Unpivot pandas DataFrame partly

Question

I have the following table DataFrame

Items	Description	Store 1 Qty	Store 1 Value	Store 2 Qty	Store 2 Value
item 1	Some item name	5	120	7	240
item 2	Some other item	9	1234	12	98

is there any easy way on Python to unpivot stores only? To make it this way:

Items	Description	Store number	Value	Qty
Item 1	Some item name	Store 1	5	120

I was thinking about unpivoting it completely and then pivoting it back keeping value and qty as values. But I assume, that some more efficient solution should be there

Answer 1

If need Qty, Value to separate columns convert first columns to MultiIndex , so possible use Series.str.rsplit by last space to MultiIndex in columns , so last reshape by DataFrame.stack :

df = df.set_index(['Items','Description'])
df.columns = df.columns.str.rsplit(n=1, expand=True)
df = df.rename_axis(('Store number',None), axis=1).stack(0).reset_index()
print (df)
    Items      Description Store number  Qty  Value
0  item 1   Some item name      Store 1    5    120
1  item 1   Some item name      Store 2    7    240
2  item 2  Some other item      Store 1    9   1234
3  item 2  Some other item      Store 2   12     98

Answer 2

You can also use pd.melt() along with pd.pivot as below:

df_ = pd.melt(df, id_vars=['Items', 'Description'])
df_[['Store number', 'Type']] = df_['variable'].str.rsplit(n=1, expand=True)
df_ = df_.pivot(index=['Items', 'Description', 'Store number'], columns='Type', values='value').reset_index()
print(df_)
Type   Items      Description Store number  Qty  Value
0     item 1   Some item name      Store 1    5    120
1     item 1   Some item name      Store 2    7    240
2     item 2  Some other item      Store 1    9   1234
3     item 2  Some other item      Store 2   12     98

Answer 3

#pip install git+https://github.com/pyjanitor-devs/pyjanitor.git
import pandas as pd
import janitor
df.pivot_longer(index = ['Items', 'Description'], 
                names_to = ('Store Number', '.value'), 
                names_pattern = r"(.+\s\d)\s(.+)")

    Items      Description Store Number  Qty  Value
0  item 1   Some item name      Store 1    5    120
1  item 2  Some other item      Store 1    9   1234
2  item 1   Some item name      Store 2    7    240
3  item 2  Some other item      Store 2   12     98

This uses the pivot_longer function from pyjanitor .

Explanation: The columns you wish to reshape has a pattern (the store number is followed by either Qty or Value); we take advantage of this in the names_pattern with a regular expression of groups ( r"(.+\\s\\d)\\s(.+)" ) - the first group points to Store 1/Store2 , while the other group points to Qty/Value .

The names_to argument specifies how the new dataframe should look - for this specific case, the .value tells the function to keep the parts of the columns associated with it as header - in this case, the .value is the second entry, so it is paired with the second group in names_pattern ; store number in names_to is paired with the first entry in names_pattern