Python 选择具有 None 值的熊猫单元格
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Selecting pandas cells with None value
提问by Michael Discenza
I have a column of a pandas dataframe that I got from a database query with blank cells. The blank cells become "None" and I want to check if each of the rows is None:
我有一列熊猫数据框，是从带有空白单元格的数据库查询中获得的。空白单元格变为“无”，我想检查每一行是否为无：
In [325]: yes_records_sample['name']
Out[325]:
41055 John J Murphy Professional Building
25260 None
41757 Armand Bayou Nature Center
31397 None
33104 Hubert Humphrey Building
16891 Williams Hall
29618 None
3770 Covenant House
39618 None
1342 Bhathal Student Services Building
20506 None
My understanding per the documentation is that I can check if each row is null with isnull()command http://pandas.pydata.org/pandas-docs/dev/missing_data.html#values-considered-missing
我对文档的理解是，我可以使用isnull()命令http://pandas.pydata.org/pandas-docs/dev/missing_data.html#values-thinked-missing检查每一行是否为空
That function, however, is not working for me:
但是，该功能对我不起作用：
In [332]: isnull(yes_records_sample['name'])
I get the following error:
我收到以下错误：
NameError Traceback (most recent call last)
<ipython-input-332-55873906e7e6> in <module>()
----> 1 isnull(yes_records_sample['name'])
NameError: name 'isnull' is not defined
I also saw that someone just replaced the "None" strings, but neither of these variations on that approach worked for me: Rename "None" value in Pandas
我还看到有人刚刚替换了“None”字符串，但该方法的这些变体都不适合我： 在 Pandas 中重命名“None”值
yes_records_sample['name'].replace('None', "--no value--")
yes_records_sample['name'].replace(None, "--no value--")
I was ultimately able to use the fillnafunction and fill each of those rows with an empty string yes_records_sample.fillna('')as a workaround and then I could check yes_records_sample['name']==''But I am profoundly confused by how 'None' works and what it means. Is there a way to easily just check if a cell in a dataframe is 'None'?
我最终能够使用该fillna函数并用空字符串填充每一行yes_records_sample.fillna('')作为解决方法，然后我可以检查yes_records_sample['name']==''但我对“无”的工作方式及其含义深感困惑。有没有办法轻松检查数据框中的单元格是否为“无”？
采纳答案by Korem
Call it like this:
像这样调用它：
yes_records_sample['name'].isnull()
回答by g.a
I couldn't find any built-in which does exactly this, so I do it manually. In case of Series, the code is this:
我找不到任何可以做到这一点的内置程序，所以我手动完成。在系列的情况下，代码是这样的：
import numpy as np
series = yes_records_sample['name']
n = np.empty_like(series)
n[...] = None
nones = series.values == n
In case of DataFrames, the code is very similar:
在 DataFrames 的情况下，代码非常相似：
import numpy as np
df = yes_records_sample
n = np.empty_like(df)
n[...] = None
nones = df == n
My problem with .isnull() is that it does not distinguish between NaN and None. This may or may not be a problem in your application.
我对 .isnull() 的问题是它不区分 NaN 和 None。这在您的应用程序中可能是也可能不是问题。
