计算 pandas 中的非空值 [英] Count non-null values in pandas

问题描述

我有数据框

    site1   time1   site2   time2   site3   time3   site4   time4   site5   time5   ... time6   site7   time7   site8   time8   site9   time9   site10  time10  target
 session_id                                                                                 

21669   56  2013-01-12 08:05:57 55.0    2013-01-12 08:05:57 NaN NaT NaN NaT NaN NaT ... NaT NaN NaT NaN NaT NaN NaT NaN NaT 0
54843   56  2013-01-12 08:37:23 55.0    2013-01-12 08:37:23 56.0    2013-01-12 09:07:07 55.0    2013-01-12 09:07:09 NaN NaT ... NaT NaN NaT NaN NaT NaN NaT NaN NaT 0
77292   946 2013-01-12 08:50:13 946.0   2013-01-12 08:50:14 951.0   2013-01-12 08:50:15 946.0   2013-01-12 08:50:15 946.0   2013-01-12 08:50:16 ... 2013-01-12 08:50:16 948.0   2013-01-12 08:50:16 784.0   2013-01-12 08:50:16 949.0   2013-01-12 08:50:17 946.0   2013-01-12 08:50:17 0
114021  945 2013-01-12 08:50:17 948.0   2013-01-12 08:50:17 949.0   2013-01-12 08:50:18 948.0   2013-01-12 08:50:18 945.0   2013-01-12 08:50:18 ... 2013-01-12 08:50:18 947.0   2013-01-12 08:50:19 945.0   2013-01-12 08:50:19 946.0   2013-01-12 08:50:19 946.0   2013-01-12 08:50:20 0

我需要计算N列，其中站点！= NaN. 我尝试使用

I need to count N of columns, where site != NaN. I try to use

df[['site%s' % i for i in range(1, 11)]].count(axis=1)

但它会向我返回10个ID

but it returns me 10 to every id

我也尝试过

train_df[sites].notnull().count(axis=1)

它也没有帮助.

期望输出

21669    2
54843    4
77292    10
114021   10

推荐答案

我只需要count:

train_df[sites].count(axis=1)

count专门计算非空值.当前实现的问题是notnull会产生布尔值，而bool肯定不是空的，这意味着它们总是被计数.

count specifically counts non-null values. The issue with your current implementation is that notnull yields boolean values, and bools are certainly not-null, meaning they are always counted.

df

        one       two     three four   five
a -0.166778  0.501113 -0.355322  bar  False
b       NaN       NaN       NaN  NaN    NaN
c -0.337890  0.580967  0.983801  bar  False
d       NaN       NaN       NaN  NaN    NaN
e  0.057802  0.761948 -0.712964  bar   True
f -0.443160 -0.974602  1.047704  bar  False
g       NaN       NaN       NaN  NaN    NaN
h -0.717852 -1.053898 -0.019369  bar  False

df.count(axis=1)

a    5
b    0
c    5
d    0
e    5
f    5
g    0
h    5
dtype: int64

然后...

df.notnull().count(axis=1)


a    5
b    5
c    5
d    5
e    5
f    5
g    5
h    5
dtype: int64

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