Please help me.

[snippsat]

In future, I will not put too many functions in my code.

I think you misunderstand a little here.
I mean that you could make function then it will isolate code,
so can have what you have written in one function and other stuff needed in a other function.
Example.

# kr3.py
import krakenex
import pandas as pd

api = krakenex.API()

def calc_pair_price():
    # Define currency pairs and their corresponding names
    currency_pairs = [
        ('XETHXXBT', 'ETH/BTC'),('XXBTZEUR', 'BTC/EUR'),
        ('XXBTZUSD', 'BTC/USD'),('XXBTZGBP', 'BTC/GBP'),
        ('XETHZEUR', 'ETH/EUR'),('XETHZGBP', 'ETH/GBP'),
        ('XETHZUSD', 'ETH/USD'),('EURGBP', 'EURGBP'),
        ('ZEURZUSD', 'EUR/USD'),('ZGBPZUSD', 'GBP/USD'),
        ('PAXGXBT', 'PAXG/BTC'),('PAXGETH', 'PAXG/ETH'),
        ('PAXGEUR', 'PAXG/EUR'),('PAXGUSD', 'PAXG/USD'),
        ('XXRPXXBT', 'XRP/BTC'),('XRPETH', 'XRP/ETH'),
        ('XXRPZEUR', 'XRP/EUR'),('XRPGBP', 'XRP/GBP'),
        ('XXRPZUSD', 'XRP/USD'),
    ]

    time_frames = ['1', '5', '15', '30', '60', '240', '1440', '10080']
    for pair, pair_name in currency_pairs:
        for tf in time_frames:
            print(f'{tf} minute m/a ({pair_name})')

            # Fetch OHLC data for the given pair and time frame
            ohlc_data = api.query_public('OHLC', {'pair': pair, 'interval': tf})

            # Convert the data to a pandas dataframe
            df = pd.DataFrame(
                ohlc_data['result'][pair],
                columns=['time', 'open', 'high', 'low', 'close', 'vwap', 'volume', 'count',]
            )
            df['time'] = pd.to_datetime(df['time'], unit='s')
            df.set_index('time', inplace=True)

            # Calculate the moving averages
            last_20_candles = df.iloc[-21:-1]
            last_20_close_prices = last_20_candles['close'].astype(float).tolist()
            avg_last_20_close_price = sum(last_20_close_prices) / len(last_20_close_prices)
            print(f'20 period m/a: {avg_last_20_close_price}')

            last_50_candles = df.iloc[-51:-1]
            last_50_close_prices = last_50_candles['close'].astype(float).tolist()
            avg_last_50_close_price = sum(last_50_close_prices) / len(last_50_close_prices)
            print(f'50 period m/a: {avg_last_50_close_price}')

            last_200_candles = df.iloc[-201:-1]
            last_200_close_prices = (last_200_candles['close'].astype(float).tolist())
            avg_last_200_close_price = sum(last_200_close_prices) / len(last_200_close_prices)
            print(f'200 period m/a: {avg_last_200_close_price}')
            print('\n')

def calc_max():
    pair = 'BTC/USD'
    interval = 15
    # make the API request for OHLC data
    ohlc_data = api.query_public('OHLC', {'pair': pair, 'interval': interval})
    # Convert the data to a pandas dataframe
    df = pd.DataFrame(ohlc_data['result'][pair], columns=['time', 'open', 'high', 'low', 'close', 'vwap', 'volume', 'count'])
    df['time'] = pd.to_datetime(df['time'], unit='s')
    df.set_index('time', inplace=True)
    max_price = df['high'].max()
    print(f"Maximum price in the <high column>: {max_price}")

if __name__ == '__main__':
    calc_max()
    #calc_pair_price() 

(dl_env) G:\div_code\dl_env
λ python kr3.py
Maximum price in the <high column>: 29975.0

So now have added a function as eg calculate Maximum price in high high column(as i don't understand what you want to add).
If uncomment calc_pair_price() it will run your code,so a way organize code that make it easier to add stuff.
Also is normal that a function return code for further processing,then can to smaller task and code get easier to read.