Online Matplotlib Playground – Create Python Plots in Your Browser
Visualize data with Python and Matplotlib right in your browser using our online Matplotlib playground. Instantly render plots — no setup needed, perfect for data science.
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📊 Introduction to Matplotlib
1. What is Matplotlib?
Matplotlib is a popular Python library used for creating static, animated, and interactive visualizations. It is especially useful in data science and scientific computing.
import matplotlib.pyplot as plt2. Line Plot
A basic line chart using X and Y values.
import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [2, 4, 6, 8]
plt.plot(x, y)
plt.title("Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.show()3. Bar Chart
Bar charts are useful for comparing discrete categories.
categories = ['A', 'B', 'C']
values = [5, 7, 3]
plt.bar(categories, values)
plt.title("Bar Chart")
plt.show()4. Scatter Plot
Use scatter plots to show relationships between two numeric variables.
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.scatter(x, y)
plt.title("Scatter Plot")
plt.show()5. Pie Chart
Pie charts are used to show proportions.
labels = ['Apple', 'Banana', 'Cherry']
sizes = [30, 40, 30]
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title("Fruit Distribution")
plt.show()6. Histogram
Histograms help understand the distribution of numerical data.
import numpy as np
data = np.random.randn(1000)
plt.hist(data, bins=30)
plt.title("Histogram")
plt.show()7. Multiple Plots
Plot multiple charts in one figure using subplots.
x = [1, 2, 3, 4]
y1 = [1, 4, 9, 16]
y2 = [2, 3, 5, 7]
plt.subplot(1, 2, 1)
plt.plot(x, y1)
plt.title("Plot 1")
plt.subplot(1, 2, 2)
plt.plot(x, y2)
plt.title("Plot 2")
plt.tight_layout()
plt.show()8. Customize Styles
You can customize colors, styles, markers, and more.
plt.plot(x, y1, color='red', linestyle='--', marker='o')
plt.title("Styled Line")
plt.show()9. Save Plot as Image
Save your plot to an image file using savefig().
plt.plot(x, y1)
plt.savefig("my_plot.png")10. 3D Plot (Advanced)
Matplotlib also supports 3D plotting via mpl_toolkits.mplot3d.
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = [1, 2, 3]
y = [2, 3, 4]
z = [5, 6, 7]
ax.scatter(x, y, z)
plt.show()