如何删除 pandas 数据框中的行? [英] How to delete rows in a pandas dataframe?
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问题描述
我有这个熊猫数据框,它实际上是一个 Excel 电子表格:
I have this pandas dataframe which is actually a excel spreadsheet:
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
列中包含 NaN
的所有行并重新索引索引虚列":
I would like to remove all the rows that have NaN
in the ID
column and reindex the "index imaginary column":
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:
I know that this can be done as follows:
df = df['ID'].dropna()
或
df[df.ID != np.nan]
或
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
df[df.ID()]
或者:
df[df.ID != '']
然后:
df.reset_index(drop=True, inplace=True)
然而,它并没有去除ID
中的NaN
.我正在获取以前的数据框.
However, It didnt removed the NaN
in ID
. I am getting the former dataframe.
更新
在:
df['ID'].values
出:
array([ '....A lot of text....',
nan,
"A lot of text...",
"More text",
'text from the site',
nan,
"text from the site"], dtype=object)
推荐答案
试试这个
df = df[df.ID != 'nan']
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