If your data represents series of variables, it can be useful to visualize the evolution the variables through point connections.
::: info Connected scatterplots are most commonly used for temporal or timeseries data. :::
By default, points are plotted linearly along the x and y coordinate.
import datetime
import jscatter
import math
import numpy as np
import pandas as pd
def get_web_timestamp(month):
return math.floor(
datetime.datetime(2023, month, 1).timestamp() * 1000
)
dates = [get_web_timestamp(month) for month in range(1, 13)]
df = DataFrame({
'date': dates * 3,
'value': np.concatenate(
(
np.random.rand(12),
np.random.rand(12) + 0.5,
np.random.rand(12) + 1
),
axis=0
),
'group': ['A'] * 12 + ['B'] * 12 + ['C'] * 12,
})
scatter = jscatter.Scatter(
data=df,
x='date',
x_scale='time',
y='value',
connect_by='group'
)
scatter.show()::: tip
By default, line connections are spline interpolated curves. If you instead want
straight lines, call scatter.options({ 'pointConnectionTolerance': 1 }).
:::
Similar to how you can visually style and encode the point color, opacity, and size, you can visually style and encode the color, opacity, and size of the point connections.
Since one might often want to use the point color as the line color, the
connection color can be set to 'inherit' in order to inherit the color
encoding.
scatter.color(by='group')
scatter.size(10)
scatter.opacity(1)
scatter.connection_color('inherit')
scatter.connection_size(5)
scatter.connection_opacity(0.5)
In additional, it's also possible to color the point connections by line segments to visually distinguish the start and end of connected points.
scatter.color(by='date', map='coolwarm')
scatter.connection_color(by='segment', map='coolwarm')