您可以使用groupby的size：

In [11]: df.groupby(["Group", "Size"]).size()
Out[11]:
Group     Size
Moderate  Medium    1
          Small     1
Short     Small     2
Tall      Large     1
dtype: int64

In [12]: df.groupby(["Group", "Size"]).size().reset_index(name="Time")
Out[12]:
      Group    Size  Time
0  Moderate  Medium     1
1  Moderate   Small     1
2     Short   Small     2
3      Tall   Large     1

更新后熊猫1.1value_counts现在接受多列

df.value_counts(["Group", "Size"])

您也可以尝试pd. Crossstab（）

Group           Size

Short          Small
Short          Small
Moderate       Medium
Moderate       Small
Tall           Large

pd.crosstab(df.Group,df.Size)


Size      Large  Medium  Small
Group                         
Moderate      0       1      1
Short         0       0      2
Tall          1       0      0

编辑：为了让你出局

pd.crosstab(df.Group,df.Size).replace(0,np.nan).\
     stack().reset_index().rename(columns={0:'Time'})
Out[591]: 
      Group    Size  Time
0  Moderate  Medium   1.0
1  Moderate   Small   1.0
2     Short   Small   2.0
3      Tall   Large   1.0

其他可能性是使用。pivot_table（）和aggfunc='size'

df_solution = df.pivot_table(index=['Group','Size'], aggfunc='size')