Excel Delete Blank Rows while Preserving Data

You could use Excel’s “Remove Duplicates” feature, but any duplicate data that is supposed to be there will also get blown away.

To assuredly remove blank rows without removing duplicate data (and maintaining the original order), follow these steps:

  1. Locate or insert a blank column
  2. Optionally name the column “Order” or leave it blank if there are no headers
  3. Type the number “1” in the first row of data
  4. Fill down to the last row of data
  5. Sort by an actual data field
  6. Now the blank rows will be grouped together, so delete the blank rows.
  7. Re-sort by the column of numbers from Smallest to Largest.
  8. Optionally delete the column of numbers

Need to undo this operation? Here’s how to Insert a Blank Row Between Every Row in Excel

Related how-to’s using the functions mentioned here:

Google Indexing Experiment

This is a search engine optimization experiment with two similar existing pages on this site to see which will get index by Google faster.

There were two bare bones pages on this site with nearly identical content (formatting was the same and word count was equal)

Neither of which were appearing in Google’s index

20160629-abbreviation-page-google-index-test
Google site search index results from 6/29/16

Pre-experiment quick stats:

  • Published: 6/23/16
  • Taxonomy WordPress Page
  • 1 internal link
  • 0 external links
  • 0 images
  • Both included in sitemap
  • Neither indexed by Google
  • No links from other pages on this site
  • AK Page word count: 40
  • MO Page word count: 40

The Experiment

Control page is Alaska and variable page is Missouri.

The variable under test is page content length as measured by word count (determined by the Yoast SEO plugin for WordPress).

The Yoast plugin recommends content be a minimum of 300 words in length.

The Missouri page was updated with Creative Commons information sourced and remixed from Wikipedia and now has 320 words of content.

Also, an equal number of links to the page from this post have been added and twitter links from the SOT account.

Experiment quick stats:

  • Started 6/29/16
  • Both pages updated: 6/29/16 (for the purpose of this test)
  • AK Page word count: 40
  • MO Page word count: 320
  • MO Page has 2 external links (both to Wikipedia)
  • This post has 241 words at time of publication

References:

https://moz.com/community/q/how-to-determine-which-pages-are-not-indexed

RW: Intel readies chip to rival NVIDIA for machine learning

After abandoning its own GPU for supercomputers, machine learning, and video games in 2009, Intel has returned to the market with a new 72-core Xeon Phi, to compete with NVIDIA’s growing portfolio of GPUs.

The Xeon Phi ‘Knights Landing’ chip, announced at the International Supercomputing Conference in Frankfurt, Germany last week, is Intel’s most powerful and expensive chip to date and is aimed at machine learning and supercomputers, two areas where Nvidia’s GPUs have flourished.

Inside the chip there is 72-cores running at 1.5GHz, alongside 16GB of integrated stacked memory. The chip supports up to 384GB of DDR4 memory, making it immensely scalable for machine learning programs.

Read the full article: Intel readies chip to rival NVIDIA for machine learning on readwrite

Technological Singularity Timeline

Year Event
1958 Earliest recorded use of the term “Singularity” by mathematician Stanislaw Ulam in his tribute to John von Neumann (1903-1957)
1993 First public use of the term “Singularity” by Venor Vinge in an address to NASA entitled “The Coming Technological Singularity: How to Survive in the Post Human Era”
2005 Inaugural “Singularity Summit” hosted by the Singularity Institute for Artificial Intelligence (now MIRI)
2006 The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil is published.
2009 Wired for Thought: How the Brain is Shaping the Future of the Internet by Jeffrey Stibel is published.
2012 MIRI study “How We’re Predicting AI—or Failing To”
2040 Median value year predicted for Artificial General Intelligence from the 2012 MIRI study.

 

References

Excel create a GETURL function

  1. Open the workbook
  2. Enter into the VBA editor (Press Alt+F11)
  3. Insert a new module (Insert > Module)
  4. Copy and Paste the Excel user defined function below
  5. Exit out of VBA (Press Alt+Q) the function will be saved
  6. Use this syntax for this custom Excel function: =GETURL(cell)

By the way

  • VBA stands for Visual Basic for Applications

References

Excel: Insert blank row between every row

There is no inbuilt way in Excel to insert a blank row between every existing row, but it is achievable without doing it manually.

The process is to create a column with repeated numerals (1 to n, where n is the number of rows with data) equal to twice the number of rows of data (2n) and sort by that column so blank lines appear between each data row.  Here’s how…

  1. Locate or insert a blank column
  2. Optionally name the column “Order” or leave it blank if there are no headers
  3. Type the number “1” in the first row of data
  4. Fill down to the last row of data
  5. Select and Copy all the numbers just created in that column
  6. Paste the selection below the data into the first blank row
  7. Sort by the column of numbers from Smallest to Largest.
  8. Optionally delete the column of numbers

Tip: if you need multiple blank rows between, repeat step 6 as necessary

Related:

Excel month name, abbreviation from date

In Excel the Text formatting formula (=TEXT()) is capible of re-formatting Excel’s built in date storage format.

Type Date Example Result Formula
Full Month Name 1/25/2016 January =TEXT(B2, “mmmm”)
Abbreviated Month 1/25/2016 Jan =TEXT(B3, “mmm”)
Two digit month number 1/25/2016 01 =TEXT(B4, “mm”)
Single/double month number 1/25/2016 1 =TEXT(B5, “m”)

Related search:

  • excel text month name

Resources:

HS: The Technology Behind Apple Photos and the Future of Deep Learning and Privacy

 

There’s a war between two visions of how the ubiquitous AI assisted future will be rendered: on the cloud or on the device. And as with any great drama it helps the story along if we have two archetypal antagonists. On the cloud side we have Google. On the device side we have Apple. Who will win? Both? Neither? Or do we all win?

If you would have asked me a week ago I would have said the cloud would win. Definitely. If you read an article like Jeff Dean On Large-Scale Deep Learning At Google you can’t help but be amazed at what Google is accomplishing. Impressive. Wide ranging. Smart. Systematic. Dominant.

Apple has been largely absent from the trend of sprinkling deep learning fairy dust on their products. This should not be all that surprising. Apple moves at their own pace. Apple doesn’t reach for early adopters, they release a technology when it’s a win for the mass consumer market.

There’s an idea because Apple is so secretive they might have hidden away vast deep learning chops we don’t even know about yet. We, of course, have no way of knowing.

What may prove more true is that Apple is going about deep learning in a different way: differential privacy + powerful on device processors + offline training with downloadable models + a commitment to really really not knowing anything personal about you + the deep learning equivalent of perfect forward secrecy.

Photos vs Photos

Building an Image Processing Pipeline Overview

This post is intended to kick off a multi-part series on building an image processing pipeline.  Presented here is an outline which will be updated and modified along the way.

My motivation and the goal is to build a process that ingests photos (currently from a mobile device), sort them into multiple categories, and perform an independent task on each photo after sorting.

The secondary goal is to build a toolkit of scripts for each of these tasks that are reusable in the future.

The programming will be in Python.

Pipeline overview:

  1. Mobile image capture device (iPhone)
  2. Program to sync photos with a computer (BitTorrent Sync)
  3. Directory spy to process newly synced images
  4. Indexing/de-duplication
  5. Pre-processing
  6. Processing
  7. Final result

Progress so far:

Numbers one and two are easily crossed off the list.  I have created a directory spy (item #3)  in the past, but it needs to be reviewed and modified to work here.