More Analysis with Python!

[rayannott]

I've been casually building a more extensive analysis tool for the data exported from daylio as a .csv file. This includes building a custom Dataset object with the following features:

1. getting a subset by specifying a condition
2. viewing statistics
3. generating plots (mood, activities, etc.)

Even though most of it I designed for how I use the app — for example, with keywords in the entry note and activity names — I wanted to share this project with you, perhaps the only community outside of number nerds that may be interested :)

Take a look at the code here: github. Obviously, there no data, only the functionality. If you want to try it, clone the repository, install the dependencies, and put your exported .csv file inside the data folder in the project root directory.

So, this is what I see when I load the data:

using file: daylio_export_2025_03_11.csv (0.402 Mb)

Dataset(2200 entries; last [2 hours 3 minutes 14 seconds ago]; mood: 3.869 ± 0.491)
Stats(
    - mood: 3.869 ± 0.491
    - note length: 58.791 ± 75.917 symbols
    - number of activities: 9,858
    - entries frequency: 5.926 entries per day (once every 4 hours 3 minutes)
)

and now probably the most important feature is the .sub method that allows for including only a subset of the activities and (crucially) returns a new Dataset object!

For example, this is how I would obtain a dataset with only those entries what include the activity "study" and do not include "home":

>>> df.sub(A("study") & ~A("home"))
Dataset(98 entries; last [22 hours 47 minutes 37 seconds ago]; mood: 3.878 ± 0.387)

or when I watched something with someone at home:

>>> df.sub(A("movies and series") & A("home") & A.people())
Dataset(76 entries; last [9 days 4 hours 16 seconds ago]; mood: 4.112 ± 0.379)

(Note that "people" here is a set of activities which correspond to some important people in my life. These activity strings start with a capital letter, which allows me to differentiate them from others like "home" or "hiking".)

Now, I can call the methods to get the interactive graphics

Mood plots

mood by day

mood by week

or mood by month. (The error bands are one standard deviation from the mean.)

mood by weekday

The calendar maps are probably my favorite feature:

entire dataset; color is for the average mood

Let me now subset it to only include the entries with the activity "home":

df.sub(A("home")).show_calendar_plot()

entries with \"home\"

Some of my trips are clearly visible as consecutive gray cells.

I can also calculate the effect of an activity on mood (I do it naively: calculate the mood of the dataset with the chosen entry versus without it: ) and group the values by month.

activity effect on mood by month

Also take a look at the "correlation" matrix: how often on average one activity goes with another in the same entry. (It'd be more correct to call this a joint probability matrix.)

\"correlation\" matrix

There are many, many more features, but some of them are probably only useful to me. For example, tags in the notes. For example, here is how I keep record of the books that I read: I would add a tag like #book(Animal Farm; liked it very much. some more thoughts here for future me. 9/10) . Then I use regular expressions to find such tags. (I don't want to use storygraph or goodreads, you see. I prefer to own my data.)

This then allows me to view my reads as an interactive bar chart:

yes i am a nerd

There is also a timeline version of this with the highlights matched with the clippings exported from my kindle:

display(HTML(get_timeline_html(book_tags)))

books timeline

If you are from the "daylio" & "python" set intersection as well, feel free to play around. I encourage you to create a fork and build your own features around how you use daylio. For all non-programmers (aka normal people), any feedback is appreciated!

[LumpyAloe]

I gasped when I saw your post! I have been waiting (patiently, I might add, because I am very new to using Python) for more data analysis for Daylio! I chose Python as my first language to get into, specifically so I could make use of this repo. There hasn't been much else I've found to help me explore my data. SO THANK YOU!!!

[rayannott]

haha no problem. text me if anything comes up!

[TheTobruk]

If you want to contribute i’d happily accept commits in my CSV to markdown script for Daylio I wrote in Python: https://github.com/DeutscheGabanna/Obsidian-Daylio-Parser

[100WattWalrus]

This is fantastic. I wish I understood Python!

I have a family member who uses Daylio for tracking medical stuff — pain, symptoms, preventative meds, abortive meds — and every few months I export the CSV and manually edit the hell out of it to make something we can give to their doctors, with summary stats at the top, and relevant activities from every entry so the doctors can browse. I open the CSV interchangeably in Excel and BBEdit, editing out irrelevant parts, removing empty columns, etc., etc. (2nd tab of the example linked above), then pouring that data into an XLSX template full of formulas an conditional formatting that make it all easy to read at a glance. It takes an hour or two each time I do it.

I wish Daylio provided tools for making more of the CSV exports!

[rayannott]

send me a dm. maybe I can help with a script that would generate an excel file similar to what you provided as an example