Extracting table values if values in two columns have identical values in other rows
Hi guys, Can someone please help me with a formula for the following task? I have over 40,000 rows of data in 'Table 1', I've extracted a sample. I need to analyse only cells which con...
LachlanB98 With 40,000+ rows of data, efficiency is definitely a concern when using a formula-based method. Might I suggest that you first concatenate the Product and Decrement columns into a single criteria range, then use the UNIQUE function with the optional [exactly_once] argument set to TRUE. From there, the table can be filtered using the ISNA / XMATCH method to exclude all records that appeared only once. For example:
However, to make this method perform as efficiently as possible when dealing with large datasets, move the arr and unq variables to their own spilled ranges in the worksheet and avoid the LET function altogether. For example:
Note: when testing this method with 40,000 rows of data, the calculation time was approx. 7 seconds with LET and only a fraction of a second without. I'm not sure if this is just a bug with the current version of MS365, but I don't remember there ever being an efficiency problem in the past using XMATCH with arrays vs ranges.
Well spotted! And I think the formula wins the time trial. When I was timing the original formula with helper ranges the calculation was taking about 230ms. Combining them into
and the time required leapt to 8000ms! Forcing the binary search and the time reduced to 150ms. I am so used to the idea that XLOOKUP and XMATCH perform sorts as part of the solution process to gain efficiency that this came as a surprise.
Once as I had come to terms with the fact that COUNTIFS does not perform well on datasets of this size (10,000ms), I tried a different strategy of sorting the dataset and comparing each record with prior and subsequent records to determine the filter criterion.
XMATCH sorts nothing inside, my assumption it takes a lot of time to map found position in sorted array back on unsorted one. That's end user job if use binary search.
Bit surprized that BYROW is relatively fast, nice approach.
SergeiBaklan Good tip. The [search_mode] never even crossed my mind when I was running my earlier tests (heavy sigh). This is the perfect situation to implement Binary search for improved performance. Thanks for that.
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I have over 40,000 rows of data in 'Table 1', I've extracted a sample. I need to analyse only cells which con...","body@stringLength":"1082","rawBody":"
Hi guys,
Can someone please help me with a formula for the following task?
I have over 40,000 rows of data in 'Table 1', I've extracted a sample.
I need to analyse only cells which contain identical values in both 'Product' and 'Decrement' in 'Table 1'. I have manually created a dataset in 'Table 2' which highlits the output I need.
Thanks in advance!!
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Hi guys,
Can someone please help me with a formula for the following task?
I have over 40,000 rows of data in 'Table 1', I've extracted a sample.
I need to analyse only cells which contain identical values in both 'Product' and 'Decrement' in 'Table 1'. I have manually created a dataset in 'Table 2' which highlits the output I need.
Thanks in advance!!
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However, if use XMATCH with binary search it works fast
\n=LET(\n pd, product & \"| \" & decrement,\n u, SORT( UNIQUE(pd,,1) ),\n f, FILTER( table, ISNA( XMATCH(pd,u,,2) ) ),\n VSTACK( {\"ID NUMBER\",\"PRODUCT\",\"DECREMENT\",\"IN USE?\"}, f )\n)","kudosSumWeight":1,"repliesCount":3,"postTime":"2024-06-04T00:15:59.153-07:00","images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"attachments":{"__typename":"AttachmentConnection","pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null},"edges":[{"__typename":"AttachmentEdge","cursor":"MjUuMXwyLjF8b3w1fF9OVl98MQ","node":{"__ref":"Attachment:{\"id\":\"attachment:message4159192AttachmentNumber1\",\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/attachments/gxcuf89792/ExcelGeneral/230826/1/sample_book_large%20xmatch.xlsx\"}"}}]},"timeToRead":1,"currentRevision":{"__ref":"Revision:revision:4159192_1"},"latestVersion":null,"metrics":{"__typename":"MessageMetrics","views":1628},"visibilityScope":"PUBLIC","isEscalated":null,"placeholder":false,"originalMessageForPlaceholder":null,"messagePolicies":{"__typename":"MessagePolicies","canModerateSpamMessage":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","key":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","args":[]}}},"customFields":[],"replies":{"__typename":"MessageConnection","edges":[{"__typename":"MessageEdge","cursor":"MjUuMXwyLjF8aXwxMHwxMzI6MHxpbnQsNDE2MDAxOSw0MTYwMDE5","node":{"__ref":"ForumReplyMessage:message:4160019"}},{"__typename":"MessageEdge","cursor":"MjUuMXwyLjF8aXwxMHwxMzI6MHxpbnQsNDE2MDAxOSw0MTU5NjAx","node":{"__ref":"ForumReplyMessage:message:4159601"}}],"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"CachedAsset:theme:customTheme1-1744326567485":{"__typename":"CachedAsset","id":"theme:customTheme1-1744326567485","value":{"id":"customTheme1","animation":{"fast":"150ms","normal":"250ms","slow":"500ms","slowest":"750ms","function":"cubic-bezier(0.07, 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LachlanB98 With 40,000+ rows of data, efficiency is definitely a concern when using a formula-based method. Might I suggest that you first concatenate the Product and Decrement columns into a single criteria range, then use the UNIQUE function with the optional [exactly_once] argument set to TRUE. From there, the table can be filtered using the ISNA / XMATCH method to exclude all records that appeared only once. For example:
However, to make this method perform as efficiently as possible when dealing with large datasets, move the arr and unq variables to their own spilled ranges in the worksheet and avoid the LET function altogether. For example:
Note: when testing this method with 40,000 rows of data, the calculation time was approx. 7 seconds with LET and only a fraction of a second without. I'm not sure if this is just a bug with the current version of MS365, but I don't remember there ever being an efficiency problem in the past using XMATCH with arrays vs ranges.
See attached...
","body@stringLength":"1718","rawBody":"
With 40,000+ rows of data, efficiency is definitely a concern when using a formula-based method. Might I suggest that you first concatenate the Product and Decrement columns into a single criteria range, then use the UNIQUE function with the optional [exactly_once] argument set to TRUE. From there, the table can be filtered using the ISNA / XMATCH method to exclude all records that appeared only once. For example:
However, to make this method perform as efficiently as possible when dealing with large datasets, move the arr and unq variables to their own spilled ranges in the worksheet and avoid the LET function altogether. For example:
Note: when testing this method with 40,000 rows of data, the calculation time was approx. 7 seconds with LET and only a fraction of a second without. I'm not sure if this is just a bug with the current version of MS365, but I don't remember there ever being an efficiency problem in the past using XMATCH with arrays vs ranges.
See attached...
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Well spotted! And I think the formula wins the time trial. When I was timing the original formula with helper ranges the calculation was taking about 230ms. Combining them into
and the time required leapt to 8000ms! Forcing the binary search and the time reduced to 150ms. I am so used to the idea that XLOOKUP and XMATCH perform sorts as part of the solution process to gain efficiency that this came as a surprise.
Once as I had come to terms with the fact that COUNTIFS does not perform well on datasets of this size (10,000ms), I tried a different strategy of sorting the dataset and comparing each record with prior and subsequent records to determine the filter criterion.
That was back down to 250ms so, no prizes, but a pretty credible performance!
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XMATCH sorts nothing inside, my assumption it takes a lot of time to map found position in sorted array back on unsorted one. That's end user job if use binary search.
\n
Bit surprized that BYROW is relatively fast, nice approach.
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SergeiBaklan Good tip. The [search_mode] never even crossed my mind when I was running my earlier tests (heavy sigh). This is the perfect situation to implement Binary search for improved performance. Thanks for that.
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