如何计算列表子列表y列平均值及数组最后指定数量值的均值？

Hey there! Let's break down your two Python problems and fix that GDAX code issue step by step.

1. Calculating the Average of a Specific "y Column" Across All Sublists #

First up, averaging values from a specific column across all your sublists. Let's assume your main list matches the sample data you shared—each sublist follows the format [timestamp, open, high, low, close, volume] (like [1521965100, 8464.99, 8470, 8464.99, 8470, 1.8307]).

If you want the average of one specific "y column" (say, the closing price at index 4) across all sublists, here's a straightforward implementation:

# Example main list with sublists matching your data format
main_list = [
    [1521965100, 8464.99, 8470, 8464.99, 8470, 1.8307],
    [1521965200, 8470, 8472.5, 8470, 8472.5, 2.104],
    [1521965300, 8472.5, 8475, 8472.5, 8475, 0.987]
]

# Define which index corresponds to your "y column" (closing price here is index 4)
y_column_index = 4

# Extract all y values and calculate the average
y_values = [sublist[y_column_index] for sublist in main_list]
y_average = sum(y_values) / len(y_values)

print(f"Average of the y column: {y_average:.2f}")

If instead you need the average of multiple y-values within each sublist (like averaging open/high/low/close for each individual candle), adjust the code to loop through each sublist and compute its own average:

for i, sublist in enumerate(main_list):
    # Grab the price values (indices 1-4: open, high, low, close)
    sublist_y_values = sublist[1:5]
    sublist_avg = sum(sublist_y_values) / len(sublist_y_values)
    print(f"Average for sublist {i+1}: {sublist_avg:.2f}")

2. Fixing the GDAX Code to Calculate Average of Last X Values #

Now let's tackle the GDAX (now Coinbase Pro) data issue. The most common pitfalls here are mixing up the API's data order and failing to handle errors properly. Here's a working version with clear explanations:

First, make sure you have the gdax library installed:

pip install gdax

Then the corrected code:

import gdax

def get_last_n_price_avg(product_id="BTC-USD", count_num=5):
    # Initialize the public GDAX client
    client = gdax.PublicClient()
    
    # Fetch historic candle data (default is 1-minute intervals, max 200 candles)
    historic_rates = client.get_product_historic_rates(product_id)
    
    # Handle API errors (e.g., rate limits, invalid product IDs)
    if isinstance(historic_rates, dict) and "message" in historic_rates:
        raise Exception(f"API Error: {historic_rates['message']}")
    
    # Critical note: GDAX returns candles in **NEWEST TO OLDEST** order!
    # So to get the latest `count_num` values, take the first `count_num` entries
    # If you need the oldest `count_num` values, use historic_rates[-count_num:] instead
    latest_n_candles = historic_rates[:count_num]
    
    # Pick your target column (matches your sample data: 1=open, 2=high, 3=low, 4=close, 5=volume)
    target_column = 4  # Using closing prices for this example
    
    # Extract the values and compute the average
    target_values = [candle[target_column] for candle in latest_n_candles]
    average = sum(target_values) / len(target_values)
    
    return average

# Test the function
try:
    avg = get_last_n_price_avg(count_num=5)
    print(f"Average of the last 5 BTC-USD closing prices: {avg:.2f}")
except Exception as e:
    print(f"Oops, something went wrong: {e}")

Common Issues Fixed Here: #

• Data Order Confusion: GDAX doesn't return candles in chronological (oldest to newest) order—newest entries come first. This is the #1 reason code for this task breaks!
• Error Handling: The API will return a dictionary with an error message if you hit rate limits or use an invalid product ID, so we check for that before processing.
• Column Index Clarity: Explicitly define which column you want to average, so you don't accidentally use the wrong value (like volume instead of price).

内容的提问来源于stack exchange，提问作者Muema