Matplotlib offers a wide range of graphs and customization options for data visualization.
Line Plot:
Line plots represent data points as a series of connected data points with lines. They are commonly used to show trends over time.
Real-Life Example: Showing the temperature changes over a week.
Options:
plt.plot(x, y, label='label'): Create a line plot.
plt.xlabel('x-label') and
plt.ylabel('y-label'): Add axis labels.
plt.title('Title'): Add a title.
plt.legend(): Display labels for multiple lines.
plt.grid(True): Add a grid.
example:
import matplotlib.pyplot as plt
days = [1, 2, 3, 4, 5, 6, 7]
temperatures = [75, 76, 74, 73, 78, 80, 79]
plt.plot(days, temperatures, label='Temperature')
plt.xlabel('Days')
plt.ylabel('Temperature (°F)')
plt.title('Weekly Temperature Changes')
plt.legend()
plt.grid(True)
plt.show()
Bar Chart:
Bar charts are used to represent data as bars of different heights. They are useful for comparing categories.
Real-Life Example: Comparing the sales of different products.
Options:
plt.bar(x, height, label='label'): Create a bar chart.
plt.xticks(x, labels): Customize x-axis labels.
example:
import matplotlib.pyplot as plt
products = ['Product A', 'Product B', 'Product C', 'Product D']
sales = [120, 200, 90, 150]
plt.bar(products, sales, label='Sales')
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Product Sales Comparison')
plt.legend()
plt.show()
Histogram:
Options:
plt.hist(data, bins, label='label'): Create a histogram.
Real-Life Example: Analyzing the distribution of exam scores.
import matplotlib.pyplot as plt
exam_scores = [78, 85, 92, 60, 72, 88, 95, 66, 78, 84, 91, 70, 75, 89, 96]
bins = [60, 70, 80, 90, 100]
plt.hist(exam_scores, bins, edgecolor='black', label='Scores')
plt.xlabel('Score Range')
plt.ylabel('Frequency')
plt.title('Exam Score Distribution')
plt.legend()
plt.show()
Scatter Plot:
Options:
plt.scatter(x, y, label='label'): Create a scatter plot.
Real-Life Example: Visualizing the relationship between a student's study hours and exam scores.
import matplotlib.pyplot as plt
study_hours = [2, 3, 4, 5, 6, 7, 8]
exam_scores = [65, 72, 80, 85, 88, 92, 95]
plt.scatter(study_hours, exam_scores, label='Scores', color='red')
plt.xlabel('Study Hours')
plt.ylabel('Exam Scores')
plt.title('Study Hours vs. Exam Scores')
plt.legend()
plt.show()
Pie Chart:
Options:
x data (slices).
labels for slice labels.
colors for slice colors.
explode to emphasize a slice.
autopct for displaying percentages.
title.
plt.pie(sizes, labels=labels, autopct='%1.1f%%'): Create a pie chart with percentage labels.
Real-Life Example: Showing the distribution of expenses in a monthly budget.
import matplotlib.pyplot as plt
expenses = [500, 300, 200, 400]
categories = ['Housing', 'Food', 'Transportation', 'Entertainment']
plt.pie(expenses, labels=categories, autopct='%1.1f%%')
plt.title('Monthly Budget Allocation')
plt.show()
assignment with subplot:
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
plt.subplot(2, 3, 1)
days = [1, 2, 3, 4, 5, 6, 7]
temperatures = [75, 76, 74, 73, 78, 80, 79]
plt.plot(days, temperatures, label='Temperature')
plt.xlabel('Days')
plt.ylabel('Temperature (°F)')
plt.title('Weekly Temperature Changes')
plt.legend()
plt.grid(True)
plt.subplot(2, 3, 2)
products = ['Product A', 'Product B', 'Product C', 'Product D']
sales = [120, 200, 90, 150]
plt.bar(products, sales, label='Sales')
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Product Sales Comparison')
plt.legend()
plt.grid(True)
plt.subplot(2, 3, 3)
exam_scores = [78, 85, 92, 60, 72, 88, 95, 66, 78, 84, 91, 70, 75, 89, 96]
bins = [60, 70, 80, 90, 100]
plt.hist(exam_scores, bins, edgecolor='black', label='Scores')
plt.xlabel('Score Range')
plt.ylabel('Frequency')
plt.title('Exam Score Distribution')
plt.legend()
plt.subplot(2, 3, 4)
study_hours = [2, 3, 4, 5, 6, 7, 8]
exam_scores = [65, 72, 80, 85, 88, 92, 95]
plt.scatter(study_hours, exam_scores, label='Scores', color='red')
plt.xlabel('Study Hours')
plt.ylabel('Exam Scores')
plt.title('Study Hours vs. Exam Scores')
plt.legend()
plt.show()
0 Comments