How to get specific rows on conditions?
1
For the data as like follows:
Name Stage Start End
Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
Hulk 2 21/10/2018 07:34:15 21/10/2018 07:54:15
Hulk 3 21/10/2018 07:58:15 21/10/2018 08:14:15
Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
Sam A2 21/10/2018 10:34:15 21/10/2018 10:45:15
Sam A3 21/10/2018 10:45:15 21/10/2018 11:00:15
Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
Bruce 1.2 21/10/2018 11:45:15 21/10/2018 12:00:15
Bruce 1.3 21/10/2018 12:00:15 21/10/2018 12:25:15
Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
Peter 1 21/10/2018 12:45:15 21/10/2018 01:05:15
Peter 1 21/10/2018 01:05:15 21/10/2018 01:15:15
How can I have first and last instance of Stage for each Name like which are starting with 1 in it and lasting with 4 ?
The dataframe should be in following way :
Name Stage Start End
Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
I tried groupby with ([Name,Stage]) but didn't get desired dataframe as above.
python-2.7 pandas dataframe row slice
asked Nov 12 '18 at 6:58
Ranjan raghav
153
add a comment |
1
For the data as like follows:
Name Stage Start End
Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
Hulk 2 21/10/2018 07:34:15 21/10/2018 07:54:15
Hulk 3 21/10/2018 07:58:15 21/10/2018 08:14:15
Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
Sam A2 21/10/2018 10:34:15 21/10/2018 10:45:15
Sam A3 21/10/2018 10:45:15 21/10/2018 11:00:15
Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
Bruce 1.2 21/10/2018 11:45:15 21/10/2018 12:00:15
Bruce 1.3 21/10/2018 12:00:15 21/10/2018 12:25:15
Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
Peter 1 21/10/2018 12:45:15 21/10/2018 01:05:15
Peter 1 21/10/2018 01:05:15 21/10/2018 01:15:15
How can I have first and last instance of Stage for each Name like which are starting with 1 in it and lasting with 4 ?
The dataframe should be in following way :
Name Stage Start End
Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
I tried groupby with ([Name,Stage]) but didn't get desired dataframe as above.
python-2.7 pandas dataframe row slice
asked Nov 12 '18 at 6:58
Ranjan raghav
153
add a comment |
1
1
1
1
For the data as like follows:
Name Stage Start End
Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
Hulk 2 21/10/2018 07:34:15 21/10/2018 07:54:15
Hulk 3 21/10/2018 07:58:15 21/10/2018 08:14:15
Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
Sam A2 21/10/2018 10:34:15 21/10/2018 10:45:15
Sam A3 21/10/2018 10:45:15 21/10/2018 11:00:15
Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
Bruce 1.2 21/10/2018 11:45:15 21/10/2018 12:00:15
Bruce 1.3 21/10/2018 12:00:15 21/10/2018 12:25:15
Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
Peter 1 21/10/2018 12:45:15 21/10/2018 01:05:15
Peter 1 21/10/2018 01:05:15 21/10/2018 01:15:15
How can I have first and last instance of Stage for each Name like which are starting with 1 in it and lasting with 4 ?
The dataframe should be in following way :
Name Stage Start End
Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
I tried groupby with ([Name,Stage]) but didn't get desired dataframe as above.
python-2.7 pandas dataframe row slice
asked Nov 12 '18 at 6:58
Ranjan raghav
153
For the data as like follows:
Name Stage Start End
Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
Hulk 2 21/10/2018 07:34:15 21/10/2018 07:54:15
Hulk 3 21/10/2018 07:58:15 21/10/2018 08:14:15
Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
Sam A2 21/10/2018 10:34:15 21/10/2018 10:45:15
Sam A3 21/10/2018 10:45:15 21/10/2018 11:00:15
Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
Bruce 1.2 21/10/2018 11:45:15 21/10/2018 12:00:15
Bruce 1.3 21/10/2018 12:00:15 21/10/2018 12:25:15
Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
Peter 1 21/10/2018 12:45:15 21/10/2018 01:05:15
Peter 1 21/10/2018 01:05:15 21/10/2018 01:15:15
How can I have first and last instance of Stage for each Name like which are starting with 1 in it and lasting with 4 ?
The dataframe should be in following way :
Name Stage Start End
Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
I tried groupby with ([Name,Stage]) but didn't get desired dataframe as above.
python-2.7 pandas dataframe row slice
python-2.7 pandas dataframe row slice
asked Nov 12 '18 at 6:58
Ranjan raghav
153
asked Nov 12 '18 at 6:58
Ranjan raghav
153
asked Nov 12 '18 at 6:58
Ranjan raghav
153
asked Nov 12 '18 at 6:58
Ranjan raghav
153
asked Nov 12 '18 at 6:58
Ranjan raghav
153
153
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
3
Use duplicated with str.contains with boolean indexing for return necessary rows first and then value_counts with map for filter only 2 row groups:
m1 = ~df['Name'].duplicated()
m2 = df['Stage'].str.contains('1')
m3 = ~df['Name'].duplicated(keep='last')
m4 = df['Stage'].str.contains('4')
df1 = df[(m1 & m2) | (m3 & m4)].copy()
df1 = df1[df1['Name'].map(df1['Name'].value_counts()) == 2]
print (df1)
Name Stage Start End
0 Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
3 Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
4 Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
7 Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
8 Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
11 Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
answered Nov 12 '18 at 7:16
jezrael
321k22263341
++ve for nice solution sir, could you please do add how are you creatingdftoo in your code as a newbie I could get a complete picture of this solution too then, will be grateful to you, thanks again.
– RavinderSingh13
Nov 12 '18 at 7:42
1
@RavinderSingh13 - Usedf = pd.read_clipboard(sep='s{2,}')- separator is regex 2 or more whitespaces
– jezrael
Nov 12 '18 at 7:43
1
Thanks a TON sir, it helped me. You are AWESOME.
– RavinderSingh13
Nov 12 '18 at 7:53
@jezrael Thanks jezrael for the lovely solution, really admire your conceptual strength : )
– Ranjan raghav
Nov 12 '18 at 8:10
1
@Ranjanraghav - You are welcome!
– jezrael
Nov 12 '18 at 8:11
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
3
Use duplicated with str.contains with boolean indexing for return necessary rows first and then value_counts with map for filter only 2 row groups:
m1 = ~df['Name'].duplicated()
m2 = df['Stage'].str.contains('1')
m3 = ~df['Name'].duplicated(keep='last')
m4 = df['Stage'].str.contains('4')
df1 = df[(m1 & m2) | (m3 & m4)].copy()
df1 = df1[df1['Name'].map(df1['Name'].value_counts()) == 2]
print (df1)
Name Stage Start End
0 Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
3 Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
4 Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
7 Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
8 Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
11 Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
answered Nov 12 '18 at 7:16
jezrael
321k22263341
++ve for nice solution sir, could you please do add how are you creatingdftoo in your code as a newbie I could get a complete picture of this solution too then, will be grateful to you, thanks again.
– RavinderSingh13
Nov 12 '18 at 7:42
1
@RavinderSingh13 - Usedf = pd.read_clipboard(sep='s{2,}')- separator is regex 2 or more whitespaces
– jezrael
Nov 12 '18 at 7:43
1
Thanks a TON sir, it helped me. You are AWESOME.
– RavinderSingh13
Nov 12 '18 at 7:53
@jezrael Thanks jezrael for the lovely solution, really admire your conceptual strength : )
– Ranjan raghav
Nov 12 '18 at 8:10
1
@Ranjanraghav - You are welcome!
– jezrael
Nov 12 '18 at 8:11
add a comment |
3
Use duplicated with str.contains with boolean indexing for return necessary rows first and then value_counts with map for filter only 2 row groups:
m1 = ~df['Name'].duplicated()
m2 = df['Stage'].str.contains('1')
m3 = ~df['Name'].duplicated(keep='last')
m4 = df['Stage'].str.contains('4')
df1 = df[(m1 & m2) | (m3 & m4)].copy()
df1 = df1[df1['Name'].map(df1['Name'].value_counts()) == 2]
print (df1)
Name Stage Start End
0 Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
3 Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
4 Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
7 Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
8 Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
11 Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
answered Nov 12 '18 at 7:16
jezrael
321k22263341
++ve for nice solution sir, could you please do add how are you creatingdftoo in your code as a newbie I could get a complete picture of this solution too then, will be grateful to you, thanks again.
– RavinderSingh13
Nov 12 '18 at 7:42
1
@RavinderSingh13 - Usedf = pd.read_clipboard(sep='s{2,}')- separator is regex 2 or more whitespaces
– jezrael
Nov 12 '18 at 7:43
1
Thanks a TON sir, it helped me. You are AWESOME.
– RavinderSingh13
Nov 12 '18 at 7:53
@jezrael Thanks jezrael for the lovely solution, really admire your conceptual strength : )
– Ranjan raghav
Nov 12 '18 at 8:10
1
@Ranjanraghav - You are welcome!
– jezrael
Nov 12 '18 at 8:11
add a comment |
3
3
3
Use duplicated with str.contains with boolean indexing for return necessary rows first and then value_counts with map for filter only 2 row groups:
m1 = ~df['Name'].duplicated()
m2 = df['Stage'].str.contains('1')
m3 = ~df['Name'].duplicated(keep='last')
m4 = df['Stage'].str.contains('4')
df1 = df[(m1 & m2) | (m3 & m4)].copy()
df1 = df1[df1['Name'].map(df1['Name'].value_counts()) == 2]
print (df1)
Name Stage Start End
0 Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
3 Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
4 Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
7 Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
8 Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
11 Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
answered Nov 12 '18 at 7:16
jezrael
321k22263341
Use duplicated with str.contains with boolean indexing for return necessary rows first and then value_counts with map for filter only 2 row groups:
m1 = ~df['Name'].duplicated()
m2 = df['Stage'].str.contains('1')
m3 = ~df['Name'].duplicated(keep='last')
m4 = df['Stage'].str.contains('4')
df1 = df[(m1 & m2) | (m3 & m4)].copy()
df1 = df1[df1['Name'].map(df1['Name'].value_counts()) == 2]
print (df1)
Name Stage Start End
0 Hulk 1 21/10/2018 06:34:15 21/10/2018 07:34:15
3 Hulk 4 21/10/2018 08:14:15 21/10/2018 08:34:15
4 Sam A1 21/10/2018 09:34:15 21/10/2018 10:34:15
7 Sam A4 21/10/2018 11:00:15 21/10/2018 11:34:15
8 Bruce 1.1 21/10/2018 11:34:15 21/10/2018 11:45:15
11 Bruce 1.4 21/10/2018 12:25:15 21/10/2018 12:45:15
answered Nov 12 '18 at 7:16
jezrael
321k22263341
edited Nov 12 '18 at 7:25
answered Nov 12 '18 at 7:16
jezrael
321k22263341
answered Nov 12 '18 at 7:16
jezrael
321k22263341
answered Nov 12 '18 at 7:16
jezrael
321k22263341
321k22263341
++ve for nice solution sir, could you please do add how are you creatingdftoo in your code as a newbie I could get a complete picture of this solution too then, will be grateful to you, thanks again.
– RavinderSingh13
Nov 12 '18 at 7:42
1
@RavinderSingh13 - Usedf = pd.read_clipboard(sep='s{2,}')- separator is regex 2 or more whitespaces
– jezrael
Nov 12 '18 at 7:43
1
Thanks a TON sir, it helped me. You are AWESOME.
– RavinderSingh13
Nov 12 '18 at 7:53
@jezrael Thanks jezrael for the lovely solution, really admire your conceptual strength : )
– Ranjan raghav
Nov 12 '18 at 8:10
1
@Ranjanraghav - You are welcome!
– jezrael
Nov 12 '18 at 8:11
add a comment |
++ve for nice solution sir, could you please do add how are you creatingdftoo in your code as a newbie I could get a complete picture of this solution too then, will be grateful to you, thanks again.
– RavinderSingh13
Nov 12 '18 at 7:42
1
@RavinderSingh13 - Usedf = pd.read_clipboard(sep='s{2,}')- separator is regex 2 or more whitespaces
– jezrael
Nov 12 '18 at 7:43
1
Thanks a TON sir, it helped me. You are AWESOME.
– RavinderSingh13
Nov 12 '18 at 7:53
@jezrael Thanks jezrael for the lovely solution, really admire your conceptual strength : )
– Ranjan raghav
Nov 12 '18 at 8:10
1
@Ranjanraghav - You are welcome!
– jezrael
Nov 12 '18 at 8:11
++ve for nice solution sir, could you please do add how are you creating
df too in your code as a newbie I could get a complete picture of this solution too then, will be grateful to you, thanks again.– RavinderSingh13
Nov 12 '18 at 7:42
++ve for nice solution sir, could you please do add how are you creating
df too in your code as a newbie I could get a complete picture of this solution too then, will be grateful to you, thanks again.– RavinderSingh13
Nov 12 '18 at 7:42
1
1
@RavinderSingh13 - Use
df = pd.read_clipboard(sep='s{2,}') - separator is regex 2 or more whitespaces– jezrael
Nov 12 '18 at 7:43
@RavinderSingh13 - Use
df = pd.read_clipboard(sep='s{2,}') - separator is regex 2 or more whitespaces– jezrael
Nov 12 '18 at 7:43
1
1
Thanks a TON sir, it helped me. You are AWESOME.
– RavinderSingh13
Nov 12 '18 at 7:53
Thanks a TON sir, it helped me. You are AWESOME.
– RavinderSingh13
Nov 12 '18 at 7:53
@jezrael Thanks jezrael for the lovely solution, really admire your conceptual strength : )
– Ranjan raghav
Nov 12 '18 at 8:10
@jezrael Thanks jezrael for the lovely solution, really admire your conceptual strength : )
– Ranjan raghav
Nov 12 '18 at 8:10
1
1
@Ranjanraghav - You are welcome!
– jezrael
Nov 12 '18 at 8:11
@Ranjanraghav - You are welcome!
– jezrael
Nov 12 '18 at 8:11
add a comment |
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