Plotly basic usage

This notebook demonstrates basic Plotly usage step-by-step. We install dependencies, create a small dataset, make a few plots, and save everything to disk for later inspection.

plotly’s API is similar but not identical to seaborn. For example, both are based on DataFrames, but what seaborn calls hue is color in plotly.

import pandas as pd
import plotly.express as px

Generate a small demo DataFrame with time series data and some categories.

df = pd.read_csv("data/blobs_measurements.csv")
df.head()

	Unnamed: 0	label	area	mean_intensity	max_intensity	min_intensity	centroid-0	centroid-1	eccentricity	solidity	perimeter	centroid_x	centroid_y
0	0	1	27.0	149.333333	168.0	128.0	26.259259	0.666667	0.973991	0.900000	20.621320	0.666667	26.259259
1	1	2	203.0	199.960591	240.0	128.0	225.876847	3.955665	0.892348	0.966667	59.591883	3.955665	225.876847
2	2	3	503.0	198.695825	248.0	128.0	97.487078	7.836978	0.801503	0.971042	86.870058	7.836978	97.487078
3	3	4	264.0	189.939394	232.0	120.0	139.545455	7.159091	0.605384	0.977778	58.041631	7.159091	139.545455
4	4	5	96.0	166.416667	200.0	128.0	44.812500	7.833333	0.628475	0.960000	34.142136	7.833333	44.812500

Create a scatter plot colored by category and sized by the “size” column.

px.scatter(
    df,
    x="area",
    y="eccentricity",
    color="perimeter"
)

Violin plots allow us to split the data into groups and visualize their distribution. For example, we can split objects into small and large groups and then visualize their perimeter.

df["size"] = ["large" if s else "small" for s in df["area"] > df["area"].median()]

px.violin(
    df,
    x="size",
    y="perimeter"
)

Exercise

Visualize area, perimeter, solidity and eccentricity. What is eccentricity most related to?