Descriptive statistics summarize observed data so analysts can quickly inspect central tendency, spread, range, and distribution before deeper investigation. Continuous data commonly uses mean, median, standard deviation, and range; categorical data uses frequency counts and proportions. section 1.1.4
Use descriptive statistics to:
- Compare the shape and variability of continuous data across periods. section 1.1.4 section 9.5.2
- Summarize categorical distributions with counts and proportions. section 1.1.4
- Establish a data baseline before simulation, forecasting, or deeper investigation. section 1.1.4 section 9.5.2
GM closing prices
Applying pandas describe() to GM closing prices shows that July–December 2023 averaged $32.93 with a standard deviation of $3.65 and a range of $26.43–$40.05, while January 2024 averaged $35.46 with a standard deviation of $0.81 and a range of $34.39–$37.94. The comparison identifies greater stability in January and supplies historical context for Monte Carlo simulation. section 9.5.2
>>> all_simulations = \
>>> simulation_df.values.flatten() #1
>>> overall_stats = pd.DataFrame(all_simulations,
>>> columns = ['Simulated Prices']).describe() #2
>>> print(overall_stats) #3
Simulated Prices
count 10500.000000
mean 35.559536
std 2.204029
min 25.996207
25% 34.261598
50% 35.691632
75% 36.821081
max 47.983021
Flatten simulated prices and use DataFrame.describe() to calculate count, mean, standard deviation, quartiles, minimum, and maximum. section 9.5.8
