merge使用方法'outer'并传递参数indicator=True这将告诉您行是否存在于两个/left only/right only中，然后您可以过滤合并的df：

In [22]:
merged = df1.merge(df2, indicator=True, how='outer')
merged[merged['_merge'] == 'right_only']

Out[22]:
  Buyer  Quantity      _merge
3  Carl         2  right_only
4  Mark         1  right_only

你可能会发现这是最好的：

df2[ ~df2.isin(df1)].dropna()

@EdChum的答案是不言自明的。但是使用not 'both'条件更有意义，而且你不需要关心比较的顺序，这就是真实的的diff应该是什么样子的。为了回答你的问题：

merged = df1.merge(df2, indicator=True, how='outer')
merged.loc = [merged['_merge'] != 'both']

diff = set(zip(df2.Buyer, df2.Quantity)) - set(zip(df1.Buyer, df1.Quantity))

这是我想到的第一个解决方案，然后你可以将diff集放回DF中进行展示。

从Pandas 1.1.0开始，有pandas.DataFrame.compare:

df1.compare(df2)

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.compare.html

如果您只关心将新买家添加到其他df，请尝试以下操作：

df_delta=df2[df2['Buyer'].apply(lambda x: x not in df1['Buyer'].values)]

一个重要的边缘用例

考虑下面的情况，在第二个 Dataframe 中有一个额外的重复条目。('Carl', 5)

df1 = DataFrame({ 'Buyer':    ['Carl', 'Carl', 'Carl'],
                  'Quantity': [   18 ,     3 ,     5 ]  })

df2 = DataFrame({ 'Buyer':    ['Carl', 'Mark', 'Carl', 'Carl', 'Carl'],
                  'Quantity': [    2 ,     1 ,    18 ,     5 ,     5 ]  })

EdChum的回答会给予你以下几点：

merged = df1.merge(df2, indicator=True, how='outer')
print(merged[merged['_merge'] == 'right_only'])

  Buyer  Quantity      _merge
4  Carl         2  right_only
5  Mark         1  right_only

正如您所看到的，解决方案忽略了额外的重复值，这取决于您正在做的事情是您想要避免的。
以下是一个更有可能实现您所需的解决方案：

df1['duplicate_counter'] = df1.groupby(list(df1.columns)).cumcount()
df2['duplicate_counter'] = df2.groupby(list(df2.columns)).cumcount()
merged = df1.merge(df2, indicator=True, how='outer')
merged[merged['_merge'] == 'right_only']

  Buyer  Quantity  duplicate_counter      _merge
3  Carl         2                  0  right_only
4  Mark         1                  0  right_only
5  Carl         5                  1  right_only

重复计数器确保每一行都是唯一的，这意味着不会删除重复的值。合并后，您可以删除duplicate_counter。

还有datacompy。它允许导出一些基于字符串的比较报告，如下所示：

DataComPy Comparison
--------------------

DataFrame Summary
-----------------

  DataFrame  Columns  Rows
0  original        5     6
1       new        4     5

Column Summary
--------------

Number of columns in common: 4
Number of columns in original but not in new: 1
Number of columns in new but not in original: 0

Row Summary
-----------

Matched on: acct_id
Any duplicates on match values: Yes
Absolute Tolerance: 0.0001
Relative Tolerance: 0
Number of rows in common: 5
Number of rows in original but not in new: 1
Number of rows in new but not in original: 0

Number of rows with some compared columns unequal: 5
Number of rows with all compared columns equal: 0

Column Comparison
-----------------

Number of columns compared with some values unequal: 3
Number of columns compared with all values equal: 1
Total number of values which compare unequal: 7

Columns with Unequal Values or Types
------------------------------------

       Column original dtype new dtype  # Unequal  Max Diff  # Null Diff
0  dollar_amt        float64   float64          1    0.0500            0
1   float_fld        float64   float64          4    0.0005            3
2        name         object    object          2    0.0000            0

Sample Rows with Unequal Values
-------------------------------

       acct_id  dollar_amt (original)  dollar_amt (new)
0  10000001234                 123.45             123.4

       acct_id  float_fld (original)  float_fld (new)
0  10000001234            14530.1555        14530.155
5  10000001238                   NaN          111.000
2  10000001236                   NaN            1.000
1  10000001235                1.0000              NaN

       acct_id name (original)            name (new)
0  10000001234  George Maharis  George Michael Bluth
3  10000001237      Bob Loblaw         Robert Loblaw

Sample Rows Only in original (First 10 Columns)
-----------------------------------------------

       acct_id  dollar_amt           name  float_fld    date_fld
4  10000001238        1.05  Lucille Bluth        NaN  2017-01-01