Iterating over timestamp in python
0
The portion of pandas dataframe is given below:
timestamp quantity price Dates Time store_price
2016-07-01 09:15:55 750 1237.50 2016-07-01 09:15:55 nan
2016-07-01 09:16:01 750 1237.35 2016-07-01 09:16:01 nan
2016-07-01 09:16:46 750 1238.15 2016-07-01 09:16:46 nan
2016-07-01 09:16:46 750 1238.00 2016-07-01 09:16:46 nan
2016-07-01 09:18:12 750 1239.70 2016-07-01 09:18:12 nan
2016-07-01 09:19:05 1500 1237.45 2016-07-01 09:19:05 nan
2016-07-01 09:19:58 750 1234.70 2016-07-01 09:19:58 nan
2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 nan
2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 nan
2016-07-01 09:20:28 750 1237.25 2016-07-01 09:20:28 nan
2016-07-01 09:21:18 750 1238.30 2016-07-01 09:21:18 nan
2016-07-01 09:22:29 750 1237.55 2016-07-01 09:22:29 nan
2016-07-01 09:22:51 750 1237.50 2016-07-01 09:22:51 nan
2016-07-01 09:23:25 750 1237.05 2016-07-01 09:23:25 nan
2016-07-01 09:23:28 750 1237.00 2016-07-01 09:23:28 nan
2016-07-01 09:24:19 750 1237.05 2016-07-01 09:24:19 nan
2016-07-01 09:24:19 2250 1237.00 2016-07-01 09:24:19 nan
2016-07-01 09:24:25 750 1237.00 2016-07-01 09:24:25 nan
2016-07-01 09:25:23 750 1236.05 2016-07-01 09:25:23 nan
2016-07-01 09:26:10 750 1237.00 2016-07-01 09:26:10 nan
2016-07-01 09:26:18 750 1237.90 2016-07-01 09:26:18 nan
2016-07-01 09:26:25 750 1237.05 2016-07-01 09:26:25 nan
2016-07-01 09:27:54 750 1233.50 2016-07-01 09:27:54 nan
2016-07-01 09:28:25 750 1233.85 2016-07-01 09:28:25 nan
2016-07-01 09:29:17 750 1234.85 2016-07-01 09:29:17 nan
2016-07-01 09:29:36 750 1235.45 2016-07-01 09:29:36 nan
2016-07-01 09:29:54 750 1235.00 2016-07-01 09:29:54 nan
2016-07-01 09:30:06 750 1236.65 2016-07-01 09:30:06 nan
2016-07-01 09:30:36 750 1236.60 2016-07-01 09:30:36 nan
2016-07-01 09:31:01 750 1236.60 2016-07-01 09:31:01 nan
2016-07-01 09:31:09 750 1236.70 2016-07-01 09:31:09 nan
2016-07-01 09:31:15 750 1237.00 2016-07-01 09:31:15 nan
I want to get dataframe like below, i.e, to store price value in a different column store_price for rows in the time range 09.20.00 to 09.30.00:
timestamp quantity price Dates Time store_price
2016-07-01 09:15:55 750 1237.50 2016-07-01 09:15:55 nan
2016-07-01 09:16:01 750 1237.35 2016-07-01 09:16:01 nan
2016-07-01 09:16:46 750 1238.15 2016-07-01 09:16:46 nan
2016-07-01 09:16:46 750 1238.00 2016-07-01 09:16:46 nan
2016-07-01 09:18:12 750 1239.70 2016-07-01 09:18:12 nan
2016-07-01 09:19:05 1500 1237.45 2016-07-01 09:19:05 nan
2016-07-01 09:19:58 750 1234.70 2016-07-01 09:19:58 nan
2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95
2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00
2016-07-01 09:20:28 750 1237.25 2016-07-01 09:20:28 1237.25
2016-07-01 09:21:18 750 1238.30 2016-07-01 09:21:18 1238.30
2016-07-01 09:22:29 750 1237.55 2016-07-01 09:22:29 1237.55
2016-07-01 09:22:51 750 1237.50 2016-07-01 09:22:51 1237.50
2016-07-01 09:23:25 750 1237.05 2016-07-01 09:23:25 1237.05
2016-07-01 09:23:28 750 1237.00 2016-07-01 09:23:28 1237.00
2016-07-01 09:24:19 750 1237.05 2016-07-01 09:24:19 1237.05
2016-07-01 09:24:19 2250 1237.00 2016-07-01 09:24:19 1237.00
2016-07-01 09:24:25 750 1237.00 2016-07-01 09:24:25 1237.00
2016-07-01 09:25:23 750 1236.05 2016-07-01 09:25:23 1236.05
2016-07-01 09:26:10 750 1237.00 2016-07-01 09:26:10 1237.00
2016-07-01 09:26:18 750 1237.90 2016-07-01 09:26:18 1237.90
2016-07-01 09:26:25 750 1237.05 2016-07-01 09:26:25 1237.05
2016-07-01 09:27:54 750 1233.50 2016-07-01 09:27:54 1233.50
2016-07-01 09:28:25 750 1233.85 2016-07-01 09:28:25 1233.85
2016-07-01 09:29:17 750 1234.85 2016-07-01 09:29:17 1234.85
2016-07-01 09:29:36 750 1235.45 2016-07-01 09:29:36 1235.45
2016-07-01 09:29:54 750 1235.00 2016-07-01 09:29:54 1235.00
2016-07-01 09:30:06 750 1236.65 2016-07-01 09:30:06 nan
2016-07-01 09:30:36 750 1236.60 2016-07-01 09:30:36 nan
2016-07-01 09:31:01 750 1236.60 2016-07-01 09:31:01 nan
2016-07-01 09:31:09 750 1236.70 2016-07-01 09:31:09 nan
2016-07-01 09:31:15 750 1237.00 2016-07-01 09:31:15 nan
python-3.x pandas dataframe
edited Nov 23 '18 at 17:58
Julian Peller
864511
asked Nov 14 '18 at 20:09
shalinishalini
42
add a comment |
0
The portion of pandas dataframe is given below:
timestamp quantity price Dates Time store_price
2016-07-01 09:15:55 750 1237.50 2016-07-01 09:15:55 nan
2016-07-01 09:16:01 750 1237.35 2016-07-01 09:16:01 nan
2016-07-01 09:16:46 750 1238.15 2016-07-01 09:16:46 nan
2016-07-01 09:16:46 750 1238.00 2016-07-01 09:16:46 nan
2016-07-01 09:18:12 750 1239.70 2016-07-01 09:18:12 nan
2016-07-01 09:19:05 1500 1237.45 2016-07-01 09:19:05 nan
2016-07-01 09:19:58 750 1234.70 2016-07-01 09:19:58 nan
2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 nan
2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 nan
2016-07-01 09:20:28 750 1237.25 2016-07-01 09:20:28 nan
2016-07-01 09:21:18 750 1238.30 2016-07-01 09:21:18 nan
2016-07-01 09:22:29 750 1237.55 2016-07-01 09:22:29 nan
2016-07-01 09:22:51 750 1237.50 2016-07-01 09:22:51 nan
2016-07-01 09:23:25 750 1237.05 2016-07-01 09:23:25 nan
2016-07-01 09:23:28 750 1237.00 2016-07-01 09:23:28 nan
2016-07-01 09:24:19 750 1237.05 2016-07-01 09:24:19 nan
2016-07-01 09:24:19 2250 1237.00 2016-07-01 09:24:19 nan
2016-07-01 09:24:25 750 1237.00 2016-07-01 09:24:25 nan
2016-07-01 09:25:23 750 1236.05 2016-07-01 09:25:23 nan
2016-07-01 09:26:10 750 1237.00 2016-07-01 09:26:10 nan
2016-07-01 09:26:18 750 1237.90 2016-07-01 09:26:18 nan
2016-07-01 09:26:25 750 1237.05 2016-07-01 09:26:25 nan
2016-07-01 09:27:54 750 1233.50 2016-07-01 09:27:54 nan
2016-07-01 09:28:25 750 1233.85 2016-07-01 09:28:25 nan
2016-07-01 09:29:17 750 1234.85 2016-07-01 09:29:17 nan
2016-07-01 09:29:36 750 1235.45 2016-07-01 09:29:36 nan
2016-07-01 09:29:54 750 1235.00 2016-07-01 09:29:54 nan
2016-07-01 09:30:06 750 1236.65 2016-07-01 09:30:06 nan
2016-07-01 09:30:36 750 1236.60 2016-07-01 09:30:36 nan
2016-07-01 09:31:01 750 1236.60 2016-07-01 09:31:01 nan
2016-07-01 09:31:09 750 1236.70 2016-07-01 09:31:09 nan
2016-07-01 09:31:15 750 1237.00 2016-07-01 09:31:15 nan
I want to get dataframe like below, i.e, to store price value in a different column store_price for rows in the time range 09.20.00 to 09.30.00:
timestamp quantity price Dates Time store_price
2016-07-01 09:15:55 750 1237.50 2016-07-01 09:15:55 nan
2016-07-01 09:16:01 750 1237.35 2016-07-01 09:16:01 nan
2016-07-01 09:16:46 750 1238.15 2016-07-01 09:16:46 nan
2016-07-01 09:16:46 750 1238.00 2016-07-01 09:16:46 nan
2016-07-01 09:18:12 750 1239.70 2016-07-01 09:18:12 nan
2016-07-01 09:19:05 1500 1237.45 2016-07-01 09:19:05 nan
2016-07-01 09:19:58 750 1234.70 2016-07-01 09:19:58 nan
2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95
2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00
2016-07-01 09:20:28 750 1237.25 2016-07-01 09:20:28 1237.25
2016-07-01 09:21:18 750 1238.30 2016-07-01 09:21:18 1238.30
2016-07-01 09:22:29 750 1237.55 2016-07-01 09:22:29 1237.55
2016-07-01 09:22:51 750 1237.50 2016-07-01 09:22:51 1237.50
2016-07-01 09:23:25 750 1237.05 2016-07-01 09:23:25 1237.05
2016-07-01 09:23:28 750 1237.00 2016-07-01 09:23:28 1237.00
2016-07-01 09:24:19 750 1237.05 2016-07-01 09:24:19 1237.05
2016-07-01 09:24:19 2250 1237.00 2016-07-01 09:24:19 1237.00
2016-07-01 09:24:25 750 1237.00 2016-07-01 09:24:25 1237.00
2016-07-01 09:25:23 750 1236.05 2016-07-01 09:25:23 1236.05
2016-07-01 09:26:10 750 1237.00 2016-07-01 09:26:10 1237.00
2016-07-01 09:26:18 750 1237.90 2016-07-01 09:26:18 1237.90
2016-07-01 09:26:25 750 1237.05 2016-07-01 09:26:25 1237.05
2016-07-01 09:27:54 750 1233.50 2016-07-01 09:27:54 1233.50
2016-07-01 09:28:25 750 1233.85 2016-07-01 09:28:25 1233.85
2016-07-01 09:29:17 750 1234.85 2016-07-01 09:29:17 1234.85
2016-07-01 09:29:36 750 1235.45 2016-07-01 09:29:36 1235.45
2016-07-01 09:29:54 750 1235.00 2016-07-01 09:29:54 1235.00
2016-07-01 09:30:06 750 1236.65 2016-07-01 09:30:06 nan
2016-07-01 09:30:36 750 1236.60 2016-07-01 09:30:36 nan
2016-07-01 09:31:01 750 1236.60 2016-07-01 09:31:01 nan
2016-07-01 09:31:09 750 1236.70 2016-07-01 09:31:09 nan
2016-07-01 09:31:15 750 1237.00 2016-07-01 09:31:15 nan
python-3.x pandas dataframe
edited Nov 23 '18 at 17:58
Julian Peller
864511
asked Nov 14 '18 at 20:09
shalinishalini
42
1
Hi there, welcome to Stack Overflow! You have provided an example of the dataframe. That's great! You mention about storing data in a different column as well as saving data from a time interval. Can you try to provide another dataframe example of showing the expected series or dataframe? Thanks!
– TrebuchetMS
Nov 15 '18 at 8:35
timestamp quantity price Dates Time store_price 2016-07-01 09:15:09 750 1231.95 2016-07-01 09:15:09 nan 2016-07-01 09:15:28 750 1242.00 2016-07-01 09:15:28 nan 2016-07-01 09:16:26 750 1237.30 2016-07-01 09:16:26 nan 2016-07-01 09:18:48 750 1239.00 2016-07-01 09:18:48 nan 2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95 2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00 I was tryingfor (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 13:43
1
Hi again. The comment area isn't suitable for data formatting. Edit your question and put the expected data up there instead?
– TrebuchetMS
Nov 23 '18 at 13:49
I created numpy array ,then tried a 'for' loop, for (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 14:20
add a comment |
0
0
0
The portion of pandas dataframe is given below:
timestamp quantity price Dates Time store_price
2016-07-01 09:15:55 750 1237.50 2016-07-01 09:15:55 nan
2016-07-01 09:16:01 750 1237.35 2016-07-01 09:16:01 nan
2016-07-01 09:16:46 750 1238.15 2016-07-01 09:16:46 nan
2016-07-01 09:16:46 750 1238.00 2016-07-01 09:16:46 nan
2016-07-01 09:18:12 750 1239.70 2016-07-01 09:18:12 nan
2016-07-01 09:19:05 1500 1237.45 2016-07-01 09:19:05 nan
2016-07-01 09:19:58 750 1234.70 2016-07-01 09:19:58 nan
2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 nan
2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 nan
2016-07-01 09:20:28 750 1237.25 2016-07-01 09:20:28 nan
2016-07-01 09:21:18 750 1238.30 2016-07-01 09:21:18 nan
2016-07-01 09:22:29 750 1237.55 2016-07-01 09:22:29 nan
2016-07-01 09:22:51 750 1237.50 2016-07-01 09:22:51 nan
2016-07-01 09:23:25 750 1237.05 2016-07-01 09:23:25 nan
2016-07-01 09:23:28 750 1237.00 2016-07-01 09:23:28 nan
2016-07-01 09:24:19 750 1237.05 2016-07-01 09:24:19 nan
2016-07-01 09:24:19 2250 1237.00 2016-07-01 09:24:19 nan
2016-07-01 09:24:25 750 1237.00 2016-07-01 09:24:25 nan
2016-07-01 09:25:23 750 1236.05 2016-07-01 09:25:23 nan
2016-07-01 09:26:10 750 1237.00 2016-07-01 09:26:10 nan
2016-07-01 09:26:18 750 1237.90 2016-07-01 09:26:18 nan
2016-07-01 09:26:25 750 1237.05 2016-07-01 09:26:25 nan
2016-07-01 09:27:54 750 1233.50 2016-07-01 09:27:54 nan
2016-07-01 09:28:25 750 1233.85 2016-07-01 09:28:25 nan
2016-07-01 09:29:17 750 1234.85 2016-07-01 09:29:17 nan
2016-07-01 09:29:36 750 1235.45 2016-07-01 09:29:36 nan
2016-07-01 09:29:54 750 1235.00 2016-07-01 09:29:54 nan
2016-07-01 09:30:06 750 1236.65 2016-07-01 09:30:06 nan
2016-07-01 09:30:36 750 1236.60 2016-07-01 09:30:36 nan
2016-07-01 09:31:01 750 1236.60 2016-07-01 09:31:01 nan
2016-07-01 09:31:09 750 1236.70 2016-07-01 09:31:09 nan
2016-07-01 09:31:15 750 1237.00 2016-07-01 09:31:15 nan
I want to get dataframe like below, i.e, to store price value in a different column store_price for rows in the time range 09.20.00 to 09.30.00:
timestamp quantity price Dates Time store_price
2016-07-01 09:15:55 750 1237.50 2016-07-01 09:15:55 nan
2016-07-01 09:16:01 750 1237.35 2016-07-01 09:16:01 nan
2016-07-01 09:16:46 750 1238.15 2016-07-01 09:16:46 nan
2016-07-01 09:16:46 750 1238.00 2016-07-01 09:16:46 nan
2016-07-01 09:18:12 750 1239.70 2016-07-01 09:18:12 nan
2016-07-01 09:19:05 1500 1237.45 2016-07-01 09:19:05 nan
2016-07-01 09:19:58 750 1234.70 2016-07-01 09:19:58 nan
2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95
2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00
2016-07-01 09:20:28 750 1237.25 2016-07-01 09:20:28 1237.25
2016-07-01 09:21:18 750 1238.30 2016-07-01 09:21:18 1238.30
2016-07-01 09:22:29 750 1237.55 2016-07-01 09:22:29 1237.55
2016-07-01 09:22:51 750 1237.50 2016-07-01 09:22:51 1237.50
2016-07-01 09:23:25 750 1237.05 2016-07-01 09:23:25 1237.05
2016-07-01 09:23:28 750 1237.00 2016-07-01 09:23:28 1237.00
2016-07-01 09:24:19 750 1237.05 2016-07-01 09:24:19 1237.05
2016-07-01 09:24:19 2250 1237.00 2016-07-01 09:24:19 1237.00
2016-07-01 09:24:25 750 1237.00 2016-07-01 09:24:25 1237.00
2016-07-01 09:25:23 750 1236.05 2016-07-01 09:25:23 1236.05
2016-07-01 09:26:10 750 1237.00 2016-07-01 09:26:10 1237.00
2016-07-01 09:26:18 750 1237.90 2016-07-01 09:26:18 1237.90
2016-07-01 09:26:25 750 1237.05 2016-07-01 09:26:25 1237.05
2016-07-01 09:27:54 750 1233.50 2016-07-01 09:27:54 1233.50
2016-07-01 09:28:25 750 1233.85 2016-07-01 09:28:25 1233.85
2016-07-01 09:29:17 750 1234.85 2016-07-01 09:29:17 1234.85
2016-07-01 09:29:36 750 1235.45 2016-07-01 09:29:36 1235.45
2016-07-01 09:29:54 750 1235.00 2016-07-01 09:29:54 1235.00
2016-07-01 09:30:06 750 1236.65 2016-07-01 09:30:06 nan
2016-07-01 09:30:36 750 1236.60 2016-07-01 09:30:36 nan
2016-07-01 09:31:01 750 1236.60 2016-07-01 09:31:01 nan
2016-07-01 09:31:09 750 1236.70 2016-07-01 09:31:09 nan
2016-07-01 09:31:15 750 1237.00 2016-07-01 09:31:15 nan
python-3.x pandas dataframe
edited Nov 23 '18 at 17:58
Julian Peller
864511
asked Nov 14 '18 at 20:09
shalinishalini
42
The portion of pandas dataframe is given below:
timestamp quantity price Dates Time store_price
2016-07-01 09:15:55 750 1237.50 2016-07-01 09:15:55 nan
2016-07-01 09:16:01 750 1237.35 2016-07-01 09:16:01 nan
2016-07-01 09:16:46 750 1238.15 2016-07-01 09:16:46 nan
2016-07-01 09:16:46 750 1238.00 2016-07-01 09:16:46 nan
2016-07-01 09:18:12 750 1239.70 2016-07-01 09:18:12 nan
2016-07-01 09:19:05 1500 1237.45 2016-07-01 09:19:05 nan
2016-07-01 09:19:58 750 1234.70 2016-07-01 09:19:58 nan
2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 nan
2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 nan
2016-07-01 09:20:28 750 1237.25 2016-07-01 09:20:28 nan
2016-07-01 09:21:18 750 1238.30 2016-07-01 09:21:18 nan
2016-07-01 09:22:29 750 1237.55 2016-07-01 09:22:29 nan
2016-07-01 09:22:51 750 1237.50 2016-07-01 09:22:51 nan
2016-07-01 09:23:25 750 1237.05 2016-07-01 09:23:25 nan
2016-07-01 09:23:28 750 1237.00 2016-07-01 09:23:28 nan
2016-07-01 09:24:19 750 1237.05 2016-07-01 09:24:19 nan
2016-07-01 09:24:19 2250 1237.00 2016-07-01 09:24:19 nan
2016-07-01 09:24:25 750 1237.00 2016-07-01 09:24:25 nan
2016-07-01 09:25:23 750 1236.05 2016-07-01 09:25:23 nan
2016-07-01 09:26:10 750 1237.00 2016-07-01 09:26:10 nan
2016-07-01 09:26:18 750 1237.90 2016-07-01 09:26:18 nan
2016-07-01 09:26:25 750 1237.05 2016-07-01 09:26:25 nan
2016-07-01 09:27:54 750 1233.50 2016-07-01 09:27:54 nan
2016-07-01 09:28:25 750 1233.85 2016-07-01 09:28:25 nan
2016-07-01 09:29:17 750 1234.85 2016-07-01 09:29:17 nan
2016-07-01 09:29:36 750 1235.45 2016-07-01 09:29:36 nan
2016-07-01 09:29:54 750 1235.00 2016-07-01 09:29:54 nan
2016-07-01 09:30:06 750 1236.65 2016-07-01 09:30:06 nan
2016-07-01 09:30:36 750 1236.60 2016-07-01 09:30:36 nan
2016-07-01 09:31:01 750 1236.60 2016-07-01 09:31:01 nan
2016-07-01 09:31:09 750 1236.70 2016-07-01 09:31:09 nan
2016-07-01 09:31:15 750 1237.00 2016-07-01 09:31:15 nan
I want to get dataframe like below, i.e, to store price value in a different column store_price for rows in the time range 09.20.00 to 09.30.00:
timestamp quantity price Dates Time store_price
2016-07-01 09:15:55 750 1237.50 2016-07-01 09:15:55 nan
2016-07-01 09:16:01 750 1237.35 2016-07-01 09:16:01 nan
2016-07-01 09:16:46 750 1238.15 2016-07-01 09:16:46 nan
2016-07-01 09:16:46 750 1238.00 2016-07-01 09:16:46 nan
2016-07-01 09:18:12 750 1239.70 2016-07-01 09:18:12 nan
2016-07-01 09:19:05 1500 1237.45 2016-07-01 09:19:05 nan
2016-07-01 09:19:58 750 1234.70 2016-07-01 09:19:58 nan
2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95
2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00
2016-07-01 09:20:28 750 1237.25 2016-07-01 09:20:28 1237.25
2016-07-01 09:21:18 750 1238.30 2016-07-01 09:21:18 1238.30
2016-07-01 09:22:29 750 1237.55 2016-07-01 09:22:29 1237.55
2016-07-01 09:22:51 750 1237.50 2016-07-01 09:22:51 1237.50
2016-07-01 09:23:25 750 1237.05 2016-07-01 09:23:25 1237.05
2016-07-01 09:23:28 750 1237.00 2016-07-01 09:23:28 1237.00
2016-07-01 09:24:19 750 1237.05 2016-07-01 09:24:19 1237.05
2016-07-01 09:24:19 2250 1237.00 2016-07-01 09:24:19 1237.00
2016-07-01 09:24:25 750 1237.00 2016-07-01 09:24:25 1237.00
2016-07-01 09:25:23 750 1236.05 2016-07-01 09:25:23 1236.05
2016-07-01 09:26:10 750 1237.00 2016-07-01 09:26:10 1237.00
2016-07-01 09:26:18 750 1237.90 2016-07-01 09:26:18 1237.90
2016-07-01 09:26:25 750 1237.05 2016-07-01 09:26:25 1237.05
2016-07-01 09:27:54 750 1233.50 2016-07-01 09:27:54 1233.50
2016-07-01 09:28:25 750 1233.85 2016-07-01 09:28:25 1233.85
2016-07-01 09:29:17 750 1234.85 2016-07-01 09:29:17 1234.85
2016-07-01 09:29:36 750 1235.45 2016-07-01 09:29:36 1235.45
2016-07-01 09:29:54 750 1235.00 2016-07-01 09:29:54 1235.00
2016-07-01 09:30:06 750 1236.65 2016-07-01 09:30:06 nan
2016-07-01 09:30:36 750 1236.60 2016-07-01 09:30:36 nan
2016-07-01 09:31:01 750 1236.60 2016-07-01 09:31:01 nan
2016-07-01 09:31:09 750 1236.70 2016-07-01 09:31:09 nan
2016-07-01 09:31:15 750 1237.00 2016-07-01 09:31:15 nan
python-3.x pandas dataframe
python-3.x pandas dataframe
edited Nov 23 '18 at 17:58
Julian Peller
864511
asked Nov 14 '18 at 20:09
shalinishalini
42
edited Nov 23 '18 at 17:58
Julian Peller
864511
asked Nov 14 '18 at 20:09
shalinishalini
42
edited Nov 23 '18 at 17:58
Julian Peller
864511
edited Nov 23 '18 at 17:58
Julian Peller
864511
edited Nov 23 '18 at 17:58
Julian Peller
864511
864511
asked Nov 14 '18 at 20:09
shalinishalini
42
asked Nov 14 '18 at 20:09
shalinishalini
42
asked Nov 14 '18 at 20:09
shalinishalini
42
42
1
Hi there, welcome to Stack Overflow! You have provided an example of the dataframe. That's great! You mention about storing data in a different column as well as saving data from a time interval. Can you try to provide another dataframe example of showing the expected series or dataframe? Thanks!
– TrebuchetMS
Nov 15 '18 at 8:35
timestamp quantity price Dates Time store_price 2016-07-01 09:15:09 750 1231.95 2016-07-01 09:15:09 nan 2016-07-01 09:15:28 750 1242.00 2016-07-01 09:15:28 nan 2016-07-01 09:16:26 750 1237.30 2016-07-01 09:16:26 nan 2016-07-01 09:18:48 750 1239.00 2016-07-01 09:18:48 nan 2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95 2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00 I was tryingfor (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 13:43
1
Hi again. The comment area isn't suitable for data formatting. Edit your question and put the expected data up there instead?
– TrebuchetMS
Nov 23 '18 at 13:49
I created numpy array ,then tried a 'for' loop, for (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 14:20
add a comment |
1
Hi there, welcome to Stack Overflow! You have provided an example of the dataframe. That's great! You mention about storing data in a different column as well as saving data from a time interval. Can you try to provide another dataframe example of showing the expected series or dataframe? Thanks!
– TrebuchetMS
Nov 15 '18 at 8:35
timestamp quantity price Dates Time store_price 2016-07-01 09:15:09 750 1231.95 2016-07-01 09:15:09 nan 2016-07-01 09:15:28 750 1242.00 2016-07-01 09:15:28 nan 2016-07-01 09:16:26 750 1237.30 2016-07-01 09:16:26 nan 2016-07-01 09:18:48 750 1239.00 2016-07-01 09:18:48 nan 2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95 2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00 I was tryingfor (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 13:43
1
Hi again. The comment area isn't suitable for data formatting. Edit your question and put the expected data up there instead?
– TrebuchetMS
Nov 23 '18 at 13:49
I created numpy array ,then tried a 'for' loop, for (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 14:20
1
1
Hi there, welcome to Stack Overflow! You have provided an example of the dataframe. That's great! You mention about storing data in a different column as well as saving data from a time interval. Can you try to provide another dataframe example of showing the expected series or dataframe? Thanks!
– TrebuchetMS
Nov 15 '18 at 8:35
Hi there, welcome to Stack Overflow! You have provided an example of the dataframe. That's great! You mention about storing data in a different column as well as saving data from a time interval. Can you try to provide another dataframe example of showing the expected series or dataframe? Thanks!
– TrebuchetMS
Nov 15 '18 at 8:35
timestamp quantity price Dates Time store_price 2016-07-01 09:15:09 750 1231.95 2016-07-01 09:15:09 nan 2016-07-01 09:15:28 750 1242.00 2016-07-01 09:15:28 nan 2016-07-01 09:16:26 750 1237.30 2016-07-01 09:16:26 nan 2016-07-01 09:18:48 750 1239.00 2016-07-01 09:18:48 nan 2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95 2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00 I was tryingfor (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 13:43
timestamp quantity price Dates Time store_price 2016-07-01 09:15:09 750 1231.95 2016-07-01 09:15:09 nan 2016-07-01 09:15:28 750 1242.00 2016-07-01 09:15:28 nan 2016-07-01 09:16:26 750 1237.30 2016-07-01 09:16:26 nan 2016-07-01 09:18:48 750 1239.00 2016-07-01 09:18:48 nan 2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95 2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00 I was tryingfor (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 13:43
1
1
Hi again. The comment area isn't suitable for data formatting. Edit your question and put the expected data up there instead?
– TrebuchetMS
Nov 23 '18 at 13:49
Hi again. The comment area isn't suitable for data formatting. Edit your question and put the expected data up there instead?
– TrebuchetMS
Nov 23 '18 at 13:49
I created numpy array ,then tried a 'for' loop, for (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 14:20
I created numpy array ,then tried a 'for' loop, for (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 14:20
add a comment |
1 Answer
1
active
oldest
votes
0
Solution
df['timestamp'] = pd.to_datetime(df['timestamp']) # Only if needed
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Explanation
First, make sure timestamp column is of time datetime:
df['timestamp'].dtypes
If it returns dtype('O'), you need to cast it to datetime using pd.to_datetime, as below:
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['timestamp'].dtypes
>>> dtype('<M8[ns]')
Now you can access the hour and the minute of the column with the .dt accessor and write a mask as below:
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
Finally, you can override the store_price column with price only for the rows that match the condition using .loc:
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Obtaining the results you want.
answered Nov 23 '18 at 15:40
Julian PellerJulian Peller
864511
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
Solution
df['timestamp'] = pd.to_datetime(df['timestamp']) # Only if needed
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Explanation
First, make sure timestamp column is of time datetime:
df['timestamp'].dtypes
If it returns dtype('O'), you need to cast it to datetime using pd.to_datetime, as below:
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['timestamp'].dtypes
>>> dtype('<M8[ns]')
Now you can access the hour and the minute of the column with the .dt accessor and write a mask as below:
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
Finally, you can override the store_price column with price only for the rows that match the condition using .loc:
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Obtaining the results you want.
answered Nov 23 '18 at 15:40
Julian PellerJulian Peller
864511
add a comment |
0
Solution
df['timestamp'] = pd.to_datetime(df['timestamp']) # Only if needed
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Explanation
First, make sure timestamp column is of time datetime:
df['timestamp'].dtypes
If it returns dtype('O'), you need to cast it to datetime using pd.to_datetime, as below:
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['timestamp'].dtypes
>>> dtype('<M8[ns]')
Now you can access the hour and the minute of the column with the .dt accessor and write a mask as below:
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
Finally, you can override the store_price column with price only for the rows that match the condition using .loc:
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Obtaining the results you want.
answered Nov 23 '18 at 15:40
Julian PellerJulian Peller
864511
add a comment |
0
0
0
Solution
df['timestamp'] = pd.to_datetime(df['timestamp']) # Only if needed
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Explanation
First, make sure timestamp column is of time datetime:
df['timestamp'].dtypes
If it returns dtype('O'), you need to cast it to datetime using pd.to_datetime, as below:
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['timestamp'].dtypes
>>> dtype('<M8[ns]')
Now you can access the hour and the minute of the column with the .dt accessor and write a mask as below:
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
Finally, you can override the store_price column with price only for the rows that match the condition using .loc:
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Obtaining the results you want.
answered Nov 23 '18 at 15:40
Julian PellerJulian Peller
864511
Solution
df['timestamp'] = pd.to_datetime(df['timestamp']) # Only if needed
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Explanation
First, make sure timestamp column is of time datetime:
df['timestamp'].dtypes
If it returns dtype('O'), you need to cast it to datetime using pd.to_datetime, as below:
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['timestamp'].dtypes
>>> dtype('<M8[ns]')
Now you can access the hour and the minute of the column with the .dt accessor and write a mask as below:
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
Finally, you can override the store_price column with price only for the rows that match the condition using .loc:
df.loc[condition, "store_price"] = df.loc[condition, "price"]
Obtaining the results you want.
answered Nov 23 '18 at 15:40
Julian PellerJulian Peller
864511
edited Nov 23 '18 at 16:14
answered Nov 23 '18 at 15:40
Julian PellerJulian Peller
864511
answered Nov 23 '18 at 15:40
Julian PellerJulian Peller
864511
answered Nov 23 '18 at 15:40
Julian PellerJulian Peller
864511
864511
add a comment |
add a comment |
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1
Hi there, welcome to Stack Overflow! You have provided an example of the dataframe. That's great! You mention about storing data in a different column as well as saving data from a time interval. Can you try to provide another dataframe example of showing the expected series or dataframe? Thanks!
– TrebuchetMS
Nov 15 '18 at 8:35
timestamp quantity price Dates Time store_price 2016-07-01 09:15:09 750 1231.95 2016-07-01 09:15:09 nan 2016-07-01 09:15:28 750 1242.00 2016-07-01 09:15:28 nan 2016-07-01 09:16:26 750 1237.30 2016-07-01 09:16:26 nan 2016-07-01 09:18:48 750 1239.00 2016-07-01 09:18:48 nan 2016-07-01 09:20:02 750 1234.95 2016-07-01 09:20:02 1234.95 2016-07-01 09:20:04 750 1234.00 2016-07-01 09:20:04 1234.00 I was tryingfor (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 13:43
1
Hi again. The comment area isn't suitable for data formatting. Edit your question and put the expected data up there instead?
– TrebuchetMS
Nov 23 '18 at 13:49
I created numpy array ,then tried a 'for' loop, for (datetime.time(9, 15, 0)<timeindex(df[i,4])<datetime.time(9, 30, 0)) df[i,5]=df[i,2] i=i+1
– shalini
Nov 23 '18 at 14:20