基于数组值的脚本开发求助：完善均值、中位数、模式计算功能

Hey there! Sounds like you're so close to wrapping up this script—nice work getting the headers and user records squared away. Let's fix that missing piece: populating the mean, median, and mode values for columns C, F, I, and L under each user's entry.

Here's a step-by-step breakdown to get you there:

1. First, Validate Data Extraction per User #

Make sure you're correctly pulling the numeric values for columns C, F, I, and L for each individual user. You'll want to filter out non-numeric entries or empty values to avoid calculation errors. For example (using Python as a reference):

# Assume user_rows contains all rows of data for the current user
col_c = [row["C"] for row in user_rows if isinstance(row["C"], (int, float))]
col_f = [row["F"] for row in user_rows if isinstance(row["F"], (int, float))]
col_i = [row["I"] for row in user_rows if isinstance(row["I"], (int, float))]
col_l = [row["L"] for row in user_rows if isinstance(row["L"], (int, float))]

2. Build Reusable Statistic Calculation Functions #

Avoid repeating code by creating a helper function to compute mean, median, and mode. Use built-in libraries where possible (like Python's statistics module) to handle edge cases:

import statistics

def get_stats(values):
    if not values:
        return (None, None, None)  # Handle empty data sets gracefully
    
    mean = round(statistics.mean(values), 2)
    median = statistics.median(values)
    
    # Handle cases where multiple modes exist
    try:
        mode = statistics.mode(values)
    except statistics.StatisticsError:
        mode = statistics.multimode(values)[0]  # Pick the first mode if multiple
    
    return (mean, median, mode)

3. Integrate Calculations Into Your Output Flow #

Right after printing the "Mean, Median and Mode" header, compute the stats for each column and print the results in a structured format. For example:

# Print the header row (you already have this part)
print("| Statistic | C       | F       | I       | L       |")
print("|-----------|---------|---------|---------|---------|")

# Calculate stats for each column
c_stats = get_stats(col_c)
f_stats = get_stats(col_f)
i_stats = get_stats(col_i)
l_stats = get_stats(col_l)

# Print each statistic row
print(f"| Mean      | {c_stats[0] or 'N/A'} | {f_stats[0] or 'N/A'} | {i_stats[0] or 'N/A'} | {l_stats[0] or 'N/A'} |")
print(f"| Median    | {c_stats[1] or 'N/A'} | {f_stats[1] or 'N/A'} | {i_stats[1] or 'N/A'} | {l_stats[1] or 'N/A'} |")
print(f"| Mode      | {c_stats[2] or 'N/A'} | {f_stats[2] or 'N/A'} | {i_stats[2] or 'N/A'} | {l_stats[2] or 'N/A'} |")

4. Handle Edge Cases #

• If a user has no valid data for a column, the N/A fallback ensures your output stays clean instead of crashing or showing messy errors.
• Adjust rounding (like round(..., 2)) to match your desired precision.

内容的提问来源于stack exchange，提问作者Geoff_S