Links: notebook, html, PDF, python, slides, GitHub

This library is well integrated with Jupyter and will probably stick for a long time. It mixes Python and Javascript. One drawback: you need to run the notebook everytime to get the graph, they don’t stay because Jupyter server is sending them, they don’t appear in the output.

source installation tutorial gallerie

%matplotlib inline
from jyquickhelper import add_notebook_menu


Le module est installé d’abord avec l’instruction pip install bqplot puis avec l’instruction jupyter nbextension enable --py bqplot. Sans cela, le notebook semble bloqué dans l’exécution du code utilisant ce module.

import bqplot

Premiers exemples de la documentation

from bqplot import pyplot as plt
import numpy as np
n = 200
x = np.linspace(0.0, 10.0, n)
y = np.cumsum(np.random.randn(n))
plt.plot(x,y, axes_options={'y': {'grid_lines': 'dashed'}})
import numpy as np
from IPython.display import display
import bqplot as bq

size = 20

x_data = np.arange(size)

x_ord = bq.OrdinalScale()
y_sc = bq.LinearScale()

bar = bq.Bars(x=x_data, y=np.random.randn(2, size), scales={'x': x_ord, 'y': y_sc},
line = bq.Lines(x=x_data, y=np.random.randn(size), scales={'x': x_ord, 'y': y_sc},
                stroke_width=3, colors=['red'], display_legend=True, labels=['Line chart'])

ax_x = bq.Axis(scale=x_ord)
ax_y = bq.Axis(scale=y_sc, orientation='vertical', tick_format='0.2f', grid_lines='solid')

fig = bq.Figure(marks=[bar, line], axes=[ax_x, ax_y])


La seconde cellule définit le graphe. La troisième met à jour le graphique avec une nouvelle série. La mise à jour est progressive.

import numpy as np
import bqplot as bq
from IPython.display import display
xs = bq.LinearScale()
ys = bq.LinearScale()
x = np.arange(100)
y = np.cumsum(np.random.randn(2, 100), axis=1) #two random walks

line = bq.Lines(x=x, y=y, scales={'x': xs, 'y': ys}, colors=['red', 'green'])
xax = bq.Axis(scale=xs, label='x', grid_lines='solid')
yax = bq.Axis(scale=ys, orientation='vertical', tick_format='0.2f', label='y', grid_lines='solid')

fig = bq.Figure(marks=[line], axes=[xax, yax], animation_duration=1000)
#update data of the line mark
line.y = np.cumsum(np.random.randn(2, 100), axis=1)


from IPython.display import display
from bqplot import (Figure, Map, Mercator, Orthographic, ColorScale, ColorAxis,
                    AlbersUSA, topo_load, Tooltip)
from pyquickhelper.filehelper import get_url_content_timeout
url = ""
sc_geo = Mercator()
x = Map(scales={'projection': sc_geo})
fig = Figure(marks=[x], title='Basic Map Example')
sc_geo = Mercator()
sc_c1 = ColorScale(scheme='YlOrRd')

map_styles = {'color': {643: 105., 4: 21., 398: 23., 156: 42., 124:78., 76: 98.},
              'scales': {'projection': sc_geo, 'color': sc_c1}}

axis = ColorAxis(scale=sc_c1)

if False:
        x = Map(map_data=topo_load('map_data/WorldMap.json'), **map_styles)
    except Exception as e:
        import os
        maps = os.path.abspath('WorldMap.json')
        get_url_content_timeout(url + 'WorldMap.json', output=maps)
        tl = topo_load(os.path.abspath("WorldMap.json"))
        x = Map(map_data=tl, **map_styles)

fig = Figure(marks=[x], axes=[axis],title='Choropleth Example')

On peut sélectionner des zones de la carte suivante. Je recommande l’usage de Firefox ou Chrome.

def_tt = Tooltip(fields=['id', 'name'])
map_mark = Map(scales={'projection': Mercator()}, tooltip=def_tt)
map_mark.interactions = {'click': 'select', 'hover': 'tooltip'}
fig = Figure(marks=[map_mark], title='Interactions Example')


from bqplot import *
from IPython.display import display
import numpy as np
import pandas as pd
from ipywidgets import *
x_sc = LinearScale()
y_sc = LinearScale()

x_data = np.arange(20)
y_data = np.random.randn(20)

dd = Dropdown(options=['First', 'Second', 'Third', 'Fourth'])
scatter_chart = Scatter(x=x_data, y=y_data, scales= {'x': x_sc, 'y': y_sc}, default_colors=['dodgerblue'],
                       names=np.arange(100, 200), names_unique=False, display_names=False, display_legend=True,
ins = Button(icon='fa-legal')
scatter_chart.tooltip = ins

scatter_chart2 = Scatter(x=x_data, y=np.random.randn(20),
                         scales= {'x': x_sc, 'y': y_sc}, default_colors=['orangered'],
                         tooltip=dd, names=np.arange(100, 200), names_unique=False, display_names=False,
                         display_legend=True, labels=['Red'])

ax_x = Axis(scale=x_sc)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')

fig = Figure(marks=[scatter_chart, scatter_chart2], axes=[ax_x, ax_y])

La cellule suivante attrape les événements générées par la souris et affiche ce qu’elle attrape.

def print_event(self, target):

# Adding call back to scatter events
# print custom mssg on hover and background click of Blue Scatter

# print custom mssg on click of an element or legend of Red Scatter

On attrape un événement pour afficher un graphe plus complexe.

# Adding figure as tooltip
x_sc = LinearScale()
y_sc = LinearScale()

x_data = np.arange(10)
y_data = np.random.randn(10)

lc = Lines(x=x_data, y=y_data, scales={'x': x_sc, 'y':y_sc})
ax_x = Axis(scale=x_sc)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
tooltip_fig = Figure(marks=[lc], axes=[ax_x, ax_y], min_height=400, min_width=400)

scatter_chart.tooltip = tooltip_fig
# Changing interaction from hover to click for tooltip
scatter_chart.interactions = {'click': 'tooltip'}