如何在jupyter notebook中查看更详细的图表

import matplotlib.pyplot as plt

# 设置全局配置
plt.rcParams['figure.figsize'] = (10, 6)  # 设置图表大小
plt.rcParams['font.size'] = 14  # 设置字体大小
plt.rcParams['axes.unicode_minus'] = False  # 解决负号'-'显示为方块的问题

# 示例图表
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.xlabel('X轴')
plt.ylabel('Y轴')
plt.title('示例图表')
plt.grid(True)  # 显示网格
plt.show()

import seaborn as sns
import pandas as pd

# 示例数据
data = pd.DataFrame({
    'X': [1, 2, 3, 4],
    'Y': [10, 20, 25, 30]
})

# 使用Seaborn绘制图表
sns.set(style="whitegrid")  # 设置网格样式
sns.lineplot(x='X', y='Y', data=data)
plt.show()

import plotly.express as px

# 示例数据
data = pd.DataFrame({
    'X': [1, 2, 3, 4],
    'Y': [10, 20, 25, 30]
})

# 使用Plotly绘制图表
fig = px.line(data, x='X', y='Y', title='示例图表')
fig.update_layout(xaxis_title='X轴', yaxis_title='Y轴')
fig.show()

from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource

# 启用Jupyter Notebook输出
output_notebook()

# 示例数据
data = {'X': [1, 2, 3, 4], 'Y': [10, 20, 25, 30]}
source = ColumnDataSource(data)

# 使用Bokeh绘制图表
p = figure(title='示例图表', x_axis_label='X轴', y_axis_label='Y轴')
p.line('X', 'Y', source=source, line_width=2)
show(p)

from tensorflow.keras.callbacks import TensorBoard

# 示例TensorBoard回调
tensorboard_callback = TensorBoard(log_dir='./logs')

# 在训练模型时使用回调
model.fit(X_train, y_train, epochs=10, callbacks=[tensorboard_callback])

%load_ext tensorboard
%tensorboard --logdir ./logs