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.
Answers:
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Method 1
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)
Method 2
Here’s another solution more fleshed out, taken from Chris Albon’s site.
Create “long” dataframe
raw_data = {'patient': [1, 1, 1, 2, 2],
'obs': [1, 2, 3, 1, 2],
'treatment': [0, 1, 0, 1, 0],
'score': [6252, 24243, 2345, 2342, 23525]}
df = pd.DataFrame(raw_data, columns = ['patient', 'obs', 'treatment', 'score'])
Make a “wide” data
df.pivot(index='patient', columns='obs', values='score')
Method 3
Karl D’s solution gets at the heart of the problem. But I find it’s far easier to pivot everything (with .pivot_table because of the two index columns) and then sort and assign the columns to collapse the MultiIndex:
df['idx'] = df.groupby('Salesman').cumcount()+1
df = df.pivot_table(index=['Salesman', 'Height'], columns='idx',
values=['product', 'price'], aggfunc='first')
df = df.sort_index(axis=1, level=1)
df.columns = [f'{x}_{y}' for x,y in df.columns]
df = df.reset_index()
Output:
Salesman Height price_1 product_1 price_2 product_2 price_3 product_3 0 Knut 6 5.0 bat 1.0 ball 3.0 wand 1 Steve 5 2.0 pen NaN NaN NaN NaN
Method 4
A bit old but I will post this for other people.
What you want can be achieved, but you probably shouldn’t want it 😉
Pandas supports hierarchical indexes for both rows and columns.
In Python 2.7.x …
from StringIO import StringIO
raw = '''Salesman Height product price
Knut 6 bat 5
Knut 6 ball 1
Knut 6 wand 3
Steve 5 pen 2'''
dff = pd.read_csv(StringIO(raw), sep='s+')
print dff.set_index(['Salesman', 'Height', 'product']).unstack('product')
Produces a probably more convenient representation than what you were looking for
price product ball bat pen wand Salesman Height Knut 6 1 5 NaN 3 Steve 5 NaN NaN 2 NaN
The advantage of using set_index and unstacking vs a single function as pivot is that you can break the operations down into clear small steps, which simplifies debugging.
Method 5
pivoted = df.pivot('salesman', 'product', 'price')
pg. 192 Python for Data Analysis
Method 6
An old question; this is an addition to the already excellent answers. pivot_wider from pyjanitor may be helpful as an abstraction for reshaping from long to wide (it is a wrapper around pd.pivot):
# pip install pyjanitor
import pandas as pd
import janitor
idx = df.groupby(['Salesman', 'Height']).cumcount().add(1)
(df.assign(idx = idx)
.pivot_wider(index = ['Salesman', 'Height'], names_from = 'idx')
)
Salesman Height product_1 product_2 product_3 price_1 price_2 price_3
0 Knut 6 bat ball wand 5.0 1.0 3.0
1 Steve 5 pen NaN NaN 2.0 NaN NaN
All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0