load csv into 2D matrix with numpy for plotting

Question

Given this CSV file:

"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

I simply want to load it as a matrix/ndarray with 3 rows and 7 columns. However, for some reason, all I can get out of numpy is an ndarray with 3 rows (one per line) and no columns.

r = np.genfromtxt(fname,delimiter=',',dtype=None, names=True)
print r
print r.shape

[ (611.88243, 9089.5601000000006, 5133.0, 864.07514000000003, 1715.3747599999999, 765.22776999999996, 1291111964948.0)
 (611.88243, 9089.5601000000006, 5133.0, 864.07514000000003, 1715.3747599999999, 765.22776999999996, 1291113113366.0)
 (611.88243, 9089.5601000000006, 5133.0, 864.07514000000003, 1715.3747599999999, 765.22776999999996, 1291120650486.0)]
(3,)

I can manually iterate and hack it into the shape I want, but this seems silly. I just want to load it as a proper matrix so I can slice it across different dimensions and plot it, just like in matlab.

Augustin · Accepted Answer · 2017-01-16 11:16:48Z

169

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.

edited Jan 16, 2017 at 11:16

Augustin

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answered Nov 30, 2010 at 16:20

Kaveh_kh

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6 Comments

Arturo Over a year ago

I would add that the skiprows=1 flag is skipping the first row, and is not a standard activation flag if you want to keep all the data. Worked perfectly!

mhstnsc Over a year ago

loadtxt does not load also the column names which happen with names=True on genfromtxt

Daniel Soutar Over a year ago

Can I ask - is open local to that single line? As in, does the file close at the end of the line?

Kaveh_kh Over a year ago

Yes, it closes the file. See also: stackoverflow.com/questions/8011797/…

Akaisteph7 Over a year ago

@fireball.1 speed tests for claims like this would be great for posterity

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mtrw · Accepted Answer · 2010-11-30 16:07:09Z

6

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])

answered Nov 30, 2010 at 16:07

mtrw

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2 Comments

dgorissen Over a year ago

Interestingly, this does not change the result in my case. I am using Python 2.5 and numpy 1.4.1 so maybe that is the problem

mtrw Over a year ago

I'm using Python 2.6 and NumPy 1.3.0! I like the older behavior better.

Mike T · Accepted Answer · 2018-05-05 06:18:23Z

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.

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