I have a list with a few records:
entries = ['Agenda', 'Measure 1', 'Measure 2']
I am trying to create a loop and have them added to a table as listed below:
for i in range(0, len(entries)):
table.cell(i, 0).text = entries[i]
The below throws an IndexError: list index out of range on the last line, wondering where am I going wrong.
First things first, when iterating over range(len(something)) this is a red flag. There must be a better way.
If you need both the index as well as the value, you can use the built-in enumerate() function: for i, val in enumerate(entries):
The error you get means you're trying to access an element i that does not exist. The list elements should be accessible just fine - it's probably that table.cell(i,0) does not exist. You can verify this by trying:
for i in range(0, len(entries)):
print(entries[i]) # works
Compared to:
for i in range(0, len(entries)):
print(table.cell(i, 0).text) # probably doesn't work
I used your old for loop to show that's not causing the error, but know that range(len()) is a bad practice: it makes the code harder to read.
I'm not sure what kind of data structure you're using for table, but look up the related documentation and see how you should add new cells.
I have built a python program processing the probability of various datasets. I input 'manually' various mean values and standard deviations, and that works, however I need to automate it so that I can upload all my data through a text or csv file. I've got so far but now have a nested for loop query I think with indices problems, but some background follows...
My code works for a small dataset where I can manually key in 6-8 parameters working but now I need to automate it and upload various inputs of unknown sizes by csv / text file. I am copying my existing code and amending it where appropriate but I have run into a problem.
I have a 2_D numpy-array where some probabilities have been reverse sorted. I have a second array which gives me the value of 68.3% of each row, and I want to trim the low value 31.7% data.
I need a solution which can handle an unspecified number of rows.
My pre-existing code worked for a single one-dimensional array was
prob_combine_sum= np.sum(prob_combine)
#Reverse sort the probabilities
prob_combine_sorted=sorted(prob_combine, reverse=True)
#Calculate 1 SD from peak Prob by multiplying Total Prob by 68.3%
sixty_eight_percent=prob_combine_sum*0.68269
#Loop over the sorted list and append the 1SD data into a list
#onesd_prob_combine
onesd_prob_combine=[]
for i in prob_combine_sorted:
onesd_prob_combine.append(i)
if sum(onesd_prob_combine) > sixty_eight_percent:
break
That worked. However, now I have a multi-dimensional array, and I want to take the 1 standard deviation data from that multi-dimensional array and stick it in another.
There's probably more than one way of doing this but I thought I would stick to the for loop, but now it's more complicated by the indices. I need to preserve the data structure, and I need to be able to handle unlimited numbers of rows in the future.
I simulated some data and if I can get this to work with this, I should be able to put it in my program.
sorted_probabilities=np.asarray([[9,8,7,6,5,4,3,2,1],
[87,67,54,43,32,22,16,14,2],[100,99,78,65,45,43,39,22,3],
[67,64,49,45,42,40,28,23,17]])
sd_test=np.asarray([30.7215,230.0699,306.5323,256.0125])
target_array=np.zeros(4).reshape(4,1)
#Task transfer data from sorted_probabilities to target array on
condition that value in each target row is less than the value in the
sd_test array.
#Ignore the problem that data transferred won't add up to 68.3%.
My real data-sample is very big. I just need a way of trimmining
and transferring.
for row in sorted_probabilities:
for element in row:
target_array[row].append[i]
if sum(target[row]) > sd_test[row]:
break
Error: IndexError: index 9 is out of bounds for axis 0 with size 4
I know it's not a very good attempt. My problem is that I need a solution which will work for any 2D array, not just one with 4 rows.
I'd be really grateful for any help.
Thank you
Edit:
Can someone help me out with this? I am struggling.
I think the reason my loop will not work is that the 'index' row I am using is not a number, but in this case a row. I will have a think about this. In meantime has anyone a solution?
Thanks
I tried the following code after reading the comments:
for counter, value in enumerate(sorted_probabilities):
for i, element in enumerate(value):
target_array[counter]=sorted_probabilities[counter][element]
if target_array[counter] > sd_test[counter]:
break
I get an error: IndexError: index 9 is out of bounds for axis 0 with size 9
I think it's because I am trying to add to numpy array of pre-determined dimensions? I am not sure. I am going to try another tack now as I can not do this with this approach. It's having to maintain the rows in the target array that is making it difficult. Each row relates to an object, and if I lose the structure it will be pointless.
I recommend you use pandas. You can read directly the csv in a dataframe and do multiple operations on columns and such, clean and neat.
You are mixing numpy arrays with python lists. Better use only one of these (numpy is preferred). Also try to debug your code, because it has either syntax and logical errors. You don't have variable i, though you're using it as an index; also you are using row as index while it is a numpy array, but not an integer.
I strongly recommend you to
0) debug your code (at least with prints)
1) use enumerate to create both of your for loops;
2) replace append with plain assigning, because you've already created an empty vector (target_array). Or initialize your target_array as empty list and append into it.
3) if you want to use your solution for any 2d array, wrap your code into a function
Try this:
sorted_probabilities=np.asarray([[9,8,7,6,5,4,3,2,1],
[87,67,54,43,32,22,16,14,2],
[100,99,78,65,45,43,39,22,3],
[67,64,49,45,42,40,28,23,17]]
)
sd_test=np.asarray([30.7215,230.0699,306.5323,256.0125])
target_array=np.zeros(4).reshape(4,1)
for counter, value in enumerate(sorted_probabilities):
for i, element in enumerate(value):
target_array[counter] = element # Here I removed the code that produced error
if target_array[counter] > sd_test[counter]:
break
I have a CSV file with 100,000 rows.
Each row in column A is a sentence comprised of both chars and integers.
I want column B to contain only integers.
I want the new columns to be in the same CSV file.
How can I accomplish this?
If I'm understanding your question correctly, I would use .isdigit() to parse the data in column A. I'm frankly not sure what the format of column A is, so I don't know exactly what you would do with this (if you gave more information I could give a more specific answer). Your solution will likely come in a similar form to this:
def find(lines):
B = []
for line in lines:
numbers = [c for c in line if c.isdigit()]
current = int(''.join(numbers))
# current is the concatenation of all
# integers found in column A from left to right
B.append(current)
return B
Let me know if this makes sense or is even in the right track for your solution. Once again, without knowing what you're trying to do, and what A looks like, I'm not sure what your actual goals are.
EDIT
I'm not going to explain the csv stuff for you, mainly because there is a fantastic resource and library for it included in python here. If you have specific questions related to writing csv, definitely post them.
It sounds like you essentially want to pull int values out of column A then add them to a new column B. There are definitely many ways to solve this, but the general form of the problem is for each row you'll filter out the int, then you'll add the filtered int into the new column. I'll list a couple:
Regex: You could use a pattern such as [0-9]+ to pull the string out of A, then use int(whatever that output is) to cast to int, then store those values in B. I'm a sucker for a good regular expression and this one is fairly straight forward. Regexr is a great resource to learn about this and test your pattern.
Use an algorithm similar to above: The above algorithm worked before, but I've updated it slightly. Now that it's been updated it'll return an array of numbers correspondent to numbers in A from left to right. This is relatively sound, but it doesn't necessarily guarantee you have the right integer, given that if the title has an int in it, it'll mess some things up. It is likely one of the more clear ways of doing this, though.
I have two lists, pPop and sPop. sPop is pPop after being sorted in ascending numerical order (they're populations of towns/cities).
I also have four other lists, pName, pType, pLat, and pLong, but I'm not really doing anything with them at this point in time.
I need to sort this list of cities by ascending population size, and I basically have been told to do it using what I know currently - which isn't that much. I've tried this using tuples and other stuff, but those fall under things I haven't been taught.
I have to compare sPop to pPop and use the information I get from that to reorder the other four lists, so I can spit them out in a .csv file.
I get the idea, I'm just not sure of the execution. I think I need to run a loop over all of sPop, with a loop inside that running for all pPop, which checks if sPop[x] = pPop[y], (x from 0 to len(sPop)) giving some kind of affirmative response when it's true. If it's true, then set pVar[y] equal to sVar[x].
After writing this out it seems fine, I'm just not entirely sure how to loop for every index in python. When I do, say,
for x in sPop
it's
x = sPop[i] i=range(0:len(sPop))
when I'd prefer x to refer to the index itself, not the value of the array/list at that index.
Short version: loop over indices in an array with
for x in array
where x refers to the index. How do? If you can't, best way to refer to the index within a loop? It's late and my brain is fried on this problem after spending about six hours trying to work this out a few days ago using different methods.
EDIT:
Alright, got it. For anyone who is somehow curious (maybe someone'll stumble across this one in five years), you loop over sPop, then over pPop, (use
for indexX, varX in enumerate(list) twice) then use
if varX ==varY
sortedList.append(initialList[varY])
Can't put down the actual code or I'd probably get smacked with a plagiarism checker.
To get the index:
for index, x in enumerate(sPop):
print index, x
for x in range(len(sPop)):
item = sPop[x]
Importing a JSON document into a pandas dataframe using records = pandas.read_json(path), where path was a pre-defined path to the JSON document, I discovered that the content of certain columns of the resulting dataframe "records" are not simply strings as expected. Instead, each "cell" in such a column is an array, containing one single element -- the string of interest. This makes selecting columns using boolean indexing difficult. For example, records[records['category']=='Python Books'] in Ipython outputs an empty dataframe; had the "cells" contained strings instead of arrays of strings, the output would have been nonempty, containing rows that correspond to python books.
I could modify the JSON document, so that "records" reads the strings in properly. But is there a way to modify "records" directly, to somehow strip the single-element arrays into the elements themselves?
Update: After clarification, I believe this might accomplish what you want while limiting it to a single iteration over the data:
nested_column_1 = records["column_name_1"]
nested_column_2 = records["column_name_2"]
clean_column_1 = []
clean_column_2 = []
for i in range(0, len(records.index):
clean_column_1.append(nested_column_1[i][0])
clean_column_2.append(nested_column_2[i][0])
Then you convert the clean_column lists to Series like you mentioned in your comment. Obviously, you make as many nested_column and clean_column lists as you need, and update them all in the loop.
You could generalize this pretty easily by keeping a record of "problem" columns and using that to create a data structure to manage the nested/clean lists, rather than declaring them explicitly as I did in my example. But I thought this might illustrate the approach more clearly.
Obviously, this assumes that all columns have the same number of elements, which maybe isn't a a valid assertion in your case.
Original Answer:
Sorry if I'm oversimplifying or misunderstanding the problem, but could you just do something like this?
simplified_list = [element[0] for element in my_array_of_arrays]
Or if you don't need the whole thing at once, just a generator instead:
simplifying_generator = (element[0] for element in my_array_of_arrays)