How to create the smallest possible number remaining zeros? - python

I was given a task to create the smallest number from two numbers remaining zeros.
But I cannot solve the task because of my code is not remaining all zeros appropriate. If the input is
245
36
the output is 23456 and that's correct. But with input:
40
305
it outputs: [0,0,3,4,5]. But should be 30045.
Here's my code:
f1 = [int(x) for x in input()]
f2 = [int(y) for y in input()]
f = f1+f2
for each in range(len(f)):
for eacc in range(each+1, len(f)):
if f[each] > f[eacc]:
f[each], f[eacc] = f[eacc], f[each]
for zero in range(len(f)):
if f[zero] == 0 and f[0] > 0:
f.remove(0)
f.insert(zero+1, 0)
break
print(f)

n1 = 40
n2 = 305
# sort lexicograhically
ns = sorted(str(n1) + str(n2))
# move the first non-zero element to the start
i = ns.count('0')
if 0 < i < len(ns):
ns[0:0] = ns.pop(i)

Remove all the zeros. Get all the permutations and find the min. Then add zero from index 1
from itertools import permutations
a=list('40')+list('305')
a=list(map(int,a))
num_of_zero=a.count(0) # get the count of zeros
for i in range(num_of_zero):
a.pop(a.index(0))
new_list=list(min(list(permutations(a)))) # get all the permutations
for i in range(num_of_zero):
new_list.insert(1,0) # insert zeros at index 1 shifting all element to the right
print(''.join(map(str,new_list)))#30045
Without permutations sorted will also work
a=list('40')+list('305')
a=list(map(int,a))
num_of_zero=a.count(0)
for i in range(num_of_zero):
a.pop(a.index(0))
new_list=sorted(a)
for i in range(num_of_zero):
new_list.insert(1,0)
print(''.join(map(str,new_list)))#30045
Using numpy
import numpy as np
a=list('40')+list('305')
a=list(map(int,a))
num_of_zero=a.count(0)
new_list=sorted(a) # sorted will return [0,0,3,4,5]
I = np.nonzero(new_list) #return non_zero_array
if(len(I[0])>0):
first_non_zero_value=new_list.pop(I[0][0]) #get index of first element
new_list.insert(0,first_non_zero_value)
print(''.join(map(str,new_list)))#30045

Here you could use itertools.permutations. First I would use map to change the ints to lists. Next I would concatenate them and have one list of 5 ints. Then using permutations we could generate all possible numbers that could be made from these 5 ints. From our new list we could now take the min using *list comprehension to filter out any item that begins with 0 using if i[0]. Since it is a tuple we have to convert the elements to str then we can join them into an int and print
from itertools import permutations
a = 40
b = 305
a = [*map(int, str(a))]
b = [*map(int, str(b))]
c = a + b
combo = list(permutations(c, len(c)))
res = min([i for i in combo if i[0]])
res = [str(i) for i in res]
print(''.join(res))
# 30045
If a = 0, b = 0 is a potential input, a try/except block would be neccessary
try:
res = min([i for i in combo if i[0]])
res = [str(i) for i in res]
print(int(''.join(res)))
except ValueError:
res = 0
print(res)

Related

TypeError: zip argument #1 must support iteration in Python

I'm trying to write code to find two indices when a value changes from 0 to 1 and save that value in a variable called idx. Then, the two rows before and after the index should be extracted and processed. The code for extracting the rows is included below:
df1=pd.DataFrame({'A':[1,3,4,7,8,11,1,15,20,15,16,87],
'B':[1,3,4,6,8,11,1,19,20,15,16,87],
'flag':[0,0,0,0,1,1,1,0,0,1,0,0]})
N = 2
s = [x for s, e in zip(idx-N,idx) for x in range(s, e+1)]
df_before_2rows=df1.loc[df1.index.intersection(s)]
This works. But, if I run this in a for-loop that processes each index one-by-one then I get an error:
df1=pd.DataFrame({'A':[1,3,4,7,8,11,1,15,20,15,16,87],
'B':[1,3,4,6,8,11,1,19,20,15,16,87],
'flag':[0,0,0,0,1,1,1,0,0,1,0,0]})
for item in idx:
N = 2
s = [x for s, e in zip(item-N,item) for x in range(s, e+1)]
df_before_2rows=df1.loc[df1.index.intersection(s)]
TypeError: zip argument #1 must support iteration
Main goal is to get two rows before when flag change from 0 to 1 and process ,and then go next check if flag change from 0 to 1 then do same as above
IIUC, you can choose a different approach using groupby with cumsum of diff:
df = pd.DataFrame({'A':[1,3,4,7,8,11,1,15,20,15,16,87],
'B':[1,3,4,6,8,11,1,19,20,15,16,87],
'flag':[0,0,0,0,1,1,1,0,0,1,0,0]})
for _, i in df.groupby(df["flag"].shift(1).diff().eq(1).cumsum()):
if i["flag"].eq(1).any(): # this is done to skip the last group with no flag of 1
print (i.tail(3))
# do your thing with i.tail(3)...
EDIT using your original method:
idx = [4,8] # I assume you retrieved the idx already
for item in idx:
N = 2
df_before_2rows=df.loc[range(item-N,item+1)]
print (df_before_2rows)
item is an element of idx; item-N is also just a number, hence the error.
for item in idx:
N = 2
s = [x for s, e in zip(item-N,item) for x in range(s, e+1)]
simplifies to:
for item in idx:
N = 2
# s = [x for x in range(item-N, item+1)]
s = list(range(item-N, item+1))
# s = np.arange(item-N, item+1)

How to check if 2 different values are from the same list and obtaining the list name

** I modified the entire question **
I have an example list specified below and i want to find if 2 values are from the same list and i wanna know which list both the value comes from.
list1 = ['a','b','c','d','e']
list2 = ['f','g','h','i','j']
c = 'b'
d = 'e'
i used for loop to check whether the values exist in the list however not sure how to obtain which list the value actually is from.
for x,y in zip(list1,list2):
if c and d in x or y:
print(True)
Please advise if there is any work around.
First u might want to inspect the distribution of values and sizes where you can improve the result with the least effort like this:
df_inspect = df.copy()
df_inspect["size.value"] = ["size.value"].map(lambda x: ''.join(y.upper() for y in x if x.isalpha() if y != ' '))
df_inspect = df_inspect.groupby(["size.value"]).count().sort_values(ascending=False)
Then create a solution for the most occuring size category, here "Wide"
long = "adasda, 9.5 W US"
short = "9.5 Wide"
def get_intersection(s1, s2):
res = ''
l_s1 = len(s1)
for i in range(l_s1):
for j in range(i + 1, l_s1):
t = s1[i:j]
if t in s2 and len(t) > len(res):
res = t
return res
print(len(get_intersection(long, short)) / len(short) >= 0.6)
Then apply the solution to the dataframe
df["defective_attributes"] = df.apply(lambda x: len(get_intersection(x["item_name.value"], x["size.value"])) / len(x["size.value"]) >= 0.6)
Basically, get_intersection search for the longest intersection between the itemname and the size. Then takes the length of the intersection and says, its not defective if at least 60% of the size_value are also in the item_name.

Exclude zeros in collections.Counter in Python

Is there a way that collections.Counter doesn't count/ignores a given value (here 0):
from collections import Counter
import numpy as np
idx = np.random.randint(4, size=(100,100))
most_common = np.zeros(100)
num_most_common = np.zeros(100)
for i in range(100):
most_common[i], num_most_common[i] = Counter(idx[i, :]).most_common(1)[0]
So if 0 is the most common value it should give the second most common value. In addition, is there a way to avoid the for loop in this case?
For positive numbers, we can use vectorized-bincount - bincount2D_vectorized -
# https://stackoverflow.com/a/46256361/ #Divakar
def bincount2D_vectorized(a):
N = a.max()+1
a_offs = a + np.arange(a.shape[0])[:,None]*N
return np.bincount(a_offs.ravel(), minlength=a.shape[0]*N).reshape(-1,N)
# Get binned counts per row, with each number representing a bin
c = bincount2D_vectorized(idx)
# Skip the first element, as that represents counts for 0s.
# Get most common element and count per row
most_common = c[:,1:].argmax(1)+1
num_most_common = c[:,1:].max(1)
# faster : num_most_common = c[np.arange(len(most_common)),most_common]
For generic int numbers, we could extend like so -
s = idx.min()
c = bincount2D_vectorized(idx-s)
c[:,-s] = 0
most_common = c.argmax(1)
num_most_common = c[np.arange(len(most_common)),most_common]
most_common += s
You can do the following, using a generator to only count something if it is not 0.
most_common = np.array([Counter(x for x in r if x).most_common(1)[0][0] for r in idx])
num_most_common = np.array([Counter(x for x in r if x).most_common(1)[0][1] for r in idx])
or even
count = np.array([Counter(x for x in r if x).most_common(1)[0] for r in idx])
most_common = count[:,0]
num_most_common = count[:,1]

How to reduce a collection of ranges to a minimal set of ranges [duplicate]

This question already has answers here:
Union of multiple ranges
(5 answers)
Closed 7 years ago.
I'm trying to remove overlapping values from a collection of ranges.
The ranges are represented by a string like this:
499-505 100-115 80-119 113-140 500-550
I want the above to be reduced to two ranges: 80-140 499-550. That covers all the values without overlap.
Currently I have the following code.
cr = "100-115 115-119 113-125 80-114 180-185 500-550 109-120 95-114 200-250".split(" ")
ar = []
br = []
for i in cr:
(left,right) = i.split("-")
ar.append(left);
br.append(right);
inc = 0
for f in br:
i = int(f)
vac = []
jnc = 0
for g in ar:
j = int(g)
if(i >= j):
vac.append(j)
del br[jnc]
jnc += jnc
print vac
inc += inc
I split the array by - and store the range limits in ar and br. I iterate over these limits pairwise and if the i is at least as great as the j, I want to delete the element. But the program doesn't work. I expect it to produce this result: 80-125 500-550 200-250 180-185
For a quick and short solution,
from operator import itemgetter
from itertools import groupby
cr = "499-505 100-115 80-119 113-140 500-550".split(" ")
fullNumbers = []
for i in cr:
a = int(i.split("-")[0])
b = int(i.split("-")[1])
fullNumbers+=range(a,b+1)
# Remove duplicates and sort it
fullNumbers = sorted(list(set(fullNumbers)))
# Taken From http://stackoverflow.com/questions/2154249
def convertToRanges(data):
result = []
for k, g in groupby(enumerate(data), lambda (i,x):i-x):
group = map(itemgetter(1), g)
result.append(str(group[0])+"-"+str(group[-1]))
return result
print convertToRanges(fullNumbers)
#Output: ['80-140', '499-550']
For the given set in your program, output is ['80-125', '180-185', '200-250', '500-550']
Main Possible drawback of this solution: This may not be scalable!
Let me offer another solution that doesn't take time linearly proportional to the sum of the range sizes. Its running time is linearly proportional to the number of ranges.
def reduce(range_text):
parts = range_text.split()
if parts == []:
return ''
ranges = [ tuple(map(int, part.split('-'))) for part in parts ]
ranges.sort()
new_ranges = []
left, right = ranges[0]
for range in ranges[1:]:
next_left, next_right = range
if right + 1 < next_left: # Is the next range to the right?
new_ranges.append((left, right)) # Close the current range.
left, right = range # Start a new range.
else:
right = max(right, next_right) # Extend the current range.
new_ranges.append((left, right)) # Close the last range.
return ' '.join([ '-'.join(map(str, range)) for range in new_ranges ]
This function works by sorting the ranges, then looking at them in order and merging consecutive ranges that intersect.
Examples:
print(reduce('499-505 100-115 80-119 113-140 500-550'))
# => 80-140 499-550
print(reduce('100-115 115-119 113-125 80-114 180-185 500-550 109-120 95-114 200-250'))
# => 80-125 180-185 200-250 500-550

How to cycle through the index of an array?

line 14 is where my main problem is.i need to cycle through each item in the array and use it's index to determine whether or not it is a multiple of four so i can create proper spacing for binary numbers.
def decimalToBinary(hu):
bits = []
h = []
while hu > 0:
kla = hu%2
bits.append(kla)
hu = int(hu/2)
for i in reversed(bits):
h.append(i)
if len(h) <= 4:
print (''.join(map(str,h)))
else:
for j in range(len(h)):
h.index(1) = h.index(1)+1
if h.index % 4 != 0:
print (''.join(map(str,h)))
elif h.index % 4 == 0:
print (' '.join(map(str,h)))
decimalToBinary( 23 )
If what you're looking for is the index of the list from range(len(h)) in the for loop, then you can change that line to for idx,j in enumerate(range(len(h))): where idx is the index of the range.
This line h.index(1) = h.index(1)+1 is incorrect. Modified your function, so at least it executes and generates an output, but whether it is correct, i dont know. Anyway, hope it helps:
def decimalToBinary(hu):
bits = []
h = []
while hu > 0:
kla = hu%2
bits.append(kla)
hu = int(hu/2)
for i in reversed(bits):
h.append(i)
if len(h) <= 4:
print (''.join(map(str,h)))
else:
for j in range(len(h)):
h_index = h.index(1)+1 # use h_index variable instead of h.index(1)
if h_index % 4 != 0:
print (''.join(map(str,h)))
elif h_index % 4 == 0:
print (' '.join(map(str,h)))
decimalToBinary( 23 )
# get binary version to check your result against.
print(bin(23))
This results:
#outout from decimalToBinary
10111
10111
10111
10111
10111
#output from bin(23)
0b10111
You're trying to join the bits to string and separate them every 4 bits. You could modify your code with Marcin's correction (by replacing the syntax error line and do some other improvements), but I suggest doing it more "Pythonically".
Here's my version:
def decimalToBinary(hu):
bits = []
while hu > 0:
kla = hu%2
bits.append(kla)
hu = int(hu/2)
h = [''.join(map(str, bits[i:i+4])) for i in range(0,len(bits),4)]
bu = ' '.join(h)
print bu[::-1]
Explanation for the h assignment line:
range(0,len(bits),4): a list from 0 to length of bits with step = 4, eg. [0, 4, 8, ...]
[bits[i:i+4] for i in [0, 4, 8]: a list of lists whose element is every four elements from bits
eg. [ [1,0,1,0], [0,1,0,1] ...]
[''.join(map(str, bits[i:i+4])) for i in range(0,len(bits),4)]: convert the inner list to string
bu[::-1]: reverse the string
If you are learning Python, it's good to do your way. As #roippi pointed out,
for index, value in enumerate(h):
will give you access to both index and value of member of h in each loop.
To group 4 digits, I would do like this:
def decimalToBinary(num):
binary = str(bin(num))[2:][::-1]
index = 0
spaced = ''
while index + 4 < len(binary):
spaced += binary[index:index+4]+' '
index += 4
else:
spaced += binary[index:]
return spaced[::-1]
print decimalToBinary(23)
The result is:
1 0111

Categories