NaN values when new column is added to pandas DataFrame based on an existing column data

I am trying to create a new column in pandas DataFrame which is based on another existing column. I am extracting characters 10:19 from column Name and adding it as a new column expiry . But most of the datas in expiry are showing as nan. I am new to python and Pandas. How can I solve this ?

allowedSegment = [14]
index_symbol = "BANKNIFTY"

fno_url = 'http://public.fyers.in/sym_details/NSE_FO.csv'
fno_symbolList = pd.read_csv(fno_url, header=None)
fno_symbolList.columns = ['FyersToken', 'Name', 'Instrument', 'lot', 'tick', 'ISIN', 'TradingSession', 'Lastupdatedate',
                           'Expirydate', 'Symbol', 'Exchange', 'Segment', 'ScripCode', 'ScripName', 'Ignore_1',
                          'StrikePrice', 'CE_PE', 'Ignore_2']

fno_symbolList = fno_symbolList[fno_symbolList['Instrument'].isin(allowedSegment) & (fno_symbolList['ScripName'] == index_symbol)]

fno_symbolList['expiry'] = fno_symbolList['Name'][10:19]

>Solution :

When dealing with strings in columns and doing operations on it, try the following:

fno_symbolList['expiry'] = fno_symbolList['Name'].str[10:19]

The .str allows you to do string operations on columns.