如何在pandas的数据框中迭代几天？
示例：
我的数据框：
time consumption
time
2016-10-17 09:00:00 2016-10-17 09:00:00 2754.483333
2016-10-17 10:00:00 2016-10-17 10:00:00 2135.966666
2016-10-17 11:00:00 2016-10-17 11:00:00 1497.716666
2016-10-17 12:00:00 2016-10-17 12:00:00 448.100000
2016-10-24 09:00:00 2016-10-24 09:00:00 1527.716666
2016-10-24 10:00:00 2016-10-24 10:00:00 1219.833333
2016-10-24 11:00:00 2016-10-24 11:00:00 1284.350000
2016-10-24 12:00:00 2016-10-24 12:00:00 14195.633333
2016-10-31 09:00:00 2016-10-31 09:00:00 2120.933333
2016-10-31 10:00:00 2016-10-31 10:00:00 1630.700000
2016-10-31 11:00:00 2016-10-31 11:00:00 1241.866666
2016-10-31 12:00:00 2016-10-31 12:00:00 1156.266666
伪代码：
for day in df:
print day
第一次迭代返回：
time consumption
time
2016-10-17 09:00:00 2016-10-17 09:00:00 2754.483333
2016-10-17 10:00:00 2016-10-17 10:00:00 2135.966666
2016-10-17 11:00:00 2016-10-17 11:00:00 1497.716666
2016-10-17 12:00:00 2016-10-17 12:00:00 448.100000
第二次迭代返回：
2016-10-24 09:00:00 2016-10-24 09:00:00 1527.716666
2016-10-24 10:00:00 2016-10-24 10:00:00 1219.833333
2016-10-24 11:00:00 2016-10-24 11:00:00 1284.350000
2016-10-24 12:00:00 2016-10-24 12:00:00 14195.633333
第三次迭代返回：
2016-10-31 09:00:00 2016-10-31 09:00:00 2120.933333
2016-10-31 10:00:00 2016-10-31 10:00:00 1630.700000
2016-10-31 11:00:00 2016-10-31 11:00:00 1241.866666
2016-10-31 12:00:00 2016-10-31 12:00:00 1156.266666
答案 0 :(得分：4)
#groupby by index date
for idx, day in df.groupby(df.index.date):
print (day)
time consumption
time
2016-10-17 09:00:00 2016-10-17 09:00:00 2754.483333
2016-10-17 10:00:00 2016-10-17 10:00:00 2135.966666
2016-10-17 11:00:00 2016-10-17 11:00:00 1497.716666
2016-10-17 12:00:00 2016-10-17 12:00:00 448.100000
time consumption
time
2016-10-24 09:00:00 2016-10-24 09:00:00 1527.716666
2016-10-24 10:00:00 2016-10-24 10:00:00 1219.833333
2016-10-24 11:00:00 2016-10-24 11:00:00 1284.350000
2016-10-24 12:00:00 2016-10-24 12:00:00 14195.633333
time consumption
time
2016-10-31 09:00:00 2016-10-31 09:00:00 2120.933333
2016-10-31 10:00:00 2016-10-31 10:00:00 1630.700000
2016-10-31 11:00:00 2016-10-31 11:00:00 1241.866666
2016-10-31 12:00:00 2016-10-31 12:00:00 1156.266666
或者：
#groupby by column time
for idx, day in df.groupby(df.time.dt.date):
print (day)
time consumption
time
2016-10-17 09:00:00 2016-10-17 09:00:00 2754.483333
2016-10-17 10:00:00 2016-10-17 10:00:00 2135.966666
2016-10-17 11:00:00 2016-10-17 11:00:00 1497.716666
2016-10-17 12:00:00 2016-10-17 12:00:00 448.100000
time consumption
time
2016-10-24 09:00:00 2016-10-24 09:00:00 1527.716666
2016-10-24 10:00:00 2016-10-24 10:00:00 1219.833333
2016-10-24 11:00:00 2016-10-24 11:00:00 1284.350000
2016-10-24 12:00:00 2016-10-24 12:00:00 14195.633333
time consumption
time
2016-10-31 09:00:00 2016-10-31 09:00:00 2120.933333
2016-10-31 10:00:00 2016-10-31 10:00:00 1630.700000
2016-10-31 11:00:00 2016-10-31 11:00:00 1241.866666
2016-10-31 12:00:00 2016-10-31 12:00:00 1156.266666
差异可以检查前两行是否使用不同的month：
for idx, day in df.groupby(df.index.day):
print (day)
time consumption
time
2016-09-17 09:00:00 2016-10-17 09:00:00 2754.483333
2016-09-17 10:00:00 2016-10-17 10:00:00 2135.966666
2016-10-17 11:00:00 2016-10-17 11:00:00 1497.716666
2016-10-17 12:00:00 2016-10-17 12:00:00 448.100000
time consumption
time
2016-10-24 09:00:00 2016-10-24 09:00:00 1527.716666
2016-10-24 10:00:00 2016-10-24 10:00:00 1219.833333
2016-10-24 11:00:00 2016-10-24 11:00:00 1284.350000
2016-10-24 12:00:00 2016-10-24 12:00:00 14195.633333
time consumption
time
2016-10-31 09:00:00 2016-10-31 09:00:00 2120.933333
2016-10-31 10:00:00 2016-10-31 10:00:00 1630.700000
2016-10-31 11:00:00 2016-10-31 11:00:00 1241.866666
2016-10-31 12:00:00 2016-10-31 12:00:00 1156.266666
for idx, day in df.groupby(df.index.date):
print (day)
time consumption
time
2016-09-17 09:00:00 2016-10-17 09:00:00 2754.483333
2016-09-17 10:00:00 2016-10-17 10:00:00 2135.966666
time consumption
time
2016-10-17 11:00:00 2016-10-17 11:00:00 1497.716666
2016-10-17 12:00:00 2016-10-17 12:00:00 448.100000
time consumption
time
2016-10-24 09:00:00 2016-10-24 09:00:00 1527.716666
2016-10-24 10:00:00 2016-10-24 10:00:00 1219.833333
2016-10-24 11:00:00 2016-10-24 11:00:00 1284.350000
2016-10-24 12:00:00 2016-10-24 12:00:00 14195.633333
time consumption
time
2016-10-31 09:00:00 2016-10-31 09:00:00 2120.933333
2016-10-31 10:00:00 2016-10-31 10:00:00 1630.700000
2016-10-31 11:00:00 2016-10-31 11:00:00 1241.866666
2016-10-31 12:00:00 2016-10-31 12:00:00 1156.266666