The hardest part and maybe biggest caveat of this is how to define the non_words, and ultimately I just went with words

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.

Can you guess which nights we have training on? Monday and Wednesday nights, yep. This will be messages saying people are in, out, or late for the session. Our matches are obviously on Saturday, which is where the bulk of our messages come in and will be mainly talking about traveling to the matches, and then post-game antics.

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.

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.

So some context for these next few sections, the WhatsApp group I used is for my (field) hockey team, so mainly talking about training, matches and the nights out we had. But you’ll see this shortly!

I thought a heatmap would display this best using Hour of Day and Day of Week. That was what I was most interested in, as other things were obvious, like month of year due to when the hockey season runs. So how I’ve constructed this is by creating new columns for those two data points, and then creating a new data frame with the grouped counts of those dimensions.

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.

Displaying as a bar chart allows you to visually see the difference in numbers, as opposed to just a table of numbers. Alex S is the captain and so sends a lot of admin messages, and I am the social sec, trying to organise a bunch of guys to get out the house and do fun things, when they clearly don’t want to. Even I was surprised just how much more Alex and I message compared to the others, but we often don’t get any responses, so it does make sense.

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.

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.

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!

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.

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.

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by Jelene — 359,897,265 views While we couldn’t celebrate everything face to face this year, we know for a fact that technology helped us celebrate in the safest of ways! This “Happy Birthday” GIF by Jelene helped us do exactly that. Sending wishes via this fun colorful GIF is sure to put a smile on the receiver’s face :)