EDIT: RESOLVED. I was being a complete moron. See comment if you want a laugh.

Here's the situation: I've webscraped some sports stats and saved them in a DF. I then add a new row which calculates the mean of each column (mean number of hits, runs, etc). I then save the data to CSV.

When looking at the data in Excel, I noticed that the means looked off. I calculated them in Excel (using both =sum(firstCell:lastCell)/#rows and =AVERAGE(firstCell:lastCell). Both of those methods agreed with each other, but were wildly different (and at a glance, closer to what I'd expect) than the df.mean() values

Here's the Python code (I've shortened the paths for brevity but they're operational in the real code):

import pandas as pd
from pathlib import Path

DATA_DIR = "[...]/data"
p = Path(DATA_DIR)

def read_data():
    lst = pd.read_html(f"[...]/batting")
    df = lst[0]
    df = df.fillna(0)
    return df

batting_df = read_data()
batting_df.loc['lgAvg'] = batting_df.mean()
# Then export to CSV.

I've just noticed, having looked at them more closely, that the lgAvg values are pretty much (but not always exactly) double the =AVERAGE() values from Excel. They're double to within 1 DP.

Any idea what's causing this?

I have this:

And I want this:

I want the average values, but specifically within the A and B categories.

Thanks!