Pyplot

pyplot is a context based functional API offering meaningful defaults. It's a concise API and very similar to matplotlib's pyplot. Users new to bqplot should use pyplot as a starting point. Users create figure and mark objects using pyplot functions.

Steps for building plots in pyplot:

Create a figure object using plt.figure()

(Optional steps)
- Scales can be customized using plt.scales function
- Axes options can customized by passing a dict to axes_options argument in the marks' functions
Create marks using pyplot functions like plot, bar, scatter etc. (All the marks created will be automatically added to the figure object created in step 1)
Render the figure object using the following approaches:
- Using plt.show function which renders the figure in the current context along with toolbar for panzoom etc.
- Using display on the figure object created in step 1 (toolbar doesn't show up in this case)

pyplot also offers many helper functions. A few are listed here:

plt.xlim: sets the domain bounds of the current 'x' scale
plt.ylim: sets the domain bounds of the current 'y' scale
plt.grids: shows/hides the axis grid lines
plt.xlabel: sets the X-Axis label
plt.ylabel: sets the Y-Axis label
plt.hline: draws a horizontal line at a specified level
plt.vline: draws a vertical line at a specified level

Let's look at the same examples which were created in the Object Model Notebook



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import bqplot.pyplot as plt



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# first, let's create two vectors x and y to plot using a Lines mark
import numpy as np
x = np.linspace(-10, 10, 100)
y = np.sin(x)

# 1. Create the figure object
fig = plt.figure(title='Simple Line Chart')

# 2. By default axes are created with basic defaults. If you want to customize the axes create 
# a dict and pass it to `axxes_options` argument in the marks
axes_opts = {'x': {'label': 'X'}, 
             'y': {'label': 'Y'}}

# 3. Create a Lines mark by calling plt.plot function
line = plt.plot(x=x, y=y, axes_options=axes_opts) # note that custom axes options are passed here

# 4. Render the figure using plt.show()
plt.show()

For creating other marks (like scatter, pie, bars, etc.), only step 2 needs to be changed. Lets look a simple example to create a bar chart:



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# first, let's create two vectors x and y to plot a bar chart
x = list('ABCDE')
y = np.random.rand(5)

# 1. Create the figure object
fig = plt.figure(title='Simple Bar Chart')

# 2. Customize the axes options
axes_opts = {'x': {'label': 'X', 'grid_lines': 'none'},
             'y': {'label': 'Y', 'tick_format': '.0%'}}

# 3. Create a Bars mark by calling plt.bar function
bar = plt.bar(x=x, y=y, padding=.2, axes_options=axes_opts)

# 4. directly display the figure object created in step 1 (note that the toolbar no longer shows up)
fig

Mutiple marks can be rendered in a figure. It's as easy as creating marks one after another. They'll all be added to the same figure!



In [ ]:

    
# first, let's create two vectors x and y
import numpy as np
x = np.linspace(-10, 10, 25)
y = 3 * x + 5
y_noise = y + 10 * np.random.randn(25) # add some random noise to y

# 1. Create the figure object
fig = plt.figure(title='Scatter and Line')

# 3. Create line and scatter marks
# additional attributes (stroke_width, colors etc.) can be passed as attributes to the mark objects as needed
line = plt.plot(x=x, y=y, colors=['green'], stroke_width=3)
scatter = plt.scatter(x=x, y=y_noise, colors=['red'], stroke='black')

# setting x and y axis labels using pyplot functions. Note that these functions 
# should be called only after creating the marks
plt.xlabel('X')
plt.ylabel('Y')

# 4. render the figure
fig

pyplot is a simpler and an intuitive API. It's available for all the marks except MarketMap. It should be used in almost all the cases by default since it offers a less verbose API compared to the Object Model. Please refer to the mark examples using pyplot and also this pyplot example notebook