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Pandas has built-in basic plotting functionality, and combining it with Matplotlib and Seaborn allows you to create richer visualization charts.\\n\\n* * *\\n\\n## Pandas Built-in Plotting\\n\\n### Line Chart\\n\\n## Example\\n\\nimport pandas as pd\\n\\nimport numpy as np\\n\\nimport matplotlib.pyplot as plt\\n\\n# Set Chinese display (requires Chinese fonts installed on the system)\\n\\n# plt.rcParams['font.sans-serif'] = ['SimHei']\\n\\n# plt.rcParams['axes.unicode_minus'] = False\\n\\n# Create time series data\\n\\n dates = pd.date_range("2024-01-01", periods=30, freq="D")\\n\\n df = pd.DataFrame({\\n\\n"Date": dates,\\n\\n"Sales Revenue": 100 + np.random.randn(30).cumsum(),\\n\\n"Visitor count": 50 + np.random.randn(30).cumsum() * 10\\n\\n})\\n\\n df = df.set_index("Date")\\n\\nprint("Data:")\\n\\nprint(df.head())\\n\\nprint("\\\\n Plotting data preparation complete, please use plt.show() Display plot")\\n\\n### Bar Chart\\n\\n## Example\\n\\nimport pandas as pd\\n\\nimport matplotlib.pyplot as plt\\n\\n# Categorical data\\n\\n data ={\\n\\n"Product": ["A","B","C","D","E"],\\n\\n"Sales Volume": [120,150,90,180,110]\\n\\n}\\n\\n df = pd.DataFrame(data)\\n\\n# Bar plot\\n\\nprint("Using df.plot.bar() Plot Bar Chart")\\n\\nprint("\\\\n Data:")\\n\\nprint(df)\\n\\n### Pie Chart\\n\\n## Example\\n\\nimport pandas as pd\\n\\n# Pie Chartdata\\n\\n s = pd.Series([30,25,20,15,10], index=["A","B","C","D","E"])\\n\\nprint("Using s.plot.pie() Plot Pie Chart")\\n\\nprint("\\\\n Data:")\\n\\nprint(s)\\n\\n* * *\\n\\n## Matplotlib Integration\\n\\n## Example\\n\\nimport pandas as pd\\n\\nimport numpy as np\\n\\n# Create Data\\n\\n df = pd.DataFrame({\\n\\n"x": range(10),\\n\\n"y1": np.random.randn(10).cumsum(),\\n\\n"y2": np.random.randn(10).cumsum() + 5\\n\\n})\\n\\nprint("Matplotlib Plotting example code:")\\n\\nprint("""\\n\\n import matplotlib.pyplot as plt\\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\\n\\n# Line plot\\n\\n ax1.plot(df, df, label="Y1", color="blue")\\n\\n ax1.plot(df, df, label="Y2", color="red")\\n\\n ax1.set_title("Line plot")\\n\\n ax1.legend()\\n\\n ax1.grid(True)\\n\\n# Scatter Plot\\n\\n ax2.scatter(df, df, alpha=0.6)\\n\\n ax2.set_title("Scatter Plot")\\n\\n ax2.set_xlabel("Y1")\\n\\n ax2.set_ylabel("Y2")\\n\\nplt.tight_layout()\\n\\n plt.show()\\n\\n """\\n\\n)\\n\\n### Subplot Layout\\n\\n## Example\\n\\nimport pandas as pd\\n\\nimport numpy as np\\n\\n# 2x2 Subplot example\\n\\nprint("2x2 Subplot layout example:")\\n\\nprint("""\\n\\n fig, axes = plt.subplots(2, 2, figsize=(10, 8))\\n# 1. Line plot\\n\\n df.plot(ax=axes[0, 0])\\n\\n axes[0, 0].set_title("Line plot")\\n\\n# 2. Bar plot\\n\\n df.plot.bar(ax=axes[0, 1])\\n\\n axes[0, 1].set_title("Bar plot")\\n\\n# 3. Histogram\\n\\n df.hist(ax=axes[1, 0], bins=10)\\n\\n axes[1, 0].set_title("Histogram")\\n\\n# 4. Pie Chart\\n\\n pd.Series([10, 20, 30]).plot.pie(ax=axes[1, 1])\\n\\n axes[1, 1].set_title("Pie Chart")\\n\\nplt.tight_layout()\\n\\n plt.show()\\n\\n """\\n\\n)\\n\\n* * *\\n\\n## Seaborn Advanced Visualization\\n\\n## Example\\n\\nimport pandas as pd\\n\\nimport numpy as np\\n\\n# Demonstrating Seaborn features\\n\\nprint("Seaborn Visualization example:")\\n\\nprint("""\\n\\n import seaborn as sns\\n\\n import matplotlib.pyplot as plt\\n# Set style\\n\\n sns.set_style("whitegrid")\\n\\n# 1. Relational plot\\n\\n fig, ax = plt.subplots(figsize=(8, 5))\\n\\n sns.lineplot(data=df, x="x", y="y1", ax=ax)\\n\\n ax.set_title("Line plot")\\n\\n# 2. Distribution plot\\n\\n sns.histplot(df, kde=True, ax=ax)\\n\\n# 3. Box plot\\n\\n sns.boxplot(data=df, x="category", y="value")\\n\\n# 4. Heatmap\\n\\n sns.heatmap(df.corr(), annot=True, cmap="coolwarm")\\n\\nplt.show()\\n\\n """\\n\\n)\\n\\n> Matplotlib provides low-level control, while Seaborn provides a high-level interface. Combining the two a