问题
Say I have a dataframe like this:
gender height weight C
2000-01-01 male 42.849980 157.500553 1
2000-01-02 male 49.607315 177.340407 1
2000-01-03 male 56.293531 171.524640 1
2000-01-04 female 48.421077 144.251986 2
2000-01-05 male 46.556882 152.526206 2
2000-01-06 female 68.448851 168.272968 1
2000-01-07 male 70.757698 136.431469 2
2000-01-08 female 58.909500 176.499753 3
2000-01-09 female 76.435631 174.094104 3
2000-01-10 male 45.306120 177.540920 2
How could I compute the mean of the height column, grouped by column C? This would yield 3 different values: the mean of those heights with C=1, that of those with C=2, and so forth.
So far I tried this but to no avail:
df['height'].mean(groupby='C')
-> returns TypeError: mean() got an unexpected keyword argument 'groupby'
回答1:
Your syntax is incorrect, there is no groupby arg for mean, you want to groupby on the col of interest and then call mean on the column of interest:
In [11]:
df.groupby('C')['height'].mean()
Out[11]:
C
1 54.299919
2 52.760444
3 67.672566
Name: height, dtype: float64
来源：https://stackoverflow.com/questions/40739558/pandas-compute-the-mean-of-a-column-grouped-by-another-column