如何删除 pandas 数据框中带有 NaN 的行？

Question

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

我想删除列NaN中的所有行ID并重新索引“索引假想列”：

    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

我知道可以按如下方式完成此操作：

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 != '']

进而：

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

但是，它并没有删除NaN中的ID. 我正在获取前一个数据框。

更新

在：

df['ID'].values

出去：

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

Answer 1

尝试df.dropna(axis = 1)。

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