def calculate_average(numbers):
return sum(numbers) / len(numbers)
# Call the function with a list of numbers
calculate_average([10, 20, 30, 40]) # Output: 25.0
calculate_average([1, 2, 3, 4, 5]) # Output: 3.0
This function calculates the average of a given list of numbers by summing all values and dividing by the length of the list.
def second_largest(numbers):
unique_numbers = list(set(numbers)) # Remove duplicates
unique_numbers.sort() # Sort the list
return unique_numbers[-2] # Return the second largest number
# Example usage
second_largest([10, 20, 30, 40]) # Output: 30
second_largest([5, 1, 9, 7]) # Output: 7
This function finds the second largest number in a given list. It removes duplicates, sorts the list, and returns the second last element.
Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps.
| Chart Type | Purpose | Best Used For | Key Characteristics | Example Code |
|---|---|---|---|---|
| Line Plot | Shows trends over time | Stock prices, temperature changes | X-axis represents time or continuous data, Y-axis represents measured value | plt.plot(x, y) |
| Bar Plot | Compares different categories | Sales by product, population by country | Bars can be vertical/horizontal, height represents value | plt.bar(categories, values) |
| Histogram | Displays frequency distribution | Exam scores, customer ages | Groups data into bins, shows frequency | plt.hist(data, bins=10) |
| Box Plot | Identifies outliers | Income analysis, detecting fraud | Shows min, max, median, and outliers | plt.boxplot(data) |
| Scatter Plot | Shows relationships between variables | Study time vs. exam score | Each point represents an observation | plt.scatter(x, y) |
| Pie Chart | Shows parts of a whole | Market share, budget allocation | Circular chart, total sum = 100% | plt.pie(data, labels) |
To install the necessary library:
pip install matplotlib