Pandas：按Cell字段数拆分DataFrame并设置指定单元格样式

原始数据 #

import pandas as pd
import numpy as np

d = {'Cell':['cell_D1_TY_L_90','cell4_D2_TY_L_90','cell6_TY_L_90','cell2_D4_TY_L_90','cell1_L_90'],
     'D1':[5, 2, 2, 6,6], 
     'D2':[np.nan, 5, 6, np.nan,3], 
     'D3':[7,np.nan, 5, 5,np.nan], 
     'D6':[17, 3, np.nan,np.nan,2],
     'diff%':[np.nan,['D2'],['D2','D3'],['D1','D3'],['D1','D2','D6']]}
df = pd.DataFrame(d)

1. 实现单元格高亮样式 #

定义行级样式函数，根据diff%列指定的列名，将对应单元格设置为红色加粗：

def highlight_diff(row):
    styles = [''] * len(row)
    highlight_cols = row['diff%']
    if not pd.isna(highlight_cols):
        for col in highlight_cols:
            if col in row.index:
                col_idx = row.index.get_loc(col)
                styles[col_idx] = 'font-weight: bold; color: red;'
    return styles

# 应用样式到整个DataFrame
styled_df = df.style.apply(highlight_diff, axis=1)

2. 拆分DataFrame并保留样式 #

通过拆分Cell列的下划线分隔字段，统计字段数量后拆分数据：

# 计算每行Cell字段的分割数量
df['cell_field_count'] = df['Cell'].str.split('_').str.len()

# 筛选Cell拆分后恰好5个字段的行，应用样式
df_5_fields = df[df['cell_field_count'] == 5].drop('cell_field_count', axis=1)
styled_df_5 = df_5_fields.style.apply(highlight_diff, axis=1)

# 筛选Cell拆分后字段数少于5个的行，应用样式
df_less_5 = df[df['cell_field_count'] < 5].drop('cell_field_count', axis=1)
styled_df_less_5 = df_less_5.style.apply(highlight_diff, axis=1)

• styled_df_5 为包含Cell列拆分后恰好5个字段的带样式DataFrame
• styled_df_less_5 为包含Cell列拆分后字段数少于5个的带样式DataFrame

内容的提问来源于stack exchange，提问作者sandeep