Python数据可视化：用Bokeh库分析房贷与信用卡违约率

from bokeh.plotting import figure, show,output_file
import numpy as np
import pandas as pd
import pandas
df = pd.read_csv(r'/Users/fuchuanruo/Desktop/可视化作业/实践/train.csv')
# 计算房贷和违约比例
housing_count = df['housing'].value_counts()
default_count = df['default'].value_counts()
housing_ratio = round(housing_count[1] / len(df), 2)
default_ratio = round(default_count[1] / len(df), 2)

# 创建条形图
p = figure(title='房贷和违约比例', x_range=['有房贷', '信用卡违约'], y_range=(0, 1), plot_width=400, plot_height=400)
p.vbar(x=['有房贷'], top=[housing_ratio], width=0.5, color='#FFC107')
p.vbar(x=['信用卡违约'], top=[default_ratio], width=0.5, color='#2196F3')

# 设置样式
p.title.text_font_size = '20pt'
p.xaxis.axis_label_text_font_size = '16pt'
p.yaxis.axis_label_text_font_size = '16pt'
p.xaxis.major_label_text_font_size = '14pt'
p.yaxis.major_label_text_font_size = '14pt'

# 输出文件
output_file('housing_default_ratio.html')

# 显示图形
show(p)