[python] How to use numpy.genfromtxt when first column is string and the remaining columns are numbers?

Basically, I have a bunch of data where the first column is a string (label) and the remaining columns are numeric values. I run the following:

data = numpy.genfromtxt('data.txt', delimiter = ',')

This reads most of the data well, but the label column just gets 'nan'. How can I deal with this?

For a dataset of this format:

CONFIG000   1080.65 1080.87 1068.76 1083.52 1084.96 1080.31 1081.75 1079.98
CONFIG001   414.6   421.76  418.93  415.53  415.23  416.12  420.54  415.42
CONFIG010   1091.43 1079.2  1086.61 1086.58 1091.14 1080.58 1076.64 1083.67
CONFIG011   391.31  392.96  391.24  392.21  391.94  392.18  391.96  391.66
CONFIG100   1067.08 1062.1  1061.02 1068.24 1066.74 1052.38 1062.31 1064.28
CONFIG101   371.63  378.36  370.36  371.74  370.67  376.24  378.15  371.56
CONFIG110   1060.88 1072.13 1076.01 1069.52 1069.04 1068.72 1064.79 1066.66
CONFIG111   350.08  350.69  352.1   350.19  352.28  353.46  351.83  350.94

This code works for my application:

def ShowData(data, names):
    i = 0
    while i < data.shape[0]:
        print(names[i] + ": ")
        j = 0
        while j < data.shape[1]:
            print(data[i][j])
            j += 1
        print("")
        i += 1

def Main():
    print("The sample data is: ")
    fname = 'ANOVA.csv'
    csv = numpy.genfromtxt(fname, dtype=str, delimiter=",")
    num_rows = csv.shape[0]
    num_cols = csv.shape[1]
    names = csv[:,0]
    data = numpy.genfromtxt(fname, usecols = range(1,num_cols), delimiter=",")
    print(names)
    print(str(num_rows) + "x" + str(num_cols))
    print(data)
    ShowData(data, names)

Python-2 output:

The sample data is:
['CONFIG000' 'CONFIG001' 'CONFIG010' 'CONFIG011' 'CONFIG100' 'CONFIG101'
 'CONFIG110' 'CONFIG111']
8x9
[[ 1080.65  1080.87  1068.76  1083.52  1084.96  1080.31  1081.75  1079.98]
 [  414.6    421.76   418.93   415.53   415.23   416.12   420.54   415.42]
 [ 1091.43  1079.2   1086.61  1086.58  1091.14  1080.58  1076.64  1083.67]
 [  391.31   392.96   391.24   392.21   391.94   392.18   391.96   391.66]
 [ 1067.08  1062.1   1061.02  1068.24  1066.74  1052.38  1062.31  1064.28]
 [  371.63   378.36   370.36   371.74   370.67   376.24   378.15   371.56]
 [ 1060.88  1072.13  1076.01  1069.52  1069.04  1068.72  1064.79  1066.66]
 [  350.08   350.69   352.1    350.19   352.28   353.46   351.83   350.94]]
CONFIG000:
1080.65
1080.87
1068.76
1083.52
1084.96
1080.31
1081.75
1079.98

CONFIG001:
414.6
421.76
418.93
415.53
415.23
416.12
420.54
415.42

CONFIG010:
1091.43
1079.2
1086.61
1086.58
1091.14
1080.58
1076.64
1083.67

CONFIG011:
391.31
392.96
391.24
392.21
391.94
392.18
391.96
391.66

CONFIG100:
1067.08
1062.1
1061.02
1068.24
1066.74
1052.38
1062.31
1064.28

CONFIG101:
371.63
378.36
370.36
371.74
370.67
376.24
378.15
371.56

CONFIG110:
1060.88
1072.13
1076.01
1069.52
1069.04
1068.72
1064.79
1066.66

CONFIG111:
350.08
350.69
352.1
350.19
352.28
353.46
351.83
350.94

data=np.genfromtxt(csv_file, delimiter=',', dtype='unicode')

It works fine for me.

You can use numpy.recfromcsv(filename): the types of each column will be automatically determined (as if you use np.genfromtxt() with dtype=None), and by default delimiter=",". It's basically a shortcut for np.genfromtxt(filename, delimiter=",", dtype=None) that Pierre GM pointed at in his answer.

If your data file is structured like this

col1, col2, col3
   1,    2,    3
  10,   20,   30
 100,  200,  300

then numpy.genfromtxt can interpret the first line as column headers using the names=True option. With this you can access the data very conveniently by providing the column header:

data = np.genfromtxt('data.txt', delimiter=',', names=True)
print data['col1']    # array([   1.,   10.,  100.])
print data['col2']    # array([   2.,   20.,  200.])
print data['col3']    # array([   3.,   30.,  300.])

Since in your case the data is formed like this

row1,   1,  10, 100
row2,   2,  20, 200
row3,   3,  30, 300

you can achieve something similar using the following code snippet:

labels = np.genfromtxt('data.txt', delimiter=',', usecols=0, dtype=str)
raw_data = np.genfromtxt('data.txt', delimiter=',')[:,1:]
data = {label: row for label, row in zip(labels, raw_data)}

The first line reads the first column (the labels) into an array of strings. The second line reads all data from the file but discards the first column. The third line uses dictionary comprehension to create a dictionary that can be used very much like the structured array which numpy.genfromtxt creates using the names=True option:

print data['row1']    # array([   1.,   10.,  100.])
print data['row2']    # array([   2.,   20.,  200.])
print data['row3']    # array([   3.,   30.,  300.])