How To Fill The Values In The List And Convert It Into The Dataframe?(part Ii)

I have a modified data frame from the previous question: How to fill the values in the list and convert it into the dataframe? Item Photo1 Photo2 Photo3 Photo4 De

Solution 1:

Use:

#created default indexdf = df.reset_index(drop=True)
df1 = df.reindex(df.columns.tolist() + ['I','PH','Descr'], axis=1)

df1['I'] = df1['I'].fillna('I')
#added column not called Description
df1['Descr'] = df1['Descr'].fillna('Description')
df1['PH'] = df1['PH'].fillna('PH')

#added values like input data
vals = [['I','Item']]
print (df1)
  Item  Photo1  Photo2  Photo3  Photo4 Description1 Description2 Description3  \
0    A  A1.jpg  A2.jpg     NaN     NaN         Nice    Beautiful          NaN   
1    B  B1.jpg  B2.jpg  B3.jpg  B4.jpg         Ugly      Damaged          NaN   
2    C  C1.jpg     NaN     NaN     NaN         Cute     Handsome         Nice   

   I  PH        Descr  
0  I  PH  Description  
1  I  PH  Description  
2  I  PH  Description  

---

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

#assigned to vals1 for avoid overwrite `vals`vals1 = [(i, y) for i, x in enumerate(photo_df.to_numpy())
          for y in vals[:3] + [['PH',z] 
          for z in photo_df.columns[x!='']]]
#removed vals[:4] for avoid duplicated rows vals2 = [(i, y) for i, x in enumerate(product_df.to_numpy())
          for y in  [['Descr',z] 
          for z in product_df.columns[x!='']]]

L = [df1.loc[df1.index[[i]], x].set_axis(range(len(x)), axis=1) for i, x in vals1]
#selected df1M = [df1.loc[df1.index[[i]], x].set_axis(range(len(x)), axis=1) for i, x in vals2]

df  = pd.concat(L)
df3 = pd.concat(M)

---

#sorting index by mergesort (only stable algo)df = pd.concat([df,df3]).sort_index(kind='mergesort')
print (df)
             0          1
0            I          A
0           PH     A1.jpg
0           PH     A2.jpg
0  Description       Nice
0  Description  Beautiful
1            I          B
1           PH     B1.jpg
1           PH     B2.jpg
1           PH     B3.jpg
1           PH     B4.jpg
1  Description       Ugly
1  Description    Damaged
2            I          C
2           PH     C1.jpg
2  Description       Cute
2  Description   Handsome
2  Description       Nice

Solution 2:

You already have the merged/concatenated dataframe. Is it just a matter of getting the sorting right?

Try this:

df['sort'] = [len(x) for x in df[0]]
df['Item'] = df.index

df.sort_values(by=['Item', 'sort'], inplace=True)
df.drop(labels=['Item', 'sort'], axis=1, inplace=True)
print(df.to_string(header=False, index=False)) 

It produces this output:

IA
         PH    A1.jpg
         PH    A2.jpg
Description      Nice
Description Beautiful
          IB
         PH    B1.jpg
         PH    B2.jpg
         PH    B3.jpg
         PH    B4.jpg
Description      Ugly
Description   Damaged
          I         C
         PH    C1.jpg
Description      Cute
Description  Handsome
Description      Nice