Python Tkinter数据可视化：交互式图表绘制

import tkinter as tk
from tkinter import ttk
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import pandas as pd

# 示例数据，实际应用中请替换成你的数据
data = pd.DataFrame({
    'gender': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
    'forehead_width_cm': [12.5, 11.8, 13.1, 12.2, 12.9, 11.6, 12.7, 12.0, 13.2, 11.9],
    'forehead_height_cm': [6.3, 5.9, 6.5, 6.1, 6.4, 5.8, 6.6, 6.0, 6.7, 5.7],
    'long_hair': [1, 0, 1, 0, 1, 1, 0, 0, 1, 0],
    'nose_wide': [0, 1, 0, 1, 0, 0, 1, 1, 0, 1],
    'nose_long': [1, 0, 1, 0, 1, 1, 0, 0, 1, 0],
    'lips_thin': [0, 1, 0, 1, 0, 0, 1, 1, 0, 1],
    'distance_nose_to_lip_long': [1, 0, 1, 0, 1, 1, 0, 0, 1, 0]
})

def vision():
    # 创建主窗口
    vision_desk = tk.Tk()
    vision_desk.title('数据可视化')
    vision_desk.geometry('800x600')

    # 创建下拉列表选择性别
    gender_label = ttk.Label(vision_desk, text='请选择性别')
    gender_label.pack(pady=10)
    gender_var = tk.StringVar(value='男')
    gender_combobox = ttk.Combobox(vision_desk, textvariable=gender_var, values=['男', '女'])
    gender_combobox.pack()

    # 创建下拉列表选择特征
    feature_label = ttk.Label(vision_desk, text='请选择图的特征')
    feature_label.pack(pady=10)
    feature_var = tk.StringVar(value='forehead_width_cm')
    feature_combobox = ttk.Combobox(vision_desk, textvariable=feature_var, values=['forehead_width_cm', 'forehead_height_cm', 'long_hair', 'nose_wide', 'nose_long', 'lips_thin', 'distance_nose_to_lip_long'])
    feature_combobox.pack()

    # 创建画布
    canvas = tk.Canvas(vision_desk, width=600, height=400)
    canvas.pack(pady=20)

    def update_plot():
        # 清空画布上的内容
        for widget in canvas.winfo_children():
            widget.destroy()

        # 获取选择的性别和特征
        gender = 1 if gender_combobox.get() == '男' else 0
        feature = feature_var.get()  # 获取更新后的特征值

        # 根据选择的特征绘制不同的图形
        if feature in ['forehead_width_cm', 'forehead_height_cm']:
            # 绘制直方图
            df = data[data['gender'] == gender][feature]
            # 计算分组间隔
            bin_width = (df.max() - df.min()) / 10
            # 绘制直方图
            fig, ax = plt.subplots()
            ax.hist(df, bins=np.arange(df.min(), df.max()+bin_width, bin_width), align='mid', histtype='bar', rwidth=0.8)
            ax.set_xlabel(feature)
            ax.set_ylabel('count')
        else:
            # 绘制柱状图
            fig, ax = plt.subplots()
            ax.bar([0, 1], [data[(data['gender'] == gender) & (data[feature] == 0)].shape[0], data[(data['gender'] == gender) & (data[feature] == 1)].shape[0]])
            ax.set_xticks([0, 1])
            ax.set_xticklabels(['0', '1'])
            ax.set_xlabel(feature)
            ax.set_ylabel('count')
            ax.set_title(f'{feature} ({gender})')

        # 将图像转换为画布上的图片
        fig_canvas = FigureCanvasTkAgg(fig, master=canvas)
        fig_canvas.draw()
        fig_canvas.get_tk_widget().pack(side='top', fill='both', expand=1)

        # 保存图像 (可选)
        # plt.savefig('temp.png')

    # 创建按钮，并绑定更新图的函数
    update_button = ttk.Button(vision_desk, text='更新图', command=update_plot)
    update_button.pack(pady=10)

    # 运行主循环
    vision_desk.mainloop()

vision()