如何计算python中每行的值的列数？

Question

我有这样的数据帧:

IndividualID Trip1 Trip2 Trip3 Trip4 Trip5 Trip6 Trip7 Trip8 Trip9
200100001    23    1     2     4     4      1    5     5     5
200100002    21    1     12    3     1      55   7     7
200100003    12    3     3     6     3     
200100004    4   
200100005    6     5     3     9     3      5    6  
200100005    23    4     4     2     4      3    6     5  

我想知道每个人的出行次数,所以我想创建一个新列,这样新表可能看起来像这样:

IndividualID Trip1  Trip2  Trip3  Trip4  Trip5  Trip6  Trip7  Trip8  Trip9 Chains
200100001     23     1      2      4      4      1     5       5     5      9
200100002     21     1      12     3      1      55    7       7            8
200100003     12     3      3      6      3                                 5
200100004     4                                                             1
200100005     6      5      3      9      3      5     6                    7
200100005     23     4      4      2      4      3     6       5            8

有没有可能的解决方案？如果有人可以提供帮助,我将非常感激!提前致谢!

Answer 1

使用iloc和count,NaN默认忽略:

df.iloc[:, 1:].count(1)

0    9
1    8
2    5
3    1
4    7
5    8
dtype: int64

如果值不是 NaN,只需将空字符串替换为NaN:

df.iloc[:, 1:].replace('', np.nan).count(1)

Answer 2

运用

df.ne('').sum(1)-1
Out[287]: 
0    9
1    8
2    5
3    1
4    7
5    8
dtype: int64

如果是NaN使用 info

df.iloc[:,1:].T.info()
<class 'pandas.core.frame.DataFrame'>
Index: 9 entries, Trip1 to Trip9
Data columns (total 6 columns):
0    9 non-null float64
1    8 non-null float64
2    5 non-null float64
3    1 non-null float64
4    7 non-null float64
5    8 non-null float64
dtypes: float64(6)
memory usage: 504.0+ bytes

Answer 3

将所有空白值替换为NaN，然后notnull使用以下方法按行对值进行计数sum(1)：

df['Chains'] = df.iloc[:,1:].replace('',np.nan).notnull().sum(1)

>>> df
   IndividualID  Trip1  Trip2  Trip3  Trip4  Trip5  Trip6  Trip7  Trip8  \
0     200100001     23    1.0    2.0    4.0    4.0    1.0    5.0    5.0   
1     200100002     21    1.0   12.0    3.0    1.0   55.0    7.0    7.0   
2     200100003     12    3.0    3.0    6.0    3.0    NaN    NaN    NaN   
3     200100004      4    NaN    NaN    NaN    NaN    NaN    NaN    NaN   
4     200100005      6    5.0    3.0    9.0    3.0    5.0    6.0    NaN   
5     200100005     23    4.0    4.0    2.0    4.0    3.0    6.0    5.0   

   Trip9  Chains  
0    5.0       9  
1    NaN       8  
2    NaN       5  
3    NaN       1  
4    NaN       7  
5    NaN       8