这是我的疑问:
获取此数据框(从此question剪裁),例如:
date type 0 1 2 3
2003-01-01 unemp 1.733275e+09 2.067889e+09 3.279421e+09 3.223396e+09
2005-01-01 unemp 1.413758e+09 2.004171e+09 2.383106e+09 2.540857e+09
2007-01-01 unemp 1.287548e+09 1.462072e+09 2.831217e+09 3.528558e+09
2009-01-01 unemp 2.651480e+09 2.846055e+09 5.882084e+09 5.247459e+09
2011-01-01 unemp 2.257016e+09 4.121532e+09 4.961291e+09 5.330930e+09
2013-01-01 unemp 7.156784e+08 1.182770e+09 1.704251e+09 2.587171e+09
2003-01-01 emp 6.012397e+09 9.692455e+09 2.288822e+10 3.215460e+10
2005-01-01 emp 5.647393e+09 9.597211e+09 2.121828e+10 3.107219e+10
2007-01-01 emp 4.617047e+09 8.030113e+09 2.005203e+10 2.755665e+10
我的目标:
总结具有不同类型(undmp / emp)的行,并创建一个新的数据帧:
答案 0 :(得分:1)
使用groupby
-sum
:
>>> df.groupby('type').sum().reset_index()
type 0 1 2 3
0 emp 16276837000 27319779000 64158530000 90783440000
1 unemp 10058755400 13684489000 21041370000 22458371000
答案 1 :(得分:1)
print df.groupby('type').sum()
0 1 2 3
type
emp 16276837000 27319779000 64158530000 90783440000
unemp 10058755400 13684489000 21041370000 22458371000
或者:
print df.groupby('type', as_index=False).sum()
type 0 1 2 3
0 emp 16276837000 27319779000 64158530000 90783440000
1 unemp 10058755400 13684489000 21041370000 22458371000