Convert dataframe column with Json Array content into separate columns

The code snippet below should work for you.

import pandas as pd
df = pd.DataFrame()
df['FIELD'] = [[{'FIELD_NAME': 'CONTACT_FIELD_1', 'FIELD_VALUE': 'EN', 'CUSTOM_FIELD_ID': 'CONTACT_FIELD_1'}, {'FIELD_NAME': 'CONTACT_FIELD_10', 'FIELD_VALUE': 'false', 'CUSTOM_FIELD_ID': 'CONTACT_FIELD_10'}]]

temp_dict = {}
counter = 0
for entry in df['FIELD'][0]:
    temp_dict[counter] = entry
    counter += 1

new_dataframe = pd.DataFrame.from_dict(temp_dict, orient='index')

new_dataframe #outputs dataframe

Edited answer to reflect edited question:

Under the assumption that each entry in CUSTOMFIELDS is a list with 1 element (which is different from original question; the entry had 2 elements), the following will work for you and create a dataframe in the requested format.

import pandas as pd

# Need to recreate example problem
df = pd.DataFrame()
df['CUSTOMFIELDS'] = [[{'FIELD_NAME': 'CONTACT_FIELD_1', 'FIELD_VALUE': 'EN', 'CUSTOM_FIELD_ID': 'CONTACT_FIELD_1'}], 
                      [{'FIELD_NAME': 'CONTACT_FIELD_1', 'FIELD_VALUE': 'FR', 'CUSTOM_FIELD_ID': 'CONTACT_FIELD_1'}]]
df['FIRST_NAME'] = ['Maria', 'John']

#begin solution
counter = 0
dataframe_solution = pd.DataFrame()
for index, row in df.iterrows():
    dataframe_solution = pd.concat([dataframe_solution, pd.DataFrame.from_dict(row['CUSTOMFIELDS'][0], orient = 'index').transpose()], sort = False, ignore_index = True)
    dataframe_solution.loc[counter,'FIRST_NAME'] = row['FIRST_NAME']
    counter += 1

Your dataframe is in dataframe_solution