英文:
New column based on a filter and an index of multiples columns?
问题
I understand your request. Here's the translated code portion for your first scenario:
我明白你的要求。以下是你的第一个情景的翻译代码部分：
```python
for each row :
if (df['value type'] == 'value train'):
#and (type,company) is the same
df['train value'] = df['value']
remove row
And here's the translated code portion for your second scenario:
以下是你的第二个情景的翻译代码部分：
```python
if df['value time'] == 'present' then add to new column
英文:
I've been trying to search/think about an answer, probably with a melt or stack, but still can't seem to do it.
Here's my DF :
d = {'type' : [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'company' : ['A', 'B', 'C', 'D', 'E','A', 'B', 'C', 'D', 'E'],
'value type': ['value car','value car','value car','value car','value car', 'value train','value train','value train','value train','value train',],
'value': [0.1, 0.2, 0.3, 0.4, 0.5, 0.15, 0.25, 0.35, 0.45, 0.55] }
df = pd.DataFrame(d)
Here is what I want (I have the array on the left, I want the one on the right):
As you can see, I want a new column "train value" based on the combination (type,company)
Something like
for each row :
if (df['value type'] == 'value train'):
#and (type,company) is the same
df['train value'] = df['value']
remove row
For example, the company A from type 1 will have a new value in a new column for its train value.
Is there a way to do this properly ?
EDIT::: There was a good answer but I didn't explain myself clearly. I want only a new column with only "one value type". For example my new DF :
d = {'type' : [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'company' : ['A', 'B', 'C', 'D', 'E','A', 'B', 'C', 'D', 'E'],
'month' : ['jan', 'feb', 'marc', 'apr', 'may', 'jan', 'feb', 'marc', 'apr', 'sep'],
'business' : ['business1', 'business2', 'business3', 'business4', 'business5', 'business6', 'business7', 'business8', 'business9', 'business10'],
'value time': ['past', 'past', 'past', 'past', 'present', 'present', 'present', 'present', 'future', 'future'],
'value': [0.1, 0.2, 0.3, 0.4, 0.11, 0.21, 0.31, 0.41, 0.45, 0.55] }
df = pd.DataFrame(d)
Heres what I want this time : 
If possible, only the values with the "present" will be in the new column. Something like
if df['value time'] == 'present' then add to new column
答案1
得分: 2
你应该对你的数据框进行重塑：
company_to_type = df.set_index('company')['type'].to_dict()
df = df.pivot(index='company', columns='value type', values='value').reset_index()
df['type'] = df.company.map(company_to_type)
df = df.rename_axis(None, axis=1)
df = df[['type', 'company', 'value train', 'value car']]
你将得到：
type company value train value car
0 1 A 0.15 0.1
1 2 B 0.25 0.2
2 3 C 0.35 0.3
3 4 D 0.45 0.4
4 5 E 0.55 0.5
英文:
You should pivot your dataframe:
company_to_type = df.set_index('company')['type'].to_dict()
df = df.pivot(index='company', columns='value type', values='value').reset_index()
df['type'] = df.company.map(company_to_type)
df = df.rename_axis(None, axis=1)
df = df[['type', 'company', 'value train', 'value car']]
and you'll get
type company value train value car
0 1 A 0.15 0.1
1 2 B 0.25 0.2
2 3 C 0.35 0.3
3 4 D 0.45 0.4
4 5 E 0.55 0.5
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