Iterating over conditions from columns and Dataframe to list conversion(pandas)

Question

I have a dataframe like this:

Item   Quantity  Price     Photo1     Photo2    Photo3    Photo4

A        2         30      A1.jpg      A2.jpg 
B        4         10      B1.jpg      B2.jpg    B3.jpg    B4.jpg
C        5         15      C1.jpg

These are my previous questions related to bringing the data frame in this format.

How to split datas from columns and add to a list from a dataframe, also repeat the list elements for a single row? (Pandas)

I first created a list:

df1 = df.reindex(['Item','Quantity','Price','Photo1','Photo2','Photo3','Photo4','I','Q','P','PH',] axis=1)
df1['I'] = df1['I'].fillna['I']
df1['Q'] = df1['Q'].fillna['Q']
df1['P'] = df1['P'].fillna['P']
df1['PH'] = df1['PH'].fillna['PH']
vals = [['I','Item'],['Q','Quantity'],['P','Price']]

I tried from the first question:

photo_df = df1.fillna('').filter(like='Photo')


vals = [y for x in photo_df.to_numpy() 
         for y in vals[:3] + [['PH',z] for z in x[x!='']] ]

the list returns

vals = [['I','Item'],['Q','Quantity'],['P','Price'],['PH','A1.jpg'],['PH','A2.jpg'],
        ['I','Item'],['Q','Quantity'],['P','Price'],['PH','B1.jpg'],['PH','B2.jpg'],['PH','B3.jpg'],['PH','B4.jpg'],
        ['I','Item'],['Q','Quantity'],['P','Price'],['PH','C1.jpg']]

I want the list as:

vals = [['I','Item'],['Q','Quantity'],['P','Price'],['PH','Photo1'],['PH','Photo2'],
        ['I','Item'],['Q','Quantity'],['P','Price'],['PH','Photo1'],['PH','Photo2'],['PH','Photo3'],['PH','Photo4'],
        ['I','Item'],['Q','Quantity'],['P','Price'],['PH','Photo1']]
   

I want to keep the header names in the list instead of the data but should iterate over data in the format from the question: How to split datas from columns and add to a list from a dataframe, also repeat the list elements for a single row? (Pandas)

Answer 1

You can just make a small change where you create the photo_df like this:

photo_df = df1.filter(like='Photo')
photo_df = photo_df.transform(lambda x: np.where(x.isnull(), x, x.name)) 
photo_df = photo_df.fillna('')

The second line just replaces the non-null value with its column name.

Output:

[['I', 'Item'], ['Q', 'Quantity'], ['P', 'Price'], ['PH', 'Photo1'], ['PH', 'Photo2'], 
['I', 'Item'], ['Q', 'Quantity'], ['P', 'Price'], ['PH', 'Photo1'], ['PH', 'Photo2'], 
['PH', 'Photo3'], ['PH', 'Photo4'], ['I', 'Item'], ['Q', 'Quantity'], ['P', 'Price'], ['PH', 'Photo1']]

Answer 2

Idea is filter columns names instead values in list comprehension - changed x[x!=''] to photo_df.columns[x!=''] :

vals = [y for x in photo_df.to_numpy() 
          for y in vals[:3] + [['PH',z] 
          for z in photo_df.columns[x!='']]]
print (vals)
[['I', 'Item'], ['Q', 'Quantity'], ['P', 'Price'], ['PH', 'Photo1'], ['PH', 'Photo2'], 
 ['I', 'Item'], ['Q', 'Quantity'], ['P', 'Price'], ['PH', 'Photo1'], ['PH', 'Photo2'], ['PH', 'Photo3'], ['PH', 'Photo4'], 
 ['I', 'Item'], ['Q', 'Quantity'], ['P', 'Price'], ['PH', 'Photo1']]