#!/usr/bin/env python
# coding: utf-8

# # "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."
# - hidden: true
# - toc: true
# - branch: master
# - badges: true
# - comments: true
# - categories: [causal inference, causal discovery, causality modelling, python]

# In[15]:


import pandas as pd
import numpy as np
import random


# In[16]:


np.random.seed(123)
random.seed(123)


# In[19]:


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(
    [
        
    ]
)


# In[13]:


seasons


# In[ ]: