pandas 计算熊猫中的非空值
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Count non-null values in pandas
提问by Petr Petrov
I have dataframe
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
I need to count N of columns, where site != NaN. I try to use
我需要计算 N 列，其中 site != NaN。我尝试使用
df[['site%s' % i for i in range(1, 11)]].count(axis=1)
but it returns me 10 to every id
但它给我每个 id 返回 10
Also I have tried
我也试过
train_df[sites].notnull().count(axis=1)
and it also didn't help.
它也没有帮助。
Desire output
欲望输出
21669 2
54843 4
77292 10
114021 10
回答by cs95
I'd do this with just count:
我会这样做count：
train_df[sites].count(axis=1)
countspecifically counts non-null values. The issue with your current implementation is that notnullyields boolean values, and bools are certainly not-null, meaning they are always counted.
count专门计算非空值。您当前实现的问题是notnull产生布尔值，而bools 肯定不是空的，这意味着它们总是被计算在内。
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
And...
和...
df.notnull().count(axis=1)
a 5
b 5
c 5
d 5
e 5
f 5
g 5
h 5
dtype: int64
