Fetch Live Option Chain Using Kotak Neo API

				
					import pandas as pd
import urllib.request
from io import StringIO

fp = client.scrip_master()
file_paths = fp['filesPaths'] 
# Initialize an empty list to store DataFrames
dfs = []

# Download and read each CSV file
for file_path in file_paths:
    with urllib.request.urlopen(file_path) as response:
        content = response.read().decode('utf-8')
        df = pd.read_csv(StringIO(content))

        # Select specific columns
        selected_columns = df[['pSymbol','pExchSeg' ,'pTrdSymbol', 'pInstType', 'lLotSize', 'pExpiryDate', 'pSegment', 'lFreezeQty']]

        # Convert 'pExpiryDate' to the desired format
        selected_columns.loc[:, 'pExpiryDate'] = pd.to_datetime(selected_columns['pExpiryDate'], unit='s') + pd.DateOffset(years=10)

        # Append the selected columns DataFrame to the list
        dfs.append(selected_columns)

# Combine all DataFrames into a single DataFrame
combined_database = pd.concat(dfs, ignore_index=True)



import numpy as np
import pandas as pd

def get_tokens_for_quotes(security_identifier, combined_database):
    # Check if the input is a security name or a trading symbol
    condition = (
        (
            (
                (combined_database['pTrdSymbol'].astype(str).str.strip().str.upper() == security_identifier.strip().upper()) |
                (combined_database['pTrdSymbol'].apply(lambda x: isinstance(x, float) and np.isnan(x)) & pd.isna(security_identifier))
            ) &
            (
                (combined_database['pTrdSymbol'].astype(str).str.strip().str.upper() == security_identifier.strip().upper()) |
                (combined_database['pTrdSymbol'].apply(lambda x: isinstance(x, float) and np.isnan(x)) & pd.isna(security_identifier))
            )
        )
    )
    
    # Filter the DataFrame based on the condition
    symbols_info = combined_database[condition][['pSymbol', 'pExchSeg']]

    if symbols_info.empty:
        return []  # Return an empty list if there are no matching records

    # Reset the index of the filtered DataFrame
    symbols_info.reset_index(drop=True, inplace=True)

    # Rename the columns
    symbols_info.rename(columns={'pSymbol': 'instrument_token', 'pExchSeg': 'exchange_segment'}, inplace=True)

    # Ensure that instrument_token values are enclosed in single quotes
    symbols_info['instrument_token'] = "" + symbols_info['instrument_token'].astype(str) + ""

    # Create a list of dictionaries in the desired format
    output_list = symbols_info.to_dict(orient='records')

    return output_list