将 csv 加载到带有 numpy 的二维矩阵中进行绘图

鉴于这个 CSV 文件:

"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

我只是想把它加载为一个矩阵/ndarray，它有3行7列。但是，由于某种原因，我能得到的只是一个 ndarray，它有3行(每行一行) ，没有列。

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,)

我可以手动迭代并把它修改成我想要的形状，但这看起来很傻。我只是想加载它作为一个合适的矩阵，这样我就可以把它切割成不同的维度，然后绘制出来，就像在 matlab 中一样。

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小开

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])
小开
最佳答案

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.

小开

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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