Suppose your data is like the following
DD,MM,YY,AMT,WHERE 30,5,2015,2,TRAVEL 30,5,2015,50,TRAVEL 30,5,2015,5,PHONE 31,5,2015,6.62,TESCO 31,5,2015,5,POUNDSHOP 31,5,2015,4.51,SAINSBURY 31,5,2015,1,PHONE
Let’s load it with numpy using genfromtxt
expdata=np.genfromtxt(fname, delimiter=',',skip_header=1,usecols=[0,1,2,3,4]) print expdata[0] print expdata[-1]
This prints the following
[ 3.00000000e+01 5.00000000e+00 2.01500000e+03 2.00000000e+00 NaN] [ 30. 5. 2015. 50. NaN]
Numeric data is read but not quite as what we wanted. Notice the NaN.
You can supply genfromtxt the datatypes using the keyword format like
dtype=([(‘f0’, ‘<i4’), (‘f1’, ‘<i4’), (‘f2’, ‘<i4’), (‘f3’, ‘<f8’), (‘f4’, ‘|S14’)])
expdata=np.genfromtxt(fname, delimiter=',',skip_header=1,usecols=[0,1,2,3,4],dtype=([('f0', '&amp;lt;i4'), ('f1', '&amp;lt;i4'), ('f2', '&amp;lt;i4'), ('f3', '&amp;lt;f8'), ('f4', '|S14')])) (30, 5, 2015, 2.0, 'TRAVEL') (30, 5, 2015, 50.0, 'TRAVEL')
But that looks too much work but there’s a simple smart way to do this. Use dtype=None
expdata=np.genfromtxt(fname, delimiter=',',skip_header=1,usecols=[0,1,2,3,4],dtype=None) (30, 5, 2015, 2.0, 'TRAVEL') (30, 5, 2015, 50.0, 'TRAVEL')
Using dtype=None is a good trick if you don’t know what your columns should be and you need some help in getting the format. This might be slower but it does work and once you have the data you can replace the dtype none with the appropriate arguments.