Python 数据帧中的唯一字符串

我有以下示例数据框

d

，由两列“col1”和“col2”组成。我想找到整个数据帧d的唯一名称列表

    d = {'col1':['Pat, Joseph', 
                 'Tony, Hoffman', 
                 'Miriam, Goodwin', 
                 'Roxanne, Padilla',
                 'Julie, Davis', 
                 'Muriel, Howell', 
                 'Salvador, Reese', 
                 'Kristopher, Mckenzie',
                 'Lucille, Thornton', 
                 'Brenda, Wilkerson'],

     'col2':['Kristopher, Mckenzie', 
             'Lucille, Thornton',
             'Pete, Fitzgerald; Cecelia, Bass; Julie, Davis', 
             'Muriel, Howell', 'Harriet, Phillips',
             'Belinda, Drake;David, Ford', 'Jared, Cummings;Joanna, Burns;Bob, Cunningham',
             'Keith, Hernandez;Pat, Joseph', 'Kristopher, Mckenzie', 'Lucille, Thornton']}

    df = pd.DataFrame(data=d)

对于col1列，我可以使用函数unique（）完成它

对于col2，某些行有多个名称，名称之间用分号分隔。e、 g.

“彼得·菲茨杰拉德；塞西莉亚，巴斯；朱莉，戴维斯

如何使用向量运算从col2中获取唯一名称？我试图避免for循环，因为实际数据集很大。

首先是

；s\*

（regex-

；

带有零个或多个空格）转换为

数据帧

，然后根据对

系列

进行重塑，最后使用

唯一

：

print (df['col2'].str.split(';\s*', expand=True).stack().unique())
['Kristopher, Mckenzie' 'Lucille, Thornton' 'Pete, Fitzgerald'
 'Cecelia, Bass' 'Julie, Davis' 'Muriel, Howell' 'Harriet, Phillips'
 'Belinda, Drake' 'David, Ford' 'Jared, Cummings' 'Joanna, Burns'
 'Bob, Cunningham' 'Keith, Hernandez' 'Pat, Joseph']

详情：

print (df['col2'].str.split(';\s*', expand=True))
                      0               1                2
0  Kristopher, Mckenzie            None             None
1     Lucille, Thornton            None             None
2      Pete, Fitzgerald   Cecelia, Bass     Julie, Davis
3        Muriel, Howell            None             None
4     Harriet, Phillips            None             None
5        Belinda, Drake     David, Ford             None
6       Jared, Cummings   Joanna, Burns  Bob, Cunningham
7      Keith, Hernandez     Pat, Joseph             None
8  Kristopher, Mckenzie            None             None
9     Lucille, Thornton            None             None

print (df['col2'].str.split(';\s*', expand=True).stack())
0  0    Kristopher, Mckenzie
1  0       Lucille, Thornton
2  0        Pete, Fitzgerald
   1           Cecelia, Bass
   2            Julie, Davis
3  0          Muriel, Howell
4  0       Harriet, Phillips
5  0          Belinda, Drake
   1             David, Ford
6  0         Jared, Cummings
   1           Joanna, Burns
   2         Bob, Cunningham
7  0        Keith, Hernandez
   1             Pat, Joseph
8  0    Kristopher, Mckenzie
9  0       Lucille, Thornton
dtype: object

替代解决方案：

print (np.unique(np.concatenate(df['col2'].str.split(';\s*').values)))
['Belinda, Drake' 'Bob, Cunningham' 'Cecelia, Bass' 'David, Ford'
 'Harriet, Phillips' 'Jared, Cummings' 'Joanna, Burns' 'Julie, Davis'
 'Keith, Hernandez' 'Kristopher, Mckenzie' 'Lucille, Thornton'
 'Muriel, Howell' 'Pat, Joseph' 'Pete, Fitzgerald']

编辑：

对于所有唯一的名称，将

堆栈

首先添加到

系列

中，形成所有列：

print (df.stack().str.split(';\s*', expand=True).stack().unique())

['Pat, Joseph' 'Kristopher, Mckenzie' 'Tony, Hoffman' 'Lucille, Thornton'
 'Miriam, Goodwin' 'Pete, Fitzgerald' 'Cecelia, Bass' 'Julie, Davis'
 'Roxanne, Padilla' 'Muriel, Howell' 'Harriet, Phillips' 'Belinda, Drake'
 'David, Ford' 'Salvador, Reese' 'Jared, Cummings' 'Joanna, Burns'
 'Bob, Cunningham' 'Keith, Hernandez' 'Brenda, Wilkerson']

---

df.col2.str.split（“；”，expand=True）.stack（）.unique（）

这是我来时的解决方案：（.Arghh下次应该很快来谢谢你的快速解决方案。因为很简单，我要问你另一个。如何在上面的数据框中找到所有唯一的名称？

print (np.unique(np.concatenate(df['col2'].str.split(';\s*').values)))
['Belinda, Drake' 'Bob, Cunningham' 'Cecelia, Bass' 'David, Ford'
 'Harriet, Phillips' 'Jared, Cummings' 'Joanna, Burns' 'Julie, Davis'
 'Keith, Hernandez' 'Kristopher, Mckenzie' 'Lucille, Thornton'
 'Muriel, Howell' 'Pat, Joseph' 'Pete, Fitzgerald']

print (df.stack().str.split(';\s*', expand=True).stack().unique())

['Pat, Joseph' 'Kristopher, Mckenzie' 'Tony, Hoffman' 'Lucille, Thornton'
 'Miriam, Goodwin' 'Pete, Fitzgerald' 'Cecelia, Bass' 'Julie, Davis'
 'Roxanne, Padilla' 'Muriel, Howell' 'Harriet, Phillips' 'Belinda, Drake'
 'David, Ford' 'Salvador, Reese' 'Jared, Cummings' 'Joanna, Burns'
 'Bob, Cunningham' 'Keith, Hernandez' 'Brenda, Wilkerson']