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matplot常备设置

news 2024/10/28 20:19:35

1. 常用包

import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.simplefilter("ignore")

2. 设置全局字体大小

from matplotlib import rcParams
# 设置全局字体大小
rcParams['font.size'] = 20  # 设置全局字体大小

3. 新建图片保存文件夹

# 确保 img 文件夹存在，如果不存在则创建它
img_dir = './img'
if not os.path.exists(img_dir):os.makedirs(img_dir)

4. 调整图像画布空白间距

plt.tight_layout()

5. 多子图

绘制箱线图

import matplotlib.pyplot as plt
import seaborn as sns
# 设置图形大小和布局
plt.figure(figsize=(20, 30))
plt.subplots_adjust(hspace=0.5)# 遍历每个列名，绘制箱线图
for i, column in enumerate(df.columns, 1):plt.subplot(8, 4, i)  # 创建子图，8行4列布局，第 i 个子图sns.boxplot(x=df[column], orient='v')  # 绘制箱线图plt.title(column)  # 设置子图标题plt.tight_layout()  # 调整子图布局，避免重叠
plt.savefig("./img/df_box.png")
plt.show()  # 显示图形

绘制混淆矩阵

for i, best_model in enumerate(best_estimators):plt.figure(figsize=(6, 4))# Predict on test sety_pred = best_model.predict(sx_test)# Compute confusion matrixcm = confusion_matrix(y_test, y_pred)# Plot confusion matrix using seaborn heatmapsns.heatmap(cm, annot=True, fmt='d', cmap='Blues')plt.title(f'Confusion Matrix - {type(best_model).__name__}')plt.xlabel('Predicted labels')plt.ylabel('True labels')# Ensure the plot doesn't overlap with previous plotsplt.tight_layout()# Save the figure if neededplt.savefig(f'./img/ConfusionMatrix_{type(best_model).__name__}2.png')# Show the plotplt.show()

6. 绘制双边y轴

fig = plt.figure()
ax1 = fig.add_subplot(111)
t = np.arange(0.01, 10.0, 0.01)
s1 = np.exp(t)
ax1.plot(t, s1, 'b-')
ax1.set_xlabel('time (s)')
ax1.set_ylabel('exp')ax2 = ax1.twinx()
s2 = np.sin(2*np.pi*t)
ax2.plot(t, s2, 'r.')
ax2.set_ylabel('sin')
plt.show()

在这里插入图片描述

import matplotlib.pyplot as plt
import numpy as np# Create some mock data
t = np.arange(0.01, 10.0, 0.01)
data1 = np.exp(t)
data2 = np.sin(2 * np.pi * t)fig, ax1 = plt.subplots()color = 'tab:red'
ax1.set_xlabel('time (s)')
ax1.set_ylabel('exp', color=color)
ax1.plot(t, data1, color=color)
ax1.tick_params(axis='y', labelcolor=color)ax2 = ax1.twinx()  # instantiate a second Axes that shares the same x-axiscolor = 'tab:blue'
ax2.set_ylabel('sin', color=color)  # we already handled the x-label with ax1
ax2.plot(t, data2, color=color)
ax2.tick_params(axis='y', labelcolor=color)fig.tight_layout()  # otherwise the right y-label is slightly clipped
plt.show()