pandas 如何删除熊猫数据框中的行？
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How to delete rows in a pandas dataframe?
提问by student
I have this pandas dataframe which is actually a excel spreadsheet:
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
I would like to remove all the rows that have NaNin the IDcolumn and reindex the "index imaginary column":
我想删除所有具有行NaN中ID列和重新索引“指数虚列”：
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
I know that this can be done as follows:
我知道这可以按如下方式完成：
df = df['ID'].dropna()
Or
或者
df[df.ID != np.nan]
Or
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''
Or
或者
df[df.ID()]
Or:
或者：
df[df.ID != '']
And then:
进而：
df.reset_index(drop=True, inplace=True)
However, It didnt removed the NaNin ID. I am getting the former dataframe.
但是，它没有删除NaNin ID。我正在获取以前的数据框。
UPDATE
更新
In:
在：
df['ID'].values
Out:
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)
回答by Mainul Islam
Try df.dropna(axis = 1).
试试df.dropna(axis = 1)。
Or, df.dropna(axis = 0, subset = "ID")See if it helps.
或者，df.dropna(axis = 0, subset = "ID")看看它是否有帮助。
回答by ??????
try this
尝试这个
df = df[df.ID != 'nan']
