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 csv
import numpy
reader = 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)
>>> r
array([[ 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 column
array([ 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 np
csv_fname = 'file.csv'
with open(csv_fname, 'w') as fp:
fp.write("""\
"A","B","C","D","E","F","timestamp"
611.88243,9089.5601,5133.0,864.07514,1715.37476,765.22777,1.291111964948E12
611.88243,9089.5601,5133.0,864.07514,1715.37476,765.22777,1.291113113366E12
611.88243,9089.5601,5133.0,864.07514,1715.37476,765.22777,1.291120650486E12
""")
# Read the CSV file into a Numpy record array
r = 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.