First, let’s reflect on the extent of this simple example: As the notes are combined and varied, the number of melodies

It’ll happen again. Some genius working within the right community at the right time will solve the riddle of how creativity works. We’ll know because they’ll be able to explain it.

More simple than I thought, I never realised you could get this on WhatsApp. On your phone, go to the WhatsApp group and click the dot dot dots in the top right. Then got to ‘More’ and to ‘Export chat’. You can then select where to save this. I chose Google Drive and put it where I would be creating my Jupyter Notebook.

Consider your love of old music. Have you ever wondered why we still love the music we listened to back in high school? Our evaluative criteria are generally imprinted during adolescence and young adulthood. We tend thereafter to feel that music is getting worse, generation after generation, as new music evolves beyond our tastes. The old music embodies the criteria we value. New music embodies new criteria, as well as disregarding some of the old.

In truth, no one understands how this complex interplay of psychological and social factors drives creativity. It’s mysterious and wonderful. When the conditions are right, at the nexus of a creative mind and the vagaries of their social circumstances, something fundamentally new and important emerges.

I have explained what these do in the comments, but to explain why: Jupyter limits the amount of outputs displayed for each cell. So if you want to output a graph of some data, as well as a supporting table nicely in one cell, then you’ll need to run the first command. The second two I use to control how much is outputted. I don’t like it how it defaults to such a low amount of rows/columns, especially when you can limit the amount displayed using functions like .head() instead anyway, so I’ve just gone unlimited. But do be careful when having a look at your data that you are limiting it when you need.

http://stream88.colomboserboli.com/eca/video-Western-United-Macarthur-FC-v-en-gb-nhc-.php

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http://stream88.colomboserboli.com/eca/v-ideos-Western-United-Macarthur-FC-v-en-gb-ood-.php

http://skrs.vidrio.org/dod/video-Western-United-Macarthur-FC-v-en-gb-cwh-.php

http://stream88.colomboserboli.com/eca/v-ideos-Western-United-Macarthur-FC-v-en-gb-uji-.php

http://skrs.vidrio.org/dod/video-Western-United-Macarthur-FC-v-en-gb-esn-.php

http://vert.actiup.com/jls/videos-Western-United-Macarthur-FC-v-en-gb-lou-.php

http://agro.ruicasa.com/vtm/video-Western-United-Macarthur-FC-v-en-gb-jvg-.php

http://old.cocir.org/media/fxa/v-ideos-Western-United-Macarthur-FC-v-en-gb-zyn-.php

http://svt.munich.es/fmi/video-Western-United-Macarthur-FC-v-en-gb-wcx30122020-.php

http://m.dentisalut.com/omy/Video-Western-United-Macarthur-FC-v-en-gb-nuz30122020-.php

http://skrs.vidrio.org/dod/videos-Western-United-Macarthur-FC-v-en-gb-gpk-.php

http://agro.ruicasa.com/vtm/video-Western-United-Macarthur-FC-v-en-gb-phu30122020-.php

http://m.dentisalut.com/omy/videos-Western-United-Macarthur-FC-v-en-gb-pqu30122020-.php

http://svt.munich.es/fmi/Video-Western-United-Macarthur-FC-v-en-gb-yov-.php

http://agro.ruicasa.com/vtm/video-Western-United-Macarthur-FC-v-en-gb-ela-.php

http://old.cocir.org/media/fxa/videos-Western-United-Macarthur-FC-v-en-gb-hvd-.php

http://stream88.colomboserboli.com/eca/v-ideos-tokatspor-v-kahta-02-spor-v-tr-tr-1cqv-6.php

http://vert.actiup.com/jls/videos-tokatspor-v-kahta-02-spor-v-tr-tr-1kxi-8.php

ue that can be used is a basket analysis. This analysis identifies the likelihood that one or more items are purchased alongside another item. A toothbrush alongside a toothpaste or mouthwash may make sense, but there could be other products that trigger the sales. The result from an analysis are suggestions for how products are mentioned on website pages, app pages, or in value coupons. Even having them appear alongside in a video would be helpful.

My favorite example of this musical riddle is known as the diabolus in musica (Latin for “the Devil in music”). It refers to a highly dissonant interval called a tritone. You’d recognize it immediately as Enter Sandman, Maria, or Purple Haze. But these melodies wouldn’t be selected during Medieval times. Not because they weren’t there in the problem space, but because the necessary evaluative criteria had not yet been selected. The Devil in music was steadfastly avoided.

Who sends the most messages? What are the most common words used? What are the most common Emojis used? When do people message most often? If you’ve ever wondered these things about your WhatsApp groups with your mates, then this is the article for you. Find out with some (relatively) simple Python!

If you like, you can even build a system that “proves” that the old music is better than the new, just by selecting for those features that privilege the old evaluative criteria. Apparently today’s songs are less melodically complex, more repetitive, they embody fewer chord changes, their dynamic range is narrower, and today’s artists are irredeemably self-involved (!). Of course, the quality of the music and the selective criteria cannot be explained in terms of these descriptive features. And of course, none of these analyses admit whatever new and largely inexplicit selective criteria may actually be active in our cultural communities. But misunderstandings about what constitutes a scientific argument are as rampant as misunderstandings of creativity. (Now get off my lawn, you kids!)

I find this cultural impediment to creativity fascinating. I wonder how many great ideas have been hatched and summarily dismissed because they weren’t aligned with the prevailing culture. Artists working in isolation internalize the creative constraints of their community, embodying their own toughest critics. The pile of crumpled paper in the wastepaper basket is a testament to this process. The challenge isn’t so much in processes of creative variation (such as propping your dog up at the piano) as in the means for selection. Incidentally, this same environment-building challenge is what limits the reach of evolutionary approaches to the problem.

Just a little comment on the setup I went for. I used a Jupyter Notebook as for this kind of analysis, that is done in little parts, I think it works really well to execute each cell. Here are a couple of tips with some special functions that help in Jupyter.

I am relatively new to Python, but this project gave me a really good intro to Python data wrangling as I had to use a lot of different techniques here as well as trying to explore the best one to use.

Some notes on what I’ve done here. I like to have two sets of the data, leaving one as the raw data that you can compare to the wrangled dataset, hence the raw_data_csv and data.

Unfortunately, this data export does not come out perfectly from WhatsApp, and when a message contains multiple lines/line breaks, it is very hard to attribute that to the correct message details. I played around with some ways to do this, but couldn’t find anything too simple for it. For the purpose of this analysis I’ve just removed them, as we can still get good enough insights without them. That is what the dropna on datetime column is doing.

It’ll happen again. Some genius working within the right community at the right time will solve the riddle of how creativity works. We’ll know because they’ll be able to explain it.