Say I have the below-mentioned list:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
Now, if user wants 4 sub-lists (n=4), then the sub-lists will be
[0,1,2,3,4,5]
[6,7,8,9,10,11]
[12,13,14,15,16,17]
[18,19,20]
Similarly, if user wants 6 sub-lists (n=6), then the sub-lists will be
[0,1,2,3]
[4,5,6,7]
[8,9,10,11]
[12,13,14,15]
[16,17,18,19]
[20]
Please let me know how can I achieve this.
list1 =[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
n=6
k = int(len(list1)/float(n))+1
i=0
for x in range(n-1):
i=(x+1)*k
print list1[i-k:i]
print list1[i:]
Try this python code. It answers your problem perfectly.
Related
to my model I added a simply DateTimeField:
expired = models.DateTimeField(default=None)
. The value of the field can be either None or a Datetime.
I'd like to filter for objects where the expired is filled with any datum, however I'm struggling to find the right filter.
I think I tried all the combinations of filter / exclude and expired__isnull=True / expired=None, but I never get back the exact number.
What's the right way to filter if the field has a DateTime in it, or not?
Django: 1.11.16
Thanks.
In my model there're 2122 lines:
Counter(Obj.objects.filter().values_list('expired'))
Counter({(datetime.datetime(2021, 9, 24, 1, 6, 50),): 1,
(datetime.datetime(2021, 9, 24, 1, 6, 51),): 1,
(datetime.datetime(2021, 9, 24, 1, 6, 32),): 1,
(datetime.datetime(2021, 9, 24, 1, 12, 3),): 1,
(datetime.datetime(2021, 9, 24, 1, 12, 44),): 1,
(datetime.datetime(2021, 12, 4, 1, 31, 25),): 1,
(datetime.datetime(2021, 12, 4, 1, 37, 49),): 1,
(datetime.datetime(2021, 12, 4, 1, 9, 55),): 1,
(None,): 2087,
(datetime.datetime(2021, 12, 4, 1, 37, 52),): 1,
(datetime.datetime(2021, 12, 4, 1, 2, 8),): 4,
(datetime.datetime(2021, 12, 4, 1, 5, 14),): 9,
(datetime.datetime(2021, 9, 28, 0, 43, 51),): 1,
(datetime.datetime(2021, 12, 4, 1, 0, 13),): 7,
(datetime.datetime(2021, 12, 4, 1, 9, 59),): 2,
(datetime.datetime(2021, 12, 3, 17, 25, 46),): 1,
(datetime.datetime(2021, 12, 4, 1, 9, 54),): 1,
(datetime.datetime(2021, 9, 24, 1, 14, 30),): 1})
.
Obj.objects.filter(expired__isnull=False).count()
returns all the lines (2122) ... .
Obj.objects.filter(expired=None).count() returns 2087 lines instead of the 35 expected.
Obj.objects.exclude(expired=None).count() returns 2122, so all the lines.
The query is good, the problem is in the model definition. It should be blank=True and null=True.
try changing the field in Model
expired = models.DateTimeField(
auto_now=False,
null=True,
blank=True
)
My column data looks like
"dayparts": [{"day": "Saturday", "hours": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]}, {"day": "Sunday", "hours": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]}, {"day": "Thursday", "hours": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]}]
I would like to have the result like
You can try:
WITH sample AS (
SELECT "1" AS id, "{\"dayparts\":[{\"day\":\"Saturday\",\"hours\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13]},{\"day\":\"Sunday\",\"hours\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13]},{\"day\":\"Thursday\",\"hours\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13]}]}" AS msg
)
SELECT id,
JSON_VALUE(dparts, '$.day') AS day,
JSON_QUERY(dparts, '$.hours') AS hours
FROM (
SELECT id,
JSON_EXTRACT_ARRAY(JSON_QUERY(msg, '$.dayparts')) AS dayparts
FROM sample) t, UNNEST(t.dayparts) dparts
(I added enclosing "{" and "}" to be able to perform JSON operations, if they are not there just concatenate them I guess)
(You can also add "JSON_EXTRACT_ARRAY" around "JSON_QUERY(dparts, '$.hours')" if you wish an actual array in the result table)
import itertools
def unnest_list(listone,listtwo):
last=[listone[x] for x in range(len(listone)) if x%2==1]
last.append([listtwo[x] for x in range(len(listtwo)) if x%2==0])
return list(itertools.chain(*last))
print(something([3, 6, 9, 12, 15, 18, 21], [4, 8, 12, 16, 20, 24, 28]))
trying to make the list unnested, at the end of the func,
last=[6, 12, 18, [4, 12, 20, 28]], after using itertools,chain(*last) it gives an error: "TypeError: 'int' object is not iterable"
I am browsing through mir.ndslice docs trying to figure out how to do a simple row selection by column.
In numpy I would do:
a = np.random.randint(0, 20, [4, 6])
# array([[ 8, 5, 4, 18, 1, 4],
# [ 2, 18, 15, 7, 18, 19],
# [16, 5, 4, 6, 11, 11],
# [15, 1, 14, 6, 1, 4]])
a[a[:,2] > 10] # select rows where the second column value is > 10
# array([[ 2, 18, 15, 7, 18, 19],
# [15, 1, 14, 6, 1, 4]])
Using mir library I naively tried:
import std.range;
import std.random;
import mir.ndslice;
auto a = generate!(() => uniform(0, 20)).take(24).array.sliced(4,6);
// [[12, 19, 3, 10, 19, 11],
// [19, 0, 0, 13, 9, 1],
// [ 0, 0, 4, 13, 1, 2],
// [ 6, 19, 14, 18, 14, 18]]
a[a[0..$,2] > 10];
But got
Error: incompatible types for `((ulong __dollar = a.length();) , a.opIndex(a.opSlice(0LU, __dollar), 2)) > (10)`: `Slice!(int*, 1LU, cast(mir_slice_kind)0)` and `int`
dmd failed with exit code 1.
So, I went through the docs and couldn't find anything that would look like np.where or similar. Is it even possible in mir?
I have data frame in pandas and I have written a function to use the information in each row to generate a new column. I want the result to be in a list format:
A B C
3 4 1
4 2 5
def Computation(row):
if row['B'] >= 3:
return [s for s in range(row['C'],50)]
else:
return [s for s in range(row['C']+2,50)]
df['D'] = df.apply(Computation, axis = 1)
However, I am getting the following error:
"could not broadcast input array from shape (308) into shape (9)"
Could you please tell me how to solve this problem?
Say you start with
In [25]: df = pd.DataFrame({'A': [3, 4], 'B': [4, 2], 'C': [1, 5]})
Then there are at least two ways to do it.
You can apply twice on the C column, but switch on the B column:
In [26]: np.where(df.B >= 3, df.C.apply(lambda c: [s for s in range(c, 50)]), df.C.apply(lambda c: [s for s in range(c + 2, 50)]))
Out[26]:
array([ [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
[7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]], dtype=object)
Or you can apply on the entire row and switch on the B value per row:
In [27]: df.apply(lambda r: [s for s in range(r.C, 50)] if r.B >= 3 else [s for s in range(r.C + 2, 50)], axis=1)
Out[27]:
0 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...
1 [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, ...
Note that the return types are different, but, in each case, you can still write
df['foo'] = <each one of the above options>