How to plot lineplot with x axis value starting from some non zero value

I am trying to generate a seasonality plot with each line corresponding to a year (i.e. lines for 2018, 2019, ...)
the x-axis values would be the week number of the year (ranging from 1 - 52).
However, I would like to start the plot at some offset e.g. x-axis values = 30, 31, ... 52, 1, 2, ..., 29

I tried to convert x axis to categorical but there would always be a gap in the lineplot between week 52 to week 1.

import pandas as pd
import numpy as np
def generate_sample_values(param1, param2, startendpoint):
    if startendpoint < 1 or startendpoint > 53:
        raise ValueError
    lst = [(x/param1)+np.random.randint(param2) for x in range(1,startendpoint)]
    lst2 = lst[:53-startendpoint][::-1]
    return lst + lst2

ls = generate_sample_values(1, 10, 30)

df = pd.DataFrame({"week":[x for x in range(1,53)], 
              "2019":generate_sample_values(1,10,30),
              "2020":generate_sample_values(0.5,10,30), 
              "2021":generate_sample_values(2,15,30),
              "2022":generate_sample_values(3,17,30)
             }).set_index("week")
px.line(df, x =df.index, y= df.columns)

in the above, i would instead like to have x axis to start at 30, 31, ... 52, 1, 2, ..., 29

To do this, first rearrange the data as per your need... start from index 30 till end and then concatenate the first 29 at the end of the dataframe. Then, you need to convert the index (week number) to string and plot it. The updated code is shown below...

def generate_sample_values(param1, param2, startendpoint):
    if startendpoint < 1 or startendpoint > 53:
        raise ValueError
    lst = [(x/param1)+np.random.randint(param2) for x in range(1,startendpoint)]
    lst2 = lst[:53-startendpoint][::-1]
    return lst + lst2

ls = generate_sample_values(1, 10, 30)

df = pd.DataFrame({"week":[x for x in range(1,53)], 
              "2019":generate_sample_values(1,10,30),
              "2020":generate_sample_values(0.5,10,30), 
              "2021":generate_sample_values(2,15,30),
              "2022":generate_sample_values(3,17,30)
             }).set_index("week")

df=pd.concat([df.iloc[29:], df.iloc[:29]])
df.index = df.index.map(str)

fig=px.line(df, x =df.index, y= df.columns)
fig.update_xaxes(tickangle=0)

This will give you the below plot...

Now, if you dont like the labels and find them as too many, you can adjust how far you want them by adding the code at the bottom...

fig.update_layout(
    xaxis = dict(tickmode = 'linear', tick0 = 30, dtick = 5)
)

...which will give you the plot below - start at 30 and show every 5th tick label.