基于id pandas更新列

Question

df_2:

order_id   date        amount name   interval is_sent
123        2020-01-02  3      white  today    false
456        NaT         2      blue   weekly   false
789        2020-10-11  0      red    monthly  false
135        2020-6-01   3      orange weekly   false

我正在合并两个数据帧，定位日期何时大于之前的结果，并查看数据类型是否发生了变化：

df_1['date'] = pd.to_datetime(df_1['date'])
df_2['date'] = pd.to_datetime(df_2['date'])
res = df_1.merge(df_2, on='order_id', suffixes=['_orig', ''])
m = res['date'].gt(res['date_orig']) | (res['date_orig'].isnull() & res['date'].notnull())
changes_df = res.loc[m, ['order_id', 'date', 'amount', 'name', 'interval', 'is_sent']]

在找到我的所有实体后，我将changes_df['is_sentd']更改为true:

changes_df['is_sent'] = True

运行以上操作后，changes_df为：

order_id   date        amount name   interval is_sent
123        2020-01-03  3      white  today    true
456        2020-12-01  2      blue   weekly   true
135        2020-6-02   3      orange weekly   true

然后我只想将df_2['date']和df_2['is_sent']中的值更新为等于changes_df['date']和changes_df['is_sent']

任何见解都将不胜感激。

Answer 1

让我们试用update和set_index

cf = changes_df[['order_id','date','is_sent']].set_index('order_id')
df_2 = df_2.set_index('order_id')
df_2.update(cf)
df_2.reset_index(inplace=True)
df_2
order_id        date  amount    name interval is_sent
0       123  2020-01-03       3   white    today    True
1       456  2020-12-01       2    blue   weekly    True
2       789  2020-10-11       0     red  monthly   False
3       135   2020-6-02       3  orange   weekly    True

Answer 2

df3 = df2.combine_first(
cap_df1).reindex(df.index)

这是我的解决方案