How to replace level 2 in a multiIndex dataframe with values from a regular dataframe?

I am writing an algorithm for processing customers at work for some data analysis, and I am grouping the data by user ID as my level 1, then by month for my level 2.

I am then doing some processing work on the data user by user, which involves a monthly time series.

The code I have below does exactly what I need it to do, save for the final step.

How can I overload the data in my original with the processed time series data in the output?

    tempDF1 = pd.read_csv('data.csv', index_col=[0,1], parse_dates=[1], thousands=',')
    
    tempDF1["Average"] = 0
    tempDF1["Score"] = 0
    
    for id, df in tempDF1.groupby(level=0):
        for date in df.loc[id].index:
            df.loc[(id,date),"Average"] = df.loc[(id,date)].Purchased/df.loc[(id,date)].Count
            df.loc[(id,date),"Score"] = df.loc[(id,date)].Count/10*log(df.loc[(id,date)].Average, 10)
        try:
            list(df.loc[(id,"2021-01-01",),:])
        except:
            df.loc[(id, "2021-01-01",),:] = 0
        try:
            list(df.loc[(id,"2023-06-01",),:])
        except:
            df.loc[(id, "2023-06-01",),:] = 0

        resampleDF = df.loc[id].resample('M', closed="left").mean().fillna(0)
        print(resampleDF)
        tempDF1.loc[id].replace(resampleDF, inplace=True)
        print(tempDF1)

Below is the sample output from the code above. The resampleDF is for ID 1785 and I am trying to replace the whole level; level 2 indexing, the data in the columns, everything wholesale. I need to put resampleDF into tempDF1 in place of what is there for 1785 (and the rest, but that should be accomplished by the above loop.

resampleDF

tempDF1

With the dataframes you provided:

import pandas as pd

tempDF1 = pd.DataFrame(
    {
        "user_id": [1785, 1785, 1857],
        "Month": ["2021-06-01", "2021-10-01", "2021-06-01"],
        "Count": [1, 2, 1],
        "Purchased": [9999, 20, 100],
        "Average": [0, 0, 0],
        "Score": [0, 0, 0],
    },
).set_index(["user_id", "Month"])

print(tempDF1)
# Output

                    Count  Purchased  Average  Score
user_id Month
1785    2021-06-01      1       9999        0      0
        2021-10-01      2         20        0      0
1857    2021-06-01      1        100        0      0

resampleDF = pd.DataFrame(
    {
        "Month": [
            "2021-01-31",
            "2021-02-28",
            "2021-03-31",
            "2021-04-30",
            "2021-05-31",
            "2021-06-30",
            "2021-07-31",
            "2021-08-31",
            "2021-09-30",
            "2021-10-31",
            "2021-11-30",
        ],
        "Count": [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0],
        "Purchased": [0.0, 0.0, 0.0, 0.0, 0.0, 9999.0, 0.0, 0.0, 0.0, 20.0, 0.0],
        "Average": [0.0, 0.0, 0.0, 0.0, 0.0, 9999.0, 0.0, 0.0, 0.0, 10.0, 0.0],
        "Score": [0.0, 0.0, 0.0, 0.0, 0.0, 0.399996, 0.0, 0.0, 0.0, 0.2, 0.0],
    }
)

Here is one way to do it with Pandas drop, assign, and concat:

tempDF1 = pd.concat(
    [
        resampleDF.assign(user_id=1785).set_index(["user_id", "Month"]),
        tempDF1.drop(index=1785),
    ]
)

print(tempDF1)
# Output

                    Count  Purchased  Average     Score
user_id Month
1785    2021-01-31    0.0        0.0      0.0  0.000000
        2021-02-28    0.0        0.0      0.0  0.000000
        2021-03-31    0.0        0.0      0.0  0.000000
        2021-04-30    0.0        0.0      0.0  0.000000
        2021-05-31    0.0        0.0      0.0  0.000000
        2021-06-30    1.0     9999.0   9999.0  0.399996
        2021-07-31    0.0        0.0      0.0  0.000000
        2021-08-31    0.0        0.0      0.0  0.000000
        2021-09-30    0.0        0.0      0.0  0.000000
        2021-10-31    2.0       20.0     10.0  0.200000
        2021-11-30    0.0        0.0      0.0  0.000000
1857    2021-06-01    1.0      100.0      0.0  0.000000