Using gridplot

The estate agents would like to examine how the relationship between property size and price varies across the four regions of Melbourne:

"Northern", "Western", "Eastern", and "Southern".

This is a great opportunity to use gridplot, displaying one subplot for each region!

This exercise is part of the course

Interactive Data Visualization with Bokeh

View Course

Exercise instructions

Import gridplot.
Create df by filtering melb for the desired region.
Complete the code to add circle glyphs to fig, representing x as the building area column and y as price, from source, and legend_label as region.
Display the subplots in a grid using two columns.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import gridplot
from ____.____ import ____
plots = []

# Complete for loop to create plots
for region in ["Northern", "Western", "Southern", "Eastern"]:
  df = melb.loc[melb["region"] == ____]
  source = ColumnDataSource(data=df)
  fig = figure(x_axis_label="Building Area (Meters Squared)", y_axis_label="Price")
  fig.circle(x="____", y="____", source=____, legend_label=____)
  fig.yaxis[0].formatter = NumeralTickFormatter(format="$0a")
  plots.append(fig)

# Display plot
output_file(filename="gridplot.html")
show(____(____, ncols=____))

Edit and Run Code