How to solve differences in calculating `mean`-CodePudding

When calculating a mean in two different ways (on a dataframe and on the same pivoted dataframe) I expect the outcomes to be identical. However, they appear to differ. Am I missing something?

Here's the dataset:

import pandas as pd # pandas version is 1.3.4

df = pd.read_csv(
    'https://data.rivm.nl/covid-19/COVID-19_aantallen_gemeente_per_dag.csv', 
    usecols = ['Date_of_publication', 'Municipality_code', 'Municipality_name', 'Province', 'Total_reported', 'Hospital_admission', 'Deceased'], 
    parse_dates = ['Date_of_publication'], 
    index_col = ['Date_of_publication'], 
    sep = ';'
).dropna()

df.tail()

I would like to calculate a mean per Date_of_publication of the column Total_reported.

Method 1:

df.Total_reported.groupby(df.index).mean()

Method 2:

df_pivot = pd.pivot_table(
    df.reset_index(), 
    values='Total_reported', 
    index='Date_of_publication', 
    columns='Municipality_name'
)

df_pivot.mean(axis=1)

CodePudding user response：

Since I cannot post a code example as a comment, I want to leverage on @mozway's answer that the reason is duplicates in the columns when doing a pivot_table

df = pd.DataFrame({"total":[50,10,30,15,10,5],"state":["UC","FI","DK","LM","NA","PL"]},index=["2021-11-29"]*3 ["2021-11-30"]*3)
df.index.name = "date"
print(df) #No duplicated "state"

#     total  state
#date       
#2021-11-29 50  UC
#2021-11-29 10  FI
#2021-11-29 30  DK
#2021-11-30 15  LM
#2021-11-30 10  NA
#2021-11-30 5   PL

df["total"].groupby(df.index).mean()

#2021-11-29    30.0
#2021-11-30    10.0


pd.pivot_table(df.reset_index(),index="date",values="total",columns="state").mean(axis=1)

#2021-11-29    30.0
#2021-11-30    10.0

as seen, it gives the same result but when we change the PL to NA in the state i.e we now have 2xNA the result for the pivot_table is changed

df = pd.DataFrame({"total":[50,10,30,15,10,5],"state":["UC","FI","DK","LM","NA","NA"]},index=["2021-11-29"]*3 ["2021-11-30"]*3) #changed the 'PL' 'NA'

#2021-11-29    30.00
#2021-11-30    11.25 #was 10.0 before