1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
|
"""
Polynomial Fit Plot with Regression Transform
=============================================
This example shows how to overlay data with multiple fitted polynomials using
the regression transform.
"""
# category: uncertainties and trends
import numpy as np
import pandas as pd
import altair as alt
# Generate some random data
rng = np.random.RandomState(1)
x = rng.rand(40) ** 2
y = 10 - 1.0 / (x + 0.1) + rng.randn(40)
source = pd.DataFrame({"x": x, "y": y})
# Define the degree of the polynomial fits
degree_list = [1, 3, 5]
base = alt.Chart(source).mark_circle(color="black").encode(
alt.X("x"), alt.Y("y")
)
polynomial_fit = [
base.transform_regression(
"x", "y", method="poly", order=order, as_=["x", str(order)]
)
.mark_line()
.transform_fold([str(order)], as_=["degree", "y"])
.encode(alt.Color("degree:N"))
for order in degree_list
]
alt.layer(base, *polynomial_fit)
|
