python - 计算 Pandas df 中的非空值

Question

我有一个包含大约 70 列的数据集，如下所示:

ID_number   Meeting1    Meeting2    Meeting3    Meeting4    Meeting5    Comments    …
123456789   9/15/2015   1/8/2016    4/27/2016   NaN         NaN         text text   …
987654321   9/22/2016   NaN         2/25/2017   NaN         NaN         text text   …
456789123   10/1/2015   11/30/2015  NaN         NaN         NaN         text text   …

我想创建一个附加列 (meeting_count)，其中包含 Meeting1-Meeting5 列的非空值计数，对应 ID_number。

通常我会使用 SQL 并执行如下操作:

select
    Meeting1,
    Meeting2,
    Meeting3,
    Meeting4,
    Meeting5,
    (
        select count(*)
        from (values (Meeting1), (Meeting2), (Meeting3), (Meeting4), (Meeting5)) as v(col)
        where v.col is not null
    ) as meeting_count
from Table

但如果在 Python 中有一种相对简单的方法来做到这一点，我宁愿那样做。

Answer 1

试试这个

df['meeting_count'] = df.filter(regex=r'^Meeting').notnull().sum(axis=1)

演示:

In [8]: df
Out[8]:
           ID_number    Meeting1   Meeting2  Meeting3  Meeting4 Meeting5 Comments
123456789  9/15/2015    1/8/2016  4/27/2016       NaN       NaN     text     text
987654321  9/22/2016         NaN  2/25/2017       NaN       NaN     text     text
456789123  10/1/2015  11/30/2015        NaN       NaN       NaN     text     text

In [9]: df['meeting_count'] = df.filter(regex=r'^Meeting').notnull().sum(axis=1)

In [10]: df
Out[10]:
           ID_number    Meeting1   Meeting2  Meeting3  Meeting4 Meeting5 Comments  meeting_count
123456789  9/15/2015    1/8/2016  4/27/2016       NaN       NaN     text     text              3
987654321  9/22/2016         NaN  2/25/2017       NaN       NaN     text     text              2
456789123  10/1/2015  11/30/2015        NaN       NaN       NaN     text     text              2