NumPy Matplotlib
Data visualization is one of the most important parts of data science.
While NumPy is used for numerical computation, Matplotlib is used to visualize data in graphical form.
Together, they form a powerful combination for:
- Data analysis
- Machine learning
- Scientific computing
- Business reporting
What is Matplotlib?
Matplotlib is a Python library used to create:
- Line plots
- Bar charts
- Scatter plots
- Histograms
- Pie charts
When combined with NumPy, it becomes very powerful for handling large datasets.
Why Use NumPy with Matplotlib?
- Fast numerical operations (NumPy)
- Easy visualization (Matplotlib)
- Real-time data plotting
- Used in AI and ML workflows
- Simplifies data analysis
1. Installing Matplotlib
pip install matplotlib
2. Basic Line Plot
import numpy as np
import matplotlib.pyplot as plt
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 6, 8, 10])
plt.plot(x, y)
plt.title("Simple Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.show()
3. Line Plot with NumPy Range
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 10, 1)
y = x ** 2
plt.plot(x, y)
plt.title("Square Function Plot")
plt.show()
4. Scatter Plot
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randint(1, 50, 10)
y = np.random.randint(1, 50, 10)
plt.scatter(x, y)
plt.title("Scatter Plot Example")
plt.show()
5. Bar Chart
import numpy as np
import matplotlib.pyplot as plt
x = np.array(["A", "B", "C", "D"])
y = np.array([10, 20, 15, 25])
plt.bar(x, y)
plt.title("Bar Chart Example")
plt.show()
6. Histogram
import numpy as np
import matplotlib.pyplot as plt
data = np.random.randn(1000)
plt.hist(data, bins=30)
plt.title("Histogram Example")
plt.show()
7. Pie Chart
import numpy as np
import matplotlib.pyplot as plt
labels = np.array(["Python", "Java", "C++", "JavaScript"])
sizes = np.array([40, 25, 20, 15])
plt.pie(sizes, labels=labels, autopct="%1.1f%%")
plt.title("Pie Chart Example")
plt.show()
8. Multiple Plots
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 5, 1)
plt.plot(x, x)
plt.plot(x, x**2)
plt.plot(x, x**3)
plt.title("Multiple Line Plots")
plt.show()
9. NumPy + Matplotlib Workflow
NumPy → Data Creation & Processing
Matplotlib → Data Visualization
10. Real-World Example: Sales Data Visualization
import numpy as np
import matplotlib.pyplot as plt
months = np.array(["Jan", "Feb", "Mar", "Apr"])
sales = np.array([200, 300, 250, 400])
plt.plot(months, sales, marker='o')
plt.title("Monthly Sales Report")
plt.show()
11. Real-World Example: Stock Prices
import numpy as np
import matplotlib.pyplot as plt
days = np.arange(1, 11)
price = np.random.randint(100, 200, 10)
plt.plot(days, price)
plt.title("Stock Price Movement")
plt.show()
Common Plot Types
| Plot Type | Purpose |
|---|---|
| Line Plot | Trends over time |
| Scatter Plot | Relationship between variables |
| Bar Chart | Comparison |
| Histogram | Data distribution |
| Pie Chart | Percentage distribution |
Advantages of NumPy + Matplotlib
- Fast data processing
- Easy visualization
- Powerful for analysis
- Essential for ML and AI
- Works with large datasets
Real-World Applications
- Data science dashboards
- Financial analysis
- Machine learning visualization
- Scientific experiments
- Business reporting
- AI model evaluation
Summary
NumPy and Matplotlib together form a powerful data analysis and visualization toolkit. NumPy handles numerical data efficiently, while Matplotlib transforms it into meaningful graphs and charts.
Both are essential tools in NumPy and visualization workflows built with Python.
Conclusion
Learning NumPy with Matplotlib is essential for anyone in data science or machine learning. It allows you to process data efficiently and visualize insights clearly using simple Python code.
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