17 Essential Python Plotting Code Snippets for Beginners

This article presents 17 essential Python plotting examples for beginners, covering a wide range of chart types and techniques.

1. Plot a straight line

Creates a simple line from (0,0) to (1,1).

import matplotlib.pyplot as plt
x = [0, 1]
y = [0, 1]
plt.plot(x, y)
plt.show()

2. Draw a rectangle

Uses Matplotlib patches to add a rectangle to a subplot.

import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig, ax = plt.subplots()
rect = patches.Rectangle((0.1, 0.1), 0.5, 0.5, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(rect)
plt.show()

3. Bar chart

Compares values across categories.

import matplotlib.pyplot as plt
x = ["A", "B", "C", "D", "E"]
y = [10, 15, 7, 12, 9]
plt.bar(x, y)
plt.title("柱状图示例")
plt.xlabel("类别")
plt.ylabel("数值")
plt.show()

4. Line chart

Shows trends over a sequence.

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [5, 3, 7, 2, 6]
plt.plot(x, y)
plt.title("折线图示例")
plt.xlabel("X轴")
plt.ylabel("Y轴")
plt.show()

5. Pie chart

Displays proportion of categories.

import matplotlib.pyplot as plt
labels = ["A", "B", "C", "D"]
sizes = [30, 20, 25, 15]
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title("饼图示例")
plt.show()

6. Scatter plot

Shows relationship between two variables using Seaborn.

import seaborn as sns
import pandas as pd
data = {'x': [1, 2, 3, 4, 5], 'y': [10, 15, 7, 12, 9]}
df = pd.DataFrame(data)
sns.scatterplot(x='x', y='y', data=df)
plt.title("散点图示例")
plt.show()

7. Box plot

Visualizes data distribution.

import seaborn as sns
import pandas as pd
data = {'value': [10, 15, 7, 12, 9, 18, 20]}
df = pd.DataFrame(data)
sns.boxplot(y='value', data=df)
plt.show()

8. Heatmap

Displays density of a 2‑D dataset.

import seaborn as sns
import pandas as pd
import numpy as np
data = np.random.rand(10, 10)
df = pd.DataFrame(data)
sns.heatmap(df)
plt.title("热力图示例")
plt.show()

9. Contour plot

Shows 2‑D contour lines of a 3‑D function.

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))
plt.contour(X, Y, Z)
plt.title("等高线图示例")
plt.show()

10. Add legend

Labels multiple curves for clarity.

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y1 = [1, 4, 9, 16]
y2 = [2, 5, 10, 17]
plt.plot(x, y1, label='曲线 1')
plt.plot(x, y2, label='曲线 2')
plt.legend()
plt.show()

11. Change color

Enhances visual appeal by setting line color.

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.plot(x, y, color='orange')
plt.show()

12. Plot from CSV file

Reads a CSV and plots date vs. sales.

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('data.csv')
plt.plot(data['日期'], data['销量'])
plt.title("销量趋势图")
plt.xlabel("日期")
plt.ylabel("销量")
plt.show()

13. Simple interactivity with Plotly

Creates an interactive bar chart.

import plotly.express as px
import pandas as pd
data = {'水果': ['苹果', '香蕉', '橙子'], '数量': [10, 15, 8]}
df = pd.DataFrame(data)
fig = px.bar(df, x='水果', y='数量')
fig.show()

14. 3D scatter plot

Displays points in three‑dimensional space.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
ax.scatter(x, y, z)
plt.title("3D 散点图示例")
plt.show()

15. 3D surface plot

Shows a 3‑D surface of a sine function.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z)
plt.title("3D 曲面图示例")
plt.show()

16. Subplots

Displays multiple plots in one figure for comparison.

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 2*np.pi, 100)
y1 = np.sin(x)
y2 = np.cos(x)
fig, axs = plt.subplots(2)
fig.suptitle('子图示例')
axs[0].plot(x, y1)
axs[0].set_title('正弦函数')
axs[1].plot(x, y2)
axs[1].set_title('余弦函数')
plt.show()

17. Real‑time updating plot

Simulates dynamic data such as temperature changes.

import matplotlib.pyplot as plt
import numpy as np
import time
fig, ax = plt.subplots()
x, y = [], []
line, = ax.plot(x, y)
for i in range(10):
    x.append(i)
    y.append(np.random.rand())
    line.set_data(x, y)
    ax.relim()
    ax.autoscale_view()
    plt.pause(0.5)
plt.show()

Conclusion: Try the code snippets and explore Python’s powerful visualization libraries.