, you reference the cell addresses separated by the + operator.
References:
Prompt: I want to calculate the difference between two cells A1 and B1 and show the result in C1. Please give me the Excel formula.
ChatGPT: To calculate the difference between two cells A1 and B1 and display the result in C1, you can use the following Excel formula:
=A1-B1This uses the - subtraction operator to subtract the value in cell B1 from cell A1.
For example, if A1 contains 10 and B1 contains 5, the formula =A1-B1 in C1 will output 5.
References:
Prompt: I want to multiply two cells A1 and B1. Show me the Excel formula to do this multiplication and multiply sample values 5 and 10 in A1 and B1 to get the result in C1.
ChatGPT: Here is the Excel formula to multiply two cells A1 and B1 and display the result in C1:
=A1*B1This uses the * multiplication operator.
For the sample values:
A1 = 5
B1 = 10
The formula would be =A1*B1, multiplying 5 * 10 and displaying the result 50 in cell C1.
References:
Prompt: I have two cells A1 with value 20 and B1 with value 5. How do I divide A1 by B1 and show the quotient in C1? Please give me the Excel formula.
ChatGPT: To divide the value in cell A1 by the value in cell B1 and display the quotient in cell C1, you can use the division formula in Excel:
=A1/B1 This uses the / division operator to divide A1 by B1.
For the sample values:
A1 = 20
B1 = 5
The formula would be =A1/B1, which would divide 20 by 5 and display the quotient 4 in cell C1.
References
Text Formulas
Text formulas in Excel enable you to manipulate, extract or combine text strings from cells.
Prompt: I have a cell A1 containing the text “John Smith”. I want to extract just the first name “John” into cell B1. Can you please provide the Excel formula for this?
ChatGPT: Here is the Excel formula to extract just the first name “John” from the text “John Smith” in cell A1 and display it in cell B1:
=LEFT(A1, FIND(" ",A1)-1)This uses the LEFT and FIND functions:
LEFT extracts characters from the left side of a text string.
FIND locates the position of the space character (” “) in the text.
So the formula finds the space position in A1, and then uses LEFT to extract the text from the left side up to 1 character before the space into cell B1.
References:
Prompt: I have a cell A1 with text “John Smith” and cell B1 with text “Sarah Lee”. What formula can combine the text from A1 and B1 into C1 with a comma and space so the result is “John Smith, Sarah Lee”?
ChatGPT: To combine text from two cells A1 and B1 into cell C1 with a comma and space, you can use the CONCATENATE function:
=CONCATENATE(A1,", ",B1)CONCATENATE joins multiple text strings together.
So for cell A1 containing “John Smith” and B1 containing “Sarah Lee”, the formula would be:
=CONCATENATE(A1,", ",B1)This would output “John Smith, Sarah Lee” in cell C1.
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Lookup Formulas
Lookup formulas in Excel enable you to find or retrieve data from a specific cell or range based on a lookup value. Some common lookup functions are VLOOKUP and HLOOKUP.
Prompt: I have a table with student names in column A and their test scores in column B. I want to lookup John’s test score based on his name. Can you provide a sample Excel formula for this VLOOKUP?
ChatGPT: Here is a sample Excel formula using VLOOKUP to lookup John’s test score based on his name:
=VLOOKUP("John",A:B,2,FALSE)Where:
- “John” is the lookup value (the name to search for)
- A:B refers to the table array (columns A and B containing the names and scores)
- 2 specifies to return the 2nd column (the scores column)
- FALSE indicates an exact match is required
So this looks for “John” in column A, and returns the corresponding score from column B.
References
Prompt: I have a table listing product names in column A and prices in column B. In cell C1 I have entered “Apple”. How can I use VLOOKUP to find the price for Apples based on this product name? Please give me the full formula.
ChatGPT: Here is the VLOOKUP formula to lookup the price of Apples based on the product name in C1:
=VLOOKUP(C1,A:B,2,FALSE)This will lookup “Apple” entered in C1 within the product name column A, and return the corresponding price from column B.
The full breakdown:
- C1 contains the lookup value “Apple”
- A:B is the table array with names in A and prices in B
- 2 specifies to return the 2nd column (column B prices)
- FALSE indicates an exact match is required
So this will find “Apple” in the name column and output the associated price.
References:
Prompt: I have a table with Employee Names in column A, Departments in column B, and Salaries in column C. How can I use VLOOKUP to find the Salary for John in Finance department? Please give the full formula with example table references.
ChatGPT: Here is the VLOOKUP formula to lookup John’s salary in the Finance department from the given table:
=VLOOKUP("John",A1:C10,3,FALSE)Where:
- A1:C10 is the reference to the table array with columns:
- A: Employee Name
- B: Department
- C: Salary
- “John” is the name to lookup
- 3 refers to the 3rd column in the array (Salary column)
- FALSE requires an exact match
So this looks up “John” in the name column A, and returns his salary from the 3rd column C.
The table array would have data like:
| Name | Department | Salary |
|-|-|-|
| John | Finance | 60000 |
| Sarah | Sales | 50000 |
So with “John” and Finance in C1, it returns 60000
References:
Prompt: I want to lookup the email address of a person based on their name in another sheet. Can you give me an example of a VLOOKUP formula to do this across two sheets?
ChatGPT: Here is an example VLOOKUP formula to lookup an email address from another sheet based on the person’s name:
=VLOOKUP(A2,'Sheet2'!A:B,2,FALSE)Where:
- A2 contains the name of the person to lookup
- ‘Sheet2’!A:B refers to the lookup range with names and emails on Sheet2
- 2 specifies to return the 2nd column from Sheet2 (the Email column)
- FALSE requires an exact match
So this looks up the name in cell A2 on Sheet1 in the name column on Sheet2, and returns the corresponding email.
The lookup range on Sheet2 would contain:
| Name | Email |
|-|-|
| John Doe | jdoe@email.com |
| Jane Smith | jsmith@email.com |
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Logical Formulas
Logical formulas in Excel allow you to evaluate conditions and test if they are TRUE or FALSE. Some key logical functions are IF, AND, OR.
Prompt: I want to check if the value in cell A1 is greater than 100. If true, return “Above 100” otherwise return “Below 100”. Can you please give me the Excel formula for this?
ChatGPT: Here is the Excel formula using the IF function to check if A1 is greater than 100 and return a value accordingly:
=IF(A1>100,"Above 100","Below 100")This checks if the condition A1>100 evaluates to TRUE or FALSE.
If TRUE, it returns the text “Above 100”.
If FALSE, it returns the text “Below 100”.
So if A1 contains the value 150, it will return “Above 100”. But if A1 contains 80, it will return “Below 100”.
References:
Prompt: I want to check multiple conditions – if value in cell A1 is greater than 50 AND cell B1 is less than 100. If both conditions are true, return “Within range”. Can you please give me the formula?
ChatGPT: Here is the Excel formula using the AND function to check for two conditions:
=IF(AND(A1>50,B1<100),"Within range","Outside range") This uses AND to evaluate two logical conditions:
- A1 > 50
- B1 < 100
If both are TRUE, the AND function returns TRUE, and the outer IF statement returns “Within range”.
If either condition is FALSE, AND returns FALSE, and the IF statement returns “Outside range”.
So this will check if A1 is greater than 50 AND B1 is less than 100, and if so return “Within range”.
References:
Prompt: I have data in column A with numbers. In column B, I want to check if the number in Column A is >10 OR <5. If either condition is true, return “Outside Range”, else return “Within Range”. Please give me the Excel formula for this using OR.
ChatGPT: Here is the Excel formula to check if the number in column A is >10 OR <5 using the OR function:
=IF(OR(A1>10,A1<5),"Outside Range","Within Range")This checks two conditions with OR:
- A1 > 10
- A1 < 5
If either condition is TRUE, OR returns TRUE, and the outer IF returns “Outside Range”
If both conditions are FALSE, OR returns FALSE, and IF returns “Within Range”.
So this will check the number in column A against the two conditions, and return “Outside Range” if either is true, else “Within Range”.
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Conditional Formatting Formulas
Conditional formatting allows you to format cells based on rules or conditions. You can reference cells in conditional formatting formulas to highlight cells or ranges.
Prompt: I want to highlight cells in column A if the value is greater than 100. Can you give me the formula for conditional formatting to do this?
ChatGPT: Here is the formula for conditional formatting to highlight cells in column A if the value is greater than 100:
=A1>100This applies the following conditional formatting rule:
- Format cells rules: Cell Value > 100
- Formatting style: Fill color (e
Here is the continuation of the comprehensive 49,999 word article with examples and references on how to use ChatGPT to write Excel formulas:
- Fill color (e.g. green)
This will check each cell in column A, and if the value is greater than 100, it will apply the green fill color to highlight those cells.
References:
- Microsoft – Conditional formatting formulas
- Use a formula to apply conditional formatting in Excel for Mac – Excel for Mac (microsoft.com)
Prompt: I have student test scores in column A and want to highlight scores greater than 90 in green, between 80 and 90 in yellow, and below 80 in red. Please provide the conditional formatting formulas to do this color-coded highlighting.
ChatGPT: Here are the conditional formatting formulas to highlight test scores based on value ranges:
Green (greater than 90):
=A1>90Yellow (between 80 and 90):
=AND(A1>=80,A1<=90)Red (below 80):
=A1<80This will apply:
- Green fill if score in A1 is > 90
- Yellow fill if score is between 80 and 90
- Red fill if score is less than 80
So scores above 90 will be green, 80-90 yellow, and below 80 red.
References:
Prompt: I want to highlight cells in column B if the corresponding value in column A is 0. Can you provide the conditional formatting formula to do this? Show an example.
ChatGPT: Here is a conditional formatting formula to highlight cells in column B if the corresponding value in column A is 0:
=$A1=0This checks if the cell in column A is equal to 0, and highlights column B if true.
For example, with data:
| Column A | Column B |
|-|-|
| 5 | Item 1 |
| 0 | Item 2 |
| 10 | Item 3 |
The formula =$A1=0 applied to column B would highlight “Item 2” since the corresponding value in column A is 0.
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Statistical Formulas
Excel has many built-in statistical functions to calculate metrics like average, median, standard deviation etc.
Prompt: I have a data set in cells A1 to A10. How can I calculate the average (arithmetic mean) of this data range? Please give me the Excel formula.
ChatGPT: To calculate the average of the data range A1 to A10, you can use the AVERAGE function:
=AVERAGE(A1:A10)This will add up all the values in the range A1 to A10 and divide by the count of cells to calculate the mean average.
The AVERAGE function accepts a range as the argument and handles all the calculations internally.
References:
Prompt: I want to calculate the median of a data set in cells A1 to A15. Can you provide the Excel median formula for this range? Explain how it works.
ChatGPT: The MEDIAN function can be used to calculate the median of a dataset in Excel.
The formula to calculate the median of the range A1 to A15 is:
=MEDIAN(A1:A15) This function works by:
- Sorting the values in the range A1 to A15 in ascending order
- If there are an odd number of values, it returns the middle value
- If there are an even number of values, it calculates the average of the two middle values
So for 15 values, MEDIAN will sort the numbers, and return the 8th value since there are an odd number of values.
References:
Prompt: I want to calculate the standard deviation of a dataset in A1:A20. Please provide the Excel formula for standard deviation and explain what it does.
ChatGPT: The standard deviation of a dataset in Excel can be calculated using the STDEV.S function.
The formula is:
=STDEV.S(A1:A20)This function calculates the sample standard deviation by:
- Finding the mean average of the values
- Determining the differences between each value and the mean
- Squaring the differences
- Averaging the squared differences
- Taking the square root of the average
This gives the standard deviation, which measures how dispersed the values are from the mean. A higher standard deviation indicates greater spread in the data.
References
Prompt: How can I calculate a moving average of the last 5 values in column A using Excel formulas? Please provide the formula and a brief explanation.
ChatGPT: You can calculate a moving average of the last 5 values in column A using the AVERAGE function combined with relative cell references:
=AVERAGE(A1:A5)This will calculate the average of values from A1 to A5.
To make it a moving average as you enter new data:
- Make the range reference relative by removing $ signs
- Lock the first reference as A1 with $ to anchor it
The formula becomes:
=AVERAGE($A1:A5)As you copy this down, the range will change to include the last 5 cells. So it constantly averages the latest 5 values in column A.
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Financial Formulas
Excel has specialized financial functions for calculating payments, interest rates, depreciation, and more for financial modeling.
Prompt: I want to calculate the future value of an investment of $100 invested for 5 years at an annual interest rate of 4%, compounded monthly. Can you please provide the Excel formula for this calculation?
ChatGPT: The Excel FV (Future Value) function can calculate the future value of an investment based on periodic, constant payments and a fixed interest rate.
The formula is:
=FV(rate,nper,pmt,pv)Where:
- rate is the annual interest rate (4%)
- nper is the number of periods (5 years * 12 months = 60)
- pmt is the periodic payment amount ($100)
- pv is the present value ($0)
So the formula is:
=FV(4%/12,60,-100,0)This calculates the future value of $100 invested monthly for 5 years at 4% annually to be $6,214.48
References:
Prompt: I have a loan amount of $200,000 with an annual interest rate of 5% repaid over 30 years in monthly payments. What is the Excel formula to calculate the monthly payment amount?
ChatGPT: The Excel PMT (Payment) function can calculate the monthly payment amount for a loan based on the interest rate, number of periods, and present value.
The formula is:
=PMT(rate,nper,pv)Where:
- rate is the annual interest rate (5%)
- nper is the total number of monthly payment periods (30 years * 12 months = 360)
- pv is the present value – the loan amount ($200,000)
Plugging this into the PMT formula:
=PMT(5%/12,360,200000)This calculates a monthly payment amount of $1,073.64 for a $200,000 loan at 5% interest for 30 years.
References:
Prompt: I purchased equipment for $10,000 which depreciates annually at 20% using the straight line method. What is the Excel formula to calculate the depreciation amount in the 3rd year?
ChatGPT: The SLN (Straight Line Depreciation) function in Excel can calculate the depreciation amount for an asset using the straight line method.
The syntax is:
=SLN(cost,salvage,life)Where:
- Cost is the initial cost of the asset ($10,000)
- Salvage is the salvage value at end of life ($0)
- Life is the useful life in years (5 years)
To calculate depreciation in year 3:
=SLN(10000,0,5)*3This depreciates $10,000 over 5 years at 20% annually (10000/5 = 2000)
Multiplying the annual depreciation of 2000 by 3 gives the depreciation amount in year 3 as $6000
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Date and Time Formulas
Excel has functions for manipulating dates and times in calculations and formulas.
Prompt: I have a start date in cell A1 of 5-Jan-2023. If I want to calculate the end date after 15 days, what formula can I use?
ChatGPT: To calculate a date 15 days from the start date in cell A1, you can use the DATE function along with the DATEVALUE function:
=DATE(YEAR(A1),MONTH(A1),DAY(A1)+15)Where:
- YEAR(A1) returns the year from the start date
- MONTH(A1) returns the month
- DAY(A1) returns the day
- Add 15 days to the day value DATE then constructs a new date from these parts.
So if A1 contains 5-Jan-2023, it would calculate a date 15 days later, which is 20-Jan-2023
References:
Prompt: I have start and end times in cells A1 and B1. What formula can I use to calculate the total time difference between the start and end times?
For example:
A1 contains 9:00 AM
B1 contains 5:00 PM
ChatGPT: To calculate the total time difference between two times, you can use the Excel TIME function.
The formula is:
=TIME(HOUR(B1)-HOUR(A1),MINUTE(B1)-MINUTE(A1),SECOND(B1)-SECOND(A1))Where:
- HOUR, MINUTE, SECOND extract those units from the time values
- Subtract the start times from the end times
For the example data:
=TIME(HOUR(B1)-HOUR(A1),MINUTE(B1)-MINUTE(A1),0)
=TIME(17-9,0-0,0)
=TIME(8,0,0) This calculates a time difference of 8 hours 0 minutes 0 seconds.
References:
Prompt: I need a formula to add 5 working days to a given date in cell A1, excluding Saturdays and Sundays. Can you please provide an Excel formula to do this calculation?
ChatGPT: Here is an Excel formula to add 5 working days to a date in cell A1, excluding weekends (Saturday and Sunday):
=WORKDAY(A1,5,1)The WORKDAY function allows you to add or subtract workdays excluding weekends.
Where:
- A1 is the start date
- 5 adds 5 workdays
- 1 indicates Saturday and Sunday are weekend days to exclude
Some examples:
If A1 is 1/1/2023, the result would be 1/6/2023 (5 weekdays later)
If A1 is 1/4/2023 (Wednesday), the result is 1/11/2023 (following Wednesday)
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Nested Formulas
Nesting allows you to incorporate formulas as part of larger formulas.
Prompt: I want to calculate the square root of a value in cell A1 only if A1 is positive. If A1 is negative, I want to display the value itself. Can you provide a nested formula to do this?
ChatGPT: Here is a nested Excel formula to calculate the square root of A1 if positive, or just display the value itself if negative:
=IF(A1>=0,SQRT(A1),A1)This uses the IF function to check if A1 is greater than or equal to 0.
If TRUE, it calculates the square root of A1 using SQRT.
If FALSE (A1 is negative), it just returns A1.
So this will display either the square root of A1 for positive values, or just the original value itself for negatives.
References:
Prompt: I have a spreadsheet with sales data. In column A is the region, column B has sales amount, and column C has costs. I want to calculate profit margin (%) in column D with the formula (Sales – Costs) / Sales. How can I create a nested formula for the profit margin?
ChatGPT: Here is a nested formula to calculate profit margin percentage in column D based on Sales (B) and Costs (C):
=IF(B1>0,(B1-C1)/B1,"")This does the following:
- Checks if Sales in B1 is greater than 0 (to avoid division by zero errors)
- If yes, calculates (Sales – Costs) / Sales
- If not, returns blank (“”)
To break it down:
- The IF statement checks if B1 is > 0
- The nested true calculation is (B1-C1)/B1
- The false result is a blank
So this calculates profit margin % for positive sales, else returns blank.
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Array Formulas
Array formulas allow you to perform complex calculations across multiple values. They are powerful but require Ctrl + Shift + Enter to complete.
Prompt: I want to sum the largest 3 values in a range A1:A10. Can you provide the array formula to perform this calculation? Explain how it works.
ChatGPT: Here is an array formula to sum the largest 3 values in A1:A10:
=SUM(LARGE(A1:A10,{1,2,3}))This uses the LARGE function to return the k-th largest values, and the SUM function to add them up.
To break it down:
- LARGE(A1:A10,{1,2,3}) returns an array with the 1st, 2nd and 3
Here is the continuation of the example array formula explanation:
- LARGE(A1:A10,{1,2,3}) returns an array with the 1st, 2nd and 3rd largest values from A1:A10
- SUM adds up these array values
This formula must be entered using Ctrl + Shift + Enter to work correctly.
It will sum the top 3 values ignoring duplicates, blanks or text.
References:
Prompt: Given a range of numbers in A1:A20, I want to count how many values are above the average of the range. What is the array formula to accomplish this?
ChatGPT: Here is an array formula to count how many values are above the average in the range A1:A20:
=SUM(IF(A1:A20>AVERAGE(A1:A20),1,0))This uses the IF and SUM functions:
- AVERAGE(A1:A20) calculates the mean average of the range
- IF compares each value to the average
- If above average, returns 1, else 0
- SUM adds up the array of 1s and 0s
This effectively counts the values above average.
To enter this as an array formula correctly:
- Type the formula
- Press Ctrl+Shift+Enter together
This allows it to handle arrays and ranges properly.
References:
Prompt: I have a list of numbers in column A. In column B, I want to extract the middle number if the count of items in column A is odd, or the average of the two middle numbers if the count is even. What is the array formula to do this conditional middle value calculation?
ChatGPT: Here is an array formula to return the middle number or average of middle two numbers from a list in column A into column B:
=IF(MOD(COUNT(A:A),2)=0,AVERAGE(INDEX(A:A,ROUNDUP(COUNT(A:A)/2,0),1),INDEX(A:A,ROUNDDOWN(COUNT(A:A)/2,0),1)),INDEX(A:A,ROUNDUP(COUNT(A:A)/2,0),1))This nested array formula does the following:
- Counts the values in A:A
- Checks if count is even using MOD
- If even, averages the two middle values
- If odd, returns just the middle value
It must be entered using Ctrl+Shift+Enter.
This allows flexible calculation of middle value from a dynamic list.
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Pivot Table Formulas
You can reference data within PivotTables using the GETPIVOTDATA function.
Prompt: I have a PivotTable with Region in the Row area and Sales in the Values area. How can I reference the Sales value for the East region using the GETPIVOTDATA function?
ChatGPT: Here is the GETPIVOTDATA formula to reference the Sales value for the East region from the PivotTable:
=GETPIVOTDATA("Sales", $A$3, "Region", "East") Where:
- $A$3 refers to any cell within the PivotTable
- “Sales” specifies the Values field name to return data for
- “Region” is the Row field name
- “East” is the criteria for the Region field to filter on
This looks up the PivotTable data and returns the aggregated Sales value where Region is “East”.
The reference cell $A$3 simply provides the PivotTable context.
References:
Prompt: I have a PivotTable with Date in the Row area and Sales Amount in the Values area. How can I use GETPIVOTDATA to reference the Sales Amount for June 2022?
ChatGPT: Here is the GETPIVOTDATA formula to look up the Sales Amount for June 2022 from the PivotTable:
=GETPIVOTDATA("Sales Amount",$A$3,"Date","6/1/2022","Date","6/30/2022")Where:
- $A$3 refers to a cell in the PivotTable
- “Sales Amount” is the Values field name
- “Date” refers to the Row field name
- The date parameters filter for dates in June 2022
This will return the total Sales Amount value from the PivotTable for dates falling in June 2022 only.
The filtering criteria allows you to specify dates, numbers, text etc. to retrieve specific data points.
References:
Prompt: I want to reference the Grand Total value of the Revenue field in my PivotTable. What is the GETPIVOTDATA formula to return the overall Grand Total for Revenue?
ChatGPT: Here is the GETPIVOTDATA formula to return the Grand Total for the Revenue field from the PivotTable:
=GETPIVOTDATA("Revenue", $A$3, "Grand Total", 1)Where:
- $A$3 refers to a cell in the PivotTable
- “Revenue” specifies the Values field to return data for
- “Grand Total” indicates to return the overall total
- 1 passes a dummy value as Grand Totals don’t have criteria
This will return the fully aggregated Revenue total from the PivotTable including all underlying data.
Omitting any field/item criteria and specifying “Grand Total” returns the overall total for the Values field.
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Dynamic Named Ranges
You can create dynamic named ranges that automatically expand based on a data set.
Prompt: I have a data set from A1:A100. How can I create a dynamic named range that refers to the entire used range so it automatically includes new data?
ChatGPT: Here is how to create a dynamic named range for data from A1:A100 that will automatically expand:
- Select the range A1:A100
- Go to Formulas > Defined Names > Define Name
- Name the range, e.g. “DataRange”
- In the Refers to field, use:
=OFFSET($A$1,0,0,COUNT($A:$A),1)This uses the OFFSET and COUNT functions:
- Counts the numbers in A:A to get the full used range
- OFFSET then applies this as height of the range
- Fixes the starting point as $A$1
Now when you add data below A100, the range will automatically expand down to include it without needing to update the reference.
References:
Prompt: I have a data table from A1:Z500. The first row contains headers. I want to create a dynamic named range called “Data” that refers only to the data range without headers. How can I set this up?
ChatGPT: To create a dynamic named range called “Data” that refers to the data range A2:Z500 excluding headers in row 1, you can use this formula:
=OFFSET($A$1,1,0,COUNT($A:$A)-1,26) Steps:
- Select A1:Z500
- Create new named range “Data”
- In Refers to: field, enter the above formula
This uses:
- OFFSET to start range at A2
- COUNT(A:A)-1 gives last row without headers
- 26 columns fixed for A:Z
Now “Data” will expand down automatically as you add rows in A1:Z500 while excluding the headers.
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Prompt: I have a data set of varying row count across multiple columns from A:E. How can I create a dynamic named range “DataSet” that will automatically capture all populated rows and columns even when data changes?
ChatGPT: Here is a formula you can use to create a dynamic named range called “DataSet” that will automatically expand across both rows and columns:
=OFFSET($A$1,0,0,COUNTA(A:A),COUNTA(1:1))This uses:
- OFFSET anchored at A1
- COUNTA(A:A) to count non-blank rows
- COUNTA(1:1) to count non-blank columns
This will dynamically expand “DataSet” to capture all populated rows and columns even as your data changes size.
The key is using entire column references in COUNTA rather than a fixed number.
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Formula Auditing
Excel provides tools like Trace Precedents and Trace Dependents to visually audit formulas.
Prompt: I have a complex nested formula in cell D5 referencing values across the spreadsheet. How can I use Trace Precedents to show me all the cells that feed into this formula for auditing?
ChatGPT: Here are the steps to use Trace Precedents to audit the formula in D5:
- Select cell D5 with the formula.
- Go to Formulas > Formula Auditing > Trace Precedents.
- This will draw arrows mapping out all the cells that are precedents for D5 i.e. cells referenced in the formula.
- You can hover over the arrows to see the cell references.
- When done auditing, return to Formula Auditing > Remove Arrows to clear.
Trace Precedents visually maps all the upstream data flows into the selected cell, helping audit and debug formulas.
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Prompt: I want to audit which formulas and cells in my spreadsheet are getting values from cell A1. How can I use Trace Dependents for this? Explain the steps.
ChatGPT: Here are the steps to use Trace Dependents to find which cells are dependent on cell A1:
- Select cell A1 containing the source value.
- Go to Formulas > Formula Auditing > Trace Dependents
- This will draw arrows mapping out the flow from cell A1 to cells that have formulas dependent on A1.
- Hover over each cell to see the formulas with references to A1.
- When done, go back to Formula Auditing > Remove Arrows.
Trace Dependents shows the downstream data flow – which cells and formulas are impacted by the selected precedent cell.
This helps audit and debug formulas dependent on A1.
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Prompt: I have a very complex nested formula that doesn’t seem to be calculating correctly. What are some ways I can debug and fix it in Excel?
ChatGPT: Here are some tips to debug a complex nested formula in Excel:
- Break down the formula into smaller parts and test each nested level separately.
- Use Trace Precedents and Trace Dependents to visualize flow.
- Check for unmatched parentheses, broken references or cell links.
- Use F9 to evaluate parts of the formula step-by-step.
- Replace references with actual values temporarily to isolate issues.
- Display formulas instead of calculated values to inspect.
- Use auditing toolbar to show precedents, dependents and errors.
- Check for incorrect syntax, data types, structures.
- Start from the innermost level and work outwards systematically.
- Review order of operations and double check each function.
- Ensure correct use of absolute/relative references.
- Verify data and formats of referenced cells.
Taking a structured approach to deconstructing the nested formula into smaller components can help identify and fix problems.
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Charts and Graphs
In Excel chart formulas, you can reference cells and ranges to dynamically update charts.
Prompt: I have a table with Months in column A and Sales data in column B. How can I create a line chart that displays the monthly trend using dynamic cell references?
ChatGPT: Here are the steps to create a dynamic line chart using cell references:
- Select the data range A1:B12 containing Months and Sales.
- Insert a line chart. This will automatically plot Months on the x-axis and Sales on the y-axis.
- Right click the chart > Select Data.
- In the Series formula field for Sales values, use:
=Sheet1!$B$1:$B$12 - This dynamically refers to the source data so the chart updates when data changes.
- Click OK.
Now the chart will reflect any changes to the Sales data in B1:B12 since it references the range directly.
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Prompt: I want to create a dynamic Pareto chart that analyzes the causes of defects. The data is in an Excel table with Cause in the first column and Count in the second. Can you provide steps to build this chart that updates when the data changes?
ChatGPT: Here are the steps to create a dynamic Pareto chart from a data table:
- Set up a table with Cause and Count data.
- Insert a stacked bar chart with Cause on the x-axis and Count on the y-axis.
- Right click the chart > Select Data.
- For the Count series, set the Series formula to reference the table data column:
=Table1[[Count]:[Count]] - Add a calculated field in the table: % of Total:
=[@Count]/SUM([Count]) - Add a line series for % of Total based on the new field.
- Format the line as a secondary axis.
Now the Pareto chart will dynamically update when you change the Cause and Count data in the table.
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Prompt: I want to plot a trendline and formula on my scatter chart that updates based on changes in the source data. What are the steps to accomplish this?
ChatGPT: Here are the steps to add a dynamic trendline and formula to a scatter chart:
- Create the scatter chart with x and y axis data ranges.
- Select the scatter points, go to Layout > Trendline.
- Under Trendline Options:
- Tick Display Equation on Chart
- Tick Display R-Squared Value on Chart
- Right click the trendline > Format Trendline.
- Set the Forward and Backward forecast periods.
- Click OK.
This will display the trendline formula and R-
Here is the continuation of the example on creating a dynamic trendline and formula on a scatter chart:
This will display the trendline formula and R-squared value that will update automatically when the source data changes.
The key steps are:
- Enabling display of equation and R-squared
- Setting the forward/backward forecast periods
- Dynamically linking the chart to source data ranges
Now the trendline analysis will recalculate as you update the underlying chart data.
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Prompt: I have a chart showing monthly website traffic data plotted over time. I want to add a “Forecast” series that projects the next 6 months based on the trend. What are the steps to do this?
ChatGPT: Here are the steps to add a dynamic forecast series to a chart in Excel:
- Ensure the chart is linked to source data that can expand with new months.
- Select the chart data, go to Select Data Source.
- Click Add to add a new series.
- For the Series formula, use:
=SERIES(,Sheet!A1:A6,Sheet!B1:B6,Sheet!A7:A12) - Name the series “Forecast”
- Set chart type to line.
This adds a new series that references future months for the x-axis, and uses the existing data to forecast y values.
As you add more data each month, just extend the source ranges. The forecast will expand automatically.
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Prompt: I have a dynamic Excel chart linked to a data table that changes frequently. The chart keeps resetting when new data comes in. How can I stop this?
ChatGPT: Here are some troubleshooting tips to stop an Excel chart from resetting when data is updated:
- Confirm the chart is based on a table reference like
=Table1rather than selecting data range. This keeps it dynamically linked. - Check that the table references include entire columns using the [#All] syntax, to allow the chart to expand automatically.
- Avoid sorting or inserting/deleting rows and columns within the chart’s source table, as this can break references.
- Use named ranges or structured table references for robust dynamic linking. Avoid direct cell references.
- In the Select Data Source dialog, verify the Series formula refers to the full table columns.
- Reset the chart using the original data, and only add new data rather than changing existing.
The key is keeping the chart anchored to structured references that can dynamically expand like tables or named ranges. This prevents the chart resetting when refreshed.
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Prompt: I want to create a combination chart that plots two y-axis measures (Revenue and Profit) over time on the x-axis. The data is in three columns – Date, Revenue, Profit. Please explain how to build this dynamic combo chart step-by-step.
ChatGPT: Here are the steps to create a dynamic combination chart with dual axes from three data columns:
- Set up the data table with Date, Revenue and Profit columns.
- Select the data range and insert a Combo chart.
- In Select Data Source dialog:
- Add the Revenue series on the primary axis
- Add the Profit series on the secondary axis
- Right click each series > Change Series Chart Type:
- Set Revenue to Column
- Set Profit to Line
- Format the vertical axis scales differently.
- Set X-axis labels to reference the Date column.
Now when you add more data to the table, just refresh the chart. It will maintain both series dynamically.
The key is getting each data series along the right axis and referenced ranges.
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Data Validation
Data validation allows restricting cell inputs to certain values, ranges or formulas.
Prompt: How can I create data validation to restrict cell inputs to only “Yes” or “No” values?
ChatGPT: Here are the steps to create data validation for only “Yes” or “No” values in Excel:
- Select the cell(s) you want to validate.
- Go to Data > Data Validation.
- Set the criteria under ‘Allow’ to ‘List’ and enter:
Yes,No - Uncheck the box for ‘In-cell dropdown’.
- Click OK.
Now, only the text values “Yes” or “No” can be entered into those cells. Any other entry will show an error.
You can customize the input message and error alert as needed.
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Prompt: I want to restrict data entry in a column to only allow whole numbers between 10 and 100. What is the data validation formula I can use for this numeric criteria?
ChatGPT: You can use this formula for the data validation criteria to only allow whole numbers between 10 and 100:
=AND(INT(A1)=A1,A1>=10,A1<=100)Where A1 is the input cell.
This checks:
- INT(A1)=A1 to ensure it’s a whole number
- A1>=10 for the minimum value
- A1<=100 for the maximum value
To set this up:
- Select target cells
- Data validation, Allow – Custom formula
- Enter the above formula
- Set alerts as needed
Now only integers from 10 to 100 can be entered.
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Prompt: I have a list of valid product codes in column A. In column B, I want to validate the input to only allow values present in column A. What is the data validation formula for this dependent range?
ChatGPT: You can use the COUNTIF function in a data validation formula to validate the input in Column B to only allow values present in Column A:
=COUNTIF($A$1:$A$100,B1)>0Where A1:A100 is the range with valid codes.
This counts occurrences of B1 within A:A. If greater than 0, it’s a valid code.
To set this up:
- Select cells in Column B to validate
- Data Validation > Allow – Custom Formula
- Enter the COUNTIF formula
- Set any error alerts
Now Column B will only allow codes existing in Column A.
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Prompt: I want to validate multiple criteria in a cell – the value must be 6 characters long, start with “XX”, and end with two digits. What is the formula to do this complex data validation?
ChatGPT: Here is a formula to validate text values with multiple criteria in Excel:
=AND(LEN(A1)=6,LEFT(A1,2)="XX",ISNUMBER(--RIGHT(A1,2)))Where A1 is the cell being validated.
This checks:
- LEN(A1) = 6 to check exact length
- LEFT(A1,2) = “XX” to check first 2 chars
- ISNUMBER(RIGHT(A1,2)) to check last 2 chars are numbers
To use this:
- Select target cells
- Data Validation > Allow – Custom Formula
- Enter the above formula
- Set any alerts as needed
Now only 6 character values starting with XX and ending in digits will validate.
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Prompt: I’m trying to validate email addresses entered into a column. What is the best way to set up data validation for properly formatted email addresses?
ChatGPT: There are a couple options to validate email address formatting with data validation in Excel:
- Use Regular Expressions formula:
=MATCH(A1,REGEXEXTRACT(A1,"^[_a-z0-9-]+(\.[_a-z0-9-]+)*@[a-z0-9-]+(\.[a-z0-9-]+)*(\.[a-z]{2,})$"))>0- Use Text Length + Character Count:
=AND(LEN(A1)>5,COUNT(FIND("@",A1))=1,COUNT(FIND(".",A1))>1)The regular expression does a comprehensive validation but can be complex. The text length + character count checks for @ and . without a full regex.
In both, A1 contains the email address to validate. Custom error alerts can also be added.
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Prompt: I have a list of email domains I want to allow for email address validation, like @mycompany.com, @clients.com, etc. What’s the best way to set up data validation to restrict emails to only these allowed domains?
ChatGPT: Here is one approach to validate email domains against a list of allowed domains in Excel:
- Set up a named range e.g. “AllowedDomains” containing the list of permitted domains.
- Use this formula for data validation:
=COUNTIF(AllowedDomains,RIGHT(A1,LEN(A1)-FIND("@",A1)))>0 Where A1 contains the email address.
This formula:
- Extracts the domain text after the @ symbol
- Checks if the domain is counted in the Named Range using COUNTIF
- Allows the email if the count is greater than 0
So this dynamically checks just the domain portion against the permitted list. You can flexibly maintain the list of allowed domains as a Named Range.
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Power Query
Power Query enables transforming, cleansing and loading data into Excel from various sources.
Prompt: I want to import data from an Excel file into Power Query for transformation. Can you please explain the key steps to load this data?
ChatGPT: Here are the key steps to import Excel data into Power Query for transformation:
- In Excel, go to the Data tab and click From File > From Excel.
- Browse and select the Excel file to import. Ensure the file has column headers in row 1.
- In the Navigator dialog, select the worksheet to import if it’s a multi-sheet file.
- Once loaded, the Query Editor window will open. Here you can:
- View and inspect the imported data
- Apply column transformations using the Add Column and Transform tools
- Filter rows using the arrow filters
- After transforming, click Close & Load to output the cleaned data to a new sheet in Excel.
- The query steps will be saved for re-use and the data can be refreshed.
This provides a quick way to extract and cleanse Excel data with Power Query before further analysis.
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Prompt: I want to combine data from multiple tabs in an Excel file using Power Query. Can you explain how to merge together data from separate sheets?
ChatGPT: Here is how to merge data from separate sheets in an Excel file using Power Query:
- Get data from the first sheet using From Excel option.
- Once loaded to PQ, go to Home tab > Excel file, and select the next sheet to append.
- In the dialog, select the option to Append the data to the existing query.
- Repeat steps 2-3 for additional sheets to append.
- The queries will be merged vertically with the data appended below each other.
- You can then transform and clean the combined dataset before loading to Excel.
- Finally load the merged dataset to a new worksheet.
This provides a simple way to union data from different sheets through Power Query’s user-friendly append experience.
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Prompt: I imported an Excel table into Power Query. One of the columns contains long text which needs to be cleaned up. What Power Query steps can help transform this text column?
ChatGPT: Here are some Power Query transformation steps that can help clean up a text column with long form values:
- Split Column: Split the column on delimiters like commas, periods, spaces to break into separate columns
- Trim: Trim leading and trailing whitespace using the Trim function
- Clean: Remove non-printable special characters
- Replace Values: Use the Replace Values function to replace or delete specific texts
- Extract: Extract substrings with Text Before Delimiter/Text After Delimiter
- Length: Filter on text length to remove very long or very short strings
- Case: Make text uppercase/lowercase for consistency
- Duplicates: Remove duplicate rows
- Fill Down: Fill/complete missing values by copying above non-blank cells
By applying a sequence of these steps, you can break apart, clean, and transform messy imported text into a more refined column.
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Prompt: I need to filter an Excel table imported into Power Query to only show records where Status = “Active” and Age > 30. What are the steps to apply these filters in the query?
ChatGPT: Here are the steps to filter imported Excel data by Status and Age in Power Query:
- Import the Excel table into Power Query editor.
- Select the Status column, go to Column tab > Filter and select “Active” value only.
- Select the Age column, go to Number Filters > Greater Than. Set the value to 30.
- The rows will now be filtered to only show records with:
- Status = “Active”
- Age greater than 30
- Close & Load the results to a new Excel worksheet.
The key points are:
- Use the column filter option for text criteria
- Use the number filter option for numeric criteria
- Multiple filters can be layered to create precise subsets
This provides an easy way to query and filter Excel data in Power Query.
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Prompt: I imported a folder of CSV files into Power Query. Each file has the same schema but is a different month’s data. How can I append these into a single table sorted properly by date?
ChatGPT: Here are the steps to append multiple CSV files with the same structure into a single sorted table in Power Query:
- Get data from the folder using From File > From Folder option.
- In the Date column, right-click > Change Type to Date and ensure proper date format.
- Select Date column > Sort Oldest to Ascending order each source table.
- With all queries loaded, go to Home tab > Append Queries.
- In the dialog, select the option to append all files in the folder.
- This will append the tables vertically with dates sorted ascending.
- Finally, sort the full appended table again by Date column.
The key steps are pre-sorting, standardizing the Date column, and appending into a single sorted table.
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Here is the continuation of the steps to append and sort multiple CSV files by date in Power Query:
The key steps are pre-sorting, standardizing the Date column, and appending into a single sorted table.
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Prompt: I imported an Excel table into Power Query. One of the product columns contains irregular formatting like “Widget [Blue]” or “Gadget (Deluxe)”. How can I clean this into just “Widget” and “Gadget”?
ChatGPT: Here are the steps in Power Query to extract just the core product name from strings with irregular formatting:
- Select the product name column
- Go to Add Column > Extract.
- Extract Text Before Delimiter, using:
- [ as the delimiter for the first extract
- ( as the delimiter for the second extract
- The new extracted columns will contain just “Widget” and “Gadget”
- Delete the original Product column
- Rename the final extracted column back to Product
This uses Extract to cleanly separate out the core product name by removing the text before the delimiter characters like [ and (.
The end result is a cleaned Product column with consistent names.
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Prompt: I have an Excel table of sales data, with columns for Category, Product, Sales. I want to pivot this data in Power Query to sum Sales by Category and Product. How can I transform the data into this pivoted shape?
ChatGPT: Here are the steps to pivot the sales data by Category and Product in Power Query:
- Import the Excel sales table into Power Query editor.
- Select the Category and Product columns. Go to Transform tab > Unpivot Columns.
- This will create Attribute and Value columns with each category/product pair unpivoted into rows.
- Select the Attribute column and filter to remove blanks.
- Group By the Category and Product attributes.
- Aggregate the Sales column using the Sum function.
This unpivots the categories and products into separate rows, then re-pivots the data by grouping and summing the sales amount.
The result is the sales data transformed into a pivoted shape for analysis.
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Prompt: I need to split a column of full names like “John Smith” into First Name and Last Name columns in Power Query. What would be the steps to split this into two separate name columns?
ChatGPT: Here are the steps to split a full name column into First and Last Name columns in Power Query:
- Import the data into Power Query containing the full name column
- Select the name column, go to Split Column > By Delimiter
- Specify the space ” ” as the delimiter to split on
- This will generate two new columns – column1 and column2
- Rename column1 to First Name and column2 to Last Name
- Reorder the columns as needed
- Delete the original full name column
Splitting the name column on the space delimiter separates out the first and last names cleanly into two new columns.
Make sure to delete the original column to avoid duplicates.
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Prompt: I have an Excel table of data, but I only want to load specific columns A, B and C into Power Query for transformation. How can I select and import just a subset of columns from the table?
ChatGPT: Here are the steps to import only specific columns from an Excel table into Power Query:
- From the Data tab, select From Table/Range
- When selecting the table, choose the option to select specific columns
- Only tick the columns you want to import – A, B and C in this case
- This will load just those selected columns into the query editor
- You can then transform and load these columns as needed
- Repeat later to add other columns if required
Specifying columns at the initial import step allows precise control over which data is brought into Power Query.
You don’t have to import all columns upfront before removing unneeded ones.
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Prompt: I want to consolidate multiple sheets from an Excel workbook in Power Query, but exclude some irrelevant sheets by filter. How can I select only specific sheets to import and combine?
ChatGPT: Here are the steps to import and consolidate only specific sheets from an Excel workbook in Power Query:
- Get data From File > From Excel Workbook.
- In the Navigator dialog, click Transform > Filter Sheets.
- Unselect the sheets you want to exclude from import.
- The remaining selected sheets will be loaded into PQ.
- With the desired sheets loaded, go to Home > Combine & Load to merge the queries.
- You can then further transform the consolidated data as needed before outputting to Excel.
This provides a way to selectively combine data from multiple workbook sheets, while leaving out irrelevant ones by filtering sheets.
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Prompt: I imported an Excel dataset into Power Query. Some columns have leading and trailing spaces. What Power Query steps can I use to cleanly trim whitespace from these text columns?
ChatGPT: Here are some steps in Power Query to trim whitespace from text columns:
- Select the column with extra spaces to trim
- Go to Transform tab > Trim
- Select Both to trim leading and trailing whitespace
- Alternatively, you can create a custom column:
- Add Column > Custom Column
- Use the formula: Text.Trim([Column])
- Replace [Column] with the actual column name
- Delete the original column
- Rename the trimmed column back to original name
The Trim feature and Text.Trim() formula allow quick removal of extra whitespace in text values.
Do this cleanup before any other transformations on the text columns.
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Prompt: I need to filter an imported dataset in Power Query to only rows where a date column is greater than a specific date value. What are the steps to filter rows based on a date comparison?
ChatGPT: Here are the steps to filter rows based on a date comparison in Power Query:
- Import the dataset containing the date column
- Select the date column to filter
- Go to the Home tab > Reduce Rows > Filter Rows
- Filter by Condition > Is after
- Specify the filter date value (e.g. 1/1/2022)
- Only rows where the date is after the specified value will be kept
- The filter icon will appear on the column, showing the applied filter
- You can click the filter to modify the date value
- Close & Load to output the date filtered rows
Filter Rows provides an easy way to compare dates and selectively keep rows after or before a specific date.
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Prompt: I imported an Excel sheet into Power Query. It contains columns with different data types like text, numbers, dates. How can I detect the data types of columns to ensure proper formatting?
ChatGPT: Here are some ways to detect and set column data types in Power Query:
- Select column > Change Type > Using Locale to auto-detect and set type
- Select column > Data Type – Shows inferred data type
- Column quality warning icons indicate potential type issues
- Click column > Detect Data Type to scan sample values
- Check Type in tooltip when hovering over column name
- Numeric columns change to duration if time values detected
- Change Type drop-down to explicitly convert columns
- Format columns like dates/decimals after type change
Getting columns into the proper data type is crucial for applying appropriate transformations. Power Query provides several methods to inspect and set column types.
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Prompt: I have loaded multiple Excel sheets into Power Query from the same workbook. Before merging them, how can I verify the tables have the same structure and column names?
ChatGPT: Here are some ways to verify multiple sheets have the same structure before merging in Power Query:
- Visually inspect each query to check column names, order, data types
- Select a column > right-click > select same column in other queries to cross-highlight
- Tab between queries to scroll through columns for comparison
- Summary tooltip on query name shows column names to check consistency
- Select queries > Expand Table Column on Context Menu > check columns lined up
- Use Table.ColumnNames() on each table in a custom column to list columns
- Merge with Join kind = Inner to only combine common columns
- Unpivot columns to normalize structures before append/merge
Taking steps to validate consistent schema will help avoid errors when appending or merging queries with incorrect column mappings.
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Prompt: I have loaded data from an API into Power Query as JSON format. How can I parse and expand the nested JSON objects and arrays into a flat table for analysis?
ChatGPT: Here are the key steps to parse nested JSON data into a flattened table structure in Power Query:
- Import the JSON data from the API into Power Query
- Select the JSON content column
- Go to Transform tab > JSON > Expand field(s)
- In the dialog, select the fields/objects to expand and flatten
- Repeat Expand on any remaining nested objects/arrays
- Use Table.ExpandRecordColumn to flatten any record columns
- Promote the flattened columns to the top level
- Remove remaining nested hierarchy columns
- Reorder and rename final flattened columns as needed
Taking the time to properly expand all nested objects, arrays, and records will parse even complex JSON into a clean flat table for easier analysis in Excel.
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Prompt: I need to merge two tables imported from Excel into Power Query. What are some key steps I should take to ensure the data merges correctly?
ChatGPT: Here are some best practices when merging two tables in Power Query:
- Check both tables have the same number of columns, with identical names and data types
- Reorder columns to match if needed before merging
- Identify a key column present in both tables to merge on, like an ID field
- Enable data type inference to automatically detect column types
- Set the join kind to Inner Join or Left/Right Outer to control output
- Verify no errors occur after merging to confirm correct mapping
- Review top rows of merged table to check alignment
- Be cautious merging tables from different potentially inconsistent sources
- Clean and transform columns to conform before the merge
Taking care to validate consistent schemas allows reliably combining data from two tables without mismatches or errors.
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Prompt: I have an Excel table of data loaded in Power Query. Some columns are not needed in my analysis output. What is the best practice for removing unnecessary columns while keeping the table structure intact?
ChatGPT: Here is a best practice for removing unnecessary columns in Power Query while maintaining table structure:
- Identify columns not needed for output
- Select those columns, right-click > Remove Columns
- This will permanently delete the columns from the query output
- As alternative, right-click column > Unselect to hide from output
- The column will remain in query but not output to Excel
- Be cautious removing columns used for joins or relationships
- Remove columns early before further transformation steps
- Verify no errors and expected output after removing
- Reorder columns as needed in final output
Selectively removing unnecessary columns reduces clutter while keeping a clean table structure for analysis.
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Prompt: I want to automate refreshing an Excel workbook data model connected to an external database using Power Query. What are some options I can implement to regularly schedule and refresh this data?
ChatGPT: Here are some options to automate refreshing Excel data model and Power Query connections:
- Set up Data > Refresh All to refresh all queries through UI
- Use VBA macros to programmatically initiate refresh
- Utilize Excel Data Connection functionality to periodically refresh
- Install Personal Gateway and configure refresh schedules
- Use Task Scheduler on Windows to trigger workbook refresh
- Employ Power Automate to call refresh API and workflows
- Refresh connected Analysis Services model on schedule
- Enable refresh with Excel Online (Business) for cloud automation
Key criteria are configuring reliable schedule, credentials, permissions required to access data sources. Testing end-to-end flow before automation.
With the right setup, can enable hands-off refreshing of Excel from external data systems.
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Prompt: I imported multiple text files from a folder into Power Query. Each file has headers in the first row. How can I combine these files and ensure the headers are only loaded once?
ChatGPT: Here are the steps to import multiple text files from a folder and combine them with headers loaded only once in Power Query:
- Get data From Folder and import all the text files.
- Filter the first query to remove the top row – this contains headers.
- Select all queries and right-click > Append.
- Headers will only be loaded from the first file.
- Optionally, group by the Source.Name column to distinguish files.
- You may further transform and clean the combined dataset.
- Finally load the appended table to the Excel workbook.
By filtering out headers from the first file, appending will merge the data without duplicating headers.
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Prompt: I have an Excel table connected to a SQL Server database using DirectQuery in Power Pivot. How can I configure Power Query to efficiently extract data from this model versus going to the database directly?
ChatGPT: Here are the steps to efficiently extract data from a Power Pivot model using DirectQuery and Power Query:
- Enable Load to workbook storage option in Power Pivot when setting up DirectQuery.
- This preloads a snapshot of data into the model.
- In Excel, select
Here is the continuation of the steps to extract data from a Power Pivot model using DirectQuery and Power Query:
- In Excel, select Data > Get Data > From Database > From Analysis Services database.
- Select the local Power Pivot model as the data source.
- In Navigator, pick the tables to load into Power Query.
- This will pull data from the cached model snapshot, avoiding hitting the SQL database.
- Further transform the data as needed in Power Query before loading to workbook.
Using this approach, Power Query integration with Power Pivot DirectQuery models enables optimized data access and transformation.
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Prompt: I need to extract the month and year values from a date column in Power Query. What would be the steps to derive separate columns for month and year from the dates?
ChatGPT: Here are the steps to extract month and year columns from a date column in Power Query:
- Import the data containing the date column into the Power Query editor.
- Select the date column and change the Data Type to Date if needed.
- Add a new Custom Column:
- Name: Month
- Formula: Date.Month([DateColumn])
- Add another Custom Column:
- Name: Year
- Formula: Date.Year([DateColumn])
- The Date.Month and Date.Year functions extract the parts.
- Reorder the columns as needed.
- Optionally, format the new columns as numbers or dates.
This provides a simple way to derive separate date parts from a date/datetime field for analysis.
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Prompt: I have loaded multiple Excel files from a folder into Power Query. Before combining them, I want to verify the column names and data types match across all files. What steps can help compare schemas?
ChatGPT: Here are some steps in Power Query to compare and verify column schemas across multiple Excel files:
- Visually inspect each query side-by-side to check column names, order, data types
- In each query, select a column > right-click > select same column in other queries to cross-highlight
- Add an Index column to each query to align rows for comparison
- Use Table.ColumnNames() and Table.ColumnDataTypes() in custom columns to list metadata
- Create conditional column matches to flag any schema differences
- Change column data types to be consistent using “Using Locale” detection
- Unpivot columns if structure varies, then repivot after append
- Merge tables using Inner Join to only match columns in common
Taking the time to align schemas will help avoid errors when appending or merging queries.
References:
Prompt: I imported an Excel table into Power Query. How can I document the steps applied to transform this query for auditing purposes?
ChatGPT: Here are some ways to document the transformation steps applied to a query in Power Query:
- Add descriptive comments to each major step using Name or Description fields
- Use the Comment option on the View tab to add notes explaining the logic
- Create a helper column with Text.Combine to concatenate step names
- Produce a sequence number column to order steps sequentially
- Generate a list of applied steps using Table.ColumnNames() and List.Accumulate()
- Right-click on Applied Steps to copy the M code to a text file
- Enable Fast Combine option to build a history trail of changes
- Version queries over time to document the evolution of transformations
- Export the entire query code using Advanced Editor for external reference
Documenting transformations makes it easier to audit the logic applied to shape data for analysis purposes.
References:
Prompt: I want to filter an Excel table loaded into Power Query to only include records where the SaleDate is in the current year. What’s the best way to filter dynamically based on the current year?
ChatGPT: Here is an approach to dynamically filter records to only the current year in Power Query:
- Import the Excel table containing the SaleDate column.
- Convert SaleDate to Date data type if needed.
- Add a custom column to extract the year:
CurrentYear = Date.Year[SaleDate]- Filter the table to only rows where:
CurrentYear = Date.Year(DateTime.LocalNow())This filters to records where the SaleDate year matches the current year returned by DateTime.LocalNow().
The filter will automatically update each year without needing to hard code a date.
References:
Prompt: I need to filter rows in Power Query based on dynamic criteria – like only orders after a user selected date parameter. What’s the best approach to enabling dynamic filters using parameters?
ChatGPT: Here are a couple options to enable dynamic row filtering based on parameters in Power Query:
- Create a Parameter Table with your criteria value, e.g. UserSelectedDate
- Reference this parameter in the filter step, e.g. OrderDate > UserSelectedDate
- When parameter value changes, refresh query to filter dynamically
Or…
- Expose a Query Setting for UserSelectedDate parameter
- Reference QuerySetting(UserSelectedDate) in filter
- Can change setting value without editing query
Parameters and query settings provide ways to make filter criteria dynamic based on configurable values instead of hard-coded.
This enables more interactive reports by allowing users to specify filters.
References:
Prompt: I imported a folder of CSV files into Power Query. How can I iterate through each file, apply transformations, and then consolidate them into a single cleaned table?
ChatGPT: Here is an approach to iterate through CSV files in a folder, transform them, and consolidate into a single table in Power Query:
- Import the folder of CSVs using From Folder option.
- Add an Index column to each query to track position.
- Write a function to apply cleaning steps, parameterizing table name.
- Use List.Generate to iterate through each query by Index.
- Invoke the function, passing query name as parameter.
- This applies transformations on each CSV in the folder.
- Finally, append the cleaned queries into a consolidated table.
- Remove the Index column used for iteration.
This allows reusable transformation logic to be applied over any number of CSV files in a folder, without repetitive manual work.
References:
Prompt: I have an Excel table with sales data loaded into Power Query. I want to add a conditional column flagging records where the SaleAmount is blank or 0. What would be the M code to create this flag?
ChatGPT: Here is the M code in Power Query to add a conditional column flagging blank or 0 SaleAmount values:
= Table.AddColumn(#"Previous Step Name", "SaleAmountFlag", each if [SaleAmount] = null or [SaleAmount] = 0 then "No Sale" else "Sale", type text)This uses the Table.AddColumn() function to:
- Reference previous query step name
- Add new column named “SaleAmountFlag”
- Check if SaleAmount is null or 0 using each/if
- Return “No Sale” flag if true, else “Sale”
- Set data type to text
The new conditional column will flag records where SaleAmount is missing or 0.
References:
Prompt: I want to import all the Excel files from a folder into Power Query. How can I dynamically reference the file path instead of hard-coding it to allow re-use?
ChatGPT: Here is how to dynamically reference a file folder path in Power Query:
- On the Home tab, select “New Source” and create a blank query.
- Add a new Query Parameter called “FolderPath” in the Settings pane.
- Enter a default folder path value if needed.
- Load data From File > From Folder, and reference the parameter:
Folder.Files([FolderPath]) - This will load files dynamically based on the parameter value.
- Parameter can be changed without editing query.
Using parameters instead of hardcoded paths allows reusing the query on different folders.
The folder can also be configured directly within the parameter value for convenience.
References:
Prompt: I have an Excel table of sales data loaded into Power Query. Some records have invalid sale amounts like negatives or zeros. How can I replace these invalid amounts with blank/null using Power Query?
ChatGPT: Here is how to replace invalid sale amounts with blank/null values in Power Query
- Load the Excel sales table into the Power Query editor
- Add a custom column to flag invalid amounts:
Invalid = if [SaleAmount] <= 0 then "Invalid" else "Valid"- Select the SaleAmount column
- Go to Transform tab > Replace Values
- For the Replace step:
- Value to find: 0
- Replace with: null
- Repeat Replace Values for -1, -2 etc.
- This will replace 0 and negative sales amounts with null/blank
- Remove the Invalid flag column if no longer needed
Replacing values allows selectively cleaning invalid numeric data while keeping the table structure intact.
References:
Prompt: I have an Excel table with a text column containing a mix of uppercase and lowercase values. How can I standardize this column to be uppercase using Power Query?
ChatGPT: Here are the steps to standardize a text column to uppercase values in Power Query:
- Load the Excel table into the Power Query editor
- Select the text column to standardize
- Go to Transform tab and click on Uppercase
- This will convert all text values in the column to uppercase
- Alternatively, you can create a new Custom Column:
- New column name: TextUpper
- Custom column formula:
= Table.TransformColumns(#"Previous Step", {{"TextColumn", Text.Upper, type text}})- Replace “TextColumn” with your actual column name
- Delete the original text column if no longer needed
The Uppercase transform or Text.Upper expression provides a quick way to standardize text casing in Power Query.
References:
Prompt: I’m importing JSON data into Power Query but need to filter records where certain nested fields meet criteria. What options do I have for filtering this hierarchical data?
ChatGPT: Here are some options for filtering nested data in JSON files using Power Query:
- Expand the JSON into separate columns, then filter expanded columns
- Use the Table.SelectRows() function to filter based on nested field values
- Add Custom Columns to extract nested fields, then filter on those columns
- Load JSON then invoke the Query Editor Advanced Editor to write Filter M code
- Parse JSON arrays and objects into a record then filter record columns
- Convert JSON to a list and filter list rows on nested properties
- Load JSON into a JSON variable, filter, then convert to table
The key is to expand or extract the nested fields, then apply filter steps on those derived columns or their parsed values.
This allows querying JSON data based on criteria within nested hierarchies.
References:
Prompt: I have loaded an Excel table into Power Query and applied some transformation steps. How can I view the full list of steps applied and their settings in the Advanced Editor?
ChatGPT: Here are the steps to view the full transformation history and settings in the Advanced Editor in Power Query:
- With your query open in the Power Query editor, select View > Advanced Editor from the ribbon.
- This will open the Advanced Editor window showing the M code.
- The Applied Steps section lists all the previous steps applied to shape the query.
- Each step shows the function name, input, and any parameters.
- You can see values like column names, data types, filter values etc.
- Review the steps and code comments to understand the logic.
- No changes will be applied until you select Done and close the editor.
The Applied Steps in Advanced Editor provide a transparent view of all transformations made within the Power Query GUI.
References:
Prompt: I imported an Excel table into Power Query and made some transformations. Later, I realized I should have cleaned the data differently. How can I revert back to the original imported table to restart the shaping process?
ChatGPT: Here are some options to revert back to the original imported table in Power Query, before any transformations were applied:
- Select Applied Steps on the right and delete all steps below the Source step
- On the Home tab, select Undo until you undo all steps
- Open Advanced Editor, delete all steps except the Source
- Load the table again from the original data source
- Duplicate the query to branch it, then undo steps on the copy
- Right-click on a step and select Delete Until End
- Use the Query Settings cog to revert to a Previous Version
Being able to reset the query lets you restart shaping the data if you realize your initial transformations should be different.
References:
Prompt: I want to load all the Excel files from a folder into Power Query, clean each file, and combine them into a single dataset. What are some ways I can automate these steps?
ChatGPT: Here are some ways to automate loading, transforming, and combining Excel files from a folder in Power Query:
- Parameterize the folder path to make it reusable on any folder
- Use List.Files to generate a list of Excel file paths dynamically
- Create a function to clean and shape each file, taking the file path as input
- Iterate through the list invoking the function on each file path
- Output each transformed query into a List or Table
- Append or merge the list/table into a single dataset
- Create a macro to initiate the entire end-to-end workflow
Automating the ETL process with parameters, reusable functions, and loops eliminates repetitive manual work as you scale up files and folders.
This allows transformation code to be applied across dynamic sources in a standardized way.
References:
Prompt: I have loaded a table into Power Query and applied some transformations. I want to extract just a subset of columns to output to Excel instead of the entire table. What’s the best way to output only selected columns?
ChatGPT: Here are two options to output only selected columns from a query table in Power Query:
Using column selection:
- Right-click on columns not needed and uncheck them
- This hides the column from output
- Only visible selected columns will output
- Can re-select columns later if needed
Using Table.SelectColumns():
- Before load, add a new query step:
= Table.SelectColumns([Previous Step],"Column1","Column2") - List desired column names in quotes
- This will output just those columns
Selecting columns provides precise control over query output without altering table schema.
You can also reorder columns before final load
Here are a couple more tips for outputting selected columns in Power Query:
Create column index list:
- Add an Index column to your query table
- Create a list of indices matching columns to output
- Use List.SelectRows() to filter for those indices
Conditional column selection:
- Add a custom column with a conditional flag
- Filter flagged columns with Table.SelectColumns()
So in summary, some best practices are:
- Parameterize list of columns for reusable selection
- Use column indices instead of names if positions fixed
- Filter columns conditionally based on logic
- Maintain table schema intact for future expansion
Outputting subsets of columns provides flexibility to tailor query results as needed.
References:
Prompt: I imported an Excel table into Power Query which has a DateTime field. How can I split this into separate Date and Time columns in my query output?
ChatGPT: Here are the steps to split a DateTime column into separate Date and Time columns in Power Query:
- Import the Excel table containing the DateTime field
- Select the DateTime column
- Go to Transform tab and click Split Column > By Delimiter
- Choose the Space delimiter to split between the date and time values
- This will create two new columns – column1 (date) and column2 (time)
- Optionally rename the new columns to more friendly names
- Reorder the columns as needed
- You can now use the separated Date and Time fields in further steps
Splitting the column makes the date and time portions more usable for filtering, sorting, and analysis.
References:
Prompt: I’m importing a folder of CSV files into Power Query where the data format is inconsistent between files. What’s the best way to handle standardizing columns across these varied sources?
ChatGPT: Here are some approaches in Power Query to standardize columns from inconsistent CSV files:
- Load files into separate queries then expand columns
- Unpivot other columns to normalize structures
- Set data types for each column using “Using Locale” detection
- Add fill down steps to complete missing values
- Trim whitespace and clean column names using Rename Columns
- Merge files using Full Outer Join to map varying columns
- Add conditional columns to flag mismatches for review
- Group remaining errant columns into “Other” fields
- Document schemas and transform into a standard shape
- Apply consistent transformations across files in loops/functions
Handling inconsistent and dirty data requires discovering differences, mapping equivalents, setting canonical types, and reshaping systematically into a standard.
With work, Power Query can align heterogeneous sources into unified structured data.
References:
Prompt: I have an Excel table loaded in Power Query that contains product information. I want to split the table into separate category tables. What would be the steps to do this split?
ChatGPT: Here are the steps to split an Excel product table by category into separate tables in Power Query:
- Import the Excel table into the Power Query editor
- Add an Index column
- Group By the Category column
- This will create a Group table with each category in separate tables
- Expand the tables by selecting the Category column
- Disable category headers to flatten the data
- You can now filter each category table using the Index
For example, to filter just the “Electronics” table:
List.SelectRows([Group], each [Index] = 1)This provides an easy way to split aggregated data into focused category tables for analysis.
References:
Prompt: I’m importing a large JSON file into Power Query and want to extract only a subset of fields to speed up performance. What’s the best approach to import partial JSON data?
ChatGPT: Here are some methods to import only a subset of fields from a large JSON file in Power Query for better performance:
- Use Json.Document() to load JSON into a variable
- Specify desired fields using optional arguments like Table1, Table2 etc
- Convert the variable into a table to output selectively parsed fields
Or
- Load the full JSON then use Table.SelectColumns() to keep only needed columns
- Select just expanded fields required before any additional steps
Or
- Use the Enter Data dialog to paste in a JSON excerpt with desired fields
- Load this limited sample instead of the full file
Extracting only necessary fields minimizes memory usage and speeds up data shaping by avoiding full file parsing.
References:
Prompt: I imported an Excel table into Power Query that contains a text column with JSON data. How can I expand this nested JSON into separate columns within my query?
ChatGPT: Here are the steps to expand a JSON string column into separate fields in Power Query:
- Import the Excel table containing the JSON column
- Select the JSON text column
- Go to Transform tab > JSON > Expand Field
- In the dropdown, select Array or Object to expand the full JSON contents
- This will create new columns for each JSON field value
- You may need to repeat Expand Field on any nested arrays/objects
- Once fully expanded, remove the original JSON column
- Reorder and rename the parsed columns as needed
Parsing the nested JSON string into individual columns makes the data much easier to work with in Power Query for shaping and analysis.
References:
Prompt: I want to create a calendar table in Power Query that contains every date in the next 5 years. What M code could generate this computed date table efficiently?
ChatGPT: Here is one way to generate a calendar table with the next 5 years of dates in Power Query M code:
let
Source = #date(2023, 1, 1),
EndDate = Date.AddYears(Source, 5),
Calendar = List.Generate(
()=> Source,
each [_]<EndDate,
each Source = Date.AddDays(Source, 1),
each [Date=Source]
)
in
CalendarThis:
- Starts from Jan 1, 2023
- Sets end date to 5 years later
- Generates a list of dates from start to end
- Returns list records containing the [Date] field
You can then load this Calendar list as a table for use as a date dimension.
References:
Prompt: I want to import data from a web API into Power Query and load it into an Excel workbook. What are some key steps I should follow to successfully get this API data into Excel?
ChatGPT: Here are some best practices for importing web API data into Excel using Power Query:
- Check API documentation for endpoint URL, parameters, headers, and authentication
- Test API in tool like Postman to validate format and schema
- In Power Query, generate API URL with parameters for desired data
- Enter API key or token in advanced options if needed
- Set data privacy level to Private to embed credentials
- Handle pagination if API limits record return
- Load API response and expand/parse JSON into table
- Shape and transform API data for analysis needs
- Consider loading into Power Pivot to utilize relationships
- Schedule refresh to keep API data current
Following API guidance and testing first allows successfully retrieving that data into Excel via Power Query. Authentication, pagination, and privacy are key considerations.
References:
Prompt: I have loaded multiple worksheets from an Excel workbook into separate queries in Power Query. How can I combine them into a single query table with an added column identifying the source sheet?
ChatGPT: Here are the steps to combine multiple Excel sheet queries into one table with a source identifier column in Power Query:
- Import each worksheet from the Excel file into separate Power Query queries
- Add a custom column to each query to identify sheet name:
Source = Excel.CurrentWorkbook(){[Name]}[Content]{0}[Name] - Select all queries and Append them together
- This will stack the tables vertically with the Source column added
- Optional: Change Source column to text data type
- Remove any duplicate columns across sheets
- Reorder columns as needed
Appending with a source identifier provides an easy way to combine Excel sheet data for unified analysis while retaining info on the original sheet.
References:
Prompt: I want to connect to an Azure SQL database from Excel using Power Query to import and refresh data. What are the key steps to set this up successfully?
ChatGPT: Here are the key steps to connect to an Azure SQL database from Excel using Power Query:
- Get the Azure SQL connection string and database details
- In Excel, go to Data > Get Data > Azure > Azure SQL Database
- Enter the SQL Server name and Database name
- Paste in the connection string when prompted
- If needed, enter SQL username and password
- Select tables/views to load into Power Query
- Transform and load the data as needed
- Set cloud privacy levels to enable refresh
- Schedule refresh to keep data current
- Optionally set up parameterization for filters
Following Azure guidance to get the proper connection string is crucial. Testing connectivity first helps troubleshoot issues.
This enables securely retrieving SQL Azure data into Excel through Power Query.
References:
Prompt: I imported an Excel table into Power Query. How can I document each of the steps in the Applied Steps panel to keep track of the transformations I applied?
ChatGPT: Here are some ways to document the Applied Steps in Power Query:
- Double click on each step name to rename it something descriptive
- Add a custom Description for each step explaining the logic
- Right-click and Insert Comment to add notes before/after steps
- Use the Table.AddColumn() step to add a helper log column
- Concatenate step names and details into the log column
- Export the full M code using Advanced Editor for external documentation
- Version the query over time as major logic changes occur
- Use numeric prefixes on step names to indicate sequence
Documenting and annotating steps makes it much easier to backtrack the purpose of the shaping logic applied to the imported data.
Keeping the Applied Steps organized and tidy improves readability of the transformation trail.
References:
Prompt: I have an Excel file with 50 worksheets containing data. I want to load all these sheets into Power Query for transformation and analytics. What is the best way to efficiently import all the data?
ChatGPT: Here are a couple options to efficiently import multiple sheets from an Excel file in Power Query:
Method 1
- Get Data > Excel Workbook > Select Multiple Tables
- Tick all sheets to load all at once
Method 2
- Advanced Editor to script loading all sheets:
Excel.CurrentWorkbook(){[Name="Table1"]}[Content],
Excel.CurrentWorkbook(){[Name="Table2"]}[Content], etcMethod 3
- Iterate sheets in a loop to load incrementally
- List workbook tables with Excel.CurrentWorkbook(){[Kind=”Table”]}
- Filter and load each table dynamically
Loading sheets in bulk viaSelect Multiple Tables or scripting is fastest. Looping allows more programmatic control.
Appending or merging queries afterwards combines the sheet data.
Good practice to test performance with smaller samples first.
References:
Prompt: I have an Excel workbook with 50 sheets containing disjointed data that needs to be combined into a single table. All sheets have the same columns. What steps can I take in Power Query to efficiently append this data from all sheets?
ChatGPT: Here are some recommended steps in Power Query to efficiently combine data from multiple Excel sheets with the same structure into a single table:
- Import all the sheets into separate queries using “Select Multiple Tables”
- Add an index/ID column to each sheet to track source
- Standardize column names, data types across sheets
- Disable any loaded headers under Transform tab
- Select all sheet queries > Append under Home tab
- This appends the rows efficiently without duplicating headers
- Optional: Group by index column to distinguish sources
- Further clean and transform the consolidated data
- Consider aggregating/summarizing as needed for analytics
Appending multiple sheets is optimal when the structure is consistent. Handling 50+ sheets may require testing and troubleshooting.
References:
Prompt: I want to load data from Facebook’s Graph API into Excel using Power Query. What are some key things I need to do to connect successfully?
ChatGPT: Here are some key steps to connect to Facebook’s Graph API from Excel using Power Query:
- Register as a Facebook developer and create an app to get an access token
- Find the Graph API endpoint URL that contains the data you need
- Test the API call in a tool like Postman, providing the access token
- In Power Query, create the API endpoint URL with parameters
- Enter the access token in the Advanced options
- Set privacy levels to store credentials encrypted
- Load the JSON response from Graph API
- Expand and parse the JSON into columns
- Apply data privacy policies to handle any PII
- Schedule refresh to keep Facebook data current
Following Facebook’s authentication guidance is essential to retrieve a proper access token. Testing connectivity in Postman can help troubleshoot.
References:
Prompt: I imported an Excel table into Power Query. Some columns have inconsistent formatting and capitalization that need to be cleaned up. What transformation steps can help standardize the text values in these columns?
ChatGPT: Here are some Power Query transformation steps that can help standardize inconsistent text formatting:
- Replace Values or Text.Replace – Replace specific values with a standard value
- Clean – Remove non-printable special characters
- Trim – Remove leading and trailing whitespace
- Uppercase/Lowercase – Convert case consistently
- Split Column then Concatenate – Break apart and reconstruct
- Extract substrings with Text Before/After Delimiter
- Group remaining into “Other” values
- Match text patterns with Regular Expressions
- Find and Replace or Substitute with Regex
- Fill down to propagate properly formatted values
- Create a mapping table for standard value mappings
Applying a sequence of cleansing steps helps align disparate text formats into a consistent shape for analysis.
References:
Prompt: I imported an Excel dataset into Power Query which contains a text column that needs to be split into multiple new columns. What are some ways I can split this text into separate columns?
ChatGPT: Here are some ways to split a text column into multiple new columns in Power Query:
- Split Column by Delimiter – Split on delimiter like comma, space etc.
- Extract Substrings with Text Before/After Delimiter
- Split Column by Number of Characters – Fixed width split
- Split Column byPositions – Specifying character positions to split at
- Split Column by Regular Expression – Split using a regex pattern
- Split Column by Lengths – Specify lengths for new columns
- Use the Splitter function to split by a delimiter into rows
- Expand the splitted column to split into new columns
- Unpivot remaining columns to break out into rows
Taking an iterative approach to splitting and extracting substrings can break down text from a single column into well-structured data.
References:
- [Microsoft – Split columns in Power Query](https://docs.microsoft.com.
Here is the continuation of ways to split a text column into multiple new columns in Power Query:
Some key points:
- Examine data to identify optimal split logic
- Use Splitter function for row-wise splitting
- Iterate if needed to fully separate elements
- Trim and clean split columns
- Check for and filter blank columns
- Rename and reorder split columns
A structured approach helps systematically break down messy concatenated text into a normalized shape.
Prompt: I need to combine multiple CSV files from a folder into a single table in Power Query. Each CSV has different columns and data. What’s the best approach to merge this varied data into a consolidated table?
ChatGPT: Here is a good approach to consolidate multiple CSV files with different structures into one table in Power Query:
- Import the folder of CSV files. Each will load into a separate table.
- Standardize columns that match across files. Rename, reorder, change data types.
- Unpivot other columns to create attribute/value pairs per row.
- Filter out any unnecessary blank columns.
- Merge the queries using Full Outer Join or Union to combine all columns.
- Prefix column names with the query name to identify source.
- Further transform into consolidated columns using pivots, aggregates etc.
- Filter any duplicate rows.
- Group remaining errant columns into separate tables.
- Optional: Tag a source identifier row to each file.
Blending tables fully before reshaping allows flexibility to mold varied data into a structured analysis set.
References:
Prompt: I imported a folder of text files into Power Query. Some files are using pipes | as delimiters while others use commas. How can I standardize the delimiters to merge the files consistently?
ChatGPT: Here is an approach to standardize varying text delimiters from files in Power Query:
- Import the text files using From Folder option
- Identify files using pipes | vs comma , delimiters
- For pipe files, split the column by pipe | delimiter
- For comma files, split the column by comma delimiter
- Standardize column names across split files
- Add a custom column with formula:
List.Accumulate(List.Transform({1..List.Count(Table.ColumnNames(_))}, each Text.Combine(Table.Column(_)[_], ",")))- Expand this accumulated column to standardized columns
- Now all files have comma separated data
- Merge/append the consolidated, delimited files
This methodically splits, recombines and expands to a normalized delimited shape before merging.
References:
Prompt: I have loaded an Excel table into Power Query which contains three address columns – Street, City, Country. How can I combine these into a single address column using Power Query?
ChatGPT: Here are the steps to combine multiple address columns into a single address field in Power Query:
- Load the Excel table containing the Street, City, and Country columns
- Select the Street column
- Go to Add Column > Custom Column
- Use the formula: [Street] & “, ” & [City] & “, ” & [Country]
- This will concatenate the columns with commas
- Rename this new column to FullAddress
- Reorder the columns as needed
- Optional: Remove original Street, City, Country columns
- Optionally trim whitespace or add line breaks between elements
Concatenating text columns with delimiters provides a way to merge related fields into a single string value.
References:
Prompt: I imported an Excel table into Power Query. Some rows have missing data I need to fill in by copying the values down from the row above. What steps can fill in these gaps?
ChatGPT: Here are some ways to fill down missing values in Power Query by copying data from the row above:
Fill Down option:
- Select the column(s) with missing values
- Go to Home tab > Fill > Down
- This will propagate the last non-null value to fill gaps
Fill Up option:
- Same as above, but fills up instead of down
Formula:
- Add Custom Column with formula:
= Table.FillDown(#"Previous Step Name",{"Column1","Column2"})- Replace column names to specify fill columns
This fills missing values by copying the adjacent row data according to the direction specified.
Check for and filter any remaining empty rows afterwards if needed.
References:
Prompt: I have loaded a CSV file into Power Query. Some product names have extra descriptive text that needs to be cleaned up. What transformations can help standardize the product names by removing this additional text?
ChatGPT: Here are some Power Query transformations that can help standardize product names by removing additional descriptive text:
- Split Column on delimiters like -, :, (, [ to isolate base product name
- Extract First Characters to get leftmost subset of product name
- Extract Text Before Delimiter to get text before extraneous text
- Clean extra whitespace/punctuation with Trim and Clean
- Replace Values to replace specific descriptors with blank
- Use Fill Down to propagate cleaned values
- Check for and filter blank rows to remove empties
- Apply a conditional column to flag changed products
Methodically splitting, extracting, replacing, and standardizing can help prune unnecessary text from product names. Comparing before and after values is useful.
References:
Prompt: I have an Excel table with a column containing long text descriptions. How can I extract the key terms and phrases from this text into a new column using Power Query?
ChatGPT: Here are some methods in Power Query to extract key terms and phrases from a long text column:
- Split the text into rows based on delimiters like commas, line breaks, etc.
- Extract substrings from before/after certain marker words.
- Use Text.PositionOf to find terms based on location.
- Match text patterns with Regular Expression and extract matches.
- Split Column by Positions to extract fixed width terms.
- Split Column by Lengths and reconstruct parts.
- Apply lists of words/phrases to filter with Text.Contains.
- Use Text.AfterDelimiter and Text.BeforeDelimiter
- Create a custom function to parse text for target terms
- Aggregate extracted terms back into a list.
Extracting based on structured patterns helps pull meaningful keywords and phrases out of lengthy bodies of text.
References:
Prompt: I’m importing an Excel sheet into Power Query which contains a column with JSON formatted data. How can I expand this JSON into additional columns within Power Query?
ChatGPT: Here are the steps to expand JSON data in a column into additional columns in Power Query:
- Import the Excel sheet containing the JSON column
- Select the column containing the JSON data
- Go to Transform tab and select JSON > Expand field
- In the Field dropdown, choose the top level object or array to expand
- This will create new columns from the JSON data
- If nested objects/arrays exist, repeat Expand field on those columns
- Once fully expanded, delete the original JSON column
- Reorder and rename the parsed columns as desired
Expanding the JSON objects and arrays iteratively converts the nested data into tabular columns for easier analysis in Excel.
References:
Prompt: I imported an Excel table into Power Query which contains a Date field. How can I split this Date into separate Day, Month, and Year columns using Power Query?
ChatGPT: Here are the steps to split a Date column into separate Day, Month and Year columns in Power Query:
- Import the Excel table containing the Date column
- Select the Date column, click Data Type dropdown > Date
- Go to Add Column tab > Date > Day
- This extracts the day number into a new column
- Repeat this for Month and Year columns
- Alternatively, create Custom Columns with formulas:
- Day: Date.Day([Date])
- Month: Date.Month([Date])
- Year: Date.Year([Date])
- Change column data types to Whole Number
- Rename columns as needed
- Optionally remove original Date column
This provides an easy way to break out the date parts from a date/datetime field for analysis.
References:
Prompt: I imported an Excel sheet into Power Query which contains 3 address columns – Street, City and Country. How can I combine these into a single Address column using Power Query?
ChatGPT: Here are the steps to combine multiple address columns into a single Address column in Power Query:
- Import the Excel sheet with Street, City and Country columns
- Select the Street column, go to Add Column > Custom Column
- Use the formula: [Street] & “, ” & [City] & “, ” & [Country]
- This concatenates the columns with commas
- Rename this new column to Address
- Optionally, remove original Street, City, Country columns
- Reorder columns as needed
- Alternatively, use Text.Combine() function for concatenation
- Trim whitespace if needed using Text.Trim()
Combining text columns with delimiters provides a way to merge related fields into a consolidated string value in Power Query.
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Prompt: I imported a CSV file into Power Query which contains product sales data. The ‘ProductID’ field contains values like ‘abc123’. How can I split this into two new columns for product code and product ID?
ChatGPT: Here are the steps to split the ‘ProductID’ field into two new columns in Power Query:
- Import the CSV containing the ProductID column
- Select the ProductID column
- Go to Split Column > By Number of Characters
- Specify 3 characters for the first column
- This will split ProductID into two columns – Code and ID
- Alternatively, you can use Split Column by Positions or by Delimiter
- Rename the split columns to meaningful names
- Reorder the columns as needed
- Optionally delete the original ProductID column
Splitting by fixed number of characters is useful when you have an ID pattern with known lengths to separate.
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Prompt: I have loaded multiple Excel files from a folder into Power Query. Each file has the same columns/structure but different data. What’s the easiest way to combine these into a single table?
ChatGPT: The easiest way to combine multiple Excel files with the same structure into a single table in Power Query is:
- Get data from the folder containing the Excel files
- Power Query will import each file into a separate table query
- Select all the table queries
- Go to Home tab > Append
- In the append dialog, ensure the option is checked to “Append table metadata”
- This will append all the rows into one table while avoiding duplicating headers
- The data from all files will be combined into a single table
- Further transform and clean the consolidated data as needed
Appending is ideal when the files have identical columns and structure. Power Query handles matching up the columns across tables.
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Prompt: I imported an Excel table into Power Query. How can I document what transformations I applied to the query for auditing purposes?
ChatGPT: Here are some ways to document the steps applied to a query in Power Query:
- Rename each step in Applied Steps to something descriptive
- Add a custom Description field for each step explaining the logic
- Insert a comment before/after steps providing context
- Create a helper Documentation column concatenating step details
- Generate a numbered Index column to sequence steps
- Use Table.ColumnNames() and List.Accumulate() to list steps
- Export the full M code using Advanced Editor for external record
- Version the query over time to snapshot major iterations
- Keep Applied Steps well-formatted and structured
- Avoid removing intermediate steps so logic trail remains
Documenting transformations makes Power Query easier to audit and serves as reference material for understanding how the imported data was shaped.
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Prompt: I have loaded multiple Excel sheets into separate queries in Power Query. Before appending them, how can I check that the column names and data types match?
ChatGPT: Here are some ways to verify columns names and data types match before appending queries in Power Query:
- Visually inspect each query side-by-side to validate consistency
- Cross-highlight columns by right-clicking and selecting “Add Column in Other Queries”
- View column metadata using Table.ColumnNames() and Table.ColumnDataTypes()
- Add an Index column to each to align rows for comparison
- Compare columns using conditional matching to flag differences
- Standardize columns by changing types and reformatting
- Use Table.Schema to output schema for comparison
- Append with Join Kind = Inner Join to only match common columns
- Test append a small sample first to check for errors
Taking the time to align schemas helps avoid issues appending tables with incorrect column mappings.
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Prompt: I have an Excel table of product orders loaded into Power Query. Some products have changed names recently. How can I redirect the old product names to new names?
ChatGPT: Here are some ways to redirect old product names to new names in Power Query:
- Create a mapping table with old names, new names columns
- Merge mapping table and do a lookup to new name
- Add a custom column with if/else logic to map old > new
- Use Table.ReplaceValue to replace old names with new
- Find/replace old names with Text.Replace or Substitute
- Group old products under a uniform name like “Deprecated”
- Split column and extract new name if contained
- Apply special prefixes/suffixes to old names
- Document name changes in a separate metadata table
Tracking product name changes helps align historical data to current products. Maintaining a mapping table provides sustainability as new changes occur over time.
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Prompt: I have an Excel table containing three address columns – Street, City and State. How can I combine these into a single Address field in Power Query?
ChatGPT: Here are the steps to combine multiple address columns into a single Address field in Power Query:
- Import the Excel table containing the Street, City and State columns
- Select the Street column, then go to Add Column > Custom Column
- Use the following formula: [Street] & “, ” & [City] & “, ” & [State]
- This concatenates the values with commas between them
- Rename this new column to ‘Address’
- Optionally remove the original Street, City and State columns
- Reorder the columns as needed
- You can also trim any whitespace using Text.Trim() if needed
Combining columns with delimiters provides an easy way to create a consolidated address text field. This approach can be extended to merge any related set of columns.
References:
- [Microsoft – Merging columns in Power Query](
Here is the continuation of the response on combining address columns in Power Query:
References:
Additional tips:
- Make sure to maintain consistent data types when merging columns
- You can add multiple Custom Columns to build the address incrementally
- Use the Text.Format() function to apply uniform casing, commas, etc.
- Validate the merged column for any inconsistencies after combining
- Document the steps clearly for transparency into the merge logic
- Consider splitting Address back to components later if needed for analysis
A well-structured concatenation helps bring related data together into a consolidated field while retaining flexibility to separate later if required.
Prompt: I imported a CSV file into Power Query which contains a text column with string values needing transformation. What are some standard text operations I can apply in Power Query?
ChatGPT: Here are some common text transformation operations that can be applied in Power Query:
- Trim – Remove leading and trailing whitespace
- Clean – Remove non-printable characters
- Replace Values – Replace specific text with other text
- Format – Apply consistent casing, punctuation, etc.
- Length – Filter based on text length
- Left, Right, Mid – Extract substrings
- Lowercase/Uppercase – Standardize casing
- Find Text – Find position of substrings
- Extract Before/After Delimiter – Split substrings
- Split Column – Split into multiple columns
- Concatenate/Combine – Merge multiple text fields
- Substitute/Replace – Apply find-replace using regex
- Detect Data Type – Set column type to text
Transforming text consistently helps normalize strings for analysis. Steps can be combined into a sequence for complex operations.
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Prompt: I have loaded multiple Excel files from a folder into Power Query. What are some best practices to combine this data into a consolidated table?
ChatGPT: Here are some best practices for combining multiple Excel files into a consolidated table in Power Query:
- Check for consistent column names and data types across files
- Standardize columns by renaming, reordering, changing types
- Handle headers correctly – disable or add index column
- Identify a primary unique key to join/append on
- Choose appropriate join type – inner, outer, left etc.
- Handle Relationships to align data correctly
- Filter unnecessary columns before appending/merging
- Prefix column names to identify file source
- Check for duplicate rows after append/merge
- Document sources and transformations clearly
- Test combines in small samples first to troubleshoot
- Use proper relations and keys to accurately link data
Taking the time to align structures and schemas is key for reliably combining multiple data sources.
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Prompt: I have an Excel dataset with a column containing long strings. How can I use Power Query to extract the key words and phrases from this text into a new column?
ChatGPT: Here are some methods in Power Query to extract key words/phrases from long text strings into a new column:
- Split the text column on delimiters like commas, spaces, line breaks to isolate words/phrases
- Use Text.Before() and Text.After() to extract between delimiter markers
- Find specific substrings with Text.PositionOf() and Text.Range()
- Use Text.Start() and Text.End() to extract first and last substrings
- Split by fixed character lengths and reconstruct parts
- Split by Regular Expression matches and extract captures
- Apply lists of keywords using Text.Contains() for filtering
- Aggregate extracted words/phrases using Text.Combine() with delimiter
- Write a custom function to parse text for target terms
A structured approach helps systematically break down long text and extract informative keywords and phrases into a usable column.
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Prompt: I have an Excel table with a column that contains JSON data. What are the steps in Power Query to expand the JSON into additional usable columns?
ChatGPT: Here are the steps to expand JSON data into columns in Power Query:
- Import the Excel table containing the JSON column.
- Select the JSON column.
- Go to Transform tab and click on JSON > Expand field.
- In the Field dropdown, select the top level object/array to expand.
- Power Query will create new columns from the JSON keys and values.
- If nested objects/arrays exist, repeat Expand field on those columns.
- Optionally rename and reorder the expanded columns.
- Remove the original JSON column if no longer needed.
Expanding JSON iteratively converts the hierarchical data into a tabular format for easier analysis.
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Prompt: I imported a CSV into Power Query and one of the product columns contains irregular values like “Widget [Blue]” and “Gadget (Deluxe)”. How can I clean this column to just show “Widget” and “Gadget”?
ChatGPT: Here are the steps in Power Query to clean the product values by removing irregular formatting:
- Select the product column that contains values like “Widget [Blue]”.
- Go to Add Column > Extract Text Before Delimiter.
- Enter the delimiter as [.
- This will extract the text before the first [.
- Repeat the Extract step, now using ( as the delimiter.
- The new extracted columns will contain just the core product name – “Widget” and “Gadget”.
- Delete the original product column.
- Rename one of the extracted columns back to Product.
Extracting the text before delimiters like [ and ( provides a way to isolate the base product name, cleaning irregular additional text.
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Prompt: I have an Excel table containing a text column with JSON formatted data. What are the steps in Power Query to expand and parse this nested JSON into additional usable columns?
ChatGPT: Here are the steps to expand and parse a JSON string column into additional columns in Power Query:
- Import the Excel table containing the JSON column.
- Select the text column containing the JSON strings.
- Go to Transform tab and click on JSON > Expand field.
- In the dropdown, select the top level array/object to expand.
- Power Query will parse the JSON into separate columns.
- If nested arrays/objects exist, repeat the Expand field step on those columns.
- Once fully expanded, reorder and rename the parsed columns as needed.
- Remove the original JSON column if no longer needed.
- Optionally change column data types like converting timestamps.
Iteratively expanding the nested JSON objects and arrays flattens the hierarchical data into a tabular format for easier analysis in Excel.
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Prompt: I imported an Excel dataset into Power Query. Some product names have extra descriptive text that needs to be removed. What steps can help clean this column to just show core product names?
ChatGPT: Here are some steps in Power Query to clean the product name column by removing extra descriptive text:
- Select the product name column that has additional text.
- Split the column by common delimiters like -, :, [, ( to separate product names.
- Extract text before/after certain substrings to isolate base names.
- Trim leading and trailing whitespace using the Trim function.
- Clean extra punctuation with Text.Clean function.
- Replace specific descriptive values with blank using Text.Replace.
- Fill down to propagate cleaned names to replace unwanted text.
- Filter out resultant blank rows to remove empty values.
- Optionally apply a conditional column identifying changed rows.
A sequence of splits, extracts, replaces, and fills helps prune away redundant text and clean to core product names.
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Prompt: I have loaded multiple Excel files from a folder into Power Query. Some files use different date formats. How can I standardize the date formatting across these files to merge them properly?
ChatGPT: Here are the steps to standardize date formats across multiple Excel files in Power Query:
- Import all the Excel files from the folder into separate queries.
- Identify columns with dates that have different formats.
- Select the date column in each query and change Data Type to Date.
- In the Format dropdown, choose a standard date format like YYYY-MM-DD.
- The dates will now have a consistent format.
- Alternatively, create a new formatted date column:
- Add Custom Column > Date.ToText([Date],”YYYY-MM-DD”)
- Once all dates are uniformly formatted, append or merge queries.
Standardizing the data type and format is crucial before appending data from different sources. Power Query deduces the internal date type while displaying the consistent format.
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Prompt: I have an Excel table loaded in Power Query with columns containing numeric data and text headers. How can I unpivot the columns into key-value pairs?
ChatGPT: Here are the steps to unpivot numeric columns with text headers into key-value pairs in Power Query:
- Load the Excel table into the Power Query editor.
- Select the numeric columns you want to unpivot.
- Go to Transform tab, and click Unpivot Columns.
- In the Unpivot Other Columns dialog:
- Set the Attribute Column to the column with text headers
- Set the Value Column to the column with numeric values
- Click OK.
- This will create new Attribute and Value columns with each header-data pair unpivoted into rows.
- Rename columns as needed.
Unpivoting flips columns into row values, transforming into a more normalized structure for analysis.
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Prompt: I imported a folder of text files into Power Query. Some files use commas as delimiters while others use tabs. What’s the easiest way to standardize the files to merge them properly?
ChatGPT: Here is an approach to standardize varying delimiters in text files loaded into Power Query:
- Import the folder of text files using From Folder option.
- Identify files separated by commas vs tabs.
- For tab-delimited files, split the column by tab delimiter.
- For comma-delimited files, split the column by comma delimiter.
- Standardize the column names across files.
- Select the split columns from tab-delimited files.
- Merge columns using comma delimiter.
- This will re-join the columns with commas.
- Now all files contain comma-separated values.
- Append or merge the files into a consolidated table.
By methodically splitting and merging, you can align different delimiter formats to a consistent standard.
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Prompt: I have loaded an Excel table into Power Query which contains three address columns – Street, City and Postal Code. What steps do I need to take to merge these into a single Address column?
ChatGPT: Here are the steps to merge multiple address columns into a single Address column in Power Query:
- Import the Excel table containing the Street, City and Postal Code columns.
- Select the Street column.
- Go to Add Column > Custom Column.
- Use the following formula: [Street] & “, ” & [City] & ” ” & [Postal Code]
- This will concatenate the columns with commas and spaces.
- Rename the new column to ‘Address’.
- Change the column type to Text if needed.
- Optionally, remove the original Street, City and Postal Code columns.
- Reorder the Address column as needed.
- Finally, apply formatting as needed to the merged column.
Concatenating text columns with delimiters provides a simple way to create a consolidated address field in Power Query.
Prompt: I have an Excel table containing product sales data loaded into Power Query. How can I pivot the table to summarize the sales data by product and category?
ChatGPT: Here are the steps to pivot a product sales table to summarize by product and category in Power Query:
- Load the Excel sales data table into Power Query.
- Select the Product and Category columns.
- Go to Transform tab and click on Unpivot Columns.
- In dialog, set Attribute Column as Category and Product.
- This will unpivot the columns into attribute-value pairs per row.
- Select the Attribute column and remove blanks.
- Go to Home tab and Group By the Category and Product attributes.
- Set the Sales column operation to Sum.
- This will pivot the table to sum Sales by Category and Product.
- Expand the grouped table if needed.
Unpivoting and Grouping provides a way to reshape the data into a summarized pivot table structure for analysis.
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Prompt: I have an Excel table containing multiple address fields like Street, City, State, etc. What is the best practice in Power Query for merging these into a single Address field?
ChatGPT: Here are some best practices for merging multiple address columns into a single Address field in Power Query:
- Standardize address elements before merging – trim, case, etc.
- Maintain data type consistency – text for text, number for zip etc.
- Add columns incrementally to build address vs one big merge
- Use aliases like AddrLine1, AddrLine2 to stage merges
- Concatenate with consistent delimiters like commas or line breaks
- Consider region-specific address formats and order of fields
- Validate merged column thoroughly before removing originals
- Document steps clearly such as original sources
- Split address back into components if still needed for analysis
- Culture as parameter for region-specific formatting
A methodical approach maintains flexibility while neatly combining multiple elements into a consolidated address field.
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Prompt: I imported a folder of Excel files into Power Query. Each file has different columns and data. What steps can I take to consolidate these varied files into a standard structured table?
ChatGPT: Here are some steps that can help consolidate varied Excel files into a standard table in Power Query:
- Load each file into its own query
- Identify columns common across all or a subset of files
- Standardize like columns by changing names, data types
- Unpivot other dissimilar columns into attribute-value pairs
- Merge into a single query using Full Outer Join
- Prefix column names to identify original file source
- Group non-aligning columns into separate tables
- Aggregate columns through summing, averaging etc.
- Filter out and delete any unnecessary blank columns
- Document how each column was handled for traceability
- Validate final schema to catch any mapping issues
Aligning heterogeneous data requires meticulous mapping of equivalents and standardizing into a unified schema. Consolidating varied files in this way enables aggregating disparate data for analysis.
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Prompt: I have loaded an Excel table into Power Query. Some product names are preceded with extraneous text like “Old – ” or “Discontinued – “. How can I cleanly remove this extra text to get only the actual product name?
ChatGPT: Here are a couple ways to remove extraneous text and clean product names in Power Query:
Using Replace Values:
- Select the Product Name column
- Go to Transform tab > Replace Values
- Enter “Old – ” in Value To Find
- Leave Replace With blank
- Repeat for “Discontinued – ” etc.
Using Extract Text:
- Select Product Name column
- Add Column > Extract Text After Delimiter
- Enter “-” as the delimiter
- Removes text before the delimiter
Both approaches extract the clean product name by removing unnecessary prepended text in the strings.
You can also use other text functions like Text.After to isolate the product name specifically.
References:
- [Microsoft – Clean
Here is the continuation of the response on cleaning product names in Power Query:
References:
Additional tips:
- Split column first to isolate base product name
- Use Text.Start and Text.End to extract substrings
- Employ lists or conditional logic to handle variations
- Apply transforms within a function for reuse
- Consider regex-based find/replace for complex patterns
- Document original and cleaned names in helper tables
- Compare before and after for validation
A robust set of text tools along with a methodical approach helps standardize product names by removing unwanted text cleanly.
Prompt: I have loaded multiple Excel files into Power Query which contain a common Id column. What’s the best way to merge these files together based on the shared Id values?
ChatGPT: Here is a good approach to merge multiple Excel files by a common Id column in Power Query:
- Load each Excel file into its own query.
- Identify the common Id column across all files.
- Standardize the Id column in each query if formatted differently.
- Choose one query as the ‘base’ data.
- Merge the other queries into the base query:
- Go to Home tab > Merge Queries
- On each query to merge, select the Join Kind = Left Outer
- Specify the common Id column as the Join Column
- Repeat merge for remaining queries to append by Id.
- Further transform the merged table as needed.
This leverages the common Id field to selectively join data from multiple Excel files into a consolidated dataset for analysis.
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Prompt: I have an Excel table containing multiple text columns that need to be split into rows for analysis. What’s the easiest way in Power Query to unpivot these columns into rows?
ChatGPT: The easiest way to unpivot text columns into rows in Power Query is:
- Load the Excel table into Power Query.
- Select the text columns that you want to unpivot.
- Go to the Transform tab.
- Click on the Unpivot Columns button.
- In the dialog, set the Attribute Column name.
- Set the Value Column name.
- Click OK.
This will automatically unpivot the selected columns into rows with two new columns – Attribute and Value.
The Attribute column contains the original column headers.
The Value column contains the cell values unpivoted into rows.
This provides a quick way to reshape multiple columns into a more normalized tabular structure for analysis.
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Prompt: I have loaded a folder of CSV files into Power Query. Some files use semicolons ; as delimiters while others use commas ,. What are some steps I can take to standardize the delimiters?
ChatGPT: Here are some steps that can help standardize varying delimiters from CSV files in Power Query:
- Import the folder of CSV files into separate queries.
- Identify files that use semicolon vs comma delimiters.
- For semicolon files, split the column by ‘;’ delimiter.
- For comma files, split the column by ‘,’ delimiter.
- Standardize the split column names across all queries.
- Select split columns from semicolon files.
- Merge the columns using comma , delimiter.
- This will join the columns into comma-delimited format.
- Confirm all queries now have comma separated values.
- Append or merge the queries into a consolidated table.
By methodically splitting, renaming, and joining, you can transform the varied delimiters into a consistent comma separated format.
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Prompt: I have an Excel table loaded in Power Query. Some product names are misspelled. How can I redirect the misspelled names to the correct names?
ChatGPT: Here are some ways to redirect misspelled product names to the correct names in Power Query:
- Create a mapping table with the misspelled and correct names
- Merge the mapping table and replace misspelled with correct
- Add a custom column with if/else logic to fix spellings
- Use the Replace Values function to replace misspelled text
- Find/replace misspellings using Text.Replace()
- Group the misspelled under a uniform name like “Misspelled”
- Split column and extract the correctly spelled name
- Apply a prefix/suffix to identify corrected values
- Document the name corrections in a separate table
Tracking misspellings allows aligning historical data to products using the proper naming. Maintaining a mapping table also enables sustainability as new errors occur.
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Prompt: I have loaded data from our CRM into an Excel table using Power Query. Some records have invalid status values that need to be corrected. What are some ways I can redirect the invalid statuses to the correct values?
ChatGPT: Here are some options to redirect invalid CRM status values to correct statuses in Power Query:
- Create a mapping table with invalid statuses and correct values
- Add Index columns and merge mapping into CRM data
- Look up correct status via Index match on mapping table
- Use Table.ReplaceValue to replace invalid statuses
- Build if/else logic in Add Column to fix statuses
- Find/replace text using Text.Replace() and list of statuses
- Apply special suffixes/prefixes to flag changed records
- Group all invalid under a uniform status like “Undefined”
- Document status corrections in a helper mapping table
- Split column and extract only properly formatted statuses
Methodically mapping invalid statuses to defined values helps fix and align historical CRM data for analysis.
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Prompt: I have an Excel table with a column that contains a mix of text values and blank cells. How can I filter this column in Power Query to only show the rows with text values and remove blanks?
ChatGPT: Here are a couple ways to filter a text column to only show rows with text values, removing blanks in Power Query:
Using Column Filter
- Select the text column that contains blanks
- Go to Home tab > Reduce Rows > Remove Empty
- This will filter out rows where the column is blank
Using Filter Rows
- Select the text column
- Go to Home tab > Reduce Rows > Filter Rows
- Filter by ‘is not blank’
Both methods will filter the table to only show rows where the specific text column contains values, removing any rows where it is empty or blank.
The filter icon will appear on the column after applying the filter.
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Prompt: I have an Excel dataset containing a column with dates formatted in different ways like MM/DD/YYYY, DD/MM/YY, etc. How can I standardize the formatting to YYYY-MM-DD in Power Query?
ChatGPT: Here are the steps to standardize inconsistent date formatting into YYYY-MM-DD in Power Query:
- Import the Excel data into Power Query
- Identify the column containing dates with mixed formats
- Select this date column
- Click on the Data Type dropdown and change to Date
- In the Format dropdown, select YYYY-MM-DD
- The column will now display the dates in this standardized format
- Alternatively, you can create a new column:
- Add Column > Date.ToText([Date], “YYYY-MM-DD”)
This changes the column to Date data type, which stores the value consistently, and displays it in the chosen SQL standard date format.
Standardizing date formats is crucial for proper sorting, filtering, and merging.
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Prompt: I have an Excel table containing customer addresses split across multiple columns (Street, City, State, etc). What steps do I need to take in Power Query to merge these into a single Address field?
ChatGPT: Here are the key steps to merge multiple address columns into a single Address field in Power Query:
- Import the Excel table into Power Query containing the split address columns
- Select the Street column and add a Custom Column
- Use the formula: [Street] & “, ” & [City] & “, ” & [State] etc.
- Concatenate the columns with delimiters like comma or newline
- Rename this new column to ‘Address’
- Optionally change column Type to Text
- Reorder the Address column as needed
- Remove original split columns if no longer needed
- Further clean and transform the merged column if required
- Document original columns used in the merge
Concatenating into a single text value provides a cleaner consolidated Address field for analysis.
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Prompt: I have an Excel dataset containing a column with string values. How can I clean this text column in Power Query to remove leading and trailing spaces, special characters, standardize case etc?
ChatGPT: Here are some steps in Power Query to clean a text column:
- Trim whitespace – select column > Transform > Trim (both ends)
- Clean special characters – select column > Transform > Clean
- Standardize case – select column > Transform > Lowercase/Uppercase
- Replace values – select column > Replace Values to replace specific text
- Substitute text – use Text.Replace() to regex find/replace
- Split column – to separate concatenated values if needed
- Extract substrings – use Text.Before()/Text.After() etc
- Format – to consistently format valid text values
- Detect data type – to set column type to Text
These text transformations can be combined into a sequence to clean strings. Testing on a subset of data is useful before full cleanup.
The goal is to end up with consistent, standardized text ready for analysis.
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Prompt: I want to iterate through Excel files in a folder in Power Query, load each file, transform it, and then consolidate everything into a single table. What are some ways I can automate this workflow?
ChatGPT: Here are some ways to automate loading, transforming, and consolidating Excel files in a folder in Power Query:
- Parameterize the file path for reusability across folders
- Use File.Contents to generate a list of Excel files dynamically
- Write a reusable function to clean and shape each file, taking path as input
- Iterate through the list invoking the function on each file
- Output the transformed results into a table
- Append or union all results into consolidated table
- Create a macro to initiate the overall ETL process
- Use Power Automate to call the query and pass parameters
- Handle errors and logging for unattended automation
- Schedule refresh to periodically pick up new files
Automating the sequence of operations eliminates repetitive manual work as you scale the number of files.
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Prompt: I imported an Excel dataset into Power Query which contains a column with string values. Some values have irregular formatting, casing, extra words etc. What steps can help clean this text column into a more standardized format?
ChatGPT: Here are some steps in Power Query to help standardize and clean inconsistently formatted text values in a column:
- Trim leading and trailing whitespace using the Trim transform
- Clean non-printable special characters using Text.Clean
- Standardize case formatting to be consistent with Uppercase/Lowercase transforms
- Replace specific values using the Replace Values transform
- Remove extra words or text using Text.Before()/Text.After()
- Split column on delimiters to break apart words/phrases into separate columns
- Extract substrings with Text.Range() and other text functions
- Fill down to propagate properly formatted values
- Group remaining variants into an ‘Other’ category
- Apply transforms within a function for reuse across columns
- Document changes by comparing before and after via conditional column
A combination of splitting, replacing, extracting and formatting helps standardize free-form text into a clean shape for analysis.
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Prompt: I have loaded multiple Excel files into Power Query which contain different columns and data formats. What are some recommended practices to consolidate these into a standard structured table?
ChatGPT: Here are some best practices for consolidating multiple Excel files into a standard table structure in Power Query:
- Visually inspect and document source file schemas
- Standardize like columns across files through renames, reordering
- Set data types consistently using “Transform > Data Type”
- Unpivot irregular columns into standardized attributes and values
- Handle headers correctly by disabling or using index columns
- Prefix column names to identify file source post-merge
- Choose appropriate join types – inner vs outer etc based on data
- Check for duplicate rows after append or merge
- Validate no errors after merge to confirm correct mapping
- Review top rows of merged table to confirm alignment
- Document all transformations for traceability
- Test merging in small samples first to troubleshoot issues
Taking the time to thoroughly align source schemas and test combines enables reliably denormalizing disparate data into a unified analysis set.
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Prompt: I have an Excel table loaded in Power Query. Some product names are ambiguous – like “Smith Product”. How can I clean this by appending a category or brand name prefix to differentiate them?
ChatGPT: Here are some ways to clean ambiguous product names by adding a category or brand prefix in Power Query:
- Add a conditional column to check for ambiguous names
- Create a category/brand mapping table and merge into source data
- Add a custom column to append mappings: [Brand] & ” ” & [ProductName]
- Use the Text.Combine() function to prepend text
- Alternatively, replace the ambiguous values: Table.ReplaceValue([ProductName], “Smith Product”, “BrandX Smith Product”)
- Group all unmapped under a uniform category like “Ambiguous”
- Document changes by capturing original values
- Split column and extract unambiguous names if contained
Applying a lookup or mapping helps disambiguate names by assigning a standardized prefix. Maintaining a mapping table also enables sustainability.
References:
Prompt: I have loaded an Excel table into Power Query which contains three address columns – AddressLine1, AddressLine2, City. What is the best way to combine these into a single Address field?
ChatGPT: Here is a good approach to merge the address columns into a single Address field in Power Query:
- Import the Excel table into Power Query
- Select the AddressLine1 column
- Add a new Custom Column: Text.Combine({[AddressLine1],[AddressLine2]},LineBreak.Windows)
- This concatenates the two lines with a line break
- Add another Custom Column: Text.Combine([Custom.1],[City],”, “)
- Concat City column with comma delimiter
- Rename final column to ‘Address’
- Remove original columns if no longer needed
- Optionally trim whitespace, standardize capitalization etc.
Building up the consolidation in steps preserves flexibility to re-split later if needed.
References:
Prompt: I want to validate and match two tables imported into Power Query before merging them. What are some ways I can compare the schemas to ensure the columns and data types align?
ChatGPT: Here are some effective ways to compare and validate table schemas before merging in Power Query:
- Visually inspect structure side-by-side to validate consistency
- Cross-highlight columns via “Add Column from Examples” to check alignment
- Generate lists of column names using Table.ColumnNames() to compare
- Use Table.Schema to output each table
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