How do I iterate through nested pandas dataframe by column?

Question

Problem: I have a 1 row dataframe dfA whose column length can vary, within each cell contains a json object. All fields in each json object are the same.

I'm trying to write code that loops through each cell, parses the JSON object, and loads ALL data into 1 single dataframe dfB, so if I have 5 columns in dfA then the final output of dfB should have 5 rows

I have the code written to where I can manually enter the column number of dfA and dfB outputs correctly:

dataA = dfA[0].values.tolist()
dfB = pd.json_normalize(dataA)
dfB

In the above example, the code pulls the 1st JSON object of dfA and correctly outputs a 1 row dataframe

I've Tried:

for column in dfA:
  dataA = dfA[column].values.tolist()
  dfB = pd.json_normalize(dataA)

This outputs just the last cell in dfA

I've tried different variations of this and either get the 1st cell, last cell, or the original dfA

Please help, thank you!

EDIT:

dfA:

	0	1	2	3	4
0	{'id':'testID434556'...	{'id':'testID568912'...	{'id':'testID652154'...	{'id':'testID756298'...	{'id':'testID812362'...

dataA = dfA[0].values.tolist()
dfB = pd.json_normalize(dataA)
dfB

	id	field2	field3	field4	field5
0	testID434556	field2data	field3data	field4data	field5data

Desired Output:

	id	field2	field3	field4	field5
0	testID434556	field2data	field3data	field4data	field5data
1	testID568912	field2data	field3data	field4data	field5data
2	testID652154	field2data	field3data	field4data	field5data
3	testID756298	field2data	field3data	field4data	field5data
4	testID812362	field2data	field3data	field4data	field5data

Answer 1

dfA[0] only gets the first column. You should convert you dataframe first row to list then feed the result to pd.json_normalize()

pd.json_normalize((dfA.iloc[0, :].values))