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Lab 3 - Statistics

Lab 3: Basic Statistical Analysis with DataFrames

Objectives

Students will learn to: - Find minimum and maximum values in a DataFrame - Calculate basic statistical measures (mean, median, standard deviation) - Identify specific rows based on conditions - Use pandas methods for statistical analysis

Prerequisites

  • Completed Lab 2 (Loading and exploring DataFrames)
  • Understanding of basic statistical concepts

Lab Steps

Step 1: Load and Prepare the Data

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import pandas as pd

# Load the healthcare data
df = pd.read_csv('healthcarepercapita2022.csv')

# Display basic info to remind ourselves of the data structure
print("Dataset shape:", df.shape)
print("\nFirst few rows:")
print(df.head())

Step 2: Find the Country with Lowest Healthcare Expenditure

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# Method 1: Using min() and boolean indexing
min_expenditure = df['Health_Exp_PerCapita_2022'].min()
lowest_country = df[df['Health_Exp_PerCapita_2022'] == min_expenditure]

print("Country with LOWEST healthcare expenditure per capita:")
print(f"Country: {lowest_country['Country_Name'].iloc[0]}")
print(f"Expenditure: ${min_expenditure:,.2f}")

# Alternative method using idxmin()
min_index = df['Health_Exp_PerCapita_2022'].idxmin()
print(f"\nAlternative method - Lowest: {df.loc[min_index, 'Country_Name']} (${min_expenditure:,.2f})")

Step 3: Find the Country with Highest Healthcare Expenditure

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# Method 1: Using max() and boolean indexing
max_expenditure = df['Health_Exp_PerCapita_2022'].max()
highest_country = df[df['Health_Exp_PerCapita_2022'] == max_expenditure]

print("\nCountry with HIGHEST healthcare expenditure per capita:")
print(f"Country: {highest_country['Country_Name'].iloc[0]}")
print(f"Expenditure: ${max_expenditure:,.2f}")

# Alternative method using idxmax()
max_index = df['Health_Exp_PerCapita_2022'].idxmax()
print(f"\nAlternative method - Highest: {df.loc[max_index, 'Country_Name']} (${max_expenditure:,.2f})")

Step 4: Calculate Mean Healthcare Expenditure

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# Calculate the average (mean)
mean_expenditure = df['Health_Exp_PerCapita_2022'].mean()

print(f"\nAverage (Mean) healthcare expenditure per capita:")
print(f"${mean_expenditure:,.2f}")

# Round to 2 decimal places for cleaner display
print(f"Rounded: ${round(mean_expenditure, 2):,.2f}")

Step 5: Calculate Median Healthcare Expenditure

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# Calculate the median (middle value)
median_expenditure = df['Health_Exp_PerCapita_2022'].median()

print(f"\nMedian healthcare expenditure per capita:")
print(f"${median_expenditure:,.2f}")

# Compare mean vs median
print(f"\nComparison:")
print(f"Mean:   ${mean_expenditure:,.2f}")
print(f"Median: ${median_expenditure:,.2f}")
if mean_expenditure > median_expenditure:
    print("Mean > Median: Data is likely right-skewed (few very high values)")
elif mean_expenditure < median_expenditure:
    print("Mean < Median: Data is likely left-skewed (few very low values)")
else:
    print("Mean ≈ Median: Data is likely normally distributed")

Step 6: Calculate Standard Deviation

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# Calculate standard deviation
std_expenditure = df['Health_Exp_PerCapita_2022'].std()

print(f"\nStandard deviation of healthcare expenditure:")
print(f"${std_expenditure:,.2f}")

# Interpret the standard deviation
print(f"\nInterpretation:")
print(f"About 68% of countries spend between ${mean_expenditure - std_expenditure:,.2f} and ${mean_expenditure + std_expenditure:,.2f} per capita")
print(f"About 95% of countries spend between ${mean_expenditure - 2*std_expenditure:,.2f} and ${mean_expenditure + 2*std_expenditure:,.2f} per capita")

Step 7: Summary Statistics (All at Once)

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# Get all basic statistics at once
print("\nComplete Statistical Summary:")
print(df['Health_Exp_PerCapita_2022'].describe())

# Create a custom summary
print(f"\n{'='*50}")
print("HEALTHCARE EXPENDITURE ANALYSIS SUMMARY")
print(f"{'='*50}")
print(f"Lowest spending country:  {df.loc[df['Health_Exp_PerCapita_2022'].idxmin(), 'Country_Name']}")
print(f"Highest spending country: {df.loc[df['Health_Exp_PerCapita_2022'].idxmax(), 'Country_Name']}")
print(f"Range: ${min_expenditure:,.2f} - ${max_expenditure:,.2f}")
print(f"Mean: ${mean_expenditure:,.2f}")
print(f"Median: ${median_expenditure:,.2f}")
print(f"Standard Deviation: ${std_expenditure:,.2f}")
print(f"Total countries analyzed: {len(df)}")

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Complete Statistical Summary:
count      238.000000
mean      1930.205882
std       2458.454731
min         39.000000
25%        270.000000
50%        930.500000
75%       2467.500000
max      12434.000000
Name: Health_Exp_PerCapita_2022, dtype: float64

==================================================
HEALTHCARE EXPENDITURE ANALYSIS SUMMARY
==================================================
Lowest spending country:  South Sudan
Highest spending country: United States
Range: $39.00 - $12,434.00
Mean: $1,930.21
Median: $930.50
Standard Deviation: $2,458.45
Total countries analyzed: 238

Discussion Questions

  1. Which country spends the least on healthcare per capita? How much do they spend?

  2. Which country spends the most on healthcare per capita? How much do they spend?

  3. What is the difference between the highest and lowest spending countries?

  4. Is the mean higher or lower than the median? What does this tell us about the distribution of healthcare spending?

  5. How many countries fall within one standard deviation of the mean?

  6. If a country spends $2,000 per capita on healthcare, is this above or below average?

Extension Activities

For Advanced Students: 

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# Find countries within certain ranges
print("\nCountries spending more than $5,000 per capita:")
high_spenders = df[df['Health_Exp_PerCapita_2022'] > 5000]
print(high_spenders[['Country_Name', 'Health_Exp_PerCapita_2022']].sort_values('Health_Exp_PerCapita_2022', ascending=False))

# Calculate what percentage of countries spend above the mean
above_mean = len(df[df['Health_Exp_PerCapita_2022'] > mean_expenditure])
percentage_above_mean = (above_mean / len(df)) * 100
print(f"\n{above_mean} countries ({percentage_above_mean:.1f}%) spend above the global average")

Key Learning Outcomes

  • Understanding the difference between mean and median
  • Learning to find extreme values in datasets
  • Interpreting standard deviation as a measure of variability
  • Using pandas methods for statistical analysis
  • Connecting statistical concepts to real-world data

This lab builds naturally from the previous exploration lab and introduces fundamental statistical analysis that students will use throughout their data science journey.