我正在使用pandas 0.17.0并且df与此类似:
df.head()
Out[339]:
A B C
DATE_TIME
2016-10-08 13:57:00 in 5.61 1
2016-10-08 14:02:00 in 8.05 1
2016-10-08 14:07:00 in 7.92 0
2016-10-08 14:12:00 in 7.98 0
2016-10-08 14:17:00 out 8.18 0
df.tail()
Out[340]:
A B C
DATE_TIME
2016-11-08 13:42:00 in 8.00 0
2016-11-08 13:47:00 in 7.99 0
2016-11-08 13:52:00 out 7.97 0
2016-11-08 13:57:00 in 8.14 1
2016-11-08 14:02:00 in 8.16 1
以下内容dtypes:
print (df.dtypes)
A object
B float64
C int64
dtype: object
当我重新索引我 …
如果我有一个df类似的：
print(df)
A B C D E
DATE_TIME
2016-08-10 13:57:00 3.6 A 1 NaN NaN
2016-08-10 13:58:00 4.7 A 1 4.5 NaN
2016-08-10 13:59:00 3.4 A 0 NaN 5.7
2016-08-10 14:00:00 3.5 A 0 NaN NaN
2016-08-10 14:01:00 2.6 A 0 4.6 NaN
2016-08-10 14:02:00 4.8 A 0 NaN 4.3
2016-08-10 14:03:00 5.7 A 1 NaN NaN
2016-08-10 14:04:00 5.5 A 1 5.7 NaN
2016-08-10 14:05:00 5.6 A 1 NaN NaN
2016-08-10 14:06:00 7.8 A 1 NaN 5.2 …我们说我有这个 df
print(df)
DATE_TIME A B
0 10/08/2016 12:04:56 1 5
1 10/08/2016 12:04:58 1 6
2 10/08/2016 12:04:59 2 3
3 10/08/2016 12:05:00 2 2
4 10/08/2016 12:05:01 3 4
5 10/08/2016 12:05:02 3 6
6 10/08/2016 12:05:03 1 3
7 10/08/2016 12:05:04 1 2
8 10/08/2016 12:05:05 2 4
9 10/08/2016 12:05:06 2 6
10 10/08/2016 12:05:07 3 4
11 10/08/2016 12:05:08 3 2
列中的值['A']随着时间的推移重复,我需要一个列,每次更改时都有一个新的ID,所以我会有类似下面的内容df
print(df)
DATE_TIME A B C
0 10/08/2016 12:04:56 1 5 …