python之如何删除 Pandas 数据框中带有 NaN 的行

我有这个 pandas 数据框，它实际上是一个 excel 电子表格:

    Unnamed: 0  Date    Num     Company     Link    ID 
0   NaN     1990-11-15  131231  apple...    http://www.example.com/201611141492/xellia...   290834 
1   NaN     1990-10-22  1231    microsoft http://www.example.com/news/arnsno...     NaN 
2   NaN     2011-10-20  123     apple   http://www.example.com/ator...  209384 
3   NaN     2013-10-27  123     apple...    http://example.com/sections/th-shots/2016/...   098 
4   NaN     1990-10-26  123     google  http://www.example.net/business/Drugmak...  098098 
5   NaN     1990-10-18  1231    google...   http://example.com/news/va-rece...  NaN 
6   NaN     2011-04-26  546     amazon...   http://www.example.com/news/home/20160425...    9809 

我想删除 ID 列中所有具有 NaN 的行，并重新索引“索引虚列”:

    Unnamed: 0  Date    Num     Company     Link    ID 
0   NaN     1990-11-15  131231  apple...    http://www.example.com/201611141492/xellia...   290834 
1   NaN     2011-10-20  123     apple   http://www.example.com/ator...  209384 
2   NaN     2013-10-27  123     apple...    http://example.com/sections/th-shots/2016/...   098 
3   NaN     1990-10-26  123     google  http://www.example.net/business/Drugmak...  098098 
4   NaN     2011-04-26  546     amazon...   http://www.example.com/news/home/20160425...    9809 

我知道这可以按如下方式完成:

df = df['ID'].dropna() 

或者

df[df.ID != np.nan] 

或者

df = df[np.isfinite(df['ID'])] 
 
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' 

或者

df[df.ID()] 

或者:

df[df.ID != ''] 

然后:

df.reset_index(drop=True, inplace=True) 

但是，它并没有删除 ID 中的 NaN。我正在获取以前的数据框。

更新

在:

df['ID'].values 

输出:

array([ '....A lot of text....', 
       nan, 
       "A lot of text...", 
       "More text", 
       'text from the site', 
       nan, 
       "text from the site"], dtype=object) 

请您参考如下方法:

试试 df.dropna(axis = 1)。

或者，df.dropna(axis = 0, subset = "ID")看看是否有帮助。