creating duration into separate half an hour bands Pandas datetime
0
Need a small help. Working on the following. Separating rows.
enter image description here
Input:
Name, Channel, Duration, Start_Time
John, A, 2, 15:55:00
John, A, 3, 15:57:00
John, A, 5, 16:00:00
Joseph, B, 10, 15:25:00
Output
Name, Channel, TB, Count, Duration
John, A, 15:30:00-16:00:00,1,5
John, A, 16:00:00-16:30:00, 1, 5
Joseph, B, 15:00:00-15:30:00, 1, 5
Joseph, B, 15:30:00-16:00:00, 1, 5
Thank you in advance
python pandas datetime pandas-groupby timedelta
edited Nov 19 '18 at 10:37
Sotos
28.9k51640
asked Nov 19 '18 at 10:33
Srikanth AyithySrikanth Ayithy
83
add a comment |
0
Need a small help. Working on the following. Separating rows.
enter image description here
Input:
Name, Channel, Duration, Start_Time
John, A, 2, 15:55:00
John, A, 3, 15:57:00
John, A, 5, 16:00:00
Joseph, B, 10, 15:25:00
Output
Name, Channel, TB, Count, Duration
John, A, 15:30:00-16:00:00,1,5
John, A, 16:00:00-16:30:00, 1, 5
Joseph, B, 15:00:00-15:30:00, 1, 5
Joseph, B, 15:30:00-16:00:00, 1, 5
Thank you in advance
python pandas datetime pandas-groupby timedelta
edited Nov 19 '18 at 10:37
Sotos
28.9k51640
asked Nov 19 '18 at 10:33
Srikanth AyithySrikanth Ayithy
83
3
Can you please explain the logic? Also please don't add pictures of data. Give a reproducible example
– Sotos
Nov 19 '18 at 10:39
add a comment |
0
0
0
Need a small help. Working on the following. Separating rows.
enter image description here
Input:
Name, Channel, Duration, Start_Time
John, A, 2, 15:55:00
John, A, 3, 15:57:00
John, A, 5, 16:00:00
Joseph, B, 10, 15:25:00
Output
Name, Channel, TB, Count, Duration
John, A, 15:30:00-16:00:00,1,5
John, A, 16:00:00-16:30:00, 1, 5
Joseph, B, 15:00:00-15:30:00, 1, 5
Joseph, B, 15:30:00-16:00:00, 1, 5
Thank you in advance
python pandas datetime pandas-groupby timedelta
edited Nov 19 '18 at 10:37
Sotos
28.9k51640
asked Nov 19 '18 at 10:33
Srikanth AyithySrikanth Ayithy
83
Need a small help. Working on the following. Separating rows.
enter image description here
Input:
Name, Channel, Duration, Start_Time
John, A, 2, 15:55:00
John, A, 3, 15:57:00
John, A, 5, 16:00:00
Joseph, B, 10, 15:25:00
Output
Name, Channel, TB, Count, Duration
John, A, 15:30:00-16:00:00,1,5
John, A, 16:00:00-16:30:00, 1, 5
Joseph, B, 15:00:00-15:30:00, 1, 5
Joseph, B, 15:30:00-16:00:00, 1, 5
Thank you in advance
python pandas datetime pandas-groupby timedelta
python pandas datetime pandas-groupby timedelta
edited Nov 19 '18 at 10:37
Sotos
28.9k51640
asked Nov 19 '18 at 10:33
Srikanth AyithySrikanth Ayithy
83
edited Nov 19 '18 at 10:37
Sotos
28.9k51640
asked Nov 19 '18 at 10:33
Srikanth AyithySrikanth Ayithy
83
edited Nov 19 '18 at 10:37
Sotos
28.9k51640
edited Nov 19 '18 at 10:37
Sotos
28.9k51640
edited Nov 19 '18 at 10:37
Sotos
28.9k51640
28.9k51640
asked Nov 19 '18 at 10:33
Srikanth AyithySrikanth Ayithy
83
asked Nov 19 '18 at 10:33
Srikanth AyithySrikanth Ayithy
83
asked Nov 19 '18 at 10:33
Srikanth AyithySrikanth Ayithy
83
83
3
Can you please explain the logic? Also please don't add pictures of data. Give a reproducible example
– Sotos
Nov 19 '18 at 10:39
add a comment |
3
Can you please explain the logic? Also please don't add pictures of data. Give a reproducible example
– Sotos
Nov 19 '18 at 10:39
3
3
Can you please explain the logic? Also please don't add pictures of data. Give a reproducible example
– Sotos
Nov 19 '18 at 10:39
Can you please explain the logic? Also please don't add pictures of data. Give a reproducible example
– Sotos
Nov 19 '18 at 10:39
add a comment |
1 Answer
1
active
oldest
votes
0
Use -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min'))
Output
Name Channel Duration Start_Time Start_time TB
0 John A 2 15:55:00 2018-11-19 15:55:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
1 John A 3 15:57:00 2018-11-19 15:57:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
2 John A 5 16:00:00 2018-11-19 16:00:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
3 Joseph B 10 15:25:00 2018-11-19 15:25:00 (2018-11-19 15:00:00, 2018-11-19 15:30:00]
If you want the exact format, do -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min')).apply(lambda x: ' - '.join(str(x).replace('(','').replace(']','').split(',')))
This will yield -
Name Channel Duration Start_Time TB
0 John A 2 15:55:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
1 John A 3 15:57:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
2 John A 5 16:00:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
3 Joseph B 10 15:25:00 2018-11-19 15:00:00 - 2018-11-19 15:30:00
answered Nov 19 '18 at 11:51
Vivek KalyanaranganVivek Kalyanarangan
4,9961827
df=df['Start_time'].astype('datetime64[D]').dtype df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='20:30:00', end='21:00:00', freq='30min')) Getting an Error - 'There are no fields in dtype datetime64[ns].' Issue is with the data types it appears to be. I tried converting into datetime formats. Still it is showing different datatype error everytime.
– Srikanth Ayithy
Nov 19 '18 at 15:24
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
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oldest
votes
0
Use -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min'))
Output
Name Channel Duration Start_Time Start_time TB
0 John A 2 15:55:00 2018-11-19 15:55:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
1 John A 3 15:57:00 2018-11-19 15:57:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
2 John A 5 16:00:00 2018-11-19 16:00:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
3 Joseph B 10 15:25:00 2018-11-19 15:25:00 (2018-11-19 15:00:00, 2018-11-19 15:30:00]
If you want the exact format, do -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min')).apply(lambda x: ' - '.join(str(x).replace('(','').replace(']','').split(',')))
This will yield -
Name Channel Duration Start_Time TB
0 John A 2 15:55:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
1 John A 3 15:57:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
2 John A 5 16:00:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
3 Joseph B 10 15:25:00 2018-11-19 15:00:00 - 2018-11-19 15:30:00
answered Nov 19 '18 at 11:51
Vivek KalyanaranganVivek Kalyanarangan
4,9961827
df=df['Start_time'].astype('datetime64[D]').dtype df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='20:30:00', end='21:00:00', freq='30min')) Getting an Error - 'There are no fields in dtype datetime64[ns].' Issue is with the data types it appears to be. I tried converting into datetime formats. Still it is showing different datatype error everytime.
– Srikanth Ayithy
Nov 19 '18 at 15:24
add a comment |
0
Use -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min'))
Output
Name Channel Duration Start_Time Start_time TB
0 John A 2 15:55:00 2018-11-19 15:55:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
1 John A 3 15:57:00 2018-11-19 15:57:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
2 John A 5 16:00:00 2018-11-19 16:00:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
3 Joseph B 10 15:25:00 2018-11-19 15:25:00 (2018-11-19 15:00:00, 2018-11-19 15:30:00]
If you want the exact format, do -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min')).apply(lambda x: ' - '.join(str(x).replace('(','').replace(']','').split(',')))
This will yield -
Name Channel Duration Start_Time TB
0 John A 2 15:55:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
1 John A 3 15:57:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
2 John A 5 16:00:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
3 Joseph B 10 15:25:00 2018-11-19 15:00:00 - 2018-11-19 15:30:00
answered Nov 19 '18 at 11:51
Vivek KalyanaranganVivek Kalyanarangan
4,9961827
df=df['Start_time'].astype('datetime64[D]').dtype df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='20:30:00', end='21:00:00', freq='30min')) Getting an Error - 'There are no fields in dtype datetime64[ns].' Issue is with the data types it appears to be. I tried converting into datetime formats. Still it is showing different datatype error everytime.
– Srikanth Ayithy
Nov 19 '18 at 15:24
add a comment |
0
0
0
Use -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min'))
Output
Name Channel Duration Start_Time Start_time TB
0 John A 2 15:55:00 2018-11-19 15:55:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
1 John A 3 15:57:00 2018-11-19 15:57:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
2 John A 5 16:00:00 2018-11-19 16:00:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
3 Joseph B 10 15:25:00 2018-11-19 15:25:00 (2018-11-19 15:00:00, 2018-11-19 15:30:00]
If you want the exact format, do -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min')).apply(lambda x: ' - '.join(str(x).replace('(','').replace(']','').split(',')))
This will yield -
Name Channel Duration Start_Time TB
0 John A 2 15:55:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
1 John A 3 15:57:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
2 John A 5 16:00:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
3 Joseph B 10 15:25:00 2018-11-19 15:00:00 - 2018-11-19 15:30:00
answered Nov 19 '18 at 11:51
Vivek KalyanaranganVivek Kalyanarangan
4,9961827
Use -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min'))
Output
Name Channel Duration Start_Time Start_time TB
0 John A 2 15:55:00 2018-11-19 15:55:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
1 John A 3 15:57:00 2018-11-19 15:57:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
2 John A 5 16:00:00 2018-11-19 16:00:00 (2018-11-19 15:30:00, 2018-11-19 16:00:00]
3 Joseph B 10 15:25:00 2018-11-19 15:25:00 (2018-11-19 15:00:00, 2018-11-19 15:30:00]
If you want the exact format, do -
df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='15:00:00', end='16:30:00', freq='30min')).apply(lambda x: ' - '.join(str(x).replace('(','').replace(']','').split(',')))
This will yield -
Name Channel Duration Start_Time TB
0 John A 2 15:55:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
1 John A 3 15:57:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
2 John A 5 16:00:00 2018-11-19 15:30:00 - 2018-11-19 16:00:00
3 Joseph B 10 15:25:00 2018-11-19 15:00:00 - 2018-11-19 15:30:00
answered Nov 19 '18 at 11:51
Vivek KalyanaranganVivek Kalyanarangan
4,9961827
answered Nov 19 '18 at 11:51
Vivek KalyanaranganVivek Kalyanarangan
4,9961827
answered Nov 19 '18 at 11:51
Vivek KalyanaranganVivek Kalyanarangan
4,9961827
answered Nov 19 '18 at 11:51
Vivek KalyanaranganVivek Kalyanarangan
4,9961827
4,9961827
df=df['Start_time'].astype('datetime64[D]').dtype df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='20:30:00', end='21:00:00', freq='30min')) Getting an Error - 'There are no fields in dtype datetime64[ns].' Issue is with the data types it appears to be. I tried converting into datetime formats. Still it is showing different datatype error everytime.
– Srikanth Ayithy
Nov 19 '18 at 15:24
add a comment |
df=df['Start_time'].astype('datetime64[D]').dtype df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='20:30:00', end='21:00:00', freq='30min')) Getting an Error - 'There are no fields in dtype datetime64[ns].' Issue is with the data types it appears to be. I tried converting into datetime formats. Still it is showing different datatype error everytime.
– Srikanth Ayithy
Nov 19 '18 at 15:24
df=df['Start_time'].astype('datetime64[D]').dtype df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='20:30:00', end='21:00:00', freq='30min')) Getting an Error - 'There are no fields in dtype datetime64[ns].' Issue is with the data types it appears to be. I tried converting into datetime formats. Still it is showing different datatype error everytime.
– Srikanth Ayithy
Nov 19 '18 at 15:24
df=df['Start_time'].astype('datetime64[D]').dtype df['TB'] = pd.cut(df['Start_time'], bins=pd.date_range(start='20:30:00', end='21:00:00', freq='30min')) Getting an Error - 'There are no fields in dtype datetime64[ns].' Issue is with the data types it appears to be. I tried converting into datetime formats. Still it is showing different datatype error everytime.
– Srikanth Ayithy
Nov 19 '18 at 15:24
add a comment |
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Can you please explain the logic? Also please don't add pictures of data. Give a reproducible example
– Sotos
Nov 19 '18 at 10:39