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.

[SuchUmami]

What I don't quite understand is if I just use this part of the code you wrote:

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() 

That I get the very same number without any of my code. I'm sorry if this is a dumb observation.

[snippsat]

Not sure what you try do.
It was just a quick function demo.
Can write it more useful like this,now is much flexible and can take different argument in.

import krakenex
import pandas as pd

api = krakenex.API()

def calc_max(pair: str, interval: int, col_index: str) -> None:
    # 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)
    print(f'{df.head()}\n')
    max_price = df[col_index].max()
    print(f"Maximum price in the <{col_index}>: {max_price}")

if __name__ == '__main__':
    pair = 'XRPGBP'
    interval = 60
    col_index = 'open'
    calc_max(pair, interval, col_index)

Output:
(dl_env) G:\div_code\dl_env
λ python calc_max.py
                        open     high      low    close     vwap         volume  count
time
2023-04-08 14:00:00  0.41009  0.41067  0.41003  0.41049  0.41031  2498.49848700     17
2023-04-08 15:00:00  0.41087  0.41087  0.41001  0.41002  0.41035  2470.63201500     19
2023-04-08 16:00:00  0.40983  0.40992  0.40913  0.40950  0.40945  5611.68209685     19
2023-04-08 17:00:00  0.40953  0.41037  0.40767  0.40767  0.40918  2416.04309153     15
2023-04-08 18:00:00  0.40755  0.40811  0.40507  0.40585  0.40658  5555.67721549     33

Maximum price in the <open>: 0.42970

Now it check max in XRPGBP open column.
Let say now want to check max in BTC/USD high column with interval 15.

import krakenex
import pandas as pd

api = krakenex.API()

def calc_max(pair: str, interval: int, col_index: str) -> None:
    # 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)
    print(f'{df.head()}\n')
    max_price = df[col_index].max()
    print(f"Maximum price in the <{col_index}>: {max_price}")

if __name__ == '__main__':
    pair = 'BTC/USD'
    interval = 15
    col_index = 'high'
    calc_max(pair, interval, col_index)

Output:
G:\div_code\dl_env
λ python calc_max.py
                        open     high      low    close     vwap       volume  count
time
2023-05-01 02:15:00  28650.2  28678.1  28568.0  28600.0  28635.1  74.45265240    541
2023-05-01 02:30:00  28599.9  28652.7  28586.8  28589.4  28615.1  30.90120898    432
2023-05-01 02:45:00  28598.0  28623.1  28536.0  28555.3  28580.7  20.00984895    350
2023-05-01 03:00:00  28564.2  28565.1  28477.4  28491.9  28537.1  41.99185887    436
2023-05-01 03:15:00  28489.1  28605.7  28450.0  28595.8  28537.2  42.52158371    402

Maximum price in the <high>: 29868.1