I have a data frame like this:

    a   b   c
12456   11  123.1
12678   19  345.67
13278   19  1235.345

or in another format

<table><tr><td>12456</td><td>11</td><td>123.1</td></tr><tr><td>12678</td><td>19</td><td>345.67</td></tr><tr><td>13278</td><td>19</td><td>1235.345</td></tr>
</table>

The first column is the index.I need to add the rows of third column and make it one if second column has same value. Could you suggest me something to do this? Following is what I have tried but doesnt work

    a,b,c=df_addweight.iloc[:,0].values,df_addweight.iloc[:, 1].values,df_addweight.iloc[:, 3].values`for u,v,w, in zip(range(1,len(a)),range(1,len(b)),range(1,len(c))):if a[u]==a[u-1] and b[v]==b[v-1]:df_addweight['W']= c[w]+c[w-1]elif a[u]==a[u-1] and b[v]!=b[v-1]:df_addweight['W']=c[w]

Use pandas:

import pandas as pddf = pd.read_csv("data.csv", delim_whitespace=True)dfa   b         c
0  12456  11   123.100
1  12678  19   345.670
2  13278  19  1235.345df.groupby('b')['c'].sum()

Output:

b
11     123.100
19    1581.015
Name: c, dtype: float64