# Defining colors palette
np.random.seed(42)
df_plot = df[['month', 'day_str', 'PJME_MW', 'day']].dropna().groupby(['day_str', 'month', 'day']).mean()[['PJME_MW']].reset_index()
df_plot = df_plot.sort_values(by='day', ascending=True)

months = df_plot['month'].unique()
colors = np.random.choice(list(mpl.colors.XKCD_COLORS.keys()), len(months), replace=False)

# Plot
plt.figure(figsize=(16,12))
for i, y in enumerate(months):
    if i > 0:        
        plt.plot('day_str', 'PJME_MW', data=df_plot[df_plot['month'] == y], color=colors[i], label=y)
        if y == 2018:
            plt.text(df_plot.loc[df_plot.month==y, :].shape[0]-.9, df_plot.loc[df_plot.month==y, 'PJME_MW'][-1:].values[0], y, fontsize=12, color=colors[i])
        else:
            plt.text(df_plot.loc[df_plot.month==y, :].shape[0]-.9, df_plot.loc[df_plot.month==y, 'PJME_MW'][-1:].values[0], y, fontsize=12, color=colors[i])

# Setting Labels
plt.gca().set(ylabel= 'PJME_MW', xlabel = 'Month')
plt.yticks(fontsize=12, alpha=.7)
plt.title("Seasonal Plot - Weekly Consumption", fontsize=20)
plt.ylabel('Consumption [MW]')
plt.xlabel('Month')
plt.show()