使用 numpy 将 csv 加载到 2D 矩阵中进行绘图 [英] load csv into 2D matrix with numpy for plotting

问题描述

鉴于此 CSV 文件:

"A","B","C","D","E","F","timestamp"611.88243,9089.5601,5133.0,864.07514,1715.37476,765.22777,1.291111964948E12611.88243,9089.5601,5133.0,864.07514,1715.37476,765.22777,1.291113113366E12611.88243,9089.5601,5133.0,864.07514,1715.37476,765.22777,1.291120650486E12

我只想将其加载为 3 行 7 列的矩阵/ndarray.然而，出于某种原因，我能从 numpy 中得到的只是一个 3 行(每行一个)没有列的 ndarray.

r = np.genfromtxt(fname,delimiter=',',dtype=None, names=True)打印打印 r.shape[ (611.88243, 9089.5601000000006, 5133.0, 864.07514000000003, 1715.3747599999999, 765.22776999991999)6199996(611.88243, 9089.5601000000006, 5133.0, 864.07514000000003, 1715.3747599999999, 765.2277699999.16916)316161(611.88243, 9089.5601000000006, 5133.0, 864.07514000000003, 1715.3747599999999, 765.227769999916296) 814.07514000000003) 81(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.我只想将它作为一个适当的矩阵加载，这样我就可以将它切片到不同的维度并绘制它，就像在 matlab 中一样.

解决方案

纯 numpy

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

查看 loadtxt 文档.

你也可以使用 python 的 csv 模块:

导入csv导入 numpyreader = csv.reader(open("test.csv", "rb"), delimiter=",")x = 列表(读者)结果 = numpy.array(x).astype("float")

您必须将其转换为您喜欢的数字类型.我想你可以在一行中写出整件事:

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

添加提示:

您也可以使用 pandas.io.parsers.read_csv 并获取关联的 numpy 数组，这样可以更快.

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.

解决方案

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.

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