Question

合并两个数据帧：我有两个数据帧需要合并某些条件，但我还没有弄清楚如何做到这一点？

df1 : 

id            positive_action    date             volume  
id_1          user 1                  2016-12-12       19720.735
              user 2                  2016-12-12       14740.800

df2 :
id            negative_action        date             volume  
id_1          user 1                  2016-12-12       10.000
              user 3                  2016-12-12       10.000     

I want : 

id            action        date             volume  
id_1          user 1         2016-12-12       19730.735
              user 2         2016-12-12       14740.800   
              user 3         2016-12-12       10.000

这里

卷跨两个数据框汇总
合并ID，日期和（积极行动和否定行动合并在一起）

我如何实现这一目标？

Answer 1

您还可以在将positive_action和negative_action列重命名为action之后连接您的DataFrame，然后执行groupby。

df1.rename(columns={'positive_action':'action'}, inplace=True)
df2.rename(columns={'negative_action':'action'}, inplace=True)
pd.concat([df1, df2]).groupby(['id', 'action', 'date']).sum().reset_index()


     id  action        date     volume
0  id_1  user 1  2016-12-12  19730.735
1  id_1  user 2  2016-12-12  14740.800
2  id_1  user 3  2016-12-12     10.000

Answer 2

这应该有效：

# not sure what indexing you are using so lets remove it
# to get on the same page, so to speak ;).
df1 = df1.reset_index()
df2 = df2.reset_index()

# do an outer merge to allow mismatches on the actions.
df = df1.merge(
    df2, left_on=['id', 'positive_action', 'date'],
    right_on=['id', 'negative_action', 'date'],
    how='outer',
)


# fill the missing actions from one with the other.
# (Will only happen when one is missing due to the way we merged.)
df['action'] = df['positive_action'].fillna(df['negative_action'])

# drop the old actions
df = df.drop('positive_action', 1)
df = df.drop('negative_action', 1)

# aggregate the volumes (I'm assuming you mean a simple sum)
df['volume'] = df['volume_x'].fillna(0) + df['volume_y'].fillna(0)

# drop the old volumes
df = df.drop('volume_x', 1)
df = df.drop('volume_y', 1)

print(df)

输出结果为：

     id        date     volume  action
0  id_1  2016-12-12  19730.735  user_1
1  id_1  2016-12-12  14740.800  user_2
2  id_1  2016-12-12     10.000  user_3

然后，您可以恢复我可能已删除的索引。

Answer 3

set_index在
rename_axis因为当我们add时，如果我们的索引级别不一致，就会让熊猫哭泣。
将pd.Series.add与参数fill_value=0
rename_axis
reset_index，您正在开展业务

v1 = df1.set_index(['positive_action', 'date']).volume.rename_axis([None, None])
v2 = df2.set_index(['negative_action', 'date']).volume.rename_axis([None, None])
v1.add(v2, fill_value=0).rename_axis(['action', 'date']).reset_index()

   action       date     volume
0  user 1 2016-12-12  19730.735
1  user 2 2016-12-12  14740.800
2  user 3 2016-12-12     10.000

设置

df1 = pd.DataFrame(dict(
        positive_action=['user 1', 'user 2'],
        date=pd.to_datetime(['2016-12-12', '2016-12-12']),
        volume=[19720.735, 14740.800]
    ))

df2 = pd.DataFrame(dict(
        negative_action=['user 1', 'user 3'],
        date=pd.to_datetime(['2016-12-12', '2016-12-12']),
        volume=[10, 10]
    ))

将具有不同列名的Dataframe与聚合列值合并

3 个答案: