Question

所以，这是我的数据框。

session_id  question_difficulty     attempt_updated_at
5c822af21c1fba22            2   1557470128000
5c822af21c1fba22            3   1557469685000
5c822af21c1fba22            4   1557470079000
5c822af21c1fba22            5   1557472999000
5c822af21c1fba22            3   1557474145000
5c822af21c1fba22            3   1557474441000
5c822af21c1fba22            4   1557474299000
5c822af21c1fba22            4   1557474738000
5c822af21c1fba22            3   1557475430000
5c822af21c1fba22            4   1557476960000
5c822af21c1fba22            5   1557477458000
5c822af21c1fba22            2   1557478118000
5c822af21c1fba22            5   1557482556000
5c822af21c1fba22            4   1557482809000
5c822af21c1fba22            5   1557482886000
5c822af21c1fba22            5   1557484232000

我想将字段“ attempt_updated_at”（是纪元时间）切成两个相等的bin，并在每个会话的那个bin中找到“ question_difficulty”的平均值。

我想分别存储第一仓和第二仓的均值。

我尝试过pd.cut，但我不知道如何使用它。

我希望我的输出像

例如，

session_id         mean1_difficulty       mean2_difficulty
5c822af21c1fba22            5.0                3.0

任何想法都值得赞赏，谢谢。

Answer 1

我相信您需要qcut和总计df1 = (df.groupby(['session_id', pd.qcut(df['attempt_updated_at'], 2, labels=False)]) ['question_difficulty'].mean() .unstack() .rename(columns=lambda x: f'mean{x+1}_difficulty')) print (df1) attempt_updated_at mean1_difficulty mean2_difficulty session_id 5c822af21c1fba22 3.5 4.125：

df1 = (df.groupby(['session_id', pd.cut(df['attempt_updated_at'], 2, labels=False)])
         ['question_difficulty'].mean()
                                .unstack()
                                .rename(columns=lambda x: f'mean{x+1}_difficulty'))
print (df1)
attempt_updated_at  mean1_difficulty  mean2_difficulty
session_id                                            
5c822af21c1fba22            3.444444          4.285714

或cut：

try {
  const result = await axios.post(`YOUR_URL`, {<Your JSON payload>});
} catch (error) {
  console.error(error);
}

函数之间的差异可以更好地解释here。

Answer 2

我认为应该这样做：

pdf.sort_values('attempt_updated_at', ascending=False, inplace=True).reset_index(drop=True)
first = pdf.iloc[:pdf.shape[0] // 2]
second = pdf.iloc[pdf.shape[0] // 2:]

res = pd.DataFrame(first.groupby('session_id')['question_difficulty'].agg('mean')) \
    .rename(columns={'question_difficulty': 'mean1_difficulty'}) \
    .join(second.groupby('session_id')['question_difficulty'].agg('mean')) \
    .rename(columns={'question_difficulty': 'mean2_difficulty'})

如何使用groupby在两个bin中剪切一列并汇总每个bin的数据？

2 个答案: