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!

Este exercício faz parte do curso

Interactive Data Visualization with Bokeh

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Instruções do exercício

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.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# 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=____))

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