用 pandas 读取以空格分隔的数据 [英] Read Space-separated Data with Pandas

问题描述

我以前用numpy.loadtxt()读取数据.但是，最近我在 SO 中发现，pandas.read_csv()的速度要快得多.

I used to read my data with numpy.loadtxt(). However, lately I found out in SO, that pandas.read_csv() is much more faster.

要读取这些数据，请使用:

To read these data I use:

pd.read_csv(filename, sep=' ',header=None)

我现在遇到的问题是，在我的情况下，分隔符可以从一个空格， x 空格到一个制表符都不同.

The problem that I encounter right now is that in my case the separator can differ from one space, x spaces to even a tab.

这是我的数据的样子:

56.00     101.85 52.40 101.85 56.000000 101.850000 1
56.00 100.74 50.60 100.74 56.000000 100.740000 2
56.00 100.74 52.10 100.74 56.000000 100.740000 3
56.00 102.96 52.40 102.96 56.000000 102.960000 4
56.00 100.74 55.40 100.74 56.000000 100.740000 5

结果如下:

     0       1     2       3     4       5   6       7   8
0   56     NaN   NaN  101.85  52.4  101.85  56  101.85   1
1   56  100.74  50.6  100.74  56.0  100.74   2     NaN NaN
2   56  100.74  52.1  100.74  56.0  100.74   3     NaN NaN
3   56  102.96  52.4  102.96  56.0  102.96   4     NaN NaN
4   56  100.74  55.4  100.74  56.0  100.74   5     NaN NaN

我必须指定我的数据> 100 MB.因此，我无法预处理数据或先清除它们. 任何想法如何解决此问题?

I have to specify that my data are >100 MB. So I can not preprocess the data or clean them first. Any ideas how to get this fixed?

推荐答案

您的原始行:

pd.read_csv(filename, sep=' ',header=None)

将分隔符指定为单个空格，因为您的csvs可以包含空格或制表符，因此您可以将正则表达式传递给sep参数，如下所示:

was specifying the separator as a single space, because your csvs can have spaces or tabs you can pass a regular expression to the sep param like so:

pd.read_csv(filename, sep='\s+',header=None)

这将分隔符定义为一个或多个空白，并且有一个方便的备忘单，其中列出了正则表达式.

This defines separator as being one single white space or more, there is a handy cheatsheet that lists regular expressions.

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