bqplot is a jupyter interactive widget library bringing d3.js visualization to the Jupyter notebook.
bqplot implements the abstractions of Wilkinson’s “The Grammar of Graphics” as interactive Jupyter widgets.
bqplot provides both
Installation:
conda install -c conda-forge bqplot
In [1]:
from __future__ import print_function
from IPython.display import display
from ipywidgets import *
from traitlets import *
import numpy as np
import pandas as pd
import bqplot as bq
import datetime as dt
In [2]:
np.random.seed(0)
size = 100
y_data = np.cumsum(np.random.randn(size) * 100.0)
y_data_2 = np.cumsum(np.random.randn(size))
y_data_3 = np.cumsum(np.random.randn(size) * 100.)
x = np.linspace(0.0, 10.0, size)
price_data = pd.DataFrame(np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.8], [0.8, 1.0]]), axis=0) + 100,
columns=['Security 1', 'Security 2'],
index=pd.date_range(start='01-01-2007', periods=150))
symbol = 'Security 1'
dates_all = price_data.index.values
final_prices = price_data[symbol].values.flatten()
In [3]:
from bqplot import pyplot as plt
In [4]:
plt.figure(1)
n = 100
plt.plot(np.linspace(0.0, 10.0, n), np.cumsum(np.random.randn(n)),
axes_options={'y': {'grid_lines': 'dashed'}})
plt.show()
In [5]:
plt.figure(title='Scatter Plot with colors')
plt.scatter(y_data_2, y_data_3, color=y_data)
plt.show()
In [6]:
plt.figure()
plt.hist(y_data, colors=['OrangeRed'])
plt.show()
In [7]:
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)
display(fig)
In [8]:
# update data of the line mark
line.y = np.cumsum(np.random.randn(2, 100), axis=1)
In [9]:
xs = bq.LinearScale()
ys = bq.LinearScale()
x, y = np.random.rand(2, 20)
scatt = bq.Scatter(x=x, y=y, scales={'x': xs, 'y': ys}, default_colors=['blue'])
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=[scatt], axes=[xax, yax], animation_duration=1000)
display(fig)
In [10]:
#data updates
scatt.x = np.random.rand(20) * 10
scatt.y = np.random.rand(20)
In [11]:
xs.min = 4
In [12]:
xs.min = None
In [13]:
xax.label = 'Some label for the x axis'
In [14]:
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')
In [15]:
def interval_change_callback(change):
db.value = str(change['new'])
intsel = bq.interacts.FastIntervalSelector(scale=xs, marks=[line])
intsel.observe(interval_change_callback, names=['selected'] )
db = widgets.Label()
db.value = str(intsel.selected)
display(db)
In [16]:
fig = bq.Figure(marks=[line], axes=[xax, yax], animation_duration=1000, interaction=intsel)
display(fig)
In [17]:
line.selected
Out[17]:
In [18]:
handdraw = bq.interacts.HandDraw(lines=line)
fig.interaction = handdraw
In [19]:
line.y[0]
Out[19]:
In [20]:
from bqplot import *
size = 100
np.random.seed(0)
x_data = range(size)
y_data = np.cumsum(np.random.randn(size) * 100.0)
## Enabling moving of points in scatter. Try to click and drag any of the points in the scatter and
## notice the line representing the mean of the data update
sc_x = LinearScale()
sc_y = LinearScale()
scat = Scatter(x=x_data[:10], y=y_data[:10], scales={'x': sc_x, 'y': sc_y}, default_colors=['blue'],
enable_move=True)
lin = Lines(scales={'x': sc_x, 'y': sc_y}, stroke_width=4, line_style='dashed', colors=['orange'])
m = Label(value='Mean is %s'%np.mean(scat.y))
def update_line(change):
with lin.hold_sync():
lin.x = [np.min(scat.x), np.max(scat.x)]
lin.y = [np.mean(scat.y), np.mean(scat.y)]
m.value='Mean is %s'%np.mean(scat.y)
update_line(None)
# update line on change of x or y of scatter
scat.observe(update_line, names='x')
scat.observe(update_line, names='y')
ax_x = Axis(scale=sc_x)
ax_y = Axis(scale=sc_y, tick_format='0.2f', orientation='vertical')
fig = Figure(marks=[scat, lin], axes=[ax_x, ax_y])
## In this case on drag, the line updates as you move the points.
with scat.hold_sync():
scat.enable_move = True
scat.update_on_move = True
scat.enable_add = False
display(m, fig)