How to calculate mean of years of a specific column?-CodePudding

I have this dataframe, i'd like to have a new one that for every country I have the 'Count' mean of the set of the years

    country Alpha-3 code    Year    Count
    0   Australia   AUS 2005    2.000000
    1   Austria AUT 2005    1.000000
    2   Belgium BEL 2005    0.000000
    3   Canada  CAN 2005    4.000000
    4   China   CHN 2005    0.000000
    5   Australia   AUS 2006    4.000000
    6   Austria AUT 2006    1.000000
    7   Belgium BEL 2006    1.000000
    8   Canada  CAN 2006    6.000000
    9   China   CHN 2006    2.000000
    10  Australia   AUS 2007    5.000000
    11  Austria AUT 2007    0.000000
    12  Belgium BEL 2007    2.000000
    13  Canada  CAN 2007    5.000000
    14  China   CHN 2007    3.000000
    15  Australia   AUS 2008    7.000000
    16  Austria AUT 2008    0.000000
    17  Belgium BEL 2008    1.000000
    18  Canada  CAN 2008    5.000000
    19  China   CHN 2008    3.000000

I'd like to have a thing like this:

country   Count
Australia 4.5
Austria 0.5
ecc.

Thanks in advance

CodePudding user response：

To calculate the mean of whole columns in the DataFrame, use pandas.Series.mean() with a list of DataFrame columns. You can also get the mean for all numeric columns using DataFrame.mean(), use axis=0 argument to calculate the column-wise mean of the DataFrame.

for more you can visit this link : https://sparkbyexamples.com/pandas/pandas-get-column-average-mean/ or https://www.statology.org/pandas-average-selected-columns/

CodePudding user response：

You can use pandas.DataFrame.groupby for this.

out1 = df.groupby("country", as_index=False)["Count"].mean() #to return a dataframe
out2 = df.groupby("country")["Count"].mean() #to return a serie

Output :

print(out1)

     country  Count
0  Australia    4.5
1    Austria    0.5
2    Belgium    1.0
3     Canada    5.0
4      China    2.0