关于 pandas 条件计算的问题 [英] Question about conditional calculation in pandas
问题描述
我有这个公式，我想将其转换为熊猫计算，
公式很简单:
NEW = A(where v=1) + A(where v=3) + A(where v=5)
I have this formula, I wanted to turn this into pandas calculation,
the formula is very easy:
NEW = A(where v=1) + A(where v=3) + A(where v=5)
我有一个像这样的数据框:
I have a data frame like this:
Type subType value A NEW
X a 1 3 =3+9+9=21
X a 3 9
X a 5 9
X b 1 4 =4+5+0=9
X b 3 5
X b 5 0
Y a 1 1 =1+2+3=6
Y a 3 2
Y a 5 3
Y b 1 4 =4+5+2=11
Y b 3 5
Y b 5 2
两个问题:
-
我知道我可以只用指定的单元格写下计算，但是我希望代码看起来更好，是否还有其他获取值的方法?
I know I can just write down the calculation with the specified cell, but I want the code looks nicer, is there other ways to get the value?
因为X&是的，如何将它们添加到原始数据框中以进行进一步计算? (我的想法是不要将它们添加到数据框中，而仅在以后需要计算时才使用该值) 编码非常新，任何答案将不胜感激！
Because there will be only two results for X & Y, how can I add them into my original dataframe for further calculation? (my thought is not to add them in the dataframe and just use the value whenever it's necessary for future calculation) Quite new to coding, any answer will be appreciated!
推荐答案
尝试一下:
>>> import pandas as pd
>>> df = pd.DataFrame({'Type':['X','X','X','Y','Y','Y'], 'value':[1,3,5,1,3,5], 'A':[3,9,4,0,2,2]})
>>> df
Type value A
0 X 1 3
1 X 3 9
2 X 5 4
3 Y 1 0
4 Y 3 2
5 Y 5 2
>>> df.groupby('Type')['A'].sum()
Type
X 16
Y 4
>>> ur_dict = df.groupby('Type')['A'].sum().to_dict()
>>> df['NEW'] = df['Type'].map(ur_dict)
>>> df
Type value A NEW
0 X 1 3 16
1 X 3 9 16
2 X 5 4 16
3 Y 1 0 4
4 Y 3 2 4
5 Y 5 2 4
希望这会有所帮助.
您正在将元组键映射到一个序列，这将给您带来错误.在执行映射之前，应将需要将字典映射到索引的列进行移位.
You are mapping tuple keys to a series, that will give you an error. You should shift the columns you need to map your dictionary into as index before doing the mapping.
参见下文:
>>> import pandas as pd
>>> df = pd.DataFrame({'Type':['X','X','X','X','X','X','Y','Y','Y','Y','Y','Y'], 'subType':['a','a','a','b','b','b','a','a','a','b','b','b'],'value':[1,3,5,1,3,5,1,3,5,1,3,5],'A':[3,9,9,4,5,0,1,2,3,4,5,2]})
>>> df
Type subType value A
0 X a 1 3
1 X a 3 9
2 X a 5 9
3 X b 1 4
4 X b 3 5
5 X b 5 0
6 Y a 1 1
7 Y a 3 2
8 Y a 5 3
9 Y b 1 4
10 Y b 3 5
11 Y b 5 2
>>> df.groupby(['Type', 'subType'])['A'].sum()
Type subType
X a 21
b 9
Y a 6
b 11
Name: A, dtype: int64
>>> ur_dict = df.groupby(['Type', 'subType'])['A'].sum().to_dict()
>>> ur_dict
{('X', 'a'): 21, ('X', 'b'): 9, ('Y', 'a'): 6, ('Y', 'b'): 11}
>>> df['NEW'] = df.set_index(['Type', 'subType']).index.map(ur_dict)
>>> df
Type subType value A NEW
0 X a 1 3 21
1 X a 3 9 21
2 X a 5 9 21
3 X b 1 4 9
4 X b 3 5 9
5 X b 5 0 9
6 Y a 1 1 6
7 Y a 3 2 6
8 Y a 5 3 6
9 Y b 1 4 11
10 Y b 3 5 11
11 Y b 5 2 11
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