CODEWITHSUNDEEP
pythonpython-3.xexcelpandas
ricsilo
ricsilo

Reputation: 105

Pandas pivot with interchanging column values from duplicate row values

I have data with duplicate parts that looks like this:

Part  |  Location |  ONHand 
A     |  XY       |  5 
A     |  XW       |  4 
B     |  XC       |  6 
B     |  XV       |  8 
C     |  XQ       |  9

And I'm trying to convert it all into one row per part, listing all the locations and quantities on hand in each location.

I tried using this code

df_f = df.assign(cc=df.groupby('Part').cumcount()+1).set_index(['Part', 'cc']).unstack()
df_f.columns = [f'{col[0]}{col[1]}' for col in df_f.columns]
df_f.to_csv('parts_multi_location.csv')

But the problem is it returns Location 1, 2, 3 and then ONHand 1, 2, 3 and so forth.

I need the end result to return Location 1, Onhand 1, Location 2, Onhand 2, so the headers should look like this:

Part | Location_1 | Onhand_1 | Location 2| Onhand 2
A    | XY         |   5      | XW        | 4
B    | XC         |   6      | XV        | 8
C    | XQ         |   9

Upvotes: 2

Views: 38

Answers (2)

Umar.H
Umar.H

Reputation: 23099

IIUC, Quang's solution is much slicker, but lets see how we could solve this using the Sorted method from the standard library.

if you use f'{col[0]}_{col[1]}' as Quang has suggested we can split by the _ to get the numerical value and use that to sort your columns.

d = {k : int(k.split('_')[1]) for k in df_f.columns}
#{'Location_1': 1, 'Location_2': 2, 'ONHand_1': 1, 'ONHand_2': 2}


df_f[sorted(d,key=d.get)]
         Location_1  ONHand_1 Location_2  ONHand_2
Part                                          
A            XY       5.0         XW       4.0
B            XC       6.0         XV       8.0
C            XQ       9.0        NaN       NaN

Upvotes: 0

Quang Hoang
Quang Hoang

Reputation: 150785

You did most of the job. The only thing missing is sort_index:

df_f = df.assign(cc=df.groupby('Part').cumcount()+1).set_index(['Part', 'cc']).unstack()

# this is what you are missing
df_f = df_f.sort_index(level=(1,0), axis=1)

df_f.columns = [f'{col[0]}{col[1]}' for col in df_f.columns]

Output:

     Location1  ONHand1 Location2  ONHand2
Part                                      
A           XY      5.0        XW      4.0
B           XC      6.0        XV      8.0
C           XQ      9.0       NaN      NaN

Upvotes: 2

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