shading the timeseries plot in python

The shaded area around the lines represent the 95% confidence interval. In order to have this area you should have more than one observation for each time point, so it is possible to compute a standard deviation and a CI for each time point. But in data you provided there are only one observation for each time point.
You can do draw an similar plot, by computing the standard deviation and sum and subtract it from the columns you want to plot. Pay attention! This is not the confidence interval (for which you need more observation), it's an interval 2 times standard deviation wide around mean value for each time point. Moreover it keeps constant width along time axis.
I honestly doubt this is a useful plot to use, since area width it's constant during time and area width it's 2 times the standard deviation, which is computed along time. In short: you shouldn't use this plot and provide more observations for each time point in order to compute a proper confidence interval.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv(r"data/data.csv", usecols = ['Hour', 'Forecast'], header = 0)

X1 = df.Forecast
mu = X1.mean()
sigma = X1.std()

timestep = df.Hour

X1_plus_sigma = X1 + sigma
X1_minus_sigma = X1 - sigma

plt.plot(timestep, X1, color = 'blue')
plt.fill_between(timestep, X1_plus_sigma, X1_minus_sigma, alpha = 0.2, color = 'blue')

plt.show()