my_cm = ListedColormap(['#bb0000', '#00FF00'])
axes = {p : ('age', 'income') if p != "Mortgage"else ('members_in_household', 'loan_accounts') for p in products}
for product in products:
ax = plt.subplot(1, len(products), products.index(product)+1)
ax.set_title(product)
axe = axes[product]
plt.xlabel(axe[0])
plt.ylabel(axe[1])
ax.scatter(df[axe[0]], df[axe[1]], c=df[product], cmap=my_cm, alpha=0.5)
plot_cloud_points(known_behaviors)
compcol = "#66ff87"
compname = "IBM"
compdf = df[df['ParentCompany'] == compname]
generate_all(compdf, compname, compcol)
bars()
createhmap1(compcol)
## Business Keywords - IBM
Plotly express, great idea:
my_cm = ListedColormap(['#bb0000', '#00FF00']) axes = {p : ('age', 'income') if p != "Mortgage"else ('members_in_household', 'loan_accounts') for p in products} for product in products: ax = plt.subplot(1, len(products), products.index(product)+1) ax.set_title(product) axe = axes[product] plt.xlabel(axe[0]) plt.ylabel(axe[1]) ax.scatter(df[axe[0]], df[axe[1]], c=df[product], cmap=my_cm, alpha=0.5)
plot_cloud_points(known_behaviors)Employee Attrition Distribution Stats
compcol = "#66ff87"compname = "IBM"compdf = df[df['ParentCompany'] == compname]generate_all(compdf, compname, compcol)bars()createhmap1(compcol)## Business Keywords - IBMcompcol = "#66ff87"