Python / Pandas Column Iteration

Question

I have a DataFrame that looks like this (with many extra columns)

          age1     age2      age3     age 4   \
Id#     
1001         5        6         2          8  
1002         7        6         1          0
1003        10        9         7          5
1004         9       12         5          9 

I am trying to write a loop that sums each column with the previous ones and returns it to a new DataFrame. I started, simply, with this:

New = pd.DataFrame()
New[0] = SFH2.ix[:,0]
for x in SFH2:
    ls = [x,x+1]
    B = SFH2[ls].sum(axis=1)
    New[x] = B

print(New)  

and i get an error

    ls = [x,x+1]

TypeError: Can't convert 'int' object to str implicitly

I know that int and str are different objects, but how can I overcome this, or is there another way to iterate through the columns? Thank!

Answer 1

It looks like cumsumthis is what you are looking for:

In [5]: df
Out[5]: 
      age1  age2  age3  age4
Id#                         
1001     5     6     2     8
1002     7     6     1     0
1003    10     9     7     5
1004     9    12     5     9

In [6]: df.cumsum(axis=1)
Out[6]: 
      age1  age2  age3  age4
Id#                         
1001     5    11    13    21
1002     7    13    14    14
1003    10    19    26    31
1004     9    21    26    35

Answer 2

You can use addwith shifted DataFrame:

print (df.shift(-1,axis=1))
      age1  age2  age3  age4
Id#                         
1001   6.0   2.0   8.0   NaN
1002   6.0   1.0   0.0   NaN
1003   9.0   7.0   5.0   NaN
1004  12.0   5.0   9.0   NaN

print (df.add(df.shift(-1,axis=1), fill_value=0))
      age1  age2  age3  age4
Id#                         
1001  11.0   8.0  10.0   8.0
1002  13.0   7.0   1.0   0.0
1003  19.0  16.0  12.0   5.0
1004  21.0  17.0  14.0   9.0

1 ( ):

print (df.shift(axis=1))
      age1  age2  age3  age4
Id#                         
1001   NaN   5.0   6.0   2.0
1002   NaN   7.0   6.0   1.0
1003   NaN  10.0   9.0   7.0
1004   NaN   9.0  12.0   5.0

print (df.add(df.shift(axis=1), fill_value=0))
      age1  age2  age3  age4
Id#                         
1001   5.0  11.0   8.0  10.0
1002   7.0  13.0   7.0   1.0
1003  10.0  19.0  16.0  12.0
1004   9.0  21.0  17.0  14.0