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(Python3.x)Splitting arrays and saving them into new arrays

I'm writing a Python script intended to split a big array of numbers into equal sub-arrays. For that purpose, I use Numpy's split method as follows:
test=numpy.array_split(raw,nslices)
where raw is the complete array containing all the values, which are float64-type by the way.
nslices is the number of sub-arrays I want to create from the raw array.
In the script, nslices may vary depending of the size of the raw array, so I would like to "automatically" save each created sub-arrays in a particular array as : resultsarray(i)in a similar way that it can be made in MATLAB/Octave.
I tried to use afor in range loop in Python but I am only able to save the last sub-array in a variable.
What is the correct way to save the sub-array for each each incrementation from 1 to nslices?
Here, the complete code as is it now (I am a Python beginner, please bother the low-level of the script).
import numpy as np
file = open("results.txt", "r")
raw = np.loadtxt(fname=file, delimiter="/n", dtype='float64')
nslices = 3
rawslice = np.array_split(raw,nslices)
for i in range(0,len(rawslice)):
resultsarray=(rawslice[i])
print(rawslice[i])
Thank you very much for your help solving this problem!

First - you screwed up delimiter :)
It should be backslash+n \n instead of /n.
Second - as Serge already mentioned in comment you can just access to split parts by index (resultarray[0] to [2]). But if you really wanted to assign each part to a separate variable you can do this in fommowing way:
result_1_of_3, result_2_of_3, result_3_of_3 = rawslice
print(result_1_of_3, result_2_of_3, result_3_of_3)
But probably it isn't the way you should go.

Converting python Dataframe to Matlab file

I am trying to convert a python Dataframe to a Matlab (.mat) file.
I initially have a txt (EEG signal) that I import using panda.read_csv:
MyDataFrame = pd.read_csv("data.txt",sep=';',decimal='.'), data.txt being a 2D array with labels. This creates a dataframe which looks like this.
In order to convert it to .mat, I tried this solution where the idea is to convert the dataframe into a dictionary of lists but after trying every aspect of this solution it's still unsuccessful.
scipy.io.savemat('EEG_data.mat', {'struct':MyDataFrame.to_dict("list")})
It did create a .mat file but it did not save my dataframe properly. The file I obtain after looks like this, so all the values are basically gone, and the remaining labels you see are empty when you look into them.
I also tried using mat4py which is designed to export python structures into Matlab files, but it did not work either. I don't understand why, because converting my dataframe to a dictionary of lists is exactly what should be done according to the mat4py documentation.

I believe that the reason the previous solutions haven't worked for you is that your DataFrame column names are not valid MATLAB struct field names, because they contain spaces and/or start with digit characters.
When I do:
import pandas as pd
import scipy.io
MyDataFrame = pd.read_csv('eeg.txt',sep=';',decimal='.')
truncDataFrame = MyDataFrame[0:1000] # reduce data size for test purposes
scipy.io.savemat('EEGdata1.mat', {'struct1':truncDataFrame.to_dict("list")})
the result in MATLAB is a struct with the 4 fields reltime, datetime, iSensor and quality. Each of these has 1000 elements, so the data from these columns has been converted, but the rest of your data is missing.
However if I first rename the DataFrame columns:
truncDataFrame.rename(columns=lambda x:'col_' + x.replace(' ', '_'), inplace=True)
scipy.io.savemat('EEGdata2.mat', {'struct2':truncDataFrame.to_dict("list")})
the result in MATLAB is a struct with 36 fields. This is not the same format as your mat4py solution but it does contain (as far as I can see) all the data from the source DataFrame.
(Note that in your question, you are creating a .mat file that contains a variable called struct and when this is loaded into MATLAB it masks the builtin struct datatype - that might also cause issues with subsequent MATLAB code.)

I finally found a solution thanks to this post. There, the poster did not create a dictionary of lists but a dictionary of integers, which worked on my side. It is a small example, easily reproductible. Then I tried to manually add lists by entering values like [1, 2], an it did not work. But what worked was when I manually added tuples !
MyDataFrame needs to be converted to a dictionary and if a dictionary of lists doesn't work, try with tuples.
For beginners : lists are contained by [] and tuples by (). Here is an image showing both.
This worked for me:
import mat4py as mp
EEGdata = MyDataFrame.apply(tuple).to_dict()
mp.savemat('EEGdata.mat',{'structs': EEGdata})
EEGdata.mat should now be readable by Matlab, as it is on my side.

Excel worksheet to Numpy array

I'm trying to do an unbelievably simple thing: load parts of an Excel worksheet into a Numpy array. I've found a kludge that works, but it is embarrassingly unpythonic:
say my worksheet was loaded as "ws", the code:
A = np.zeros((37,3))
for i in range(2,39):
for j in range(1,4):
A[i-2,j-1]= ws.cell(row = i, column = j).value
loads the contents of "ws" into array A.
There MUST be a more elegant way to do this. For instance, csvread allows to do this much more naturally, and while I could well convert the .xlsx file into a csv one, the whole purpose of working with openpyxl was to avoid that conversion. So there we are, Collective Wisdom of the Mighty Intertubes: what's a more pythonic way to perform this conceptually trivial operation?
Thank you in advance for your answers.
PS: I operate Python 2.7.5 on a Mac via Spyder, and yes, I did read the openpyxl tutorial, which is the only reason I got this far.

You could do
A = np.array([[i.value for i in j] for j in ws['C1':'E38']])
EDIT - further explanation.
(firstly thanks for introducing me to openpyxl, I suspect I will use it quite a bit from time to time)
the method of getting multiple cells from the worksheet object produces a generator. This is probably much more efficient if you want to work your way through a large sheet as you can start straight away without waiting for it all to load into your list.
to force a generator to make a list you can either use list(ws['C1':'E38']) or a list comprehension as above
each row is a tuple (even if only one column wide) of
Cell objects. These have a lot more about them than just a number but if you want to get the number for your array you can use the .value attribute. This is really the crux of your question, csv files don't contain the structured info of an excel spreadsheet.
there isn't (as far as I can tell) a built in method for extracting values from a range of cells so you will have to do something effectively as you have sketched out.
The advantages of doing it my way are: no need to work out the dimension of the array and make an empty one to start with, no need to work out the corrected index number of the np array, list comprehensions faster. Disadvantage is that it needs the "corners" defining in "A1" format. If the range isn't know then you would have to use iter_rows, rows or columns
A = np.array([[i.value for i in j[2:5]] for j in ws.rows])
if you don't know how many columns then you will have to loop and check values more like your original idea

If you don't need to load data from multiple files in an automated manner, the package tableconvert I recently wrote may help. Just copy and paste the relevant cells from the excel file into a multiline string and use the convert() function.
import numpy as np
from tableconvert.converter import convert
array = convert("""
123 456 3.14159
SOMETEXT 2,71828 0
""")
print(type(array))
print(array)
Output:
<class 'numpy.ndarray'>
[[ 123. 456. 3.14159]
[ nan 2.71828 0. ]]

numpy array values to be converted from string to float?

I have a dataset like the one shown below
http://i.stack.imgur.com/1uxCK.png
I am able to read them into an numpy array but the datatype is of type string when it has read from the CSV file. I am unable to convert the same into float since without that i would not be able to proceed further.Mind you there are blank spaces between the two data columns shown in the first screenshot.
The numpy array structure when printed looks like in the screenshot given below:
http://i.stack.imgur.com/JFfzw.png
Note: (Observe the Single Quotation Marks between the start and end of each data line in the screenshot which is a proof that numpy has stored the data as a string rather than float)
Any help would be appreciated in helping me convert the data from string to float type?????? have Tried many things but yet all in vain!!!!!!!!

numpy.loadtxt(filename) should work out of the box: it yields numbers.

Python numpy: Data file with multiple datasets

Suppose you have a data file which includes several data sets separated by the string "--" in the following format:
--
<x0_val> <y0_val>
<x1_val> <y1_val>
<x2_val> <y2_val>
--
<x0_val> <y0_val>
<x1_val> <y1_val>
<x2_val> <y2_val>
...
How can you read the whole file into an array of arrays so that you can plot all data sets afterwards to the same picture with a for loop looping over the outer array ?
genfromtxt('data.dat', delimiter=("--"))
gives lots of
Line #1550 (got 1 columns instead of 2)

I will update ...
I would first split the file into multiple files, which can reside in memory as objects or on the filesystems as new files.
You can locate the string -- with the module re.
Then you can use the link I posted above.

If you're 100% certain that you have no negative values in your file, you can try a quick:
np.genfromtxt(your_file, comments="-")
The comments="-" will force genfromtxt to ignore all the characters after -, which of course will give weird results if you have negative variables. Moreover, the result will be just a lump of your dataset in a single array
Otherwise, the safest route is to iterate on your file and store the lines that do not match -- in one list per block, something along the lines:
blocks = []
current = []
for line in your_file:
if line.startswith("-"):
blocks.append(np.array(current))
current = []
else:
current += line.split()
You may have to get rid of the first block if empty.
You could also check a mmap based solution already posted.