深入探索：Python高级数据可视化技巧与定制化应用

import matplotlib.pyplot as plt
import numpy as np
​
# 生成随机数据
np.random.seed(0)
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100) * 1000  # 点的大小
​
# 自定义颜色映射
colors = z
​
plt.scatter(x, y, s=z, c=colors, cmap='viridis', alpha=0.5)
plt.colorbar()  # 显示颜色条
plt.show()

import matplotlib.pyplot as plt
​
# 数据
categories = ['A', 'B', 'C', 'D', 'E']
values = [7, 13, 5, 17, 10]
​
# 创建柱状图
plt.bar(categories, values)
​
# 自定义标签
plt.xlabel('Categories', fontsize=14, color='blue')
plt.ylabel('Values', fontsize=14, color='green')
plt.title('Customized Labels', fontsize=16, color='red')
​
plt.show()

import matplotlib.pyplot as plt
import numpy as np
​
# 定义自定义颜色映射函数
def custom_color_map(value):
    if value < 0.5:
        return 'red'
    else:
        return 'blue'
​
# 生成随机数据
np.random.seed(0)
x = np.random.rand(10)
y = np.random.rand(10)
values = np.random.rand(10)
​
# 使用自定义颜色映射函数
colors = [custom_color_map(value) for value in values]
​
# 创建散点图
plt.scatter(x, y, c=colors)
plt.show()

import matplotlib.pyplot as plt
​
# 数据
categories = ['Category A', 'Category B', 'Category C', 'Category D', 'Category E']
values = [7, 13, 5, 17, 10]
​
# 创建柱状图
plt.bar(categories, values)
​
# 添加格式化文本到标签
for i, value in enumerate(values):
    plt.text(i, value + 0.5, f'{value} units', ha='center')
​
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Chart with Formatted Labels')
​
plt.show()

import matplotlib.pyplot as plt
import numpy as np
​
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
​
plt.figure(figsize=(10, 5))
​
plt.subplot(1, 2, 1)
plt.plot(x, y1)
plt.title('Sin Function')
​
plt.subplot(1, 2, 2)
plt.plot(x, y2)
plt.title('Cos Function')
​
plt.show()

import matplotlib.pyplot as plt
​
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [1, 2, 3, 4, 5]
​
plt.plot(x, y1, label='y = x^2')
plt.plot(x, y2, label='y = x')
​
plt.legend(loc='upper left')
plt.show()

import matplotlib.pyplot as plt
import numpy as np
​
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
​
x = np.random.standard_normal(100)
y = np.random.standard_normal(100)
z = np.random.standard_normal(100)
​
ax.scatter(x, y, z)
​
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
​
plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.animation import FuncAnimation
​
fig, ax = plt.subplots()
xdata, ydata = [], []
ln, = plt.plot([], [], 'r-', animated=True)
​
def init():
    ax.set_xlim(0, 2*np.pi)
    ax.set_ylim(-1, 1)
    return ln,
​
def update(frame):
    xdata.append(frame)
    ydata.append(np.sin(frame))
    ln.set_data(xdata, ydata)
    return ln,
​
ani = FuncAnimation(fig, update, frames=np.linspace(0, 2*np.pi, 128),
                    init_func=init, blit=True)
plt.show()

import plotly.express as px

df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", size="petal_length",
                 hover_data=['petal_width'], title="Interactive Scatter Plot with Plotly Express")
fig.show()

import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="whitegrid")

tips = sns.load_dataset("tips")
sns.barplot(x="day", y="total_bill", hue="sex", data=tips)
plt.title("Bar Plot with Customized Style")
plt.show()