Water usage again

Creating a tree map using the Squarify and Seaborn libraries to choose where best to save water.

Importing libraries and packages

# Warnings
import warnings

# Mathematical operations and data manipulation
import pandas as pd

# Plotting
import matplotlib.pyplot as plt
import seaborn as sns
import squarify

sns.set()
warnings.filterwarnings("ignore")

Set paths

# Path to datasets directory
data_path = "./datasets"
# Path to assets directory (for saving results to)
assets_path = "./assets"

Loading dataset

dataset = pd.read_csv(f"{data_path}/water_usage.csv", index_col=0)

Exploring dataset

# Shape of the dataset
print("Shape of the dataset: ", dataset.shape)
# View
dataset

Shape of the dataset:  (6, 2)

	Usage	Percentage
0	Leak	12
1	Clothes Washer	17
2	Faucet	19
3	Shower	20
4	Toilet	24
5	Other	8

Preprocessing

# Create a list of labels by accessing each column from the dataset.
# The astype('str') function casts the fetched data into a type string.
labels = dataset["Usage"] + " (" + dataset["Percentage"].astype("str") + "%)"
labels

0              Leak (12%)
1    Clothes Washer (17%)
2            Faucet (19%)
3            Shower (20%)
4            Toilet (24%)
5              Other (8%)
dtype: object

Visualisation

# Creating a tree map visualization using the plot() function of
# the squarify library
plt.figure(dpi=200)
# Create tree map
squarify.plot(
    sizes=dataset["Percentage"],
    label=labels,
    color=sns.light_palette("green", dataset.shape[0]),
)
plt.axis("off")
# Add title
plt.title("Water usage")
# Show plot
plt.show()