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Question

I'm extracting values from a pandas dataframe and want to include an index so that I know from which row in the dataframe the values belong to. For example, I'm getting the lists within a dataframe and extracting the individual values with the code below:

list_test = defaultdict(list)
for i in range(0, len(first_test)):
    for con, val in zip(first_test.content[i].values(), first_test.value[i].values()):
        for un_con, un_val in zip(con, val):
            list_test["data_content"].append(un_con)
            list_test["data_values"].append(un_val)

Expected output:

       Index      data_content         data_values
0      0          Condition:            Used: An item that has been previously used. S...
1      0          Card Size:            Standard
2      0          Set:                  Base Set
3      0          Autograph Format:     Does not apply
4      0          Certification Number: Does not apply
5      0          HP:                   Does not apply
6      0          Cost:                 Unknown
7      0          Graded:               No
.
.
.

Here's a sample of the data I'm working with (Apologies for the long format):

first_test = pd.DataFrame({
    'index': {
        0: 0,
        1: 1
    },
    'content': {
        0: {
            'contents': ['Condition:',
                'Card Size:',
                'Set:',
                'Autograph Format:',
                'Certification Number:',
                'HP:',
                'Cost:',
                'Graded:',
                'Language:',
                'Autograph Authentication:',
                'Convention/Event:',
                'Autographed:',
                'Signed By:',
                'Creature/Monster Type:',
                'Custom Bundle:',
                'Year Manufactured:',
                'Vintage:',
                'Autograph Authentication Number:',
                'Card Name:',
                'Manufacturer:'
            ]
        },
        1: {
            'contents': ['Condition:',
                'Card Size:',
                'Set:',
                'HP:',
                'Vintage:',
                'Language:',
                'Manufacturer:',
                'Features:',
                'Finish:',
                'Character:',
                'Attribute/MTG:Color:',
                'Autographed:',
                'Creature/Monster Type:',
                'Year Manufactured:',
                'Graded:',
                'Card Name:',
                'Stage:',
                'Card Type:',
                'Speciality:',
                'Card Condition:'
            ]
        }
    },
    'value': {
        0: {
            'values': ['Used: An item that has been previously used. See the seller’s listing for full details and ... ',
                'Standard',
                'Base Set',
                'Does not apply',
                'Does not apply',
                'Does not apply',
                'Unknown',
                'No',
                'French',
                'Does not apply',
                'Does not apply',
                'No',
                'Does not apply',
                'Unknown',
                'No',
                '2008',
                'Yes',
                'Does not apply',
                'Magby',
                'Pokémon / Nintendo'
            ]
        },
        1: {
            'values': ['Used: An item that has been used previously. See the seller’s listing for full details and ... ',
                'Standard',
                'Vivid Voltage',
                '140',
                'No',
                'English',
                'creatures gamefreak 2020 nintendo',
                'Altered Art, Box Topper, Full Art, Shadowless, Unlimited',
                'Regular',
                'Drednaw',
                'Blue',
                'No',
                'Turtle',
                '2020',
                'No',
                'Drednaw',
                'Stage 1',
                'Pokémon',
                'V',
                'Near Mint'
            ]
        }
    }
})

Answer 1

list_test = []
for i, row in first_test.iterrows():
    for con, val in zip(row['content'].values(), row['value'].values()):
        for un_con, un_val in zip(con, val):
            list_test += [
                dict(index=i,
                     data_content=un_con,
                     data_values=un_val,
            )]

pd.DataFrame(list_test)