您可以使用groupby的size

import pandas as pd

# load the sample data
data = {'Group': ['Short', 'Short', 'Moderate', 'Moderate', 'Tall'], 'Size': ['Small', 'Small', 'Medium', 'Small', 'Large']}
df = pd.DataFrame(data)

选项1：

dfg = df.groupby(by=["Group", "Size"]).size()

# which results in a pandas.core.series.Series
Group     Size
Moderate  Medium    1
          Small     1
Short     Small     2
Tall      Large     1
dtype: int64

备选方案二：

dfg = df.groupby(by=["Group", "Size"]).size().reset_index(name="Time")

# which results in a pandas.core.frame.DataFrame
      Group    Size  Time
0  Moderate  Medium     1
1  Moderate   Small     1
2     Short   Small     2
3      Tall   Large     1

备选方案3：

dfg = df.groupby(by=["Group", "Size"], as_index=False).size()

# which results in a pandas.core.frame.DataFrame
      Group    Size  Time
0  Moderate  Medium     1
1  Moderate   Small     1
2     Short   Small     2
3      Tall   Large     1

• 更新 * 后pandas 1.1 value_counts现在接受多列

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

您也可以尝试pd.crosstab()

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：为了让你的输出

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')