Mammalia

Generate a regression plot to visualize whether there is any linear relationship between body mass and maximum longevity of animals in the dataset. Only consider samples for the Mammalia class and a body mass of less than 200,000.

Importing libraries and packages

# Warnings
import warnings

# Mathematical operations and data manipulation
import pandas as pd
import numpy as np

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

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}/anage_data.csv", index_col=0)

Exploring dataset

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

Shape of the dataset:  (4218, 29)

	HAGRID	Kingdom	Phylum	Class	Order	Family	Genus	Species	Common name	Female maturity (days)	...	Growth rate (1/days)	Maximum longevity (yrs)	Specimen origin	Sample size	Data quality	IMR (per yr)	MRDT (yrs)	Metabolic rate (W)	Body mass (g)	Temperature (K)
0	3	Animalia	Arthropoda	Branchiopoda	Diplostraca	Daphniidae	Daphnia	pulicaria	Daphnia	NaN	...	NaN	0.19	unknown	medium	acceptable	NaN	NaN	NaN	NaN	NaN
1	5	Animalia	Arthropoda	Insecta	Diptera	Drosophilidae	Drosophila	melanogaster	Fruit fly	7.0	...	NaN	0.30	captivity	large	acceptable	0.05	0.04	NaN	NaN	NaN
2	6	Animalia	Arthropoda	Insecta	Hymenoptera	Apidae	Apis	mellifera	Honey bee	NaN	...	NaN	8.00	unknown	medium	acceptable	NaN	NaN	NaN	NaN	NaN
3	8	Animalia	Arthropoda	Insecta	Hymenoptera	Formicidae	Cardiocondyla	obscurior	Cardiocondyla obscurior	NaN	...	NaN	0.50	captivity	medium	acceptable	NaN	NaN	NaN	NaN	NaN
4	9	Animalia	Arthropoda	Insecta	Hymenoptera	Formicidae	Lasius	niger	Black garden ant	NaN	...	NaN	28.00	unknown	medium	acceptable	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4214	4239	Animalia	Porifera	Hexactinellida	Lyssacinosida	Rossellidae	Scolymastra	joubini	Hexactinellid sponge	NaN	...	NaN	15000.00	wild	medium	questionable	NaN	NaN	NaN	NaN	NaN
4215	4241	Plantae	Pinophyta	Pinopsida	Pinales	Pinaceae	Pinus	longaeva	Great Basin bristlecone pine	NaN	...	NaN	5062.00	wild	medium	acceptable	NaN	999.00	NaN	NaN	NaN
4216	4242	Fungi	Ascomycota	Saccharomycetes	Saccharomycetales	Saccharomycetaceae	Saccharomyces	cerevisiae	Baker's yeast	NaN	...	NaN	0.04	captivity	large	acceptable	NaN	NaN	NaN	NaN	NaN
4217	4243	Fungi	Ascomycota	Schizosaccharomycetes	Schizosaccharomycetales	Schizosaccharomycetaceae	Schizosaccharomyces	pombe	Fission yeast	NaN	...	NaN	NaN	unknown	small	low	NaN	NaN	NaN	NaN	NaN
4218	4244	Fungi	Ascomycota	Sordariomycetes	Sordariales	Lasiosphaeriaceae	Podospora	anserina	Filamentous fungus	NaN	...	NaN	NaN	unknown	small	low	NaN	NaN	NaN	NaN	NaN

4218 rows × 29 columns

Preprocessing

longevity = "Maximum longevity (yrs)"
mass = "Body mass (g)"
data = dataset[dataset["Class"] == "Mammalia"]
data = data[
    np.isfinite(data[longevity])
    & np.isfinite(data[mass])
    & (data[mass] < 200000)
]

Visualisation

# Create regression plot
plt.figure(figsize=(10, 6), dpi=300)
# Create scatter plot
sns.regplot(mass, longevity, data=data)
# Show plot
plt.show()