与@Corralien的方法相同，但使用margins和排序列：

out = (pd.concat([df1, df2, df3])
        .pivot_table(index="Seller", values="Vaule", columns="Date",
                     aggfunc="sum", margins=True, margins_name="Total", sort=False)
        .drop(columns="Total").reindex(list(df["Seller"]) + ["Total"])
        .sort_index(key= lambda x: pd.to_datetime(x, format="%b-%d"), axis=1)
        .add_prefix("Value").reset_index().rename_axis(columns=None)
)

输出：

print(out)

     Seller  Value_Jan-02  Value_Feb-02  Value_Oct-02
0      Hari          2.00           NaN        222.00
1        Om         12.00           NaN        120.00
2       Cat          0.00           NaN         10.00
3       Mat         14.00        144.00        143.00
4      John           NaN         10.00         30.00
5     Messi           NaN           NaN           NaN
6      John           NaN         10.00         30.00
7   Ronaldo           NaN           NaN           NaN
8   Shannon           NaN           NaN           NaN
9    Catthy           NaN           NaN           NaN
10      Ray           NaN         22.00           NaN
11    Total         28.00        188.00        525.00

您可以使用pd.concat合并df1，df2和df3，然后按主 Dataframe 的Seller列重新索引（dfM）：

# 1st pass: aggregate all dataframes whatever the dates
out = (pd.concat([df1, df2, df3])
         .pivot_table(index='Seller', columns='Date', values='Vaule', 
                      sort=False, fill_value=0)
         .reindex(dfM['Seller'], fill_value=0))

# 2nd pass: reorganize the output
out = (pd.concat([out, out.sum().to_frame('Total').T])
         .sort_index(axis=1, key=lambda x: pd.to_datetime(x, format='%b-%y'))
         .add_prefix('Value_').rename_axis(index='Seller', columns=None).reset_index())

输出：

>>> out
     Seller  Value_Jan-02  Value_Feb-02  Value_Oct-02
0      Hari             2             0           222
1        Om            12             0           120
2       Cat             0             0            10
3       Mat            14           144           143
4      John             0            10            30
5     Messi             0             0             0
6      John             0            10            30
7   Ronaldo             0             0             0
8   Shannon             0             0             0
9    Catthy             0             0             0
10      Ray             0            22             0
11    Total            28           186           555