按熊猫数据框的索引对所有列的值进行分组

Question

I want to basically build a distribution of total no. of videos a user has watched. Watch is signified by 1 else 0. Users are index of the data frame.

Assume the data is like this:

A   B   C
User1   1   1   0
User2   0   1   0
User3   1   0   1

I want for each use a count of all the 1 in that row.

I am doing something like this but it doesn't seem to work. I dont want to use some applymap function as that seem to be slow.

d.groupby(d.index).sum(axis=1)

Gives error that axis not recognized

Answer 1

If you have duplicates in index, you can use groupby with double sum :

print (df)
       A  B  C
User1  1  1  0
User1  1  1  1
User2  0  1  0
User3  1  0  1

print (df.groupby(df.index).sum().sum(1))
User1    5
User2    1
User3    2
dtype: int64

If there are no duplicates, use only sum - Psidom comment :

df.sum(axis=1)

EDIT:

import matplotlib.pyplot as plt

df.sum(axis=1).plot.hist()
plt.show()

Answer 2

Use the transpose method of the DataFrame.

In [38]: d = pd.DataFrame({'A':[1,0,1],'B':[1,1,0],'C':[0,0,1]},index=['User1','User2','User3'])

In [39]: d
Out[39]:
       A  B  C
User1  1  1  0
User2  0  1  0
User3  1  0  1

In [40]: d.transpose()
Out[40]:
   User1  User2  User3
A      1      0      1
B      1      1      0
C      0      0      1

In [41]: d.transpose().sum()
Out[41]:
User1    2
User2    1
User3    2
dtype: int64

Or, as Psidom suggested, sum the columns of your DataFrame.

In [55]: d.sum(axis=1)
Out[55]:
User1    2
User2    1
User3    2
dtype: int64