Advanced Visualizations¶

▶️ Run the code cell below to import packages used in today's exercise.

In [1]:

import pandas as pd
import numpy as np
import plotly
import plotly.express as px
import plotly.graph_objects as go
import base64
import unittest

# plotly.io is a low-level interface for interacting with figures/
# plotly.io.templates lists available plotly templates
# https://plotly.com/python-api-reference/plotly.io.html
import plotly.io as pio

pd.set_option('display.max_columns', 50)

tc = unittest.TestCase()

# use plotly version 5.x.x
tc.assertEqual(plotly.__version__[:2], '5.', 'Plotly version mismatch')
tc.assertIsNotNone(go.Figure, 'Check whether you have correctly imported plotly.graph_objects with an alias go.')
tc.assertIsNotNone(px.scatter, 'Check whether you have correctly imported plotly.express with an alias px.')

▶️ Run the code below to import Chicago Airbnb listings dataset.

In [2]:

df_listings = pd.read_csv('https://github.com/bdi475/datasets/raw/main/case-studies/airbnb-sql/Chicago.csv')
df_listings_backup = df_listings.copy()
df_listings.head(3)

Out[2]:

	name	neighbourhood	room_type	bedrooms	bathrooms	accommodates	minimum_nights	price	availability_365	number_of_reviews	review_score	latitude	longitude	is_superhost
0	Hyde Park - Walk to UChicago, 10 min to McCormick	Hyde Park	Private room	1.0	1.0	1	2	65.0	355	181	100.0	41.78790	-87.58780	1
1	394 Great Reviews. 127 y/o House. 40 yds to tr...	South Lawndale	Entire home/apt	3.0	1.0	7	2	117.0	184	395	96.0	41.85495	-87.69696	1
2	Tiny Studio Apartment 94 Walk Score	West Town	Entire home/apt	3.0	1.0	2	2	70.0	365	389	93.0	41.90289	-87.68182	1

🎯 Exercise 1: Number of rows and columns¶

👇 Tasks¶

✔️ Store the number of rows in df_listings to a new variable named num_rows.
✔️ Store the number of columns in df_listings to a new variable named num_cols.
✔️ Both num_rows and num_cols must be ints.
✔️ Use .shape, not len().

In [3]:

# YOUR CODE BEGINS
num_rows = df_listings.shape[0]
num_cols = df_listings.shape[1]
# YOUR CODE ENDS

print(f'There are {num_rows} rows and {num_cols} columns in the dataset.')

There are 3217 rows and 14 columns in the dataset.

🧭 Check Your Work¶

In [4]:

# DO NOT CHANGE THE CODE IN THIS CELL
tc.assertEqual(num_rows, len(df_listings_backup.index), f'Number of rows should be {len(df_listings_backup.index)}')
tc.assertEqual(num_cols, len(df_listings_backup.columns), f'Number of columns should be {len(df_listings_backup.columns)}')

🎯 Exercise 2: Sample listings priced under $200¶

👇 Tasks¶

✔️ Sample 100 rows from df_listings where the price is under 200 (df_listings['price'] < 200).
✔️ Store the sampled result to a new DataFrame named df_under_200_sample.

🚀 Hint¶

my_sampled = my_dataframe[my_dataframe["price"] < 200].sample(100)

In [5]:

# YOUR CODE BEGINS
df_under_200_sample = df_listings[df_listings["price"] < 200].sample(100)
# YOUR CODE ENDS

display(df_under_200_sample.head(3))

	name	neighbourhood	room_type	bedrooms	bathrooms	accommodates	minimum_nights	price	availability_365	number_of_reviews	review_score	latitude	longitude	is_superhost
1533	Edgewater, private, charming coach house 2 ( o...	Edgewater	Entire home/apt	1.0	1.0	3	2	89.0	301	40	99.0	41.98715	-87.66177	1
2532	GOATHOTE \| SPACIOUS/VINTAGE STYLE 2/1APT \| CHI...	West Town	Entire home/apt	2.0	1.0	10	1	58.0	356	55	84.0	41.89488	-87.68642	0
3043	Private Condo Apartment on Belmont Cragin Area	Belmont Cragin	Entire home/apt	3.0	2.0	8	2	92.0	72	12	100.0	41.93793	-87.78125	1

🧭 Check Your Work¶

In [6]:

# DO NOT CHANGE THE CODE IN THIS CELL
tc.assertEqual(df_under_200_sample.shape, (100, df_listings_backup.shape[1]), 'Incorrect number of rows and/or columns')
tc.assertFalse((df_under_200_sample['price'] >= 200).any(), 'Listing priced equal to or greater than 200 found')

🎯 Exercise 3: Scatter Plot¶

👇 Tasks¶

✔️ Using df_under_200_sample, create a scatter plot with the following axes:
- x: Number of bedrooms
- y: Number of bathrooms
✔️ Use price to differentiate size of each point.
✔️ Use room type to differentiate color of each point.
✔️ Set an appropriate title.
✔️ Use the plotly_dark theme.
✔️ Set the width to 800 and height to 400.
✔️ Store your figure to a variable named fig.
✔️ Display the figure using fig.show()

In [7]:

# YOUR CODE BEGINS
fig = px.scatter(
    df_under_200_sample,
    title='Bedrooms & Bathrooms Scatter Plot',
    x="bedrooms",
    y="bathrooms",
    size="price",
    color="room_type",
    template='plotly_dark',
    width=800,
    height=400
)
fig.show()
# YOUR CODE ENDS

🔑 Sample output¶

🧭 Check Your Work¶

In [8]:

# DO NOT CHANGE THE CODE IN THIS CELL
tc.assertEqual(len(fig.data), df_under_200_sample['room_type'].nunique(), f'Did you specify a column to differentiate colors?')
tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')

num_points = 0
for i in range(len(fig.data)):
    tc.assertEqual(fig.data[i].type, 'scatter', 'Must be a scatter3d plot')
    num_points += fig.data[i].x.shape[0]

tc.assertEqual(num_points, 100, 'There must be 100 points')
tc.assertEqual(fig.layout.width, 800, 'Incorrect width')
tc.assertEqual(fig.layout.height, 400, 'Incorrect height')
tc.assertEqual(fig.layout.template, pio.templates['plotly_dark'], 'Incorrect plotly theme (template)')

🎯 Exercise 4: 3D Scatter Plots¶

This exercise is highly similar to the previous one. Instead of using size to differentiate price of each point, we'll use add a new axis. This makes it a 3D scatter plot! 🤡

👇 Tasks¶

✔️ Using df_under_200_sample, create a 3D scatter plot with the following axes:
- x: Number of bedrooms
- y: Number of bathrooms
- z: Price
✔️ Use room type to differentiate colors.
✔️ Set an appropriate title.
✔️ Use the plotly_dark theme.
✔️ Set the width to 800 and height to 600.
✔️ Store your figure to a variable named fig.
✔️ Display the figure using fig.show()

In [9]:

# YOUR CODE BEGINS
fig = px.scatter_3d(
    df_under_200_sample,
    title='Bedrooms, Bathrooms, Price 3D Scatter Plot',
    x="bedrooms",
    y="bathrooms",
    z="price",
    color="room_type",
    template='plotly_dark',
    width=800,
    height=600
)
fig.show()
# YOUR CODE ENDS

🔑 Sample output¶

🧭 Check Your Work¶

In [10]:

# DO NOT CHANGE THE CODE IN THIS CELL
tc.assertEqual(len(fig.data), df_under_200_sample['room_type'].nunique(), f'Did you specify a column to differentiate colors?')
tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')

num_points = 0
for i in range(len(fig.data)):
    tc.assertEqual(fig.data[i].type, 'scatter3d', 'Must be a scatter3d plot')
    num_points += fig.data[i].x.shape[0]

tc.assertEqual(num_points, 100, 'There must be 100 points')
tc.assertEqual(fig.layout.width, 800, 'Incorrect width')
tc.assertEqual(fig.layout.height, 600, 'Incorrect height')
tc.assertEqual(fig.layout.template, pio.templates['plotly_dark'], 'Incorrect plotly theme (template)')

🎯 Exercise 5: Find a list of top 20 neighbourhoods¶

👇 Tasks¶

✔️ Find the top 20 neighbourhoods in df_listings (by number of listings).
✔️ Store the result to a new variable named top_20_neighbourhoods.
✔️ top_20_neighbourhoods should be a Python list type.
✔️ We'll give you the fully-working code below.

🔥 Solution¶

Code

In [11]:

# YOUR CODE BEGINS
top_20_neighbourhoods = df_listings['neighbourhood'].value_counts().head(20).index.tolist()
# YOUR CODE ENDS

top_20_neighbourhoods

Out[11]:

['West Town',
 'Lake View',
 'Logan Square',
 'Near North Side',
 'Lincoln Park',
 'Near West Side',
 'Lower West Side',
 'Edgewater',
 'Uptown',
 'North Center',
 'Irving Park',
 'Loop',
 'Avondale',
 'Rogers Park',
 'Near South Side',
 'Bridgeport',
 'Lincoln Square',
 'Grand Boulevard',
 'Hyde Park',
 'Armour Square']

🧭 Check Your Work¶

In [12]:

# DO NOT CHANGE THE CODE IN THIS CELL
decoded_code = base64.b64decode(b'dG9wXzIwX25laWdoYm91cmhvb2RzX2NoZWNrID0gZGZfbGlzd\
GluZ3NbJ25laWdoYm91cmhvb2QnXS52YWx1ZV9jb3VudHMoKS5oZWFkKDIwKS5pbmRleC50b2xpc3QoKQ==')

eval(compile(decoded_code, '<string>', 'exec'))

tc.assertEqual(set(top_20_neighbourhoods), set(top_20_neighbourhoods_check), 'Incorrect neighbourhoods')

🎯 Exercise 6: Filter listings in top 20 neighbourhoods¶

👇 Tasks¶

✔️ Using df_listings, filter only the rows where the neighbourhood is in top_20_neighbourhoods and the price is less than 300.
✔️ Store the filtered result to a new variable named df_filtered.
✔️ We'll give you the fully-working code below.

🔥 Solution¶

Code

In [13]:

# YOUR CODE BEGINS
df_filtered = df_listings[(df_listings['neighbourhood'].isin(top_20_neighbourhoods)) \
                          & (df_listings['price'] < 300)]
# YOUR CODE ENDS

display(df_filtered.head(3))
print(df_filtered.shape)

	name	neighbourhood	room_type	bedrooms	bathrooms	accommodates	minimum_nights	price	availability_365	number_of_reviews	review_score	latitude	longitude	is_superhost
0	Hyde Park - Walk to UChicago, 10 min to McCormick	Hyde Park	Private room	1.0	1.0	1	2	65.0	355	181	100.0	41.78790	-87.58780	1
2	Tiny Studio Apartment 94 Walk Score	West Town	Entire home/apt	3.0	1.0	2	2	70.0	365	389	93.0	41.90289	-87.68182	1
3	Barbara's Hideaway - Old Town	Lincoln Park	Entire home/apt	1.0	1.0	4	4	95.0	282	54	93.0	41.91769	-87.63788	1

(2391, 14)

🧭 Check Your Work¶

In [14]:

# DO NOT CHANGE THE CODE IN THIS CELL
decoded_code = base64.b64decode(b'dG9wXzIwX25laWdoYm91cmhvb2RzX2NoZWNrID0g\
ZGZfbGlzdGluZ3NfYmFja3VwWyduZWlnaGJvdXJob29kJ10udmFsdWVfY291bnRzKCkuaGVhZC\
gyMCkuaW5kZXgudG9saXN0KCkKCmRmX2ZpbHRlcmVkX2NoZWNrID0gZGZfbGlzdGluZ3NfYmFj\
a3VwWyhkZl9saXN0aW5nc19iYWNrdXBbJ25laWdoYm91cmhvb2QnXS5pc2luKHRvcF8yMF9uZW\
lnaGJvdXJob29kc19jaGVjaykpICYgKGRmX2xpc3RpbmdzX2JhY2t1cFsncHJpY2UnXSA8IDMwMCld')

eval(compile(decoded_code, '<string>', 'exec'))

tc.assertEqual(df_filtered.shape, df_filtered_check.shape, 'Incorrect number of rows and/or columns')
pd.testing.assert_frame_equal(
    df_filtered.sort_values(df_filtered.columns.tolist()).reset_index(drop=True),
    df_filtered_check.sort_values(df_filtered_check.columns.tolist()).reset_index(drop=True)
)

🎯 Exercise 7: Neighbourhood breakdown¶

👇 Tasks¶

✔️ Using df_filtered, create a pie chart that shows the breakdown of neighbourhoods (by number of listings).
✔️ Set an appropriate title.
✔️ Set the width to 800 and height to 700.
✔️ Store your figure to a variable named fig.
✔️ Display the figure using fig.show()

In [15]:

# YOUR CODE BEGINS
fig = px.pie(
    df_filtered,
    names='neighbourhood',
    title='Neighbourhood breakdown',
    width=800,
    height=700
)

fig.show()
# YOUR CODE ENDS

🔑 Sample output¶

🧭 Check Your Work¶

In [16]:

# DO NOT CHANGE THE CODE IN THIS CELL
tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
tc.assertEqual(fig.data[0].type, 'pie', 'Must be a pie chart')
tc.assertEqual(set(fig.data[0].labels), set(top_20_neighbourhoods), 'Must only use the top 20 neighbourhoods')

tc.assertEqual(fig.layout.width, 800, 'Incorrect width')
tc.assertEqual(fig.layout.height, 700, 'Incorrect height')

🎯 Exercise 8: Neighbourhood vs price heatmap¶

👇 Tasks¶

✔️ Using df_filtered, create a heatmap to visualize the distribution of listings price by neighbourhood.
✔️ Use neighbourhood on the x-axis and price on the y-axis.
✔️ Set an appropriate title.
✔️ Set the height to 600 (do not specify width).
✔️ Store your figure to a variable named fig.
✔️ Display the figure using fig.show()

In [17]:

# YOUR CODE BEGINS
fig = px.density_heatmap(
    df_filtered,
    x='neighbourhood',
    y='price',
    title='Neighbourhood vs Price Heatmap',
    height=600
)
fig.show()
# YOUR CODE ENDS

🔑 Sample output¶

🧭 Check Your Work¶

In [18]:

# DO NOT CHANGE THE CODE IN THIS CELL
decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHguZGVuc2l0eV9oZWF0bWFwKAogICAgZGZfZ\
mlsdGVyZWQsCiAgICB4PSduZWlnaGJvdXJob29kJywKICAgIHk9J3ByaWNlJywKICAgIGhlaWdodD02MDAKKQ==')

eval(compile(decoded_code, '<string>', 'exec'))

tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
tc.assertEqual(fig.data[0].type, 'histogram2d', 'Must be a heatmap (histogram2d)')
tc.assertEqual(set(fig.data[0].x), set(fig_check.data[0].x), 'Incorrect x-axis value(s)')
tc.assertEqual(set(fig.data[0].y), set(fig_check.data[0].y), 'Incorrect y-axis value(s)')
tc.assertEqual(fig.layout.height, fig_check.layout.height, 'Incorrect height')

🎯 Exercise 9: Listing metrics by neighbourhood and room type¶

👇 Tasks¶

✔️ Using df_filtered, calculate the following aggregated values by neighbourhood and room_type.
- num_listings: Number of listings
- bedrooms: Average number of bedrooms
- bathrooms: Average number of bathrooms
- price: Average price
✔️ Store the resulting DataFrame to a new variable named df_by_neighbourhood_room_type.
✔️ We'll give you the fully-working code below.

🔥 Solution¶

df_by_neighbourhood_room_type = df_filtered.groupby(['neighbourhood', 'room_type'], as_index=False) \
    .agg({
        'name': 'count',
        'bedrooms': 'mean',
        'bathrooms': 'mean',
        'accommodates': 'mean',
        'price': 'mean'
    }).rename(columns={
        'name': 'num_listings'
    })

In [19]:

# YOUR CODE BEGINS
df_by_neighbourhood_room_type = df_filtered.groupby(['neighbourhood', 'room_type'], as_index=False) \
    .agg({
        'name': 'count',
        'bedrooms': 'mean',
        'bathrooms': 'mean',
        'accommodates': 'mean',
        'price': 'mean'
    }).rename(columns={
        'name': 'num_listings'
    })
# YOUR CODE ENDS

display(df_by_neighbourhood_room_type.head(5))

	neighbourhood	room_type	num_listings	bedrooms	bathrooms	accommodates	price
0	Armour Square	Entire home/apt	22	3.090909	1.545455	9.000000	167.454545
1	Armour Square	Private room	25	1.000000	1.760000	1.920000	44.480000
2	Avondale	Entire home/apt	59	1.932203	1.152542	4.932203	92.084746
3	Avondale	Private room	9	1.111111	1.055556	1.666667	70.888889
4	Avondale	Shared room	1	1.000000	1.000000	1.000000	30.000000

🧭 Check Your Work¶

In [20]:

# DO NOT CHANGE THE CODE IN THIS CELL
decoded_code = base64.b64decode(b'dG9wXzIwX25laWdoYm91cmhvb2RzX2NoZWNrID0gZGZfbGlzdGluZ3Nf\
YmFja3VwWyduZWlnaGJvdXJob29kJ10udmFsdWVfY291bnRzKCkuaGVhZCgyMCkuaW5kZXgudG9saXN0KCkKCmRmX2\
ZpbHRlcmVkX2NoZWNrID0gZGZfbGlzdGluZ3NfYmFja3VwWyhkZl9saXN0aW5nc19iYWNrdXBbJ25laWdoYm91cmhv\
b2QnXS5pc2luKHRvcF8yMF9uZWlnaGJvdXJob29kc19jaGVjaykpICYgKGRmX2xpc3RpbmdzX2JhY2t1cFsncHJpY2\
UnXSA8IDMwMCldCgpkZl9ieV9uZWlnaGJvdXJob29kX3Jvb21fdHlwZV9jaGVjayA9IGRmX2ZpbHRlcmVkLmdyb3Vw\
YnkoWyduZWlnaGJvdXJob29kJywgJ3Jvb21fdHlwZSddLCBhc19pbmRleD1GYWxzZSkgXAogICAgLmFnZyh7CiAgIC\
AgICAgJ25hbWUnOiAnY291bnQnLAogICAgICAgICdiZWRyb29tcyc6ICdtZWFuJywKICAgICAgICAnYmF0aHJvb21z\
JzogJ21lYW4nLAogICAgICAgICdhY2NvbW1vZGF0ZXMnOiAnbWVhbicsCiAgICAgICAgJ3ByaWNlJzogJ21lYW4nCi\
AgICB9KS5yZW5hbWUoY29sdW1ucz17CiAgICAgICAgJ25hbWUnOiAnbnVtX2xpc3RpbmdzJwogICAgfSk=')

eval(compile(decoded_code, '<string>', 'exec'))

tc.assertEqual(
    df_by_neighbourhood_room_type.shape,
    df_by_neighbourhood_room_type_check.shape,
    'Incorrect number of rows and/or columns'
)
pd.testing.assert_frame_equal(
    df_by_neighbourhood_room_type.sort_values(df_by_neighbourhood_room_type.columns.tolist()).reset_index(drop=True),
    df_by_neighbourhood_room_type_check.sort_values(df_by_neighbourhood_room_type_check.columns.tolist()).reset_index(drop=True)
)

🎯 Exercise 10: Listings Breakdown by Neighbourhood and Room Type (Bar Chart)¶

👇 Tasks¶

✔️ Using df_by_neighbourhood_room_type, create a bar chart describing the number of listings by neighbourhood (broken down into room types).
✔️ Use num_listings on the x-axis and neighbourhood on the y-axis.
✔️ Use colors to show the breakdown of room types for each neighbourhood.
✔️ Sort the neighbourhoods by number of listings in descending order using fig.update_yaxes(...).
✔️ Set an appropriate title.
✔️ Set the height to 600 (do not specify width).
✔️ Use the plotly_dark theme.
✔️ Store your figure to a variable named fig.
✔️ Display the figure using fig.show()

In [21]:

# YOUR CODE BEGINS
fig = px.bar(
    df_by_neighbourhood_room_type,
    x='num_listings',
    y='neighbourhood',
    color='room_type',
    template='plotly_dark',
    title='Listings Breakdown by Neighbourhood and Room Type',
    height=600
)

fig.update_yaxes(categoryorder='total ascending')

fig.show()
# YOUR CODE ENDS

🔑 Sample output¶

🧭 Check Your Work¶

In [22]:

tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
tc.assertEqual(fig.layout.height, 600, 'Incorrect height')

decoded_code = base64.b64decode(b'CmZpZ19jaGVjayA9IHB4LmJhcigKICAgIGRmX2J5X25laWdoYm91\
cmhvb2Rfcm9vbV90eXBlLAogICAgeD0nbnVtX2xpc3RpbmdzJywKICAgIHk9J25laWdoYm91cmhvb2QnLAogIC\
AgY29sb3I9J3Jvb21fdHlwZScsCiAgICB0ZW1wbGF0ZT0ncGxvdGx5X2RhcmsnLAogICAgdGl0bGU9J0xpc3Rp\
bmdzIEJyZWFrZG93biBieSBOZWlnaGJvdXJob29kIGFuZCBSb29tIFR5cGUnLAogICAgaGVpZ2h0PTYwMAopCg\
pmaWdfY2hlY2sudXBkYXRlX3lheGVzKGNhdGVnb3J5b3JkZXI9J3RvdGFsIGFzY2VuZGluZycpCg=='
)
eval(compile(decoded_code, '<string>', 'exec'))

for fig_index in range(len(fig.data)):
    fig_data = fig.data[fig_index]
    fig_check_data = fig_check.data[fig_index]
    
    tc.assertEqual(fig_data.type, fig_check_data.type, f'Must be a {fig_check_data.type} chart')
    
    np.testing.assert_array_equal(
        fig_data.x,
        fig_check_data.x,
        'x value(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig_data.y,
        fig_check_data.y,
        'y value(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig_data.name,
        fig_check_data.name,
        'Name(s) mismatch'
    )
    
tc.assertEqual(fig.layout.template, pio.templates['plotly_dark'], 'Incorrect plotly theme (template)')

🎯 Exercise 11: Listings Breakdown by Neighbourhood and Room Type (Sunburst Chart)¶

👇 Tasks¶

✔️ Using df_by_neighbourhood_room_type, create a sunburst chart describing the breakdown of the listings by neighbourhood and room type.
✔️ Set an appropriate title.
✔️ Set both the width and height to 800.
✔️ Store your figure to a variable named fig.
✔️ Display the figure using fig.show()

In [23]:

# YOUR CODE BEGINS
fig = px.sunburst(
    df_by_neighbourhood_room_type,
    path=['neighbourhood', 'room_type'],
    title='Listings Breakdown by Neighbourhood and Room Type',
    values='num_listings',
    width=800,
    height=800
)

fig.show()
# YOUR CODE ENDS

🔑 Sample output¶

🧭 Check Your Work¶

In [24]:

tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
tc.assertEqual(fig.data[0].type, 'sunburst', 'Must be a sunburst chart')
tc.assertEqual(fig.layout.width, 800, 'Incorrect width')
tc.assertEqual(fig.layout.height, 800, 'Incorrect height')

decoded_code = base64.b64decode(b'CmZpZ19jaGVjayA9IHB4LnN1bmJ1cnN0KAogICAg\
ZGZfYnlfbmVpZ2hib3VyaG9vZF9yb29tX3R5cGUsCiAgICBwYXRoPVsnbmVpZ2hib3VyaG9vZC\
csICdyb29tX3R5cGUnXSwKICAgIHRpdGxlPSdMaXN0aW5ncyBCcmVha2Rvd24gYnkgTmVpZ2hi\
b3VyaG9vZCBhbmQgUm9vbSBUeXBlJywKICAgIHZhbHVlcz0nbnVtX2xpc3RpbmdzJywKICAgIH\
dpZHRoPTgwMCwKICAgIGhlaWdodD05MDAKKQo='
)
eval(compile(decoded_code, '<string>', 'exec'))

np.testing.assert_array_equal(
    fig.data[0].labels,
    fig_check.data[0].labels,
    'Label(s) mismatch'
)

np.testing.assert_array_equal(
    fig.data[0].parents,
    fig_check.data[0].parents,
    'Parent(s) mismatch'
)

np.testing.assert_array_equal(
    fig.data[0].values,
    fig_check.data[0].values,
    'Value(s) mismatch'
)

▶️ Run the code below to create a treemap chart describing the distribution of listings by neighbourhood.

🎯 Exercise 12: Listings Breakdown by Neighbourhood (Treemap Chart)¶

👇 Tasks¶

✔️ Using df_by_neighbourhood_room_type, create a treemap chart that shows the breakdown of neighbourhoods by number of listings.
✔️ Set an appropriate title.
✔️ Set the height to 700.
✔️ Store your figure to a variable named fig.
✔️ Display the figure using fig.show()

In [25]:

# YOUR CODE BEGINS
fig = px.treemap(
    df_by_neighbourhood_room_type,
    path=['neighbourhood'],
    title='Top 20 neighbourhoods breakdown',
    values='num_listings',
    height=700
)

fig.show()
# YOUR CODE ENDS

🔑 Sample output¶

🧭 Check Your Work¶

In [26]:

tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')

decoded_code = base64.b64decode(b'CmZpZ19jaGVjayA9IHB4LnRyZWVtYXAoCiAgICBkZl9\
ieV9uZWlnaGJvdXJob29kX3Jvb21fdHlwZSwKICAgIHBhdGg9WyduZWlnaGJvdXJob29kJ10sCiAg\
ICB0aXRsZT0nVG9wIDIwIG5laWdoYm91cmhvb2RzIGJyZWFrZG93bicsCiAgICB2YWx1ZXM9J251b\
V9saXN0aW5ncycsCiAgICBoZWlnaHQ9NzAwCikK'

)
eval(compile(decoded_code, '<string>', 'exec'))

tc.assertEqual(fig.data[0].type, 'treemap', 'Must be a treemap')

np.testing.assert_array_equal(
    fig.data[0].ids,
    fig_check.data[0].ids,
    'ID value(s) mismatch'
)

np.testing.assert_array_equal(
    fig.data[0].labels,
    fig_check.data[0].labels,
    'Label value(s) mismatch'
)

np.testing.assert_array_equal(
    fig.data[0].parents,
    fig_check.data[0].parents,
    'Parent value(s) mismatch'
)

np.testing.assert_array_equal(
    fig.data[0].values,
    fig_check.data[0].values,
    'Size value(s) mismatch'
)

🎯 Exercise 13: Listings Breakdown by Neighbourhood and Room Type (Treemap Chart)¶

👇 Tasks¶

✔️ Using df_by_neighbourhood_room_type, create a treemap chart that shows the breakdown of neighbourhoods by number of listings and then by room types.
✔️ Set an appropriate title.
✔️ Set the height to 700.
✔️ Store your figure to a variable named fig.
✔️ Display the figure using fig.show()

In [27]:

# YOUR CODE BEGINS
fig = px.treemap(
    df_by_neighbourhood_room_type,
    path=['neighbourhood', 'room_type'],
    title='Top 20 neighbourhoods breakdown',
    values='num_listings',
    height=700
)

fig.show()
# YOUR CODE ENDS

🔑 Sample output¶

🧭 Check Your Work¶

In [28]:

tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')

decoded_code = base64.b64decode(b'CmZpZ19jaGVjayA9IHB4LnRyZWVtYXAoCiAgICBkZl9ie\
V9uZWlnaGJvdXJob29kX3Jvb21fdHlwZSwKICAgIHBhdGg9WyduZWlnaGJvdXJob29kJywgJ3Jvb21f\
dHlwZSddLAogICAgdGl0bGU9J1RvcCAyMCBuZWlnaGJvdXJob29kcyBicmVha2Rvd24nLAogICAgdmF\
sdWVzPSdudW1fbGlzdGluZ3MnLAogICAgaGVpZ2h0PTcwMAopCg==')
eval(compile(decoded_code, '<string>', 'exec'))

tc.assertEqual(fig.data[0].type, 'treemap', 'Must be a treemap')

np.testing.assert_array_equal(
    fig.data[0].ids,
    fig_check.data[0].ids,
    'ID value(s) mismatch'
)

np.testing.assert_array_equal(
    fig.data[0].labels,
    fig_check.data[0].labels,
    'Label value(s) mismatch'
)

np.testing.assert_array_equal(
    fig.data[0].parents,
    fig_check.data[0].parents,
    'Parent value(s) mismatch'
)

np.testing.assert_array_equal(
    fig.data[0].values,
    fig_check.data[0].values,
    'Size value(s) mismatch'
)