import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


color_dict = {"Norway": "#2B314D", "Denmark": "#A54836", "Sweden": "#5375D4" }

xy_ticklabel_color, grand_totals_color, grid_color, datalabels_color ='#757C85',"#101628", "#E9ECED", "#3F4452"

data = {
    "year": [2004, 2022, 2004, 2022, 2004, 2022],
    "countries" : [ "Denmark", "Denmark", "Norway", "Norway","Sweden", "Sweden"],
    "sites": [4,10,5,8,13,15]
}
df= pd.DataFrame(data)


df['pct_change'] = df.groupby('countries', sort=False)['sites'].apply(
     lambda x: x.pct_change()).to_numpy()*100

df['sub_total'] = df.groupby('countries')['sites'].transform('sum')

df['diff'] = df.groupby(['countries'])['sites'].diff()
#map the colors of a dict to a dataframe
df['color']= df.countries.map(color_dict)
df

fig, ax = plt.subplots( figsize=(5,5),facecolor = "#FFFFFF", zorder= 1)
fig.tight_layout(pad=6.0)

for row in df.iloc[1::2].itertuples():

    ax.scatter(
        row.diff, 
        row.pct_change, 
        color = row.color,
        zorder = 2)

    offset = .2
    ax.annotate(
        row.countries, 
        (row.diff + offset, row.pct_change), 
        ha= "left", 
        va="center", 
        color= datalabels_color
        )

label_style = dict(
    size = 10, 
    color = xy_ticklabel_color)

ax.set_xlabel("Absolute change", **label_style)
ax.set_ylabel("Percentual change", **label_style)

ax.tick_params(
    axis='both', 
    which='major',
    length=0, 
    labelsize=8,
    colors =xy_ticklabel_color,
    pad=12
    )

ax.grid(color = grid_color)

ax.set(xlim = (0,8), ylim = (0,2))

y_ticks = np.arange(0, 250, 50)
ax.yaxis.set_ticks(ticks, labels = [f'{tick}%' for tick in ticks])
x_ticks = np.arange(0, 10, 2)
ax.xaxis.set_ticks(x_ticks, labels = x_ticks)

for axis in ['top', 'bottom', 'left', 'right']:
    ax.spines[axis].set_color(grid_color) 
    ax.spines[axis].set_zorder(0)

fig