import matplotlib

import matplotlib.pyplot as plt

import pandas as pd

import squarify

dat='https://raw.githubusercontent.com/gVallverdu/cookbook/master/Evolution_et_structure_de_la_population/Evolution_structure_population.csv'

df = pd.read_csv(dat, sep=";")

df = df.set_index("libgeo")

df = df[["superf", "p11_pop"]]

df2 = df.sort_values(by="superf", ascending=False)

# treemap parameters

x = 0.

y = 0.

width = 100.

height = 100.

cmap = matplotlib.cm.viridis

# color scale on the population

# min and max values without Pau

mini, maxi = df2.drop("PAU").p11_pop.min(), df2.drop("PAU").p11_pop.max()

norm = matplotlib.colors.Normalize(vmin=mini, vmax=maxi)

colors = [cmap(norm(value)) for value in df2.p11_pop]

colors[1] = "#FBFCFE"

# labels for squares

labels = ["%s\n%d km2\n%d hab" % (label) for label in zip(df2.index, df2.superf, df2.p11_pop)]

labels[11] = "MAZERES-\nLEZONS\n%d km2\n%d hab" % (df2["superf"]["MAZERES-LEZONS"], df2["p11_pop"]["MAZERES-LEZONS"])

# make plot

fig = plt.figure(figsize=(12, 10))

fig.suptitle("Population et superficie des communes de la CAPP", fontsize=20)

ax = fig.add_subplot(111, aspect="equal")

ax = squarify.plot(df2.superf, color=colors, label=labels, ax=ax, alpha=.7)

ax.set_xticks([])

ax.set_yticks([])

ax.set_title("L'aire de chaque carré est proportionnelle à la superficie de la commune\n", fontsize=14)

# color bar

# create dummy invisible image with a color map

img = plt.imshow([df2.p11_pop], cmap=cmap)

img.set_visible(False)

fig.colorbar(img, orientation="vertical", shrink=.96)

fig.text(.76, .9, "Population", fontsize=14)

fig.text(.5, 0.1,

"Superficie totale %d km2, Population de la CAPP : %d hab" % (df2.superf.sum(), df2.p11_pop.sum()),

fontsize=14,

ha="center")

fig.text(.5, 0.07,

"Source : http://opendata.agglo-pau.fr/",

fontsize=14,

ha="center")

plt.show()

squarify.plot(

sizes=[100,13,22,35,5],

label=["group A", "group B", "group C", "group D", "group E"], alpha=.7 )

plt.axis('off')

plt.show()

df = pd.DataFrame({'nb_people':[8,3,4,2], 'group':["group A", "group B", "group C", "group D"] })

squarify.plot(sizes=df['nb_people'], label=df['group'], alpha=.8 )

plt.axis('off')

plt.show()