熊猫：计算按另一列分组的列的平均值

Question

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'

Answer 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