Python 如何使用聚合函数在 Pandas 的列中按相同的值“合并”行？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/46826773/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
How can I "merge" rows by same value in a column in Pandas with aggregation functions?
提问by Martin Thoma
I would like to group rows in a dataframe, given one column. Then I would like to receive an edited dataframe for which I can decide which aggregation function makes sense. The default should be just the value of the first entry in the group.
给定一列，我想对数据框中的行进行分组。然后我想接收一个编辑过的数据帧，我可以决定哪个聚合函数有意义。默认值应该只是组中第一个条目的值。
(it would be nice if the solution also worked for a combination of two columns)
（如果该解决方案也适用于两列的组合，那就太好了）
Example
例子
#!/usr/bin/env python
"""Test data frame grouping."""
# 3rd party modules
import pandas as pd
df = pd.DataFrame([{'id': 1, 'price': 123, 'name': 'anna', 'amount': 1},
{'id': 1, 'price': 7, 'name': 'anna', 'amount': 2},
{'id': 2, 'price': 42, 'name': 'bob', 'amount': 30},
{'id': 3, 'price': 1, 'name': 'charlie', 'amount': 10},
{'id': 3, 'price': 2, 'name': 'david', 'amount': 100}])
print(df)
gives the dataframe:
给出数据框：
amount id name price
0 1 1 anna 123
1 2 1 anna 7
2 30 2 bob 42
3 10 3 charlie 1
4 100 3 david 2
And I would like to get:
我想得到：
amount id name price
3 1 anna 130
30 2 bob 42
110 3 charlie 3
So:
所以：
- Entries with the same value in the
idcolumn belong together. After that operation, there should still be anidcolumn, but it should have only unique values. - All values in
amountandpricewhich have the sameidget summed up - For
name, just the first one (by the current order of the dataframe) is taken.
id列中具有相同值的条目属于一起。在该操作之后，仍然应该有一个id列，但它应该只有唯一的值。- 中的所有值
amount和price具有相同的id总和 - 对于
name，仅采用第一个（按数据帧的当前顺序）。
Is this possible with Pandas?
熊猫可以做到这一点吗？
回答by Martin Thoma
You are looking for
你正在寻找
aggregation_functions = {'price': 'sum', 'amount': 'sum', 'name': 'first'}
df_new = df.groupby(df['id']).aggregate(aggregation_functions)
which gives
这使
price name amount
id
1 130 anna 3
2 42 bob 30
3 3 charlie 110
回答by jezrael
For same columns ordering is necessary add reindex, because aggregate by dict:
对于相同的列排序是必要的 add reindex，因为聚合dict：
d = {'price': 'sum', 'name': 'first', 'amount': 'sum'}
df_new = df.groupby('id', as_index=False).aggregate(d).reindex(columns=df.columns)
print (df_new)
amount id name price
0 3 1 anna 130
1 30 2 bob 42
2 110 3 charlie 3
