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import numpy as np
from bqplot import (OrdinalScale, LinearScale, Bars,
Figure, Axis, ColorScale, ColorAxis, CATEGORY10)
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size = 100
np.random.seed(0)
x_data = range(size)
y_data = np.random.randn(size)
y_data_2 = np.random.randn(size)
y_data_3 = np.cumsum(np.random.randn(size) * 100.)
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x_ord = OrdinalScale()
y_sc = LinearScale()
bar = Bars(x=np.arange(10), y=np.random.rand(10), scales={'x': x_ord, 'y': y_sc})
ax_x = Axis(scale=x_ord)
ax_y = Axis(scale=y_sc, tick_format='0.2f', orientation='vertical')
Figure(marks=[bar], axes=[ax_x, ax_y], padding_x=0.025, padding_y=0.025)
To generate a horizontal bar chart, pass orientation='horizontal' to the bar.
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x_ord = OrdinalScale()
y_sc = LinearScale()
bar = Bars(x=np.arange(10), y=np.arange(-5, 5), scales={'x': x_ord, 'y': y_sc},
orientation='horizontal')
ax_x = Axis(scale=x_ord, orientation='vertical')
ax_y = Axis(scale=y_sc, tick_format='0.2f')
Figure(marks=[bar], axes=[ax_x, ax_y], padding_x=0.025, padding_y=0.025)
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x_ord = LinearScale()
y_sc = LinearScale()
bar = Bars(x=x_data[:20], y=np.abs(y_data_2[:20]), scales={'x': x_ord, 'y': y_sc}, base=1.0)
ax_x = Axis(scale=x_ord)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
Figure(marks=[bar], axes=[ax_x, ax_y])
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# changing the base
bar.base = 2.0
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bar.align = 'right'
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# Increasing the spacing between the bars
x_ord = OrdinalScale()
y_sc = LinearScale()
bar = Bars(x=x_data[:20], y=y_data[:20], scales={'x': x_ord, 'y': y_sc}, padding=0.2)
ax_x = Axis(scale=x_ord)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
Figure(marks=[bar], axes=[ax_x, ax_y])
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# add bar labels
bar.label_display = True
bar.label_display_format = '.1f'
bar.label_font_style = {'fill': 'pink', 'font-size': '10px'}
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# changing basic properties like stroke and opacity
bar.stroke = 'orange'
bar.opacities = [0.5, 0.2]
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bar.orientation = 'horizontal'
ax_x.orientation = 'vertical'
ax_y.orientation = 'horizontal'
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x_ord = OrdinalScale()
y_sc = LinearScale()
bar = Bars(x=x_data, y=[y_data[:20], y_data_2[:20]],
scales={'x': x_ord, 'y': y_sc}, padding=0.2,
colors=CATEGORY10)
ax_x = Axis(scale=x_ord)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
Figure(marks=[bar], axes=[ax_x, ax_y])
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bar.type = 'grouped' # equivalent to saying
# bar = Bars(x=x_data, y=y_data, scales={'x': x_ord, 'y': y_sc}, padding=0.2, type='grouped')
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x_ord = OrdinalScale()
y_sc = LinearScale()
bar = Bars(x=x_data, y=[y_data[:20], y_data_2[:20]],
scales={'x': x_ord, 'y': y_sc}, padding=0.2,
colors=CATEGORY10, type='stacked', orientation='horizontal')
ax_x = Axis(scale=x_ord, orientation='vertical')
ax_y = Axis(scale=y_sc, tick_format='0.2f')
Figure(marks=[bar], axes=[ax_x, ax_y])
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bar.type = 'grouped'
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## Color mode has 3 values. 'group', 'element' and 'no_group'.
## 'group' means colors are assigned per group.
## 'element' means colors are assigned per group element.
## 'no_group' means means colors are assigned per bar, discarding the fact that there are groups or stacks
x_ord = OrdinalScale()
y_sc = LinearScale()
bar = Bars(x=x_data, y=[y_data[:20], y_data_2[:20]],
scales={'x': x_ord, 'y': y_sc}, padding=0.2,
colors=CATEGORY10, color_mode='group')
ax_x = Axis(scale=x_ord)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
Figure(marks=[bar], axes=[ax_x, ax_y])
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bar.color_mode = 'element'
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bar.color_mode = 'no_group'
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## for 1-d array for Y.
x_ord = OrdinalScale()
y_sc = LinearScale()
bar = Bars(x=x_data, y=y_data[:20], scales={'x': x_ord, 'y': y_sc}, padding=0.2,
color_mode='element', labels=['Values'],
display_legend=True)
ax_x = Axis(scale=x_ord)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
Figure(marks=[bar], axes=[ax_x, ax_y])
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# In this example, the color is just the magnitude of the y data
x_ord = OrdinalScale()
y_sc = LinearScale()
col_sc = ColorScale(scheme='Reds')
bar = Bars(x=x_data[:20], y=y_data[:20], color=np.abs(y_data[:20]),
scales={'x': x_ord, 'y': y_sc, 'color': col_sc}, padding=0.2)
ax_x = Axis(scale=x_ord)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
ax_c = ColorAxis(scale=col_sc, tick_format='0.2f')
margin = dict(top=50, bottom=80, left=50, right=50)
Figure(marks=[bar], axes=[ax_x, ax_y, ax_c], fig_margin=margin)
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# By default color is applied along the axis=1
x_ord = OrdinalScale()
y_sc = LinearScale()
col_sc = ColorScale(scheme='Reds')
y_vals = [y_data[:20], y_data_2[:20], y_data_3[:20] / 100.0]
color_data = np.mean(y_vals, axis=1)
bar = Bars(x=x_data, y=y_vals, color=color_data,
scales={'x': x_ord, 'y': y_sc, 'color': col_sc}, padding=0.2,
labels=['Dim 1', 'Dim 2', 'Dim 3'], display_legend=True)
ax_x = Axis(scale=x_ord)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
ax_c = ColorAxis(scale=col_sc, tick_format='0.2f')
margin = dict(top=50, bottom=80, left=50, right=50)
Figure(marks=[bar], axes=[ax_x, ax_y, ax_c], fig_margin=margin)
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# Applying color along the axis=0
x_ord = OrdinalScale()
y_sc = LinearScale()
col_sc = ColorScale(mid=0.0)
y_vals = [y_data[:20], y_data_2[:20], y_data_3[:20] / 100.0]
color_data = np.mean(y_vals, axis=0)
bar = Bars(x=x_data, y=y_vals, color=color_data,
scales={'x': x_ord, 'y': y_sc, 'color': col_sc},
padding=0.2, color_mode='group', stroke='orange')
ax_x = Axis(scale=x_ord)
ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')
ax_c = ColorAxis(scale=col_sc, tick_format='0.1f')
margin = dict(top=50, bottom=80, left=50, right=50)
Figure(marks=[bar], axes=[ax_x, ax_y, ax_c], fig_margin=margin)