Наложить два графика друг на друга в seaborn

Есть два источника данных log и log1 в формате json. По этим данным требуется построить графики, наложив один на другой. Возможно ли такое реализовать в seaborn ? Код:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn

d = {
"log": [
    {
        "platform_time" : "2022-06-20 00:00:03.055",
        "value" : 1.598E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:04.859",
        "value" : 1.5987E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:04.980",
        "value" : 1.5988E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:09.391",
        "value" : 1.5989E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:09.593",
        "value" : 1.5987E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:12.193",
        "value" : 1.5988E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:12.203",
        "value" : 1.5989E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:14.130",
        "value" : 1.599E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:14.140",
        "value" : 1.5991E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:19.181",
        "value" : 1.5991E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:19.185",
        "value" : 1.5992E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:19.196",
        "value" : 1.5993E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:25.066",
        "value" : 1.5993E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:25.115",
        "value" : 1.5994E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:25.123",
        "value" : 1.5993E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:25.129",
        "value" : 1.5995E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:25.408",
        "value" : 1.5987E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:25.415",
        "value" : 1.5988E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:25.419",
        "value" : 1.5989E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:25.430",
        "value" : 1.599E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:29.381",
        "value" : 1.5991E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:29.392",
        "value" : 1.5992E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:32.292",
        "value" : 1.5993E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:32.294",
        "value" : 1.5992E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:32.294",
        "value" : 1.5993E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:32.303",
        "value" : 1.5994E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:33.535",
        "value" : 1.5995E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:33.546",
        "value" : 1.5996E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:34.276",
        "value" : 1.5994E-4
    },
    {
        "platform_time" : "2022-06-20 00:00:34.438",
        "value" : 1.5995E-4
    }
]}


n = {
"log1": [
    {
        "platform_time" : "2022-06-20 00:00:00.699",
        "value" : 2.357E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:05.756",
        "value" : 2.357E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:09.366",
        "value" : 2.358E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:10.427",
        "value" : 2.357E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:11.414",
        "value" : 2.358E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:11.571",
        "value" : 2.357E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:12.172",
        "value" : 2.358E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:12.209",
        "value" : 2.357E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:12.220",
        "value" : 2.358E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:13.872",
        "value" : 2.357E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:17.257",
        "value" : 2.358E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:20.639",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:25.162",
        "value" : 2.358E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:25.172",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:30.268",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:33.619",
        "value" : 2.36E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:34.273",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:39.371",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:44.375",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:46.978",
        "value" : 2.36E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:47.036",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:47.082",
        "value" : 2.36E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:47.099",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:47.147",
        "value" : 2.36E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:49.991",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:55.080",
        "value" : 2.359E-5
    },
    {
        "platform_time" : "2022-06-20 00:00:58.574",
        "value" : 2.358E-5
    },
    {
        "platform_time" : "2022-06-20 00:01:00.901",
        "value" : 2.357E-5
    },
    {
        "platform_time" : "2022-06-20 00:01:02.158",
        "value" : 2.356E-5
    },
    {
        "platform_time" : "2022-06-20 00:01:02.183",
        "value" : 2.357E-5
    }
]}


df = pd.DataFrame(d['log'])
sn.set()
p = sn.lineplot(x='platform_time',y='value', data=df )
p.set_xlabel('platform_time', fontsize = 8)
p.set_ylabel('value', fontsize = 8)
p.tick_params(labelsize=5)
plt.show()