How do I deal with non-stationery data for time series analysis (autocorrelation function (ACF) and partial autocorrelation function (PACF)

Question

I am trying to plot the autocorrelation function (ACF) and partial autocorrelation function (PACF) for 30 lags of 'Nile'

import statsmodels.api as sm
import matplotlib.pyplot as plt

nile = sm.datasets.get_rdataset("Nile").data

from statsmodels.tsa.stattools import adfuller

def check_stationarity(series):
# Copied from https://machinelearningmastery.com/time-series-data-stationary-python/

    result = adfuller(series.values)

    print('ADF Statistic: %f' % result[0])
    print('p-value: %f' % result[1])
    print('Critical Values:')
    for key, value in result[4].items():
        print('\t%s: %.3f' % (key, value))

    if (result[1] <= 0.05) & (result[4]['5%'] > result[0]):
        print("\u001b[32mStationary\u001b[0m")
    else:
        print("\x1b[31mNon-stationary\x1b[0m")
    
    check_stationarity(nile['time'])

ADF Statistic: 0.257617 p-value: 0.975314 Critical Values: 1%: -3.505 5%: -2.894 10%: -2.584

Non-stationary

Given that it is non-stationary, what should I do and to plot the autocorrelation function (ACF) and partial autocorrelation function (PACF) for 30 lags