[英]How to move pandas data from index to column after multiple groupby
I have the following pandas dataframe:我有以下熊猫数据框:
token year uses books
386 xanthos 1830 3 3
387 xanthos 1840 1 1
388 xanthos 1840 2 2
389 xanthos 1868 2 2
390 xanthos 1875 1 1
Method #1 : reset_index()
方法#1 : reset_index()
>>> g
uses books
sum sum
token year
xanthos 1830 3 3
1840 3 3
1868 2 2
1875 1 1
[4 rows x 2 columns]
>>> g = g.reset_index()
>>> g
token year uses books
sum sum
0 xanthos 1830 3 3
1 xanthos 1840 3 3
2 xanthos 1868 2 2
3 xanthos 1875 1 1
[4 rows x 4 columns]
Method #2 : don't make the index in the first place, using as_index=False
方法#2 :首先不要创建索引,使用as_index=False
>>> g = dfalph[['token', 'year', 'uses', 'books']].groupby(['token', 'year'], as_index=False).sum()
>>> g
token year uses books
0 xanthos 1830 3 3
1 xanthos 1840 3 3
2 xanthos 1868 2 2
3 xanthos 1875 1 1
[4 rows x 4 columns]
I defer form the accepted answer.我推迟了接受的答案。 While there are 2 ways to do this, these will not necessarily result in same output.虽然有两种方法可以做到这一点,但这些方法不一定会产生相同的输出。 Specially when you are using Grouper
in groupby
特别是当您在groupby
中使用Grouper
时
index=False
reset_index()
example df
示例df
+---------+---------+-------------+------------+
| column1 | column2 | column_date | column_sum |
+---------+---------+-------------+------------+
| A | M | 26-10-2018 | 2 |
| B | M | 28-10-2018 | 3 |
| A | M | 30-10-2018 | 6 |
| B | M | 01-11-2018 | 3 |
| C | N | 03-11-2018 | 4 |
+---------+---------+-------------+------------+
They do not work the same way.它们的工作方式不同。
df = df.groupby(
by=[
'column1',
'column2',
pd.Grouper(key='column_date', freq='M')
],
as_index=False
).sum()
The above will give以上会给
+---------+---------+------------+
| column1 | column2 | column_sum |
+---------+---------+------------+
| A | M | 8 |
| B | M | 3 |
| B | M | 3 |
| C | N | 4 |
+---------+---------+------------+
While,尽管,
df = df.groupby(
by=[
'column1',
'column2',
pd.Grouper(key='column_date', freq='M')
]
).sum().reset_index()
Will give会给
+---------+---------+-------------+------------+
| column1 | column2 | column_date | column_sum |
+---------+---------+-------------+------------+
| A | M | 31-10-2018 | 8 |
| B | M | 31-10-2018 | 3 |
| B | M | 30-11-2018 | 3 |
| C | N | 30-11-2018 | 4 |
+---------+---------+-------------+------------+
You need to add drop=True
:您需要添加drop=True
:
df.reset_index(drop=True)
df = df.groupby(
by=[
'column1',
'column2',
pd.Grouper(key='column_date', freq='M')
]
).sum().reset_index(drop=True)
If you have the MultiIndex<\/code> and want to reset only a specific index level you can use the parameter
level<\/code> in
reset_index<\/code> .
如果您有
MultiIndex<\/code>并且只想重置特定索引级别,则可以使用
reset_index<\/code>中的参数
level<\/code> 。
For example:例如:
index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'), ('two', 'a'), ('two', 'b')], names=['A', 'B'])
s = pd.DataFrame(np.arange(1.0, 5.0), index=index, columns=['C'])
C
A B
one a 1.0
b 2.0
two a 3.0
b 4.0
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