Regression Plot with Confidence Interval in Python + Matplotlib + Seaborn

plot_regression_with_confidence_interval.py

import matplotlib.pyplot as plt
import seaborn as sns


def parse_points(s):
    return map(lambda x: map(int, x.strip().split(",")), filter(len, s.split("\n")))


def get_xy(points):
    x = []
    y = []
    
    for i in points:
        t = list(i)
        x.append(t[0])
        y.append(t[1])
        
    return x, y


a = """
    1,10
    2,20
    3,30
"""

b = """
    1,50
    2,60
    3,70
"""

xa, ya = get_xy(parse_points(a))
xb, yb = get_xy(parse_points(b))

fig, axs = plt.subplots(figsize=(15, 10))

plt.title("A Compare (CI=95%)")
plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")

sns.regplot(x=xa, y=ya, color='blue', ax=axs, label="A")
sns.regplot(x=xb, y=yb, color='green', ax=axs, label="B")

plt.legend()