Causality modelling in Python for data scientists
Data science is increasingly commonplace in industry and the enterprise. Industrial data scientists have a vast toolbox for descriptive and predictive analyses at their disposal. However, data science tools for decision-making in industry and the enterprise are less well established. Here we survey Python packages that can aid industrial data scientists facilitate intelligent decision-making through causality modelling.
import pandas as pd
import numpy as np
import random
np.random.seed(123)
random.seed(123)
no_samples = 10000
seasons = np.random.choice(['winter', 'spring', 'summer', 'fall'], size=(no_samples,))
color = np.array(
[
random.choice(['yellow', 'pink'])
if season in ['spring', 'summer']
else random.choice(['navy', 'grey'])
for season in seasons
]
)
price = np.random.lognormal(size=(no_samples,))
rank = np.array(
[
]
)
seasons