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Group counter for ranges of values
Parsing Wikipedia data in PythonPi-calculating programTriangle rasterization using a scanline algorithm for numpy array indexingCompute the box covering on a graph using CPythonRetrive data for Table from m2m through field is slowSplit list of integers at certain value efficientlyPerformance of stable marriage solution in Python 3Rearrange page numbers for printing a bookCountdown numbers game (Solution generator)Python script for monitoring systemd services (cpu/memory usage)
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ margin-bottom:0;
}
$begingroup$
I have this grouping thing, which is would be a switch case if it weren't for the ranges or a Counter of some sorts, but as there are those ranges I don't know how to implement this any more efficiently.
def getGroups(user_array):
# group1 is group 1-10 x
group1 = 0
# group2 is group 10-50 x
group2 = 0
# group3 is group 50-100 x
group3 = 0
# group4 is group 100-200 x
group4 = 0
# group5 is group 200-500 x
group5 = 0
# group6 is group 500 - 1000 x
group6 = 0
# group7 is group 1000+ x
group7 = 0
for user in user_array:
if user.x_count == 0:
pass
elif user.x_count <= 10:
group1 += 1
elif user.x_count <= 50:
group2 += 1
elif user.x_count <= 100:
group3 += 1
elif user.x_count <= 200:
group4 += 1
elif user.x_count <= 500:
group5 += 1
elif user.x_count <= 1000:
group6 += 1
else:
group7 += 1
return [group1, group2, group3, group4, group5, group6, group7]
python
New contributor
$endgroup$
add a comment |
$begingroup$
I have this grouping thing, which is would be a switch case if it weren't for the ranges or a Counter of some sorts, but as there are those ranges I don't know how to implement this any more efficiently.
def getGroups(user_array):
# group1 is group 1-10 x
group1 = 0
# group2 is group 10-50 x
group2 = 0
# group3 is group 50-100 x
group3 = 0
# group4 is group 100-200 x
group4 = 0
# group5 is group 200-500 x
group5 = 0
# group6 is group 500 - 1000 x
group6 = 0
# group7 is group 1000+ x
group7 = 0
for user in user_array:
if user.x_count == 0:
pass
elif user.x_count <= 10:
group1 += 1
elif user.x_count <= 50:
group2 += 1
elif user.x_count <= 100:
group3 += 1
elif user.x_count <= 200:
group4 += 1
elif user.x_count <= 500:
group5 += 1
elif user.x_count <= 1000:
group6 += 1
else:
group7 += 1
return [group1, group2, group3, group4, group5, group6, group7]
python
New contributor
$endgroup$
$begingroup$
For what type of application are you using/planning to use this?
$endgroup$
– Alex
yesterday
$begingroup$
@Alex Does it matter? I'm trying to group some users for a small project for visualisation.
$endgroup$
– creyD
yesterday
$begingroup$
Indeed, it does. Your application can greatly influence what aspects matter most about your code. Random examples: hyper-critical, real-time, background application, code it and never touch again? Aim for fast execution. You/others should be able to "play" with it? Ease of use and clarity are prime considerations here.
$endgroup$
– Alex
yesterday
$begingroup$
Also, doesuser_array
only containint
values, or can it contain floating point numbers?
$endgroup$
– AJNeufeld
yesterday
$begingroup$
@Alex Ah ok, the focus is mainly performance as we use it to group the results of some mined data... And yes it only contains int values.
$endgroup$
– creyD
18 hours ago
add a comment |
$begingroup$
I have this grouping thing, which is would be a switch case if it weren't for the ranges or a Counter of some sorts, but as there are those ranges I don't know how to implement this any more efficiently.
def getGroups(user_array):
# group1 is group 1-10 x
group1 = 0
# group2 is group 10-50 x
group2 = 0
# group3 is group 50-100 x
group3 = 0
# group4 is group 100-200 x
group4 = 0
# group5 is group 200-500 x
group5 = 0
# group6 is group 500 - 1000 x
group6 = 0
# group7 is group 1000+ x
group7 = 0
for user in user_array:
if user.x_count == 0:
pass
elif user.x_count <= 10:
group1 += 1
elif user.x_count <= 50:
group2 += 1
elif user.x_count <= 100:
group3 += 1
elif user.x_count <= 200:
group4 += 1
elif user.x_count <= 500:
group5 += 1
elif user.x_count <= 1000:
group6 += 1
else:
group7 += 1
return [group1, group2, group3, group4, group5, group6, group7]
python
New contributor
$endgroup$
I have this grouping thing, which is would be a switch case if it weren't for the ranges or a Counter of some sorts, but as there are those ranges I don't know how to implement this any more efficiently.
def getGroups(user_array):
# group1 is group 1-10 x
group1 = 0
# group2 is group 10-50 x
group2 = 0
# group3 is group 50-100 x
group3 = 0
# group4 is group 100-200 x
group4 = 0
# group5 is group 200-500 x
group5 = 0
# group6 is group 500 - 1000 x
group6 = 0
# group7 is group 1000+ x
group7 = 0
for user in user_array:
if user.x_count == 0:
pass
elif user.x_count <= 10:
group1 += 1
elif user.x_count <= 50:
group2 += 1
elif user.x_count <= 100:
group3 += 1
elif user.x_count <= 200:
group4 += 1
elif user.x_count <= 500:
group5 += 1
elif user.x_count <= 1000:
group6 += 1
else:
group7 += 1
return [group1, group2, group3, group4, group5, group6, group7]
python
python
New contributor
New contributor
edited yesterday
200_success
131k17157422
131k17157422
New contributor
asked yesterday
creyDcreyD
1185
1185
New contributor
New contributor
$begingroup$
For what type of application are you using/planning to use this?
$endgroup$
– Alex
yesterday
$begingroup$
@Alex Does it matter? I'm trying to group some users for a small project for visualisation.
$endgroup$
– creyD
yesterday
$begingroup$
Indeed, it does. Your application can greatly influence what aspects matter most about your code. Random examples: hyper-critical, real-time, background application, code it and never touch again? Aim for fast execution. You/others should be able to "play" with it? Ease of use and clarity are prime considerations here.
$endgroup$
– Alex
yesterday
$begingroup$
Also, doesuser_array
only containint
values, or can it contain floating point numbers?
$endgroup$
– AJNeufeld
yesterday
$begingroup$
@Alex Ah ok, the focus is mainly performance as we use it to group the results of some mined data... And yes it only contains int values.
$endgroup$
– creyD
18 hours ago
add a comment |
$begingroup$
For what type of application are you using/planning to use this?
$endgroup$
– Alex
yesterday
$begingroup$
@Alex Does it matter? I'm trying to group some users for a small project for visualisation.
$endgroup$
– creyD
yesterday
$begingroup$
Indeed, it does. Your application can greatly influence what aspects matter most about your code. Random examples: hyper-critical, real-time, background application, code it and never touch again? Aim for fast execution. You/others should be able to "play" with it? Ease of use and clarity are prime considerations here.
$endgroup$
– Alex
yesterday
$begingroup$
Also, doesuser_array
only containint
values, or can it contain floating point numbers?
$endgroup$
– AJNeufeld
yesterday
$begingroup$
@Alex Ah ok, the focus is mainly performance as we use it to group the results of some mined data... And yes it only contains int values.
$endgroup$
– creyD
18 hours ago
$begingroup$
For what type of application are you using/planning to use this?
$endgroup$
– Alex
yesterday
$begingroup$
For what type of application are you using/planning to use this?
$endgroup$
– Alex
yesterday
$begingroup$
@Alex Does it matter? I'm trying to group some users for a small project for visualisation.
$endgroup$
– creyD
yesterday
$begingroup$
@Alex Does it matter? I'm trying to group some users for a small project for visualisation.
$endgroup$
– creyD
yesterday
$begingroup$
Indeed, it does. Your application can greatly influence what aspects matter most about your code. Random examples: hyper-critical, real-time, background application, code it and never touch again? Aim for fast execution. You/others should be able to "play" with it? Ease of use and clarity are prime considerations here.
$endgroup$
– Alex
yesterday
$begingroup$
Indeed, it does. Your application can greatly influence what aspects matter most about your code. Random examples: hyper-critical, real-time, background application, code it and never touch again? Aim for fast execution. You/others should be able to "play" with it? Ease of use and clarity are prime considerations here.
$endgroup$
– Alex
yesterday
$begingroup$
Also, does
user_array
only contain int
values, or can it contain floating point numbers?$endgroup$
– AJNeufeld
yesterday
$begingroup$
Also, does
user_array
only contain int
values, or can it contain floating point numbers?$endgroup$
– AJNeufeld
yesterday
$begingroup$
@Alex Ah ok, the focus is mainly performance as we use it to group the results of some mined data... And yes it only contains int values.
$endgroup$
– creyD
18 hours ago
$begingroup$
@Alex Ah ok, the focus is mainly performance as we use it to group the results of some mined data... And yes it only contains int values.
$endgroup$
– creyD
18 hours ago
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
If your data is strictly integer values, you can use user.x_count in range(...)
to test whether or not the user.x_count
value is a member of the range(...)
set. Ie)
def getUsers(user_array):
group1 = sum(1 for user in user_array if user.x_count in range(1, 11))
group2 = sum(1 for user in user_array if user.x_count in range(11, 51))
# ... etc ...
This unfortunately will require several passes through your user_array
data, so will not work if that data is ephemeral, such as iterator or generator based.
A more complex method will categorize the user.x_count
value into a group value, and then increment the appropriate group counter. bisect
will find an insertion index in a sorted array, so we can leverage this to turn a user.x_count
into a group based on its corresponding insertion index. This will function properly if floating point values are encountered.
import bisect
def getUsers(user_array):
thresholds = (0, 10, 50, 100, 200, 500, 1000)
groups = [0] * (len(thresholds) + 1)
for user in user_array:
groups[bisect.bisect_left(thresholds, user.x_count)] += 1
return groups[1:]
Notice there are no more group-specific variables, like group1
. Instead, all counters are created based on data, allowing you to add additional groups without modifying lines of code; you just modify data.
$endgroup$
$begingroup$
That second solution looks really good. We don't need the temporary variables anyways and as the result is the same array, that is very good. The first option however is probably to slow, as it is a very large array. Even tho the code would be more readable. Thank you for your answer!
$endgroup$
– creyD
18 hours ago
1
$begingroup$
I also like the second solution. If you're not bound to the standard library, maybe also have a look at Python packages like pandas which are widely used in data analytics.
$endgroup$
– Alex
15 hours ago
$begingroup$
@Alex Thank you :)
$endgroup$
– creyD
11 hours ago
add a comment |
Your Answer
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
If your data is strictly integer values, you can use user.x_count in range(...)
to test whether or not the user.x_count
value is a member of the range(...)
set. Ie)
def getUsers(user_array):
group1 = sum(1 for user in user_array if user.x_count in range(1, 11))
group2 = sum(1 for user in user_array if user.x_count in range(11, 51))
# ... etc ...
This unfortunately will require several passes through your user_array
data, so will not work if that data is ephemeral, such as iterator or generator based.
A more complex method will categorize the user.x_count
value into a group value, and then increment the appropriate group counter. bisect
will find an insertion index in a sorted array, so we can leverage this to turn a user.x_count
into a group based on its corresponding insertion index. This will function properly if floating point values are encountered.
import bisect
def getUsers(user_array):
thresholds = (0, 10, 50, 100, 200, 500, 1000)
groups = [0] * (len(thresholds) + 1)
for user in user_array:
groups[bisect.bisect_left(thresholds, user.x_count)] += 1
return groups[1:]
Notice there are no more group-specific variables, like group1
. Instead, all counters are created based on data, allowing you to add additional groups without modifying lines of code; you just modify data.
$endgroup$
$begingroup$
That second solution looks really good. We don't need the temporary variables anyways and as the result is the same array, that is very good. The first option however is probably to slow, as it is a very large array. Even tho the code would be more readable. Thank you for your answer!
$endgroup$
– creyD
18 hours ago
1
$begingroup$
I also like the second solution. If you're not bound to the standard library, maybe also have a look at Python packages like pandas which are widely used in data analytics.
$endgroup$
– Alex
15 hours ago
$begingroup$
@Alex Thank you :)
$endgroup$
– creyD
11 hours ago
add a comment |
$begingroup$
If your data is strictly integer values, you can use user.x_count in range(...)
to test whether or not the user.x_count
value is a member of the range(...)
set. Ie)
def getUsers(user_array):
group1 = sum(1 for user in user_array if user.x_count in range(1, 11))
group2 = sum(1 for user in user_array if user.x_count in range(11, 51))
# ... etc ...
This unfortunately will require several passes through your user_array
data, so will not work if that data is ephemeral, such as iterator or generator based.
A more complex method will categorize the user.x_count
value into a group value, and then increment the appropriate group counter. bisect
will find an insertion index in a sorted array, so we can leverage this to turn a user.x_count
into a group based on its corresponding insertion index. This will function properly if floating point values are encountered.
import bisect
def getUsers(user_array):
thresholds = (0, 10, 50, 100, 200, 500, 1000)
groups = [0] * (len(thresholds) + 1)
for user in user_array:
groups[bisect.bisect_left(thresholds, user.x_count)] += 1
return groups[1:]
Notice there are no more group-specific variables, like group1
. Instead, all counters are created based on data, allowing you to add additional groups without modifying lines of code; you just modify data.
$endgroup$
$begingroup$
That second solution looks really good. We don't need the temporary variables anyways and as the result is the same array, that is very good. The first option however is probably to slow, as it is a very large array. Even tho the code would be more readable. Thank you for your answer!
$endgroup$
– creyD
18 hours ago
1
$begingroup$
I also like the second solution. If you're not bound to the standard library, maybe also have a look at Python packages like pandas which are widely used in data analytics.
$endgroup$
– Alex
15 hours ago
$begingroup$
@Alex Thank you :)
$endgroup$
– creyD
11 hours ago
add a comment |
$begingroup$
If your data is strictly integer values, you can use user.x_count in range(...)
to test whether or not the user.x_count
value is a member of the range(...)
set. Ie)
def getUsers(user_array):
group1 = sum(1 for user in user_array if user.x_count in range(1, 11))
group2 = sum(1 for user in user_array if user.x_count in range(11, 51))
# ... etc ...
This unfortunately will require several passes through your user_array
data, so will not work if that data is ephemeral, such as iterator or generator based.
A more complex method will categorize the user.x_count
value into a group value, and then increment the appropriate group counter. bisect
will find an insertion index in a sorted array, so we can leverage this to turn a user.x_count
into a group based on its corresponding insertion index. This will function properly if floating point values are encountered.
import bisect
def getUsers(user_array):
thresholds = (0, 10, 50, 100, 200, 500, 1000)
groups = [0] * (len(thresholds) + 1)
for user in user_array:
groups[bisect.bisect_left(thresholds, user.x_count)] += 1
return groups[1:]
Notice there are no more group-specific variables, like group1
. Instead, all counters are created based on data, allowing you to add additional groups without modifying lines of code; you just modify data.
$endgroup$
If your data is strictly integer values, you can use user.x_count in range(...)
to test whether or not the user.x_count
value is a member of the range(...)
set. Ie)
def getUsers(user_array):
group1 = sum(1 for user in user_array if user.x_count in range(1, 11))
group2 = sum(1 for user in user_array if user.x_count in range(11, 51))
# ... etc ...
This unfortunately will require several passes through your user_array
data, so will not work if that data is ephemeral, such as iterator or generator based.
A more complex method will categorize the user.x_count
value into a group value, and then increment the appropriate group counter. bisect
will find an insertion index in a sorted array, so we can leverage this to turn a user.x_count
into a group based on its corresponding insertion index. This will function properly if floating point values are encountered.
import bisect
def getUsers(user_array):
thresholds = (0, 10, 50, 100, 200, 500, 1000)
groups = [0] * (len(thresholds) + 1)
for user in user_array:
groups[bisect.bisect_left(thresholds, user.x_count)] += 1
return groups[1:]
Notice there are no more group-specific variables, like group1
. Instead, all counters are created based on data, allowing you to add additional groups without modifying lines of code; you just modify data.
edited 23 hours ago
answered yesterday
AJNeufeldAJNeufeld
6,6791722
6,6791722
$begingroup$
That second solution looks really good. We don't need the temporary variables anyways and as the result is the same array, that is very good. The first option however is probably to slow, as it is a very large array. Even tho the code would be more readable. Thank you for your answer!
$endgroup$
– creyD
18 hours ago
1
$begingroup$
I also like the second solution. If you're not bound to the standard library, maybe also have a look at Python packages like pandas which are widely used in data analytics.
$endgroup$
– Alex
15 hours ago
$begingroup$
@Alex Thank you :)
$endgroup$
– creyD
11 hours ago
add a comment |
$begingroup$
That second solution looks really good. We don't need the temporary variables anyways and as the result is the same array, that is very good. The first option however is probably to slow, as it is a very large array. Even tho the code would be more readable. Thank you for your answer!
$endgroup$
– creyD
18 hours ago
1
$begingroup$
I also like the second solution. If you're not bound to the standard library, maybe also have a look at Python packages like pandas which are widely used in data analytics.
$endgroup$
– Alex
15 hours ago
$begingroup$
@Alex Thank you :)
$endgroup$
– creyD
11 hours ago
$begingroup$
That second solution looks really good. We don't need the temporary variables anyways and as the result is the same array, that is very good. The first option however is probably to slow, as it is a very large array. Even tho the code would be more readable. Thank you for your answer!
$endgroup$
– creyD
18 hours ago
$begingroup$
That second solution looks really good. We don't need the temporary variables anyways and as the result is the same array, that is very good. The first option however is probably to slow, as it is a very large array. Even tho the code would be more readable. Thank you for your answer!
$endgroup$
– creyD
18 hours ago
1
1
$begingroup$
I also like the second solution. If you're not bound to the standard library, maybe also have a look at Python packages like pandas which are widely used in data analytics.
$endgroup$
– Alex
15 hours ago
$begingroup$
I also like the second solution. If you're not bound to the standard library, maybe also have a look at Python packages like pandas which are widely used in data analytics.
$endgroup$
– Alex
15 hours ago
$begingroup$
@Alex Thank you :)
$endgroup$
– creyD
11 hours ago
$begingroup$
@Alex Thank you :)
$endgroup$
– creyD
11 hours ago
add a comment |
creyD is a new contributor. Be nice, and check out our Code of Conduct.
creyD is a new contributor. Be nice, and check out our Code of Conduct.
creyD is a new contributor. Be nice, and check out our Code of Conduct.
creyD is a new contributor. Be nice, and check out our Code of Conduct.
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For what type of application are you using/planning to use this?
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– Alex
yesterday
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@Alex Does it matter? I'm trying to group some users for a small project for visualisation.
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– creyD
yesterday
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Indeed, it does. Your application can greatly influence what aspects matter most about your code. Random examples: hyper-critical, real-time, background application, code it and never touch again? Aim for fast execution. You/others should be able to "play" with it? Ease of use and clarity are prime considerations here.
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– Alex
yesterday
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Also, does
user_array
only containint
values, or can it contain floating point numbers?$endgroup$
– AJNeufeld
yesterday
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@Alex Ah ok, the focus is mainly performance as we use it to group the results of some mined data... And yes it only contains int values.
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– creyD
18 hours ago