Company sensitivity analysis
You'll now examine what would happen to profits for the company from the previous exercise at a variety of mean_inflation and mean_volume values. This will help the company plan for several levels of inflation and sales volumes since no company can ever be certain what inflation or sales volumes they will have in the future.
The mean inflation percentages you'd like to explore are 0, 1, 2, 5, 10, 15, 20, 50, while the sales values for use as the mean volume value are 100, 200, 500, 800, 1000. As a reminder, here is the profit_next_year_mc() function definition, which has already been loaded for you.
def profit_next_year_mc(mean_inflation, mean_volume, n):
profits = []
for i in range(n):
# Generate inputs by sampling from the multivariate normal distribution
rate_sales_volume = st.multivariate_normal.rvs(mean=[mean_inflation,mean_volume], cov=cov_matrix,size=1000)
# Deterministic calculation of company profit
price = 100 * (100 + rate_sales_volume[:,0])/100
volume = rate_sales_volume[:,1]
loan_and_cost = 50 * volume + 45 * (100 + 3 * rate_sales_volume[:,0]) * (volume/100)
profit = (np.mean(price * volume - loan_and_cost))
profits.append(profit)
return profits
The following packages have been imported: pandas as pd, numpy as np, scipy.stats as st, matplotlib.pyplot as plt, and seaborn as sns.
This exercise is part of the course
Monte Carlo Simulations in Python
Exercise instructions
- Complete the Monte Carlo simulation by running
profit_next_year_mc()to calculate profit 100 times, each time looping through the list ofinflvalues and the list ofvolvalues. - Use a
displotto visualize the simulation results saved in theProfitcolumn of the resulting DataFrame.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
x1 = []
x2 = []
y = []
for infl in [0, 1, 2, 5, 10, 15, 20, 50]:
for vol in [100, 200, 500, 800, 1000]:
# Run profit_next_year_mc so that it samples 100 times for each infl and vol combination
avg_prof = np.mean(____)
x1.append(infl)
x2.append(vol)
y.append(avg_prof)
df_sa = pd.concat([pd.Series(x1), pd.Series(x2), pd.Series(y)], axis=1)
df_sa.columns = ["Inflation", "Volume", "Profit"]
# Create a displot of the simulation results for "Profit"
____
plt.show()