We Keep Coding

How to use append in for loop for multiple value list when only a single value is returning?

I am trying to create a function that when passed a list of names of any length, will return a list of only those names that are exactly four characters in length. I want the output list to be comma delimited.
I have attached code of what I have tried. I tried to get the function to iterate through the list parameter passed to the function 'x' and then if the length of that list value is four characters long, add it to the output list.
I expected the output list to be ['Ryan','Mary'] but it's just ['Ryan'] as is.
I'm new to all this and don't understand why what I've done isn't working.
output = []
def friend(x):
for i in range(len(x)):
if len(x[i]) == 4:
return output.append(x[i])
list = ["Ryan","Kieran","Jason","Mary"]
friend(list)
print(output)

You don't want to return so early. you are not giving your function enough passes through your list to get more than 1 element with 4 letters. You want to take time to build your list before returning it. I would build up the output list then, once we are done looping entirely, return it:
def friend(x):
output = []
for i in range(len(x)):
if len(x[i]) == 4:
output.append(x[i])
return output
list = ["Ryan","Kieran","Jason","Mary"]
myOutput = friend(list)
print(myOutput)```

There are actually numerous ways to tackle this issue
One way is list comprehension:
list = ["Ryan","Kieran","Jason","Mary"]
print([name for name in list if len(name) == 4]) # iterate list using variable name, check if len(name) == 4, and add it to the list if it's true
Another way is making use of yield:
def friend(x):
for i in range(len(x)):
if len(x[i]) == 4:
yield x[i] # note that this returns a generator, not a list. Basically, you can only iterate once

Another way, using list comprehension, you can shorten your function like :
def friend(x):
return [k for k in x if len(k)==4]
list_ = ["Ryan","Kieran","Jason","Mary"]
print(friend(list_))
OUTPUT :
['Ryan', 'Mary']

Firstly I want to add that you should not use list (and other types names) as variable name. Also alternatively to list comprehension you might use filter function, example usage:
lst = ["Ryan","Kieran","Jason","Mary"]
output = list(filter(lambda x:len(x)==4,lst))
print(output) #['Ryan', 'Mary']
Note that filter requires function which takes element of lst as argument and returns False (jettison) or True (retain). I want to note that personally I find list comprehension more readable in this case and I just wanted to show function filter which could be encountered in situation requiring choice of certain elements from list.

You can use a list comprehension
output = [name for name in list_of_names if len(name)==4]
print(output)

Check number not a sum of 2 ints on a list

Given a list of integers, I want to check a second list and remove from the first only those which can not be made from the sum of two numbers from the second. So given a = [3,19,20] and b = [1,2,17], I'd want [3,19].
Seems like a a cinch with two nested loops - except that I've gotten stuck with break and continue commands.
Here's what I have:
def myFunction(list_a, list_b):
for i in list_a:
for a in list_b:
for b in list_b:
if a + b == i:
break
else:
continue
break
else:
continue
list_a.remove(i)
return list_a
I know what I need to do, just the syntax seems unnecessarily confusing. Can someone show me an easier way? TIA!

You can do like this,
In [13]: from itertools import combinations
In [15]: [item for item in a if item in [sum(i) for i in combinations(b,2)]]
Out[15]: [3, 19]
combinations will give all possible combinations in b and get the list of sum. And just check the value is present in a
Edit
If you don't want to use the itertools wrote a function for it. Like this,
def comb(s):
for i, v1 in enumerate(s):
for j in range(i+1, len(s)):
yield [v1, s[j]]
result = [item for item in a if item in [sum(i) for i in comb(b)]]

Comments on code:
It's very dangerous to delete elements from a list while iterating over it. Perhaps you could append items you want to keep to a new list, and return that.
Your current algorithm is O(nm^2), where n is the size of list_a, and m is the size of list_b. This is pretty inefficient, but a good start to the problem.
Thee's also a lot of unnecessary continue and break statements, which can lead to complicated code that is hard to debug.
You also put everything into one function. If you split up each task into different functions, such as dedicating one function to finding pairs, and one for checking each item in list_a against list_b. This is a way of splitting problems into smaller problems, and using them to solve the bigger problem.
Overall I think your function is doing too much, and the logic could be condensed into much simpler code by breaking down the problem.
Another approach:
Since I found this task interesting, I decided to try it myself. My outlined approach is illustrated below.
1. You can first check if a list has a pair of a given sum in O(n) time using hashing:
def check_pairs(lst, sums):
lookup = set()
for x in lst:
current = sums - x
if current in lookup:
return True
lookup.add(x)
return False
2. Then you could use this function to check if any any pair in list_b is equal to the sum of numbers iterated in list_a:
def remove_first_sum(list_a, list_b):
new_list_a = []
for x in list_a:
check = check_pairs(list_b, x)
if check:
new_list_a.append(x)
return new_list_a
Which keeps numbers in list_a that contribute to a sum of two numbers in list_b.
3. The above can also be written with a list comprehension:
def remove_first_sum(list_a, list_b):
return [x for x in list_a if check_pairs(list_b, x)]
Both of which works as follows:
>>> remove_first_sum([3,19,20], [1,2,17])
[3, 19]
>>> remove_first_sum([3,19,20,18], [1,2,17])
[3, 19, 18]
>>> remove_first_sum([1,2,5,6],[2,3,4])
[5, 6]
Note: Overall the algorithm above is O(n) time complexity, which doesn't require anything too complicated. However, this also leads to O(n) extra auxiliary space, because a set is kept to record what items have been seen.

You can do it by first creating all possible sum combinations, then filtering out elements which don't belong to that combination list
Define the input lists
>>> a = [3,19,20]
>>> b = [1,2,17]
Next we will define all possible combinations of sum of two elements
>>> y = [i+j for k,j in enumerate(b) for i in b[k+1:]]
Next we will apply a function to every element of list a and check if it is present in above calculated list. map function can be use with an if/else clause. map will yield None in case of else clause is successful. To cater for this we can filter the list to remove None values
>>> list(filter(None, map(lambda x: x if x in y else None,a)))
The above operation will output:
>>> [3,19]
You can also write a one-line by combining all these lines into one, but I don't recommend this.

you can try something like that:
a = [3,19,20]
b= [1,2,17,5]
n_m_s=[]
data=[n_m_s.append(i+j) for i in b for j in b if i+j in a]
print(set(n_m_s))
print("after remove")
final_data=[]
for j,i in enumerate(a):
if i not in n_m_s:
final_data.append(i)
print(final_data)
output:
{19, 3}
after remove
[20]

How to merge n lists together item by item for each list

I want to make one large list for entering into a database with values from 4 different lists. I want it to be like
[[list1[0], list2[0], list3[0], list4[0]], [list1[1], list2[1], list3[1], list4[1]], etc.....]
Another issue is that currently the data is received like this:
[ [ [list1[0], list1[1], [list1[3]]], [[list2[0]]], etc.....]
I've tried looping through each list using indexs and adding them to a new list based on those but it hasn't worked, I'm pretty sure it didn't work because some of the lists are different lengths (they're not meant to be but it's automated data so sometimes there's a mistake).
Anyone know what's the best way to go about this? Thanks.

First list can be constructed using zip function as follows (for 4 lists):
list1 = [1,2,3,4]
list2 = [5,6,7,8]
list3 = [9,10,11,12]
list4 = [13,14,15,16]
res = list(zip(list1,list2,list3,list4))
For arbitrtary number of lists stored in another list u can use *-notation to unpack outer list:
lists = [...]
res = list(zip(*lists))
To construct list of lists for zipping from you data in second issue use flatten concept to it and then zip:
def flatten(l):
res = []
for el in l:
if(isinstance(el, list)):
res += flatten(el)
else:
res.append(el)
return res
auto_data = [...]
res = list(zip(*[flatten(el) for el in auto_data]))
Some clarification at the end:
zip function construct results of the smallest length between all inputs, then you need to extend data in list comprehension in last code string to be one length to not lose some info.

So if I understand correctly, this is your input:
l = [[1.1,1.2,1.3,1.4],[2.1,2.2,2.3,2.4],[3.1,3.2,3.3,3.4],[4.1,4.2,4.3,4.4]]
and you would like to have this output
[[1.1,2.1,3.1,4.1],...]
If so, this could be done by using zip
zip(*l)

Make a for loop which only gives you the counter variable. Use that variable to index the lists. Make a temporary list , fill it up with the values from the other lists. Add that list to the final one. With this you will et the desired structure.
nestedlist = []
for counter in range(0,x):
temporarylist = []
temporarylist.append(firstlist[counter])
temporarylist.append(secondlist[counter])
temporarylist.append(thirdlist[counter])
temporarylist.append(fourthlist[counter])
nestedlist.append(temporarylist)
If all the 4 lists are the same length you can use this code to make it even nicer.
nestedlist = []
for counter in range(0,len(firstlist)): #changed line
temporarylist = []
temporarylist.append(firstlist[counter])
temporarylist.append(secondlist[counter])
temporarylist.append(thirdlist[counter])
temporarylist.append(fourthlist[counter])
nestedlist.append(temporarylist)

This comprehension should work, with a little help from zip:
mylist = [i for i in zip(list1, list2, list3, list4)]
But this assumes all the list are of the same length. If that's not the case (or you're not sure of that), you can "pad" them first, to be of same length.
def padlist(some_list, desired_length, pad_with):
while len(some_list) < desired_length:
some_list.append(pad_with)
return some_list
list_of_lists = [list1, list2, list3, list4]
maxlength = len(max(list_of_lists, key=len))
list_of_lists = [padlist(l, maxlength, 0) for l in list_of_lists]
And now do the above comprehension statement, works well in my testing of it
mylist = [i for i in zip(*list_of_lists)]

If the flatten concept doesn't work, try this out:
import numpy as np
myArray = np.array([[list1[0], list2[0], list3[0], list4[0]], [list1[1], list2[1], list3[1], list4[1]]])
np.hstack(myArray)
Also that one should work:
np.concatenate(myArray, axis=1)
Just for those who will search for the solution of this problem when lists are of the same length:
def flatten(lists):
results = []
for numbers in lists:
for output in numbers:
results.append(output)
return results
print(flatten(n))

Python List Indexing or Appending?

What is the best way to add values to a List in terms of processing time, memory usage and just generally what is the best programming option.
list = []
for i in anotherArray:
list.append(i)
or
list = range(len(anotherArray))
for i in list:
list[i] = anotherArray[i]
Considering that anotherArray is for example an array of Tuples. (This is just a simple example)

It really depends on your use case. There is no generic answer here as it depends on what you are trying to do.
In your example, it looks like you are just trying to create a copy of the array, in which case the best way to do this would be to use copy:
from copy import copy
list = copy(anotherArray)
If you are trying to transform the array into another array you should use list comprehension.
list = [i[0] for i in anotherArray] # get the first item from tuples in anotherArray
If you are trying to use both indexes and objects, you should use enumerate:
for i, j in enumerate(list)
which is much better than your second example.
You can also use generators, lambas, maps, filters, etc. The reason all of these possibilities exist is because they are all "better" for different reasons. The writters of python are pretty big on "one right way", so trust me, if there was one generic way which was always better, that is the only way that would exist in python.
Edit: Ran some results of performance for tuple swap and here are the results:
comprehension: 2.682028295999771
enumerate: 5.359116118001111
for in append: 4.177091988000029
for in indexes: 4.612594166001145
As you can tell, comprehension is usually the best bet. Using enumerate is expensive.
Here is the code for the above test:
from timeit import timeit
some_array = [(i, 'a', True) for i in range(0,100000)]
def use_comprehension():
return [(b, a, i) for i, a, b in some_array]
def use_enumerate():
lst = []
for j, k in enumerate(some_array):
i, a, b = k
lst.append((b, a, i))
return lst
def use_for_in_with_append():
lst = []
for i in some_array:
i, a, b = i
lst.append((b, a, i))
return lst
def use_for_in_with_indexes():
lst = [None] * len(some_array)
for j in range(len(some_array)):
i, a, b = some_array[j]
lst[j] = (b, a, i)
return lst
print('comprehension:', timeit(use_comprehension, number=200))
print('enumerate:', timeit(use_enumerate, number=200))
print('for in append:', timeit(use_for_in_with_append, number=200))
print('for in indexes:', timeit(use_for_in_with_indexes, number=200))
Edit2:
It was pointed out to me the the OP just wanted to know the difference between "indexing" and "appending". Really, those are used for two different use cases as well. Indexing is for replacing objects, whereas appending is for adding. However, in a case where the list starts empty, appending will always be better because the indexing has the overhead of creating the list initially. You can see from the results above that indexing is slightly slower, mostly because you have to create the first list.

Best way is list comprehension :
my_list=[i for i in anotherArray]
But based on your problem you can use a generator expression (is more efficient than list comprehension when you just want to loop over your items and you don't need to use some list methods like indexing or len or ... )
my_list=(i for i in anotherArray)

I would actually say the best is a combination of index loops and value loops with enumeration:
for i, j in enumerate(list): # i is the index, j is the value, can't go wrong

What does a for loop within a list do in Python?

Can someone explain the last line of this Python code snippet to me?
Cell is just another class. I don't understand how the for loop is being used to store Cell objects into the Column object.
class Column(object):
def __init__(self, region, srcPos, pos):
self.region = region
self.cells = [Cell(self, i) for i in xrange(region.cellsPerCol)] #Please explain this line.

The line of code you are asking about is using list comprehension to create a list and assign the data collected in this list to self.cells. It is equivalent to
self.cells = []
for i in xrange(region.cellsPerCol):
self.cells.append(Cell(self, i))
Explanation:
To best explain how this works, a few simple examples might be instructive in helping you understand the code you have. If you are going to continue working with Python code, you will come across list comprehension again, and you may want to use it yourself.
Note, in the example below, both code segments are equivalent in that they create a list of values stored in list myList.
For instance:
myList = []
for i in range(10):
myList.append(i)
is equivalent to
myList = [i for i in range(10)]
List comprehensions can be more complex too, so for instance if you had some condition that determined if values should go into a list you could also express this with list comprehension.
This example only collects even numbered values in the list:
myList = []
for i in range(10):
if i%2 == 0: # could be written as "if not i%2" more tersely
myList.append(i)
and the equivalent list comprehension:
myList = [i for i in range(10) if i%2 == 0]
Two final notes:
You can have "nested" list comrehensions, but they quickly become hard to comprehend :)
List comprehension will run faster than the equivalent for-loop, and therefore is often a favorite with regular Python programmers who are concerned about efficiency.
Ok, one last example showing that you can also apply functions to the items you are iterating over in the list. This uses float() to convert a list of strings to float values:
data = ['3', '7.4', '8.2']
new_data = [float(n) for n in data]
gives:
new_data
[3.0, 7.4, 8.2]

It is the same as if you did this:
def __init__(self, region, srcPos, pos):
self.region = region
self.cells = []
for i in xrange(region.cellsPerCol):
self.cells.append(Cell(self, i))
This is called a list comprehension.