84

鉴于此 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

我只是想将它加载为具有 3 行和 7 列的矩阵/ndarray。但是，由于某种原因，我能从 numpy 中得到的只是一个 3 行（每行一个）且没有列的 ndarray。

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,)

我可以手动迭代并将其修改为我想要的形状，但这似乎很愚蠢。我只想将它加载为一个合适的矩阵，这样我就可以在不同的维度上对其进行切片并绘制它，就像在 matlab 中一样。

4

3 回答 3

164

纯麻木 

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

查看loadtxt文档。

您还可以使用 python 的 csv 模块： 

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

您必须将其转换为您喜欢的数字类型。我想您可以将整个内容写在一行中：

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

添加提示：

您还可以使用并获取更快pandas.io.parsers.read_csv的关联数组。numpy

于 2010-11-30T16:20:18.913 回答
6

我认为dtype在有名称行的地方使用会混淆例程。尝试

>>> 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])
于 2010-11-30T16:07:09.420 回答
4

您可以使用np.genfromtxt将带有标题的 CSV 文件读入NumPy 结构化数组。例如：

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))

看起来像这样：

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')])

您可以像这样访问命名列r['E']：

array([1715.37476, 1715.37476, 1715.37476])

注意：此答案以前使用np.recfromcsv将数据读入NumPy 记录数组。虽然该方法没有任何问题，但结构化数组在速度和兼容性方面通常比记录数组更好。

于 2016-02-22T03:04:44.480 回答