DataFRame.concat()

[nafshar]

I am trying to convert from DataFrame.append() which has been deprecated to DataFrame.concat() and having some issues with an otherwise working code as below. The commented section works just fine with no issues, except that I get the deprecated warning.

def chunks(lst, n):
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        yield lst[i:i + n]

symbol_groups = list(chunks(stocks['Ticker'], 100))
symbol_chunks = []
for i in range(0, len(symbol_groups)):
    symbol_chunks.append(','.join(symbol_groups[i]))

final_dataframe = pd.DataFrame(columns = my_columns)

for symbol_chunk in symbol_chunks:
    api_url = f'https://api.iex.cloud/v1/data/core/quote/{symbol_chunk}'
    response = requests.get(api_url, params=params)
    data = pd.DataFrame(response.json())
    data = data.set_index('symbol')
    #display(data) <------------------ At this point everything displays fine.

    for symbol in symbol_chunk.split(','):
        new_row = pd.Series([symbol, 
                             data.loc[symbol]['latestPrice'], 
                             data.loc[symbol]['marketCap'], 
                             'N/A'], 
                            index=my_columns)

        final_dataframe = pd.concat([final_dataframe, new_row], ignore_index=True)

        '''
        The following section works well, although I get a warning for Deprecation on the append()

        final_dataframe = final_dataframe.append(
                                        pd.Series([symbol, 
                                                   data.loc[symbol]['latestPrice'], 
                                                   data.loc[symbol]['marketCap'], 
                                                   'N/A'], 
                                                  index = my_columns), 
                                        ignore_index = True)
        '''
display(final_dataframe)

Output:
Ticker	Price	Market Capitalization	Number Of Shares to Buy	0
0           NaN  NaN       NaN                         NaN                     A
1           NaN  NaN       NaN                         NaN              119.35
2           NaN  NaN       NaN                         NaN     35253103878
3           NaN  NaN       NaN                         NaN                   N/A

...	...	...	...	...	...
2015	     NaN	NaN	      NaN	                    NaN	                   N/A
2016	     NaN	NaN	      NaN	                    NaN	                   ZTS
2017	     NaN	NaN	      NaN	                    NaN	                 171.0
2018	     NaN	NaN	      NaN	                    NaN	      79021175940