Python 获取pd.dataFrame的最后一列并将其添加到另一个pd.dataFrame

我有一个Excel文件，如下所示：

CompanyName    High Priority     QualityIssue
Customer1         Yes             User
Customer1         Yes             User
Customer2         No              User
Customer3         No              Equipment
Customer1         No              Neither
Customer3         No              User
Customer3         Yes             User
Customer3         Yes             Equipment
Customer4         No              User

我想计算

CompanyName

中每个实例在每种类型的

QualityIssue

中出现的次数，并按出现次数排序

例如，通过使用此代码：

df.groupby(["CompanyName ", "QualityIssue"]).size().to_frame('Count')

我得到：

Out:

CompanyName       QualityIssue    Count
Customer2         User            1
Customer1         Neither         1
Customer4         User            1
Customer1         User            2
Customer3         Equipment       2
Customer3         User            2

然后假设我还有另一份上面的副本在记忆中

我想要的是将第二个查询的最后一列添加到第一个查询的末尾（实际上它不是它的副本，只是一个示例）：

这里的问题是如果我这样做

df['Count'] 

它不会只打印该列，它会打印所有内容，就像

print df  

---

因此，我无法找到一种方法，仅获取数据帧的最后一列，以便将其添加到另一列。

使用

groupby

和

size

df.groupby(['CompanyName', 'QualityIssue']).size()

CompanyName  QualityIssue
Customer1    Neither         1
             User            2
Customer2    User            1
Customer3    Equipment       2
             User            2
Customer4    User            1
dtype: int64

---

假如我们还记得另一个呢

c1 = df.groupby(['CompanyName', 'QualityIssue']).size()
c2 = c1.copy()

然后使用

pd.concat

pd.concat([c1, c2], keys=['Count1', 'Count2']).unstack(0, fill_value=0)

                          Count1  Count2
CompanyName QualityIssue                
Customer1   Neither            1       1
            User               2       2
Customer2   User               1       1
Customer3   Equipment          2       2
            User               2       2
Customer4   User               1       1

---

reset_index

如果希望索引返回到数据帧中

pd.concat([c1, c2], keys=['Count1', 'Count2']).unstack(0, fill_value=0) \
    .reset_index()

  CompanyName QualityIssue  Count1  Count2
0   Customer1      Neither       1       1
1   Customer1         User       2       2
2   Customer2         User       1       1
3   Customer3    Equipment       2       2
4   Customer3         User       2       2
5   Customer4         User       1       1

pd.concat([c1, c2], keys=['Count1', 'Count2']).unstack(0, fill_value=0) \
    .reset_index()

  CompanyName QualityIssue  Count1  Count2
0   Customer1      Neither       1       1
1   Customer1         User       2       2
2   Customer2         User       1       1
3   Customer3    Equipment       2       2
4   Customer3         User       2       2
5   Customer4         User       1       1