load csv into 2D matrix with numpy for plotting

Pure numpy

numpy.loadtxt(open("test.csv", "rb"), delimiter=",", skiprows=1)

Check out the loadtxt documentation.

You can also use python's csv module:

import csvimport numpyreader = csv.reader(open("test.csv", "rb"), delimiter=",")x = list(reader)result = numpy.array(x).astype("float")

You will have to convert it to your favorite numeric type. I guess you can write the whole thing in one line:

result = numpy.array(list(csv.reader(open("test.csv", "rb"), delimiter=","))).astype("float")

Added Hint:

You could also use pandas.io.parsers.read_csv and get the associated numpy array which can be faster.

I think using dtype where there is a name row is confusing the routine. Try

>>> r = np.genfromtxt(fname, delimiter=',', names=True)>>> rarray([[  6.11882430e+02,   9.08956010e+03,   5.13300000e+03,          8.64075140e+02,   1.71537476e+03,   7.65227770e+02,          1.29111196e+12],       [  6.11882430e+02,   9.08956010e+03,   5.13300000e+03,          8.64075140e+02,   1.71537476e+03,   7.65227770e+02,          1.29111311e+12],       [  6.11882430e+02,   9.08956010e+03,   5.13300000e+03,          8.64075140e+02,   1.71537476e+03,   7.65227770e+02,          1.29112065e+12]])>>> r[:,0]    # Slice 0'th columnarray([ 611.88243,  611.88243,  611.88243])

You can read a CSV file with headers into a NumPy structured array with np.genfromtxt. For example:

import numpy as npcsv_fname = 'file.csv'with open(csv_fname, 'w') as fp:    fp.write("""\"A","B","C","D","E","F","timestamp"611.88243,9089.5601,5133.0,864.07514,1715.37476,765.22777,1.291111964948E12611.88243,9089.5601,5133.0,864.07514,1715.37476,765.22777,1.291113113366E12611.88243,9089.5601,5133.0,864.07514,1715.37476,765.22777,1.291120650486E12""")# Read the CSV file into a Numpy record arrayr = np.genfromtxt(csv_fname, delimiter=',', names=True, case_sensitive=True)print(repr(r))

which looks like this:

array([(611.88243, 9089.5601, 5133., 864.07514, 1715.37476, 765.22777, 1.29111196e+12),       (611.88243, 9089.5601, 5133., 864.07514, 1715.37476, 765.22777, 1.29111311e+12),       (611.88243, 9089.5601, 5133., 864.07514, 1715.37476, 765.22777, 1.29112065e+12)],      dtype=[('A', '<f8'), ('B', '<f8'), ('C', '<f8'), ('D', '<f8'), ('E', '<f8'), ('F', '<f8'), ('timestamp', '<f8')])

You can access a named column like this r['E']:

array([1715.37476, 1715.37476, 1715.37476])

Note: this answer previously used np.recfromcsv to read the data into a NumPy record array. While there was nothing wrong with that method, structured arrays are generally better than record arrays for speed and compatibility.