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Suggestions and Tips to Manage Jupyter Pocket book Visualizations | by Matthew Andres Moreno | Jan, 2024

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Optimize your information science workflow by automating matplotlib output — with 1 line of code. Right here’s how.

Naming issues is tough. After a protracted sufficient day, we’ve all ended up with the highly-descriptive likes of “graph7(1)_FINAL(2).png” and “output.pdf” Look acquainted?

We are able to do higher — and fairly simply, really.

Once we use data-oriented “seaborn-esque” plotting mechanisms, the elements for a descriptive filename are all there. A typical name appears like this,

sns.scatterplot(information=suggestions, x="total_bill", y="tip", hue="time")

Proper there we all know we’ve acquired “total_bill” on the x axis, “timecolour coded, and so forth. So what if we used the plotting operate identify and people semantic column keys to arrange the output for us?

Right here’s what that workflow appears like, utilizing the teeplot software.

import seaborn as sns; import teeplot as tp
tp.save = {".eps": True, ".pdf": True} # set customized output conduct
tp.tee(sns.scatterplot,
information=sns.load_data("suggestions"), x="total_bill", y="tip", hue="time")

teeplots/hue=time+viz=scatterplot+x=total-bill+y=tip+ext=.eps
teeplots/hue=time+viz=scatterplot+x=total-bill+y=tip+ext=.pdf

We’ve really executed three issues on this instance — 1) we rendered the plot within the pocket book and 2) we’ve saved our visualization to file with a significant filename and 3) we’ve hooked our visualization right into a framework the place pocket book outputs could be managed at a world degree (on this case, enabling eps/pdf output).

This text will clarify tips on how to harness the teeplot Python bundle to get higher organized and unlock your psychological workload to give attention to extra fascinating issues.

I’m the first writer and maintainer of the venture, which I’ve utilized in my very own workflow for a number of years and located helpful sufficient to bundle and share extra extensively with the group. teeplot is open supply below the MIT license.

teeplot is designed to simplify work with information visualizations created with libraries like matplotlib, seaborn, and pandas. It acts as a wrapper round your plotting calls to deal with output administration for you.

teeplot workflow in motion

Right here’s tips on how to use teeplot in 3 steps,

  1. Select Your Plotting Operate: Begin by choosing your most popular plotting operate, whether or not it’s from matplotlib, seaborn, pandas, and so forth. or one you wrote your self.
  2. Add Your Plotting Arguments: Move your plotting operate as the primary argument to tee, adopted by the arguments you need to use to your visualization.
  3. Computerized Plotting and Saving: teeplot captures your plotting operate and its arguments, executes the plot, after which takes care of wrangling the plot outputs for you.

That’s it!

Subsequent, let’s have a look at 3 temporary examples that show: a) primary use, b) customized post-processing, and c) customized plotting capabilities.

On this instance, we cross a DataFrame df’s member operate df.plot.field as our plotter and two semantic keys: “age” and “gender.” teeplot takes care of the remainder.

# tailored pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.field.html
import pandas as pd; from teeplot import teeplot as tp

age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85]
df = pd.DataFrame({"gender": record("MMMMMMMMFFFFFF"), "age": age_list})

tp.tee(df.plot.field, # plotter...
column="age", by="gender", figsize=(4, 3)) # ...forwa

teeplots/by=gender+column=age+viz=field+ext=.pdf
teeplots/by=gender+column=age+viz=field+ext=.png

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