Python繪製圖表的高效方法與技術實戰

import numpy as np
import matplotlib.pyplot as plt

# 生成x和y数据
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 绘制折线图
plt.plot(x, y)

# 设置图表标题和轴标签
plt.title("Sin Function")
plt.xlabel("Time")
plt.ylabel("Amplitude")

# 显示图表
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 生成x和y数据
x = np.random.normal(0, 1, 100)
y = np.random.normal(0, 1, 100)

# 绘制散点图
plt.scatter(x, y)

# 设置图表标题和轴标签
plt.title("Scatter Plot")
plt.xlabel("X")
plt.ylabel("Y")

# 显示图表
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 生成数据
categories = ["Apple", "Orange", "Banana"]
counts = [10, 15, 8]

# 绘制柱状图
plt.bar(categories, counts)

# 设置图表标题和轴标签
plt.title("Fruit Counts")
plt.xlabel("Fruit")
plt.ylabel("Count")

# 显示图表
plt.show()

import numpy as np
import seaborn as sns

# 生成数据
data = np.random.normal(0, 1, 100)

# 绘制箱线图
sns.boxplot(data)

# 设置图表标题和轴标签
plt.title("Boxplot")
plt.ylabel("Value")

# 显示图表
plt.show()

import numpy as np
import seaborn as sns

# 生成数据
data = np.random.random((10, 10))

# 绘制热力图
sns.heatmap(data, cmap="coolwarm")

# 设置图表标题
plt.title("Heatmap")

# 显示图表
plt.show()

import seaborn as sns

# 加载数据集
tips = sns.load_dataset("tips")

# 绘制分类图
sns.catplot(x="day", y="total_bill", hue="smoker", kind="bar", data=tips)

# 设置图表标题和轴标签
plt.title("Total Bill by Day and Smoker")
plt.xlabel("Day")
plt.ylabel("Total Bill")

# 显示图表
plt.show()

import plotly.express as px

# 加载数据集
tips = px.data.tips()

# 绘制饼图
fig = px.pie(tips, values='tip', names='day', title='Tips by Day')

# 显示图表
fig.show()

import numpy as np
import plotly.graph_objects as go

# 生成数据
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))

# 绘制3D图
fig = go.Figure(data=[go.Surface(x=X, y=Y, z=Z)])

# 设置图表标题
fig.update_layout(title='3D Surface Plot')

# 显示图表
fig.show()