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up vote 0 down vote favorite

Given a .csv file that look like this:

49.18;-3.34
45.73;2.63
47.88;-11.49
45.74;3.06
48.73;-9.56
49.20;-3.42

I use the following code:

data = np.genfromtxt("file.csv",  delimiter=';')

And the resulting array looks like this:

[[ 49.18  -3.34]
 [ 45.73   2.63]
 [ 47.88 -11.49]
 [ 45.74   3.06]
 [ 48.73  -9.56]
 [ 49.2   -3.42]
 [ 45.71   2.53]
 [ 47.87 -11.67]]

But what I want is this:

 [[ 49.18 , -3.34]
 [ 45.73 ,  2.63]
 [ 47.88 , -11.49]
 [ 45.74 ,  3.06]
 [ 48.73 , -9.56]
 [ 49.2  , -3.42]
 [ 45.71 ,  2.53]
 [ 47.87 , -11.67]]

What am I doing wrong? Thanks a lot, and I hope you can help me

share|improve this question
3  
Everything is fine. The print form of numpy doesn't shows the comma –  wolfrevo Feb 6 at 12:27

2 Answers 2

up vote 0 down vote accepted

What is the output of

data.shape

I believe it is already working as you wish.

share|improve this answer
    
This is the output: (125L, 2L) –  user3626454 Feb 6 at 12:33
    
So, 125 rows by 2 columns? Is the problem that you want it to print a certain way or that you want it in a numpy array to work with? Is type(data) an numpy.ndarray? –  EngineeredE Feb 6 at 12:40
    
That's right, it's 125 rows and 2 columns. The problem is that I cannot access the columns separately using indexes. It is a numpy.ndarray –  user3626454 Feb 6 at 12:48
    
Please provide an example the indexing and the error it produces. data[:,0] should access the first column –  EngineeredE Feb 6 at 12:49
    
Thanks, it was the indexes what I was doing wrong! –  user3626454 Feb 6 at 13:03
up vote 0 down vote

If you really need an array instead of a numpy-array, then you can convert your np.array to a list with tolist():

my_list = data.tolist()

Please remember that with a list you only can index every dimension with following notation:

print my_list[0][0]

and with a numpy-array you can use both notations:

print data[0,0]
print data[0][0]
share|improve this answer
    
tolist() produces a list, not an array. Or rather a list of lists. Python has a separate class called array. But with the wide availability of numpy, that is rarely used. I know I'm being picky, but terminology does matter when programming. –  hpaulj Feb 6 at 17:22
    
You're absolutely right. Thanks for you comment. –  wolfrevo Feb 7 at 15:39

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