在 Pandas 数据列中访问 total_seconds()
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Accessing total_seconds() in pandas data column
提问by user2994013
I want to create a new column in a pandas data frame that is the elapsed time from the start of the data frame. I am importing a log file into a data frame which has datatime info, but accessing the total_seconds()function in s_df['delta_t']is not working. It works if I access the individual elements of the column (s_df['delta_t'].iloc[8].total_seconds()), but I want to create a new column with total_seconds() and my attempts are failing.
我想在 Pandas 数据框中创建一个新列，它是从数据框开始经过的时间。我正在将日志文件导入具有数据时间信息的数据框中，但无法访问该total_seconds()函数s_df['delta_t']。如果我访问列 ( s_df['delta_t'].iloc[8].total_seconds())的各个元素，它会起作用，但是我想使用 total_seconds() 创建一个新列并且我的尝试失败了。
s_df['t'] = s_df.index # s_df['t] is a column of datetime
s_df['delta_t'] = ( s_df['t'] - s_df['t'].iloc[0]) # time since start of data frame
s_df['elapsed_seconds'] = # want column s_df['delta_t'].total_seconds()
回答by EdChum
use .dtaccessor:
使用.dt访问器：
s_df['elapsed_seconds'] = s_df['delta_t'].dt.total_seconds()
Example:
例子：
In [82]:
df = pd.DataFrame({'date': pd.date_range(dt.datetime(2010,1,1), dt.datetime(2010,2,1))})
df['delta'] = df['date'] - df['date'].iloc[0]
df
Out[82]:
date delta
0 2010-01-01 0 days
1 2010-01-02 1 days
2 2010-01-03 2 days
3 2010-01-04 3 days
4 2010-01-05 4 days
5 2010-01-06 5 days
6 2010-01-07 6 days
7 2010-01-08 7 days
8 2010-01-09 8 days
9 2010-01-10 9 days
10 2010-01-11 10 days
11 2010-01-12 11 days
12 2010-01-13 12 days
13 2010-01-14 13 days
14 2010-01-15 14 days
15 2010-01-16 15 days
16 2010-01-17 16 days
17 2010-01-18 17 days
18 2010-01-19 18 days
19 2010-01-20 19 days
20 2010-01-21 20 days
21 2010-01-22 21 days
22 2010-01-23 22 days
23 2010-01-24 23 days
24 2010-01-25 24 days
25 2010-01-26 25 days
26 2010-01-27 26 days
27 2010-01-28 27 days
28 2010-01-29 28 days
29 2010-01-30 29 days
30 2010-01-31 30 days
31 2010-02-01 31 days
In [83]:
df['total_seconds'] = df['delta'].dt.total_seconds()
df
Out[83]:
date delta total_seconds
0 2010-01-01 0 days 0
1 2010-01-02 1 days 86400
2 2010-01-03 2 days 172800
3 2010-01-04 3 days 259200
4 2010-01-05 4 days 345600
5 2010-01-06 5 days 432000
6 2010-01-07 6 days 518400
7 2010-01-08 7 days 604800
8 2010-01-09 8 days 691200
9 2010-01-10 9 days 777600
10 2010-01-11 10 days 864000
11 2010-01-12 11 days 950400
12 2010-01-13 12 days 1036800
13 2010-01-14 13 days 1123200
14 2010-01-15 14 days 1209600
15 2010-01-16 15 days 1296000
16 2010-01-17 16 days 1382400
17 2010-01-18 17 days 1468800
18 2010-01-19 18 days 1555200
19 2010-01-20 19 days 1641600
20 2010-01-21 20 days 1728000
21 2010-01-22 21 days 1814400
22 2010-01-23 22 days 1900800
23 2010-01-24 23 days 1987200
24 2010-01-25 24 days 2073600
25 2010-01-26 25 days 2160000
26 2010-01-27 26 days 2246400
27 2010-01-28 27 days 2332800
28 2010-01-29 28 days 2419200
29 2010-01-30 29 days 2505600
30 2010-01-31 30 days 2592000
31 2010-02-01 31 days 2678400
