[FIXED] How to create a plot that summarizes multiple daily profiles?

Issue

I am a type 1 diabetic and wear a continuous glucose monitor that measures my blood glucose levels every 5 minutes. The company that makes the CGM generates a report with a graph that looks like the figure at the bottom of this post. My goal is to learn how to recreate this graph for myself in a Jupyter notebook.

The data that I have, for example, looks like this:

Timestamp	Glucose Value (mg/dL)
2021-07-11 00:11:25	116.0
2021-07-11 00:16:25	118.0
2021-07-11 00:21:25	121.0
2021-07-11 00:26:24	123.0
2021-07-11 00:31:25	124.0

The graph is using data from a 30 day period and summarizing the distribution of values at each point in time. Is there a name for this type of graph, and how can I create it myself using Pandas/matplotlib/seaborn?

So far, I have tried creating a graph with the IQR split by day which is rather easy - using ploty:

glucose['Day'] = glucose['Timestamp'].dt.day_name()

fig = px.box(glucose, x="Day", y="Glucose Value (mg/dL)",
         points="all", color='Day')
fig.show()

But now I am unsure how to easily calculate the IQR for specific time periods and average them.

Thank you so much for your help!

Solution

Answering my own question with help from the links that Joe provided in the comments:

I was able to group the dataframe by hour, then use .quantile to generate a new dataframe with rows as hours and columns as 10%, 25%, 50%, 75%, and 90%. From there it was a matter of simple formatting with matplotlib to copy the original one.

grouped = df.groupby([df['Timestamp'].dt.hour])
i = grouped['bgl'].quantile([.1, .25, .5, .75, .9]).unstack()

Thanks a lot Joe!

Answered By - walker967

Answer Checked By - Robin (PHPFixing Admin)