We Keep Coding

Trouble printing strings and percentages - python

Here's my Code:
from collections import Counter
Value = (3, 19, 23, 27, 36, 10, 25, 29, 39, 41, 9, 16, 20, 31, 34, 2, 8, 9, 33, 37, 4, 21, 27, 28, 35, 2, 12, 21, 23, 38, 4, 5, 12, 16, 20, 1, 8, 21, 33, 38, 1, 9, 12, 18, 36, 5, 17, 20, 22, 25, 14, 21, 27, 31, 41, 11, 12, 23, 28, 41, 9, 22, 25, 34, 35, 5, 12, 19, 22, 39, 5, 13, 14, 17, 33, 4, 16, 26, 27, 37, 9, 18, 31, 34, 39, 9, 17, 33, 35, 40, 16, 20, 22, 37, 40, 2, 14, 19, 22, 32, 4, 5, 21, 29, 36, 2, 9, 22, 32, 35, 9, 13, 24, 36, 40, 3, 7, 9, 10, 28, 4, 20, 21, 23, 41, 12, 18, 26, 28, 34, 3, 23, 24, 27, 37, 4, 5, 26, 34, 36, 3, 5, 15, 25, 34, 4, 7, 9, 23, 32, 5, 7, 19, 29, 34, 2, 4, 25, 34, 38, 1, 4, 24, 32, 33, 20, 23, 31, 37, 40, 1, 20, 31, 36, 37, 1, 18, 30, 32, 41, 4, 8, 24, 26, 33, 3, 12, 13, 27, 34, 3, 6, 16, 24, 28, 10, 17, 20, 30, 32, 8, 10, 27, 28, 29, 6, 21, 23, 30, 32, 1, 22, 29, 33, 36, 1, 6, 9, 28, 30, 9, 12, 14, 19, 31, 1, 2, 10, 29, 37, 2, 7, 19, 20, 27, 7, 9, 30, 35, 37, 14, 23, 25, 29, 30, 4, 11, 19, 27, 32, 11, 15, 23, 26, 35, 14, 18, 22, 26, 28, 4, 17, 18, 23, 40, 10, 11, 16, 37, 39, 2, 12, 15, 27, 29, 3, 11, 20, 21, 26, 11, 17, 29, 31, 38, 9, 14, 15, 31, 32, 14, 25, 29, 35, 40, 1, 5, 8, 17, 27, 6, 14, 16, 18, 20, 13, 15, 29, 33, 35, 10, 18, 26, 38, 41, 13, 16, 20, 26, 27, 4, 12, 27, 32, 33, 7, 10, 26, 30, 31, 5, 7, 17, 37, 38, 3, 6, 10, 27, 38, 5, 15, 23, 33, 36, 11, 15, 31, 34, 38, 8, 9, 11, 15, 25, 6, 7, 21, 30, 33, 4, 17, 21, 34, 35, 3, 8, 16, 22, 34, 5, 23, 28, 31, 39, 7, 18, 24, 33, 40, 5, 24, 30, 38, 41, 3, 11, 25, 27, 37, 19, 26, 34, 36, 40, 20, 27, 30, 32, 38, 1, 16, 17, 26, 30, 6, 11, 14, 17, 28, 11, 18, 27, 33, 38, 4, 6, 7, 8, 25, 7, 28, 30, 35, 39, 20, 27, 31, 38, 41, 18, 27, 35, 37, 38, 17, 30, 32, 38, 39, 11, 12, 24, 33, 36, 1, 3, 8, 25, 34, 2, 3, 7, 15, 35, 8, 18, 19, 27, 32, 1, 3, 11, 15, 26, 15, 17, 21, 29, 36, 4, 16, 22, 24, 25, 3, 4, 19, 31, 35, 1, 15, 26, 33, 35, 8, 15, 29, 39, 40, 14, 19, 27, 32, 34, 8, 12, 22, 23, 28, 13, 28, 29, 31, 40, 1, 4, 16, 18, 26, 5, 8, 25, 31, 39, 5, 8, 16, 32, 33, 6, 11, 15, 21, 37, 19, 27, 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24, 31, 36, 5, 11, 12, 15, 37, 2, 15, 16, 17, 20, 8, 10, 16, 38, 39, 2, 12, 14, 16, 39, 15, 17, 27, 31, 38, 1, 3, 16, 32, 35, 6, 14, 16, 23, 30, 12, 22, 31, 34, 37, 3, 10, 12, 22, 39, 4, 20, 28, 38, 39, 2, 7, 18, 35, 38, 6, 7, 10, 16, 31, 2, 7, 13, 34, 38, 17, 18, 24, 26, 29, 11, 17, 21, 26, 35, 2, 3, 12, 29, 31, 13, 15, 20, 25, 38, 15, 22, 30, 34, 39, 18, 22, 35, 37, 39, 3, 13, 14, 35, 37, 1, 5, 30, 31, 32, 6, 7, 12, 28, 34, 1, 3, 16, 23, 32, 1, 7, 8, 13, 22, 1, 4, 11, 20, 36, 6, 14, 27, 28, 29, 11, 14, 24, 25, 38, 1, 12, 26, 28, 29, 1, 6, 8, 22, 37, 5, 11, 22, 35, 37, 3, 7, 11, 15, 34, 4, 7, 22, 31, 33, 2, 7, 11, 19, 29, 18, 19, 21, 22, 25, 13, 17, 18, 19, 34, 1, 20, 24, 30, 38, 11, 13, 14, 35, 36, 3, 11, 28, 31, 34, 2, 6, 11, 27, 36, 1, 9, 11, 16, 25, 15, 16, 20, 26, 38, 4, 6, 31, 34, 36, 8, 14, 19, 23, 31, 11, 22, 23, 28, 39, 2, 16, 19, 36, 38, 8, 12, 33, 36, 37, 3, 11, 21, 30, 39, 3, 8, 9, 14, 29, 7, 9, 20, 21, 37, 8, 13, 14, 30, 35, 3, 11, 26, 37, 38, 2, 8, 15, 33, 34, 2, 9, 14, 17, 26, 2, 6, 15, 34, 35, 1, 7, 23, 24, 37, 5, 7, 10, 26, 30, 8, 10, 11, 22, 33, 5, 8, 20, 30, 33, 11, 15, 21, 28, 39, 15, 16, 17, 32, 33, 17, 23, 28, 29, 39, 4, 7, 14, 18, 39, 8, 17, 27, 35, 37, 5, 7, 18, 21, 37, 4, 17, 18, 25, 31, 20, 26, 28, 31, 35, 5, 11, 14, 19, 23, 4, 7, 17, 27, 30, 6, 26, 27, 33, 35, 22, 25, 35, 36, 38, 4, 13, 15, 23, 26, 7, 9, 28, 32, 39, 14, 22, 25, 35, 37, 10, 23, 25, 35, 38, 5, 16, 17, 20, 21, 6, 10, 27, 28, 35, 2, 9, 20, 31, 37, 1, 19, 26, 31, 36, 3, 12, 24, 27, 29, 10, 17, 18, 31, 37, 3, 13, 17, 26, 32, 2, 7, 19, 25, 31, 2, 8, 19, 23, 27, 9, 16, 28, 34, 35, 1, 12, 33, 35, 39, 11, 15, 24, 32, 36, 4, 8, 12, 13, 35, 8, 14, 18, 22, 28, 1, 20, 22, 31, 32, 2, 8, 16, 17, 35, 1, 10, 16, 20, 25, 6, 18, 23, 33, 38, 5, 10, 21, 22, 30, 13, 17, 18, 19, 35, 4, 10, 12, 20, 37, 20, 22, 24, 26, 32, 18, 26, 28, 30, 36, 1, 2, 21, 25, 33, 3, 4, 9, 16, 35, 5, 12, 14, 28, 29, 2, 10, 17, 21, 38, 1, 9, 11, 28, 34, 2, 33, 35, 37, 38, 2, 6, 10, 35, 38, 4, 13, 16, 19, 35, 4, 7, 9, 12, 15, 4, 7, 21, 30, 32, 6, 13, 22, 30, 32, 2, 9, 13, 18, 33, 3, 10, 29, 37, 38, 1, 13, 23, 27, 32, 14, 15, 17, 24, 27, 8, 10, 14, 26, 31, 5, 15, 17, 31, 35, 12, 27, 31, 33, 36, 9, 12, 13, 15, 38, 13, 16, 27, 29, 32, 8, 14, 20, 24, 26, 12, 19, 23, 24, 33, 2, 9, 14, 16, 19, 9, 24, 31, 38, 39, 13, 22, 25, 26, 35, 10, 14, 17, 24, 36, 8, 11, 16, 17, 32, 1, 11, 27, 28, 31, 15, 23, 34, 35, 36, 1, 7, 14, 17, 35, 1, 24, 36, 37, 39, 9, 13, 16, 23, 36, 12, 13, 23, 24, 37, 1, 7, 13, 31, 33, 8, 12, 16, 27, 28, 6, 13, 28, 29, 30, 9, 10, 22, 23, 39, 1, 3, 5, 27, 35, 6, 13, 16, 34, 39, 14, 23, 25, 34, 37, 9, 17, 18, 36, 37, 11, 16, 24, 30, 36, 9, 19, 22, 26, 34, 11, 20, 22, 26, 29, 5, 24, 28, 29, 32, 10, 23, 25, 30, 33, 6, 8, 22, 31, 33, 1, 8, 10, 23, 38, 5, 9, 12, 32, 33, 5, 29, 31, 33, 35, 1, 4, 19, 23, 37, 24, 29, 30, 32, 36, 5, 12, 13, 23, 33, 1, 17, 18, 25, 26, 6, 9, 20, 33, 39, 3, 25, 30, 34, 39, 1, 5, 11, 14, 26, 4, 12, 13, 31, 35, 19, 20, 23, 24, 36, 1, 2, 6, 24, 34, 15, 32, 35, 36, 37, 1, 8, 14, 25, 38, 5, 8, 21, 23, 34, 1, 4, 10, 34, 39, 6, 7, 24, 36, 37, 6, 8, 22, 26, 38, 11, 17, 24, 27, 35, 12, 13, 16, 21, 29, 17, 18, 19, 28, 38, 23, 25, 31, 32, 39, 22, 24, 30, 31, 37, 3, 17, 21, 27, 31, 4, 24, 27, 28, 33, 13, 16, 26, 27, 31, 6, 22, 24, 34, 39, 20, 27, 30, 35, 37, 1, 4, 7, 15, 26, 8, 14, 16, 32, 34, 5, 23, 27, 37, 38, 4, 7, 20, 29, 35, 2, 11, 18, 25, 36, 2, 4, 5, 6, 10, 8, 11, 21, 25, 31, 5, 12, 23, 24, 27, 16, 18, 19, 28, 39, 1, 11, 13, 18, 39, 4, 10, 12, 28, 30, 9, 13, 25, 26, 30, 9, 11, 16, 20, 38, 4, 7, 18, 33, 39, 4, 11, 17, 31, 37, 15, 31, 34, 36, 38, 9, 11, 29, 34, 37, 4, 12, 21, 23, 31, 11, 12, 13, 31, 38, 12, 18, 31, 33, 39, 5, 9, 14, 16, 30, 7, 9, 11, 17, 30, 2, 5, 7, 14, 22, 22, 24, 28, 29, 31, 1, 10, 20, 21, 32, 12, 29, 31, 32, 33, 8, 13, 20, 30, 33, 5, 6, 13, 23, 28, 6, 7, 18, 22, 25, 14, 18, 19, 22, 35, 4, 9, 11, 26, 31, 1, 21, 23, 24, 36, 12, 25, 26, 29, 34, 3, 11, 13, 20, 26, 12, 14, 23, 26, 33, 13, 22, 24, 29, 37, 1, 16, 17, 22, 29, 2, 12, 17, 32, 34, 2, 10, 21, 25, 29, 8, 25, 26, 34, 39, 2, 28, 31, 33, 34, 7, 11, 17, 18, 21, 3, 6, 8, 12, 37, 8, 10, 14, 17, 35, 20, 21, 25, 26, 38, 15, 19, 22, 23, 37, 9, 12, 15, 20, 25, 6, 9, 18, 23, 31, 19, 20, 27, 36, 38, 1, 5, 12, 28, 37, 1, 3, 6, 24, 25, 14, 23, 26, 32, 39, 8, 24, 29, 35, 36, 18, 21, 32, 36, 37)
V = Counter(Value)
print(V)
#For 'x' amount of number sets (from spreadsheet).
#List Size(Ex. 917) do 917x5
myInt = 4585
#Divide for percentage
percentdiv = [x / myInt for x in Value]
#Times new value by 100 and format 2 decimal places to get percentage
for i in percentdiv:
Percentage = ("%{:.2f}".format(i * 100))
for x in Value:
print (x, Percentage,)
I'm trying to figure out how to get python3 to print something like:
1 = %0.34, 2 = %0.21, 3 = %0.03
4 = %0.22, 5 = %0.13, 6 = %0.01
But for some reason I get:
1 %0.07
3 %0.07
8 %0.07
25 %0.07
34 %0.07
2 %0.07
3 %0.07
7 %0.07
15 %0.07
35 %0.07
8 %0.07
18 %0.07
19 %0.07
27 %0.07
32 %0.07
1 %0.07
I noticed too that the numbers were looping themselves.
I like what Counter does I like the formatting, but I need the percentage not just the number of times. Can someone help, please?

I think you need the percentage of each value's count in the Value list.Please correct if I am wrong,
for x,i in zip(Value,percentdiv):
print("{}={:.2f}".format(x,i*100),end='')

By default print function have the \n in a end so it should be,
import sys
for i in percentdiv:
Percentage = ("%{:.2f}".format(i * 100))
for x in Value:
sys.stdout.write(x, Percentage,)
sys.stdout.write("\n")
From here code copied

Related

I want to do bucketing on one of the column of my dataframe. I have 2 columns, Category & Rank.
I want to do bucketing in each category. So i first groupby on category & then use qcut on each group to do bucketing. After groupby, my dataframe's group looks like (final_data.groupby(['category'])['Ranks'].groups)
{'north': [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 19, 21, 22, 23, 24, 25, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44], 'south': [0, 5, 12, 15, 16, 20, 26, 27, 0, 1, 9, 10, 21, 26, 27, 28, 35, 45, 46, 47]}
I'm applying this code to bucketing
final_data.groupby(['category'])['Rank'].transform(lambda g: pd.qcut(g, q=[0.0, .1, .25, .5, .75, .9, 1.0], labels= ["Top 10", "11-25", "26-50", "50-75", "75-90" ,"Bottom10"]))
The above code is throwing the error

I am trying to append values to a dictionary inside a loop, but somehow it's only appending one of the values. I recreated the setup using the same numbers I am dynamically getting.
The output from "print(vertex_id_from_shell)" is "{0: [4], 1: [12], 2: [20]}". I need to keep the keys, but add the remaining numbers to the values.
Thanks.
shells = {0: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], 1: [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27], 2: [28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]}
uvsID = [0, 1, 3, 2, 2, 3, 5, 4, 4, 5, 7, 6, 6, 7, 9, 8, 1, 10, 11, 3, 12, 0, 2, 13, 14, 15, 16, 17, 17, 16, 18, 19, 19, 18, 20, 21, 21, 20, 22, 23, 15, 24, 25, 16, 26, 14, 17, 27, 28, 29, 30, 31, 31, 30, 32, 33, 33, 32, 34, 35, 35, 34, 36, 37, 29, 38, 39, 30, 40, 28, 31, 41]
vertsID = [0, 1, 3, 2, 2, 3, 5, 4, 4, 5, 7, 6, 6, 7, 1, 0, 1, 7, 5, 3, 6, 0, 2, 4, 8, 9, 11, 10, 10, 11, 13, 12, 12, 13, 15, 14, 14, 15, 9, 8, 9, 15, 13, 11, 14, 8, 10, 12, 16, 17, 19, 18, 18, 19, 21, 20, 20, 21, 23, 22, 22, 23, 17, 16, 17, 23, 21, 19, 22, 16, 18, 20]
vertex_id_from_shell = {}
for shell in shells:
selection_shell = shells.get(shell)
#print(selection_shell)
for idx, item in enumerate(selection_shell):
if item in uvsID:
uv_index = uvsID.index(item)
vertex_ids = vertsID[uv_index]
vertex_id_from_shell[shell] = [ ( vertex_ids ) ]
print(vertex_id_from_shell)
#{0: [4], 1: [12], 2: [20]}
#desired result
{0: [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 7, 0, 1, 7, 5, 6, 4], 1: [8, 9, 11, 10, 13, 12, 15, 14, 9, 8, 15, 13, 14, 12], 2: [16, 17, 19, 18, 21, 20, 23, 22, 17, 16, 23, 21, 22, 20]}

You're overwriting vertex_id_from_shell[shell] each time through the loop, not appending to it.
Use collections.defaultdict() to automatically create the dictionary elements with an empty list if necessary, then you can append.
from collections import defaultdict
vertex_id_from_shell = defaultdict(list)
for shell, selection_shell in shells.items():
for item in selection_shell:
if item in uvsID:
uv_index = uvsID.index(item)
vertex_ids = vertsID[uv_index]
vertex_id_from_shell[shell].append(vertex_ids)

You are setting vertex_id_from_shell[shell] to a new list, containing only one item every time. Instead, you should append to it.But first, of course that list needs to exist, so you should check and create it if it doesn't already exist.
shells = {0: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], 1: [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27], 2: [28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]}
uvsID = [0, 1, 3, 2, 2, 3, 5, 4, 4, 5, 7, 6, 6, 7, 9, 8, 1, 10, 11, 3, 12, 0, 2, 13, 14, 15, 16, 17, 17, 16, 18, 19, 19, 18, 20, 21, 21, 20, 22, 23, 15, 24, 25, 16, 26, 14, 17, 27, 28, 29, 30, 31, 31, 30, 32, 33, 33, 32, 34, 35, 35, 34, 36, 37, 29, 38, 39, 30, 40, 28, 31, 41]
vertsID = [0, 1, 3, 2, 2, 3, 5, 4, 4, 5, 7, 6, 6, 7, 1, 0, 1, 7, 5, 3, 6, 0, 2, 4, 8, 9, 11, 10, 10, 11, 13, 12, 12, 13, 15, 14, 14, 15, 9, 8, 9, 15, 13, 11, 14, 8, 10, 12, 16, 17, 19, 18, 18, 19, 21, 20, 20, 21, 23, 22, 22, 23, 17, 16, 17, 23, 21, 19, 22, 16, 18, 20]
vertex_id_from_shell = {}
for shell in shells:
selection_shell = shells.get(shell)
#print(selection_shell)
for idx, item in enumerate(selection_shell):
if item in uvsID:
uv_index = uvsID.index(item)
vertex_ids = vertsID[uv_index]
# if the list does not exist, create it
if shell not in vertex_id_from_shell:
vertex_id_from_shell[shell] = []
# append to list
vertex_id_from_shell[shell].append(vertex_ids)
print(vertex_id_from_shell)
# {0: [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 7, 5, 6, 4],
# 1: [8, 9, 11, 10, 13, 12, 15, 14, 9, 8, 15, 13, 14, 12],
# 2: [16, 17, 19, 18, 21, 20, 23, 22, 17, 16, 23, 21, 22, 20]}

I create a ngram from Python and the output its something like this:
'vida': 113, 'sistema': 104, 'economía': 91, 'nacional': 84, 'mujeres': 76, 'derechos': 75, 'salud': 75, 'paz': 67, 'colombia': 66, 'social': 66, 'trabajo': 63, 'protección': 62, 'política': 61, 'país': 55, 'acceso': 53, 'cambio': 49, 'sociedad': 49, 'derecho': 49, 'educación': 47, 'cuidado': 46, 'así': 45, 'productiva': 44, 'condiciones': 44, 'cultura': 41, 'participación': 39, 'agua': 39, 'gobierno': 38, 'desarrollo': 38, 'integral': 37, 'construcción': 37, 'personas': 37, 'naturaleza': 35, 'territorios': 35, 'popular': 35, 'público': 35, 'pública': 34, 'territorio': 33, 'pueblos': 33, 'servicios': 33, 'todas': 33, 'población': 33, 'indígenas': 32, 'garantizaremos': 32, 'climático': 31, 'vivienda': 31, 'justicia': 31, 'políticas': 31, 'través': 31, 'producción': 30, 'cultural': 30, 'empleo': 30, 'calidad': 29, 'internacional': 29, 'manera': 29, 'pacto': 28, 'públicos': 28, 'mediante': 28, 'impulsaremos': 28, 'plan': 27, 'procesos': 27, 'territorial': 26, 'seguridad': 26, 'reconocimiento': 26, 'igualdad': 25, 'conocimiento': 25, 'afrodescendientes': 25, 'economías': 25, 'toda': 25, 'sociales': 25, 'nivel': 25, 'garantizar': 25, 'comunidades': 25, 'atención': 25, 'uso': 25, 'territoriales': 25, 'tierra': 24, 'víctimas': 24, 'transición': 24, 'trabajadores': 24, 'mayor': 24, 'programas': 24, 'enfoque': 24, 'base': 24, 'democratización': 23, 'productividad': 23, 'arte': 23, 'negros': 23, 'avanzaremos': 23, 'rurales': 23, 'públicas': 23, 'formas': 23, 'sector': 23, 'formación': 23, 'garantía': 22, 'lucha': 22, 'palenqueros': 22, 'pensión': 22, 'fin': 22, 'transformación': 22, 'marco': 22, 'culturales': 22, 'género': 21, 'sistemas': 21, 'ambiental': 21, 'modelo': 21, 'mundial': 20, 'niños': 20, 'gran': 20, 'alimentaria': 20, 'zonas': 20, 'bajo': 20, 'reforma': 20, 'potencia': 19, 'crédito': 19, 'primera': 19, 'raizales': 19, 'mayores': 19, 'fundamental': 19, 'organizaciones': 19, 'educativo': 19, 'comunitarias': 19, 'ambientales': 19, 'espacio': 18, 'industria': 18, 'jóvenes': 18, 'infancia': 18, 'niñas': 18, 'patrimonio': 18, 'alimentación': 18, 'económica': 18, 'instrumentos': 18, 'bienes': 18, 'generación': 18, 'recursos': 18, 'proyectos': 18, 'promoveremos': 18, 'laboral': 18, 'deporte': 17, 'realidad': 17, 'diversidad': 17, 'humana': 17, 'reparación': 17, 'programa': 17, 'soberanía': 17, 'garantizando': 17, 'diálogo': 17, 'mercado': 17, 'regional': 17, 'económico': 16, 'saber': 16, 'campo': 16, 'campesinado': 16, 'tiempo': 16, 'parte': 16, 'familias': 16, 'energía': 16, 'autonomía': 16, 'local': 16, 'rural': 16, 'populares': 16, 'apoyo': 16, 'fortalecimiento': 16, 'libre': 15, 'oportunidades': 15, 'haremos': 15, 'rrom': 15, 'adultos': 15, 'basada': 15, 'capacidad': 15, 'millones': 15, 'libertad': 15, 'acuerdo': 15, 'superar': 15, 'valor': 15, 'energética': 15, 'fortaleceremos': 15, 'centros': 15, 'gestión': 15, 'ser': 15, 'garantías': 14, 'agenda': 14, 'corrupción': 14, 'nuevo': 14, 'crearemos': 14, 'hombres': 14, 'superior': 14, 'general': 14, 'tierras': 14, 'espacios': 14, 'capacidades': 14, 'ingresos': 14, 'nacionales': 14, 'mejorar': 14, 'prácticas': 14, 'servicio': 14, 'control': 14, 'alrededor': 13, 'desigualdad': 13, 'convivencia': 13, 'nación': 13, 'diversidades': 13, 'saberes': 13, 'puedan': 13, 'grandes': 13, 'institucionalidad': 13, 'áreas': 13, 'riesgo': 13, 'infraestructura': 13, 'financiamiento': 13, 'transporte': 13, 'cada': 13, 'particular': 13, 'red': 12, 'vivir': 12, 'defensa': 12, 'fundamentales': 12, 'democracia': 12, 'efectiva': 12, 'permita': 12, 'colombiana': 12, 'productores': 12, 'universal': 12, 'creación': 12, 'mínimo': 12, 'permitan': 12, 'especial': 12, 'articulación': 12, 'distribución': 12, 'regionales': 12, 'hacer': 12, 'actividades': 12, 'locales': 12, 'productos': 12, 'intercultural': 12, 'tecnologías': 12, 'entidades': 12, 'sectores': 12, 'memoria': 12, 'negocio': 11, 'física': 11, 'poblaciones': 11, 'violencia': 11, 'trabajos': 11, 'impuestos': 11, 'promoción': 11, 'frente': 11, 'cuidados': 11, 'bienestar': 11, 'digno': 11, 'reducción': 11, 'dentro': 11, 'investigación': 11, 'debe': 11, 'medidas': 11, 'implica': 11, 'comunitarios': 11, 'cobertura': 11, 'incluyendo': 11, 'nuevas': 11, 'autoridades': 11, 'aprovechamiento': 11
I want to create a cdv file from it, that cotain two columns "words" and "Count" in a csv file, Im looking for a way fo how to do it in Python o R
Thanks for the help!

In tidyverse, we can split by : with separate_rows and split the column again into two with separate
library(dplyr)
library(tidyr)
library(stringr)
tibble(col1 = str1) %>%
separate_rows(col1, sep = ",\\s*") %>%
separate(col1, into = c("key", "value"), sep = ":\\s*", convert = TRUE) %>%
mutate(key = str_remove_all(key, "'"))
-output
# A tibble: 261 × 2
key value
<chr> <dbl>
1 vida 113
2 sistema 104
3 economía 91
4 nacional 84
5 mujeres 76
6 derechos 75
7 salud 75
8 paz 67
9 colombia 66
10 social 66
# … with 251 more rows

You can try
x <- "the string in your question"
df <- read.table(textConnection(gsub("," ,"\n" , x)) , sep = ":")
write.csv(df, "myngram" , row.names = F)

Here is one more tidyverseapproach:
Same as #akrun we separate the rows by , , then we use str_extract from stringr package twice with regex:
library(tidyverse)
str1 %>%
as_tibble() %>%
separate_rows(value, sep=", ") %>%
mutate(key = str_extract(value, '[A-Za-z]+'),
value = as.numeric(str_extract(value, '[0-9]+')), .before=1)
key value
<chr> <dbl>
1 vida 113
2 sistema 104
3 econom 91
4 nacional 84
5 mujeres 76
6 derechos 75
7 salud 75
8 paz 67
9 colombia 66
10 social 66
# ... with 251 more rows

I am trying to multiply factors from the first nested list with the second nested list. The result I get is [0,0,0,0]. Help appreciated.
Faktorer = [[1,2,3,4,5,6,7,8,9,10],[1,2,3,4,5,6,7,8,9,10]]
res = []
for Faktorer[0][i] in Faktorer:
for Faktorer[1][j] in Faktorer:
res.append(i*j)
print(res)

Using a nested loop is right, but your syntax is all mixed up.
>>> Faktorer = [[1,2,3,4,5,6,7,8,9,10],[1,2,3,4,5,6,7,8,9,10]]
>>> res = []
>>> for i in Faktorer[0]:
... for j in Faktorer[1]:
... res.append(i * j)
...
>>> res
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 9, 18, 27, 36, 45, 54, 63, 72, 81, 90, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
You can also do this as a list comprehension like this:
>>> [i * j for i in Faktorer[0] for j in Faktorer[1]]
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 9, 18, 27, 36, 45, 54, 63, 72, 81, 90, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
If Faktorer had an arbitrary number of sublists (making it impossible to do this with a fixed number of nested loops), or if you just didn't want to use nested loops, you could use product to generate the combinations of all the factors and reduce to multiply them:
>>> from functools import reduce
>>> from itertools import product
>>> [reduce(int.__mul__, f, 1) for f in product(*Faktorer)]
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 9, 18, 27, 36, 45, 54, 63, 72, 81, 90, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

What you described in a Kronecker product. If you are allowed to use libraries, you could use numpy.kron:
import numpy as np
res = list(np.kron(Faktorer[0], Faktorer[1]))
print(res)
OUTPUT:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 9, 18, 27, 36, 45, 54, 63, 72, 81, 90, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

I need to divide my dataframe into 2 dataframe based on their index
Df1 with this index:[5, 15, 22, 23, 24]
Df2 with this index:[0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 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, 50, 51, 52, 53, 54]
Unable to find solution! Any help would be appreciated

If input is list of index values is possible use Index.isin in boolean indexing (if not exist some values in original index also working correct):
idx = [5, 15, 22, 23, 24]
mask = df.index.isin(idx)
df1 = df[mask]
df2 = df[~mask]
Solution with DataFrame.loc is possible without : and is necessary all values exist in original index:
L1 = [5, 15, 22, 23, 24]
L2 = [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20,
21, 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, 50, 51, 52, 53, 54]
df1 = df.loc[L1]
df2 = df.loc[L2]

You can use .loc:
df_1 = df.loc[[5, 15, 22, 23, 24], :]
df_2 = df.loc[[0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 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, 50, 51, 52, 53, 54], :]
Here is the documentation: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html