每日一题33：数据统计之广告效果

一、每日一题

返回结果示例如下：

示例 1：

输入：
Ads 表:
+-------+---------+---------+
| ad_id | user_id | action  |
+-------+---------+---------+
| 1     | 1       | Clicked |
| 2     | 2       | Clicked |
| 3     | 3       | Viewed  |
| 5     | 5       | Ignored |
| 1     | 7       | Ignored |
| 2     | 7       | Viewed  |
| 3     | 5       | Clicked |
| 1     | 4       | Viewed  |
| 2     | 11      | Viewed  |
| 1     | 2       | Clicked |
+-------+---------+---------+
输出：
+-------+-------+
| ad_id | ctr   |
+-------+-------+
| 1     | 66.67 |
| 3     | 50.00 |
| 2     | 33.33 |
| 5     | 0.00  |
+-------+-------+
解释：
对于 ad_id = 1, ctr = (2/(2+1)) * 100 = 66.67
对于 ad_id = 2, ctr = (1/(1+2)) * 100 = 33.33
对于 ad_id = 3, ctr = (1/(1+1)) * 100 = 50.00
对于 ad_id = 5, ctr = 0.00, 注意 ad_id = 5 没有被点击 (Clicked) 或查看 (Viewed) 过
注意我们不关心 action 为 Ingnored 的广告

解答：

import pandas as pddef ads_performance(ads_df: pd.DataFrame) -> pd.DataFrame:
# 过滤掉 Ignored 行filtered_df = ads_df[ads_df['action'] != 'Ignored']# 计算每个 ad_id 的点击次数和查看次数summary = filtered_df.groupby(['ad_id', 'action']).size().unstack(fill_value=0)# 确保有 Clicked 和 Viewed 列，如果不存在则创建并填充为0summary['Clicked'] = summary.get('Clicked', 0)summary['Viewed'] = summary.get('Viewed', 0)
# 计算CTRsummary['CTR'] = (summary['Clicked'] / (summary['Clicked'] + summary['Viewed'])).fillna(0) * 100# 处理没有被点击或查看的广告，赋值为0full_ad_ids = ads_df['ad_id'].unique()ctr_df = summary.reindex(full_ad_ids, fill_value = 0).reset_index()ctr_df['CTR'] = ctr_df['CTR'].fillna(0).round(2)# 排序，按CTR降序和ad_id升序排序ctr_df = ctr_df.sort_values(by = ['CTR', 'ad_id'], ascending = [False, True])[['ad_id', 'CTR']]return ctr_df# 测试代码
data = {'ad_id': [1, 2, 3, 5, 1, 2, 3, 1, 2, 1],'user_id': [1, 2, 3, 5, 7, 7, 5, 4, 11, 2],'action': ['Clicked', 'Clicked', 'Viewed', 'Ignored', 'Ignored', 'Viewed', 'Clicked', 'Viewed', 'Viewed', 'Clicked']
}
ads_df = pd.DataFrame(data)result = ads_performance(ads_df)
print(result)

二、总结

注意分母为0的情况时如何处理。