How to get index of multiple, possibly different, elements in numpy? - python

I have a numpy array with many rows in it that look roughly as follows:
0, 50, 50, 2, 50, 1, 50, 99, 50, 50
50, 2, 1, 50, 50, 50, 98, 50, 50, 50
0, 50, 50, 98, 50, 1, 50, 50, 50, 50
0, 50, 50, 50, 50, 99, 50, 50, 2, 50
2, 50, 50, 0, 98, 1, 50, 50, 50, 50
I am given a variable n<50. Each row, of length 10, has the following in it:
Every number from 0 to n, with one possibly missing. In the example above, n=2.
Possibly a 98, which will be in the place of the missing number, if there is a number missing.
Possibly a 99, which will be in the place of the missing number, if there is a number missing, and there is not already a 98.
Many 50's.
What I want to get is an array with all the indices of the 0s in the first row, all the indices of the 1s in the second row, all the indices of the 2s in the third row, etc. For the above example, my desired output is this:
0, 6, 0, 0, 3
5, 2, 5, 5, 5
3, 1, 3, 8, 0
You may have noticed the catch: sometimes, exactly one of the numbers is replaced either by a 98, or a 99. It's pretty easy to write a for loop which determines which number, if any, was replaced, and uses that to get the array of indices.
Is there a way to do this with numpy?

The follwing numpy solution rather aggressively uses the assumptions listed in OP. If they are not 100% guaranteed some more checks may be in order.
The mildly clever bit (even if I say so myself) here is to use the data array itself for finding the right destinations of their indices. For example, all the 2's need their indices stored in row 2 of the output array. Using this we can bulk store most of the indices in a single operation.
Example input is in array data:
n = 2
y,x = data.shape
out = np.empty((y,n+1),int)
# find 98 falling back to 99 if necessary
# and fill output array with their indices
# if neither exists some nonsense will be written but that does no harm
# most of this will be overwritten later
out.T[...] = ((data-98)&127).argmin(axis=1)
# find n+1 lowest values in each row
idx = data.argpartition(n,axis=1)[:,:n+1]
# construct auxiliary indexer
yr = np.arange(y)[:,None]
# put indices of low values where they belong
out[yr,data[yr,idx[:,:-1]]] = idx[:,:-1]
# ^ ^ ^ ^ ^ ^ ^ ^ ^ ^
# the clever bit
# rows with no missing number still need the last value
nomiss, = (data[range(y),idx[:,n]] == n).nonzero()
out[nomiss,n] = idx[nomiss,n]
# admire
print(out.T)
outputs:
[[0 6 0 0 3]
[5 2 5 5 5]
[3 1 3 8 0]]

I don't think you're getting away without a for-loop here. But here's how you could go about it.
For each number in n, find all of the locations where it is known. Example:
locations = np.argwhere(data == 1)
print(locations)
[[0 5]
[1 2]
[2 5]
[4 5]]
You can then turn this into a map for easy lookup per number in n:
known = {
i: dict(np.argwhere(data == i))
for i in range(n + 1)
}
pprint(known)
{0: {0: 0, 2: 0, 3: 0, 4: 3},
1: {0: 5, 1: 2, 2: 5, 4: 5},
2: {0: 3, 1: 1, 3: 8, 4: 0}}
Do the same for the unknown numbers:
unknown = dict(np.argwhere((data == 98) | (data == 99)))
pprint(unknown)
{0: 7, 1: 6, 2: 3, 3: 5, 4: 4}
And now for each location in the result, you can lookup the index in the known list and fallback to the unknown.
result = np.array(
[
[known[i].get(j, unknown.get(j)) for j in range(len(data))]
for i in range(n + 1)
]
)
print(result)
[[0 6 0 0 3]
[5 2 5 5 5]
[3 1 3 8 0]]
Bonus: Getting fancy with dictionary constructor and unpacking:
from collections import OrderedDict
unknown = np.argwhere((data == 98) | (data == 99))
results = np.array([
[*OrderedDict((*unknown, *np.argwhere(data == i))).values()]
for i in range(n + 1)
])
print(results)

Related

Python Numpy - Slicing assignment not assigning correctly

I have a 2d numpy array called arm_resets that has positive integers. The first column has all positive integers < 360. For all columns other than the first, I need to replace all values over 360 with the value that is in the same row in the 1st column. I thought this would be a relatively easy thing to do, here's what I have:
i = 300
over_360 = arm_resets[:, [i]] >= 360
print(arm_resets[:, [i]][over_360])
print(arm_resets[:, [0]][over_360])
arm_resets[:, [i]][over_360] = arm_resets[:, [0]][over_360]
print(arm_resets[:, [i]][over_360])
And here's what prints:
[3600 3609 3608 ... 3600 3611 3605]
[ 0 9 8 ... 0 11 5]
[3600 3609 3608 ... 3600 3611 3605]
Since all numbers that are being shown in the first print (first 3 and last 3) are above 360, they should be getting replaced by the 2nd print in the 3rd print. Why is this not working?
edit: reproducible example:
df = pd.DataFrame({"start":[1,2,5,6],"freq":[1,5,6,9]})
periods = 6
arm_resets = df[["start"]].values
freq = df[["freq"]].values
arm_resets = np.pad(arm_resets,((0,0),(0,periods-1)))
for i in range(1,periods):
arm_resets[:,[i]] = arm_resets[:,[i-1]] + freq
#over_360 = arm_resets[:,[i]] >= periods
#arm_resets[:,[i]][over_360] = arm_resets[:,[0]][over_360]
arm_resets
Given commented out code here's what prints:
array([[ 1, 2, 3, 4, 5, 6],
[ 2, 7, 12, 17, 22, 27],
[ 3, 9, 15, 21, 27, 33],
[ 4, 13, 22, 31, 40, 49]])
What I would expect:
array([[ 1, 2, 3, 4, 5, 1],
[ 2, 2, 2, 2, 2, 2],
[ 3, 3, 3, 3, 3, 3],
[ 4, 4, 4, 4, 4, 4]])
Now if it helps, the final 2d array I'm actually trying to create is a 1/0 array that indicates which are filled in, so in this example I'd want this:
array([[ 0, 1, 1, 1, 1, 1],
[ 0, 0, 1, 0, 0, 0],
[ 0, 0, 0, 1, 0, 0],
[ 0, 0, 0, 0, 1, 0]])
The code I use to achieve this from the above arm_resets is this:
fin = np.zeros((len(arm_resets),periods),dtype=int)
for i in range(len(arm_resets)):
fin[i,a[i]] = 1
The slice arm_resets[:, [i]] is a fancy index, and therefore makes a copy of the ith column of the data. arm_resets[:, [i]][over_360] = ... therefore calls __setitem__ on a temporary array that is discarded as soon as the statement executes. If you want to assign to the mask, call __setitem__ on the sliced object directly:
arm_resets[over_360, [i]] = ...
You also don't need to make the index into a list. It's generally better to use simple indices, especially when doing assignments, since they create views rather than copies:
arm_resets[over_360, i] = ...
With slicing, even the following should work, since it calls __setitem__ on a view:
arm_resets[:, i][over_360] = ...
This index does not help you process each row of the data, since i is a column. In fact, you can process the entire matrix in one step, without looping, if you use indices rather than a boolean mask. The reason that indices are useful is that you can match the item from the correct row in the first column:
rows, cols = np.nonzero(arm_resets[:, 1:] >= 360)
arm_resets[rows, cols] = arm_resets[rows, 1]
You can use np.where()
first_col = arm_resets[:,0] # first col
first_col = first_col.reshape(first_col.size,1) #Transfor in 2d array
arm_resets = np.where(arm_resets >= 360,first_col,arm_resets)
You can see in detail how np.where work here, but basically it compare arm_resets >= 360, if true it put first_col value in place (there another detail here with broadcasting) if false it put arm_resets value.
Edit: As suggested by Mad Physicist. You can use arm_resets[:,0,None] directly instead of creating first_col variable.
arm_resets = np.where(arm_resets >= 360,arm_resets[:,0,None],arm_resets)

Filtering sorted array with a defined threshold in python

I have a sorted list, for example x = [120, 99, 90, 90, 40, 5, 5, 5, 1, 1].
I want to find the index of the last element that is greater than a variable, for example limit = 30. In this example, I want to get 4, because that's the index of 40 (and 5 is lower than 30).
How can I do that efficiently in python?
I can do looping like this:
def filtering(x, limit):
for i,elem in enumerate(x):
if elem <=limit:
return i-1
return i
But I was thinking if there is any better way / numpy function to do this. Thanks!
Since you mentioned NumPy:
def last_ind(arr, limit):
length = len(arr)
sorter = np.arange(length)[::-1]
pos = np.searchsorted(arr, limit, sorter=sorter)
return length - pos - 1
Since searchsorted looks for an array sorted in ascending order but yours is sorted in descending order, we pass the indices of the list reversed to it with sorter. Also, searchsorted does the finding but it returns the index that you'd put the new number on; you, however, need the index of the previous element, hence -1. Lastly, since searchsorted worked as if it was sorted other way around, we correct the position by subtracting it from the length at the end.
Also, it returns -1 when no element in array is larger than the limit.
Sample runs:
>>> x
[120, 99, 90, 90, 40, 5, 5, 5, 1, 1]
>>> last_ind(x, 30)
4
>>> last_ind(x, 100)
0
>>> last_ind(x, 92)
1
>>> last_ind(x, 90)
3
>>> last_ind(x, 3)
7
>>> last_ind(x, 500)
-1
>>> last_ind(x, -51)
9
I would suggest to use numpy
>>> x = np.array([120, 99, 90, 90, 40, 5, 5, 5, 1, 1])
>>> np.where(x > 30)
(array([0, 1, 2, 3, 4]),)
The last returned element is the index you are looking for.

Eliminating Consecutive Numbers

If you have a range of numbers from 1-49 with 6 numbers to choose from, there are nearly 14 million combinations. Using my current script, I currently have only 7.2 million combinations remaining. Of the 7.2 million remaining combinations, I want to eliminate all 3, 4, 5, 6, dual, and triple consecutive numbers.
Example:
3 consecutive: 1, 2, 3, x, x, x
4 consecutive: 3, 4, 5, 6, x, x
5 consecutive: 4, 5, 6, 7, 8, x
6 consecutive: 5, 6, 7, 8, 9, 10
double separate consecutive: 1, 2, 5, 6, 14, 18
triple separate consecutive: 1, 2, 9, 10, 22, 23
Note: combinations such as 1, 2, 12, 13, 14, 15 must also be eliminated or else they conflict with the rule that double and triple consecutive combinations to be eliminated.
I'm looking to find how many combinations of the 7.2 million remaining combinations have zero consecutive numbers (all mixed) and only 1 consecutive pair.
Thank you!
import functools
_MIN_SUM = 120
_MAX_SUM = 180
_MIN_NUM = 1
_MAX_NUM = 49
_NUM_CHOICES = 6
_MIN_ODDS = 2
_MAX_ODDS = 4
#functools.lru_cache(maxsize=None)
def f(n, l, s = 0, odds = 0):
if s > _MAX_SUM or odds > _MAX_ODDS:
return 0
if n == 0 :
return int(s >= _MIN_SUM and odds >= _MIN_ODDS)
return sum(f(n-1, i+1, s+i, odds + i % 2) for i in range(l, _MAX_NUM+1))
result = f(_NUM_CHOICES, _MIN_NUM)
print('Number of choices = {}'.format(result))
While my answer should work, I think someone might be able to offer a faster solution.
Consider the following code:
not_allowed = []
for x in range(48):
not_allowed.append([x, x+1, x+2])
# not_allowed = [ [0,1,2], [1,2,3], ... [11,12,13], ... [47,48,49] ]
my_numbers = [[1, 2, 5, 9, 11, 33], [1, 3, 7, 8, 9, 31], [12, 13, 14, 15, 23, 43]]
for x in my_numbers:
for y in not_allowed:
if set(y) <= set(x): # if [1,2,3] is a subset of [1,2,5,9,11,33], etc.
# drop x
This code will remove all instances that contain double consecutive numbers, which is all you really need to check for, because triple, quadruple, etc. all imply double consecutive. Try implementing this and let me know how it works.
The easiest approach is probably to generate and filter. I used numpy to try to vectorize as much of this as I could:
import numpy as np
from itertools import combinations
combos = np.array(list(combinations(range(1, 50), 6))) # build all combos
# combos is shape (13983816, 6)
filt = np.where(np.bincount(np.where(np.abs(
np.subtract(combos[:, :-1], combos[:, 1:])) == 1)[0]) <= 1)[0] # magic!
filtered = combos[filt]
# filtered is shape (12489092, 6)
Breaking down that "magic" line
First we subtract the first five items in the list from the last five items to get the differences between them. We do this for the entire set of combinations in one shot with np.subtract(combos[:, :-1], combos[:, 1:]). Note that itertools.combinations produces sorted combinations, on which this depends.
Next we take the absolute value of these differences to make sure we only look at positive distances between numbers with np.abs(...).
Next we grab the indicies from this operation for the entire dataset that indicate a difference of 1 (consecutive numbers) with np.where(... == 1)[0]. Note that np.where returns a tuple where the first item are all of the rows, and the second item are all of the corresponding columns for our condition. This is important because any row value that shows up more than once tells us that we have more than one consecutive number in that row!
So we count how many times each row shows up in our results with np.bincount(...), which will return something like [5, 4, 4, 4, 3, 2, 1, 0] indicating how many consecutive pairs are in each row of our combinations dataset.
Finally we grab only the row numbers where there are 0 or 1 consecutive values with np.where(... <= 1)[0].
I am returning way more combinations than you seem to indicate, but I feel fairly confident that this is working. By all means, poke holes in it in the comments and I will see if I can find fixes!
Bonus, because it's all vectorized, it's super fast!

Generating random numbers to obtain a fixed sum(python) [duplicate]

This question already has answers here:
Generate random numbers summing to a predefined value
(7 answers)
Closed 4 years ago.
I have the following list:
Sum=[54,1536,36,14,9,360]
I need to generate 4 other lists, where each list will consist of 6 random numbers starting from 0, and the numbers will add upto the values in sum. For eg;
l1=[a,b,c,d,e,f] where a+b+c+d+e+f=54
l2=[g,h,i,j,k,l] where g+h+i+j+k+l=1536
and so on upto l6. And I need to do this in python. Can it be done?
Generating a list of random numbers that sum to a certain integer is a very difficult task. Keeping track of the remaining quantity and generating items sequentially with the remaining available quantity results in a non-uniform distribution, where the first numbers in the series are generally much larger than the others. On top of that, the last one will always be different from zero because the previous items in the list will never sum up to the desired total (random generators usually use open intervals in the maximum). Shuffling the list after generation might help a bit but won't generally give good results either.
A solution could be to generate random numbers and then normalize the result, eventually rounding it if you need them to be integers.
import numpy as np
totals = np.array([54,1536,36,14]) # don't use Sum because sum is a reserved keyword and it's confusing
a = np.random.random((6, 4)) # create random numbers
a = a/np.sum(a, axis=0) * totals # force them to sum to totals
# Ignore the following if you don't need integers
a = np.round(a) # transform them into integers
remainings = totals - np.sum(a, axis=0) # check if there are corrections to be done
for j, r in enumerate(remainings): # implement the correction
step = 1 if r > 0 else -1
while r != 0:
i = np.random.randint(6)
if a[i,j] + step >= 0:
a[i, j] += step
r -= step
Each column of a represents one of the lists you want.
Hope this helps.
This might not be the most efficient way but it will work
totals = [54, 1536, 36, 14]
nums = []
x = np.random.randint(0, i, size=(6,))
for i in totals:
while sum(x) != i: x = np.random.randint(0, i, size=(6,))
nums.append(x)
print(nums)
[array([ 3, 19, 21, 11, 0, 0]), array([111, 155, 224, 511, 457,
78]), array([ 8, 5, 4, 12, 2, 5]), array([3, 1, 3, 2, 1, 4])]
This is a way more efficient way to do this
totals = [54,1536,36,14,9,360, 0]
nums = []
for i in totals:
if i == 0:
nums.append([0 for i in range(6)])
continue
total = i
temp = []
for i in range(5):
val = np.random.randint(0, total)
temp.append(val)
total -= val
temp.append(total)
nums.append(temp)
print(nums)
[[22, 4, 16, 0, 2, 10], [775, 49, 255, 112, 185, 160], [2, 10, 18, 2,
0, 4], [10, 2, 1, 0, 0, 1], [8, 0, 0, 0, 0, 1], [330, 26, 1, 0, 2, 1],
[0, 0, 0, 0, 0, 0]]

Python Recursion in pascal's triangle [duplicate]

As a learning experience for Python, I am trying to code my own version of Pascal's triangle. It took me a few hours (as I am just starting), but I came out with this code:
pascals_triangle = []
def blank_list_gen(x):
while len(pascals_triangle) < x:
pascals_triangle.append([0])
def pascals_tri_gen(rows):
blank_list_gen(rows)
for element in range(rows):
count = 1
while count < rows - element:
pascals_triangle[count + element].append(0)
count += 1
for row in pascals_triangle:
row.insert(0, 1)
row.append(1)
pascals_triangle.insert(0, [1, 1])
pascals_triangle.insert(0, [1])
pascals_tri_gen(6)
for row in pascals_triangle:
print(row)
which returns
[1]
[1, 1]
[1, 0, 1]
[1, 0, 0, 1]
[1, 0, 0, 0, 1]
[1, 0, 0, 0, 0, 1]
[1, 0, 0, 0, 0, 0, 1]
[1, 0, 0, 0, 0, 0, 0, 1]
However, I have no idea where to go from here. I have been banging my head against the wall for hours. I want to emphasize that I do NOT want you to do it for me; just push me in the right direction. As a list, my code returns
[[1], [1, 1], [1, 0, 1], [1, 0, 0, 1], [1, 0, 0, 0, 1], [1, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 1]]
Thanks.
EDIT: I took some good advice, and I completely rewrote my code, but I am now running into another problem. Here is my code.
import math
pascals_tri_formula = []
def combination(n, r):
return int((math.factorial(n)) / ((math.factorial(r)) * math.factorial(n - r)))
def for_test(x, y):
for y in range(x):
return combination(x, y)
def pascals_triangle(rows):
count = 0
while count <= rows:
for element in range(count + 1):
[pascals_tri_formula.append(combination(count, element))]
count += 1
pascals_triangle(3)
print(pascals_tri_formula)
However, I am finding that the output is a bit undesirable:
[1, 1, 1, 1, 2, 1, 1, 3, 3, 1]
How can I fix this?
OK code review:
import math
# pascals_tri_formula = [] # don't collect in a global variable.
def combination(n, r): # correct calculation of combinations, n choose k
return int((math.factorial(n)) / ((math.factorial(r)) * math.factorial(n - r)))
def for_test(x, y): # don't see where this is being used...
for y in range(x):
return combination(x, y)
def pascals_triangle(rows):
result = [] # need something to collect our results in
# count = 0 # avoidable! better to use a for loop,
# while count <= rows: # can avoid initializing and incrementing
for count in range(rows): # start at 0, up to but not including rows number.
# this is really where you went wrong:
row = [] # need a row element to collect the row in
for element in range(count + 1):
# putting this in a list doesn't do anything.
# [pascals_tri_formula.append(combination(count, element))]
row.append(combination(count, element))
result.append(row)
# count += 1 # avoidable
return result
# now we can print a result:
for row in pascals_triangle(3):
print(row)
prints:
[1]
[1, 1]
[1, 2, 1]
Explanation of Pascal's triangle:
This is the formula for "n choose k" (i.e. how many different ways (disregarding order), from an ordered list of n items, can we choose k items):
from math import factorial
def combination(n, k):
"""n choose k, returns int"""
return int((factorial(n)) / ((factorial(k)) * factorial(n - k)))
A commenter asked if this is related to itertools.combinations - indeed it is. "n choose k" can be calculated by taking the length of a list of elements from combinations:
from itertools import combinations
def pascals_triangle_cell(n, k):
"""n choose k, returns int"""
result = len(list(combinations(range(n), k)))
# our result is equal to that returned by the other combination calculation:
assert result == combination(n, k)
return result
Let's see this demonstrated:
from pprint import pprint
ptc = pascals_triangle_cell
>>> pprint([[ptc(0, 0),],
[ptc(1, 0), ptc(1, 1)],
[ptc(2, 0), ptc(2, 1), ptc(2, 2)],
[ptc(3, 0), ptc(3, 1), ptc(3, 2), ptc(3, 3)],
[ptc(4, 0), ptc(4, 1), ptc(4, 2), ptc(4, 3), ptc(4, 4)]],
width = 20)
[[1],
[1, 1],
[1, 2, 1],
[1, 3, 3, 1],
[1, 4, 6, 4, 1]]
We can avoid repeating ourselves with a nested list comprehension:
def pascals_triangle(rows):
return [[ptc(row, k) for k in range(row + 1)] for row in range(rows)]
>>> pprint(pascals_triangle(15))
[[1],
[1, 1],
[1, 2, 1],
[1, 3, 3, 1],
[1, 4, 6, 4, 1],
[1, 5, 10, 10, 5, 1],
[1, 6, 15, 20, 15, 6, 1],
[1, 7, 21, 35, 35, 21, 7, 1],
[1, 8, 28, 56, 70, 56, 28, 8, 1],
[1, 9, 36, 84, 126, 126, 84, 36, 9, 1],
[1, 10, 45, 120, 210, 252, 210, 120, 45, 10, 1],
[1, 11, 55, 165, 330, 462, 462, 330, 165, 55, 11, 1],
[1, 12, 66, 220, 495, 792, 924, 792, 495, 220, 66, 12, 1],
[1, 13, 78, 286, 715, 1287, 1716, 1716, 1287, 715, 286, 78, 13, 1],
[1, 14, 91, 364, 1001, 2002, 3003, 3432, 3003, 2002, 1001, 364, 91, 14, 1]]
Recursively defined:
We can define this recursively (a less efficient, but perhaps more mathematically elegant definition) using the relationships illustrated by the triangle:
def choose(n, k): # note no dependencies on any of the prior code
if k in (0, n):
return 1
return choose(n-1, k-1) + choose(n-1, k)
And for fun, you can see each row take progressively longer to execute, because each row has to recompute nearly each element from the prior row twice each time:
for row in range(40):
for k in range(row + 1):
# flush is a Python 3 only argument, you can leave it out,
# but it lets us see each element print as it finishes calculating
print(choose(row, k), end=' ', flush=True)
print()
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
1 5 10 10 5 1
1 6 15 20 15 6 1
1 7 21 35 35 21 7 1
1 8 28 56 70 56 28 8 1
1 9 36 84 126 126 84 36 9 1
1 10 45 120 210 252 210 120 45 10 1
1 11 55 165 330 462 462 330 165 55 11 1
1 12 66 220 495 792 924 792 495 220 66 12 1
1 13 78 286 715 1287 1716 1716 1287 715 286 78 13 1
1 14 91 364 1001 2002 3003 3432 3003 2002 1001 364 91 14 1
1 15 105 455 1365 3003 5005 6435 6435 5005 3003 1365 455 105 15 1
1 16 120 560 1820 4368 8008 11440 12870 11440 8008 4368 1820 560 120 16 1
1 17 136 680 2380 6188 12376 19448 24310 24310 19448 12376 6188 2380 680 136 17 1
1 18 153 816 3060 8568 18564 31824 43758 48620 43758 31824 18564 8568 3060 816 ...
Ctrl-C to quit when you get tired of watching it, it gets very slow very fast...
I know you want to implement yourself, but the best way for me to explain is to walk through an implementation. Here's how I would do it, and this implementation relies on my fairly complete knowledge of how Python's functions work, so you probably won't want to use this code yourself, but it may get you pointed in the right direction.
def pascals_triangle(n_rows):
results = [] # a container to collect the rows
for _ in range(n_rows):
row = [1] # a starter 1 in the row
if results: # then we're in the second row or beyond
last_row = results[-1] # reference the previous row
# this is the complicated part, it relies on the fact that zip
# stops at the shortest iterable, so for the second row, we have
# nothing in this list comprension, but the third row sums 1 and 1
# and the fourth row sums in pairs. It's a sliding window.
row.extend([sum(pair) for pair in zip(last_row, last_row[1:])])
# finally append the final 1 to the outside
row.append(1)
results.append(row) # add the row to the results.
return results
usage:
>>> for i in pascals_triangle(6):
... print(i)
...
[1]
[1, 1]
[1, 2, 1]
[1, 3, 3, 1]
[1, 4, 6, 4, 1]
[1, 5, 10, 10, 5, 1]
Without using zip, but using generator:
def gen(n,r=[]):
for x in range(n):
l = len(r)
r = [1 if i == 0 or i == l else r[i-1]+r[i] for i in range(l+1)]
yield r
example:
print(list(gen(15)))
output:
[[1], [1, 1], [1, 2, 1], [1, 3, 3, 1], [1, 4, 6, 4, 1], [1, 5, 10, 10, 5, 1], [1, 6, 15, 20, 15, 6, 1], [1, 7, 21, 35, 35, 21, 7, 1], [1, 8, 28, 56, 70, 56, 28, 8, 1], [1, 9, 36, 84, 126, 126, 84, 36, 9, 1], [1, 10, 45, 120, 210, 252, 210, 120, 45, 10, 1], [1, 11, 55, 165, 330, 462, 462, 330, 165, 55, 11, 1], [1, 12, 66, 220, 495, 792, 924, 792, 495, 220, 66, 12, 1], [1, 13, 78, 286, 715, 1287, 1716, 1716, 1287, 715, 286, 78, 13, 1], [1, 14, 91, 364, 1001, 2002, 3003, 3432, 3003, 2002, 1001, 364, 91, 14, 1]]
DISPLAY AS TRIANGLE
To draw it in beautiful triangle(works only for n < 7, beyond that it gets distroted. ref draw_beautiful for n>7)
for n < 7
def draw(n):
for p in gen(n):
print(' '.join(map(str,p)).center(n*2)+'\n')
eg:
draw(10)
output:
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
1 5 10 10 5 1
for any size
since we need to know the max width, we can't make use of generator
def draw_beautiful(n):
ps = list(gen(n))
max = len(' '.join(map(str,ps[-1])))
for p in ps:
print(' '.join(map(str,p)).center(max)+'\n')
example (2) :
works for any number:
draw_beautiful(100)
Here is my attempt:
def generate_pascal_triangle(rows):
if rows == 1: return [[1]]
triangle = [[1], [1, 1]] # pre-populate with the first two rows
row = [1, 1] # Starts with the second row and calculate the next
for i in range(2, rows):
row = [1] + [sum(column) for column in zip(row[1:], row)] + [1]
triangle.append(row)
return triangle
for row in generate_pascal_triangle(6):
print row
Discussion
The first two rows of the triangle is hard-coded
The zip() call basically pairs two adjacent numbers together
We still have to add 1 to the beginning and another 1 to the end because the zip() call only generates the middle of the next row
# combining the insights from Aaron Hall and Hai Vu,
# we get:
def pastri(n):
rows = [[1]]
for _ in range(1, n+1):
rows.append([1] +
[sum(pair) for pair in zip(rows[-1], rows[-1][1:])] +
[1])
return rows
# thanks! learnt that "shape shifting" data,
# can yield/generate elegant solutions.
def pascal(n):
if n==0:
return [1]
else:
N = pascal(n-1)
return [1] + [N[i] + N[i+1] for i in range(n-1)] + [1]
def pascal_triangle(n):
for i in range(n):
print pascal(i)
Beginner Python student here. Here's my attempt at it, a very literal approach, using two For loops:
pascal = [[1]]
num = int(input("Number of iterations: "))
print(pascal[0]) # the very first row
for i in range(1,num+1):
pascal.append([1]) # start off with 1
for j in range(len(pascal[i-1])-1):
# the number of times we need to run this loop is (# of elements in the row above)-1
pascal[i].append(pascal[i-1][j]+pascal[i-1][j+1])
# add two adjacent numbers of the row above together
pascal[i].append(1) # and cap it with 1
print(pascal[i])
Here is an elegant and efficient recursive solution. I'm using the very handy toolz library.
from toolz import memoize, sliding_window
#memoize
def pascals_triangle(n):
"""Returns the n'th row of Pascal's triangle."""
if n == 0:
return [1]
prev_row = pascals_triangle(n-1)
return [1, *map(sum, sliding_window(2, prev_row)), 1]
pascals_triangle(300) takes about 15 ms on a macbook pro (2.9 GHz Intel Core i5). Note that you can't go much higher without increasing the default recursion depth limit.
I am cheating from the popular fibonacci sequence solution. To me, the implementation of Pascal's triangle would have the same concept of fibonacci's. In fibonacci we use a single number at a time and add it up to the previous one. In pascal's triangle use a row at a time and add it up to the previous one.
Here is a complete code example:
>>> def pascal(n):
... r1, r2 = [1], [1, 1]
... degree = 1
... while degree <= n:
... print(r1)
... r1, r2 = r2, [1] + [sum(pair) for pair in zip(r2, r2[1:]) ] + [1]
... degree += 1
Test
>>> pascal(3)
[1]
[1, 1]
[1, 2, 1]
>>> pascal(4)
[1]
[1, 1]
[1, 2, 1]
[1, 3, 3, 1]
>>> pascal(6)
[1]
[1, 1]
[1, 2, 1]
[1, 3, 3, 1]
[1, 4, 6, 4, 1]
[1, 5, 10, 10, 5, 1]
Note: to have the result as a generator, change print(r1) to yield r1.
# call the function ! Indent properly , everything should be inside the function
def triangle():
matrix=[[0 for i in range(0,20)]for e in range(0,10)] # This method assigns 0's to all Rows and Columns , the range is mentioned
div=20/2 # it give us the most middle columns
matrix[0][div]=1 # assigning 1 to the middle of first row
for i in range(1,len(matrix)-1): # it goes column by column
for j in range(1,20-1): # this loop goes row by row
matrix[i][j]=matrix[i-1][j-1]+matrix[i-1][j+1] # this is the formula , first element of the matrix gets , addition of i index (which is 0 at first ) with third value on the the related row
# replacing 0s with spaces :)
for i in range(0,len(matrix)):
for j in range(0,20):
if matrix[i][j]==0: # Replacing 0's with spaces
matrix[i][j]=" "
for i in range(0,len(matrix)-1): # using spaces , the triangle will printed beautifully
for j in range(0,20):
print 1*" ",matrix[i][j],1*" ", # giving some spaces in two sides of the printing numbers
triangle() # calling the function
would print something like this
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
Here is a simple way of implementing the pascal triangle:
def pascal_triangle(n):
myList = []
trow = [1]
y = [0]
for x in range(max(n,0)):
myList.append(trow)
trow=[l+r for l,r in zip(trow+y, y+trow)]
for item in myList:
print(item)
pascal_triangle(5)
Python zip() function returns the zip object, which is the iterator of tuples where the first item in each passed iterator is paired together, and then the second item in each passed iterator are paired together. Python zip is the container that holds real data inside.
Python zip() function takes iterables (can be zero or more), makes an iterator that aggregates items based on the iterables passed, and returns the iterator of tuples.
I did this when i was working with my son on intro python piece. It started off as rather simple piece, when we targeted -
1
1 2
1 2 3
1 2 3 4
However, as soon as we hit the actual algorithm, complexity overshot our expectations. Anyway, we did build this -
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
1 5 10 10 5 1
1 6 15 20 15 6 1
Used some recursion -
def genRow(row:list) :
# print(f"generatig new row below {row}")
# printRow(row)
l = len(row) #2
newRow : list = []
i = 0
# go through the incoming list
while i <= l:
# print(f"working with i = {i}")
# append an element in the new list
newRow.append(1)
# set first element of the new row to 1
if i ==0:
newRow[i] = 1
# print(f"1:: newRow = {newRow}")
# if the element is in the middle somewhere, add the surroundng two elements in
# previous row to get the new element
# e.g. row 3[2] = row2[1] + row2[2]
elif i <= l-1:
# print(f"2:: newRow = {newRow}")
newRow[i] = row[i-1] + row[i]
else:
# print(f"3 :: newRow = {newRow}")
newRow[i] = 1
i+=1
# print(newRow)
return newRow
def printRow(mx : int, row:list):
n = len(row)
spaces = ' ' *((mx - n)*2)
print(spaces,end=' ')
for i in row:
print(str(i) + ' ',end = ' ')
print(' ')
r = [1,1]
mx = 7
printRow(mx,[1])
printRow(mx,r)
for a in range(1,mx-1):
# print(f"working for Row = {a}")
if len(r) <= 2:
a1 = genRow(r)
r=a1
else:
a2 = genRow(a1)
a1 = a2
printRow(mx,a1)
Hopefully it helps.

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