将列从一个数据帧添加到python中的数据帧 [英] Add column from one data frame to group-by data frame in python
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问题描述
group.by 的每日降水量的总和。 一个数据框看起来像这样（在device_ids之间有更多行）：
code>>>> df1
device_id雨日月份
0 9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
1 9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
6 e7z581f0-2693-42ad-9896-0048550ccda7 0.0 31 12 2016
11 e7z581f0-2693-42ad-9896-0048550ccda7 0.0 31 12 2016
12 ceez972b-135f-45b3-be4w-7c23102676bq 0.2 31 12 2016
13 ceez972b-135f-45b3-be4w-7c23102676bq 0.0 31 12 2016
18 ceez972b-135f-45b3-be4w-7c23102676bq 0.0 31 12 2016
19 1d28dz3a-c923-4967-a7bb-5881d232c9a7 0.0 31 12 2016
24 1d28dz3a-c923-4967-a7bb-5881d232c9a7 0.0 31 12 2016
25 a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 0.0 31 12 2016
29 a044ag4f-fd7c-4ae4 -bff3-9158cebad3b1 0.0 31 12 2016
29 a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 0.0 31 12 2016
... ... ... ... ... ...
3903 9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
3904 9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
3905 9z849362-b05d -4317-96f5-f267c1adf8d6 0.0 31 12 2016
另外看起来像这样：
>>> df2
rain
device_id
1d28dz3a-c923-4967-a7bb-5881d232c9a7 0.0
9z849362-b05d-4317-96f5-f267c1adf8d6 0.0
a044ag4f-fd7c-4ae4-bff3 -9158cebad3b1 1.2
ceez972b-135f-45b3-be4w-7c23102676bq 2.2
e7z581f0-2693-42ad-9896-0048550ccda7 0.2
...我通过使用：
df2 = df1.groupby（[ 'device_id']）[[rain]] sum（）
我想要我的最终数据框架看起来像这样：
>>> df3
rain day month year
device_id
1d28dz3a-c923-4967-a7bb-5881d232c9a7 0.0 31 12 2016
9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 1.2 31 12 2016
ceez972b-135f-45b3-be4w-7c23102676bq 2.2 31 12 2016
e7z581f0-2693-42ad-9896-0048550ccda7 0.2 31 12 2016
这就是说我想从 df1中的日月列添加到 df2 。我不知道我是否应该使用 merge ， append 或做其他事情。
解决方案
也许这会奏效吗？
df.groupby（['device_id'，'day'，'month'，' year']）sum（）
rain
device_id日月月
1d28dz3a-c923-4967-a7bb-5881d232c9a7 31 12 2016 0.0
9z849362-b05d-4317-96f5- f267c1adf8d6 31 12 2016 0.0
a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 31 12 2016 0.0
ceez972b-135f-45b3-be4w-7c23102676bq 31 12 2016 0.2
e7z581f0-2693-42ad-9896 -0048550ccda7 31 12 2016 0.0
或者你可以添加 reset_index 将这些列返回到DataFrame，如
df.groupby（['device_id'，'day'，'month '，'year']）sum（）。reset_index（）
0 1d28dz3a-c923-4967-a7bb-5881d232c9a7 31 12 2016 0.0
1 9z849362-b05d-4317-96f5- f267c1adf8d6 31 12 2016 0.0
2 a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 31 12 2016 0.0
3 ceez972b-135f-45b3-be4w-7c23102676bq 31 12 2016 0.2
4 e7z581f0-2693-42ad-9896-0048550ccda7 31 12 2016 0.0
或者以下内容应该完全符合您的索引/列结构。
code> df.groupby（['device_id'，'day'，'month'，'year']）sum（）。reset_index（[1，2，3]）
I have two data frames in python. The first is raw rainfall data for a single day of year and the second is the sum of daily rainfall using group.by.
One data frame looks like this (with many more rows in between device_ids):
>>> df1
device_id rain day month year
0 9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
1 9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
6 e7z581f0-2693-42ad-9896-0048550ccda7 0.0 31 12 2016
11 e7z581f0-2693-42ad-9896-0048550ccda7 0.0 31 12 2016
12 ceez972b-135f-45b3-be4w-7c23102676bq 0.2 31 12 2016
13 ceez972b-135f-45b3-be4w-7c23102676bq 0.0 31 12 2016
18 ceez972b-135f-45b3-be4w-7c23102676bq 0.0 31 12 2016
19 1d28dz3a-c923-4967-a7bb-5881d232c9a7 0.0 31 12 2016
24 1d28dz3a-c923-4967-a7bb-5881d232c9a7 0.0 31 12 2016
25 a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 0.0 31 12 2016
29 a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 0.0 31 12 2016
29 a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 0.0 31 12 2016
... ... ... ... ... ...
3903 9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
3904 9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
3905 9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
And the other looks something like this:
>>> df2
rain
device_id
1d28dz3a-c923-4967-a7bb-5881d232c9a7 0.0
9z849362-b05d-4317-96f5-f267c1adf8d6 0.0
a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 1.2
ceez972b-135f-45b3-be4w-7c23102676bq 2.2
e7z581f0-2693-42ad-9896-0048550ccda7 0.2
... which I got by using:
df2 = df1.groupby(['device_id'])[["rain"]].sum()
I want my final data frame to look like this:
>>> df3
rain day month year
device_id
1d28dz3a-c923-4967-a7bb-5881d232c9a7 0.0 31 12 2016
9z849362-b05d-4317-96f5-f267c1adf8d6 0.0 31 12 2016
a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 1.2 31 12 2016
ceez972b-135f-45b3-be4w-7c23102676bq 2.2 31 12 2016
e7z581f0-2693-42ad-9896-0048550ccda7 0.2 31 12 2016
Which is to say that I want the "day month year" columns from df1 to be added to df2. I'm not sure if I should use merge, append, or do something else.
解决方案
Maybe this will work? groupby day month and year as well.
df.groupby(['device_id', 'day', 'month', 'year']).sum()
rain
device_id day month year
1d28dz3a-c923-4967-a7bb-5881d232c9a7 31 12 2016 0.0
9z849362-b05d-4317-96f5-f267c1adf8d6 31 12 2016 0.0
a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 31 12 2016 0.0
ceez972b-135f-45b3-be4w-7c23102676bq 31 12 2016 0.2
e7z581f0-2693-42ad-9896-0048550ccda7 31 12 2016 0.0
Or you could add reset_index to return these columns to the DataFrame like
df.groupby(['device_id', 'day', 'month', 'year']).sum().reset_index()
0 1d28dz3a-c923-4967-a7bb-5881d232c9a7 31 12 2016 0.0
1 9z849362-b05d-4317-96f5-f267c1adf8d6 31 12 2016 0.0
2 a044ag4f-fd7c-4ae4-bff3-9158cebad3b1 31 12 2016 0.0
3 ceez972b-135f-45b3-be4w-7c23102676bq 31 12 2016 0.2
4 e7z581f0-2693-42ad-9896-0048550ccda7 31 12 2016 0.0
Or the following should match your index / column structure exactly.
df.groupby(['device_id', 'day', 'month', 'year']).sum().reset_index([1, 2, 3])
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