Plotting in Python

This section focuses on Python-based visualizations for building science and indoor environmental quality data.

The examples in this library will mainly use:

matplotlib as the core plotting engine
pandas for data handling
seaborn for high-level statistical plots where helpful
plotly for interactive plots that are handy for web apps

Core ideas

1. Data as DataFrames

Most examples assume:

one row = one observation
one column = one variable
data stored as pandas DataFrames

Common preprocessing tasks before plotting:

cleaning and filtering
aggregation with groupby
reshaping via melt() and pivot()

Clear data structure simplifies plotting code.

2. matplotlib as the foundation

There are two main ways to use matplotlib:

Stateful style (quick)

import matplotlib.pyplot as plt

plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.title("Example")
plt.show()

Object-oriented style (recommended)

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("Example")
fig.tight_layout()

The object-oriented interface is more flexible and easier to reuse in real projects.

3. seaborn as a helper layer

seaborn simplifies many plot types:

distributions
regression plots
categorical comparisons

Example:

import seaborn as sns

sns.scatterplot(data=df, x="temp", y="comfort", hue="building")

seaborn uses matplotlib internally and can be customized the same way.

4. Styling and consistency

In matplotlib, visual defaults can be controlled globally:

import matplotlib as mpl

mpl.rcParams["figure.figsize"] = (8, 5)
mpl.rcParams["font.size"] = 11

Later, a centralized style file (e.g. lab_style.mplstyle) can be created and reused.

5. Exporting figures

Always specify output format and quality:

fig.savefig(
    "figures/example_plot.pdf",
    bbox_inches="tight",
    dpi=300
)

Guidelines:

use PDF/SVG for vector output
control figure size explicitly
separate source code from output figures

6. Reproducibility principles

Good practice includes:

version control (Git)
scripts or notebooks that run top-to-bottom
functions for repeated patterns
avoidance of manual tweaks

Reusable helpers (e.g. plot helpers, style functions) lead to cleaner code and consistent figures.

Where this Python section is going

Future additions may include:

reusable plotting utilities
standardized styling helpers
Python/R side-by-side examples
curated references and learning resources

The aim is to turn this into a living reference, not just static examples.