pandas 从长到宽重塑,通过两个变量 [英] Pandas long to wide reshape, by two variables
问题描述
我有长格式的数据,我正在尝试将数据重塑为宽格式,但似乎没有使用melt/stack/unstack 的直接方法来做到这一点:
Salesman Height 产品价格克努特 6 蝙蝠 5克努特 6 球 1克努特 6 魔杖 3史蒂夫 5 笔 2
变成:
Salesman Height product_1 price_1 product_2 price_2 product_3 price_3克努特 6 球棒 5 球 1 魔杖 3史蒂夫 5 笔 2 NA NA NA NA
我认为 Stata 可以用 reshape 命令做这样的事情.
一个简单的枢轴可能足以满足您的需求,但我这样做是为了重现您想要的输出:
df['idx'] = df.groupby('销售员').cumcount()
只需添加组内计数器/索引即可完成大部分工作,但列标签不会如您所愿:
print df.pivot(index='Salesman',columns='idx')[['product','price']]产品价格idx 0 1 2 0 1 2推销员Knut 球棒 5 1 3史蒂夫笔 NaN NaN 2 NaN NaN
为了更接近您想要的输出,我添加了以下内容:
df['prod_idx'] = 'product_' + df.idx.astype(str)df['prc_idx'] = 'price_' + df.idx.astype(str)product = df.pivot(index='Salesman',columns='prod_idx',values='product')prc = df.pivot(index='Salesman',columns='prc_idx',values='price')reshape = pd.concat([product,prc],axis=1)reshape['Height'] = df.set_index('Salesman')['Height'].drop_duplicates()打印重塑product_0 product_1 product_2 price_0 price_1 price_2 高度推销员Knut 球棒 5 1 3 6史蒂夫笔 NaN NaN 2 NaN NaN 5
如果您想将过程推广到更多变量,我认为您可以执行以下操作(尽管它可能不够高效):
df['idx'] = df.groupby('销售员').cumcount()tmp = []对于 ['product','price'] 中的 var:df['tmp_idx'] = var + '_' + df.idx.astype(str)tmp.append(df.pivot(index='Salesman',columns='tmp_idx',values=var))reshape = pd.concat(tmp,axis=1)
<块引用>
@Luke 说:
我认为 Stata 可以用 reshape 命令做这样的事情.
您可以,但我认为您还需要一个组内计数器来在 stata 中进行重塑以获得所需的输出:
+-------------------------------------------+|业务员idx身高产品价格||-------------------------------------------|1. |克努特 0 6 蝙蝠 5 |2. |Knut 1 6 球 1 |3. |克努特 2 6 魔杖 3 |4. |史蒂夫 0 5 笔 2 |+--------------------------------------------+
如果你添加 idx
那么你可以在 stata
中做 reshape:
重塑宽产品价格,i(salesman) j(idx)
I have data in long format and am trying to reshape to wide, but there doesn't seem to be a straightforward way to do this using melt/stack/unstack:
Salesman Height product price
Knut 6 bat 5
Knut 6 ball 1
Knut 6 wand 3
Steve 5 pen 2
Becomes:
Salesman Height product_1 price_1 product_2 price_2 product_3 price_3
Knut 6 bat 5 ball 1 wand 3
Steve 5 pen 2 NA NA NA NA
I think Stata can do something like this with the reshape command.
A simple pivot might be sufficient for your needs but this is what I did to reproduce your desired output:
df['idx'] = df.groupby('Salesman').cumcount()
Just adding a within group counter/index will get you most of the way there but the column labels will not be as you desired:
print df.pivot(index='Salesman',columns='idx')[['product','price']]
product price
idx 0 1 2 0 1 2
Salesman
Knut bat ball wand 5 1 3
Steve pen NaN NaN 2 NaN NaN
To get closer to your desired output I added the following:
df['prod_idx'] = 'product_' + df.idx.astype(str)
df['prc_idx'] = 'price_' + df.idx.astype(str)
product = df.pivot(index='Salesman',columns='prod_idx',values='product')
prc = df.pivot(index='Salesman',columns='prc_idx',values='price')
reshape = pd.concat([product,prc],axis=1)
reshape['Height'] = df.set_index('Salesman')['Height'].drop_duplicates()
print reshape
product_0 product_1 product_2 price_0 price_1 price_2 Height
Salesman
Knut bat ball wand 5 1 3 6
Steve pen NaN NaN 2 NaN NaN 5
Edit: if you want to generalize the procedure to more variables I think you could do something like the following (although it might not be efficient enough):
df['idx'] = df.groupby('Salesman').cumcount()
tmp = []
for var in ['product','price']:
df['tmp_idx'] = var + '_' + df.idx.astype(str)
tmp.append(df.pivot(index='Salesman',columns='tmp_idx',values=var))
reshape = pd.concat(tmp,axis=1)
@Luke said:
I think Stata can do something like this with the reshape command.
You can but I think you also need a within group counter to get the reshape in stata to get your desired output:
+-------------------------------------------+
| salesman idx height product price |
|-------------------------------------------|
1. | Knut 0 6 bat 5 |
2. | Knut 1 6 ball 1 |
3. | Knut 2 6 wand 3 |
4. | Steve 0 5 pen 2 |
+-------------------------------------------+
If you add idx
then you could do reshape in stata
:
reshape wide product price, i(salesman) j(idx)
这篇关于 pandas 从长到宽重塑,通过两个变量的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!