CraigO CraigO - 2 years ago 245
Python Question

Using numpy.genfromtxt to read a csv file with strings containing commas

I am trying to read in a csv file with

numpy.genfromtxt
but some of the fields are strings which contain commas. The strings are in quotes, but numpy is not recognizing the quotes as defining a single string. For example, with the data in 't.csv':

2012, "Louisville KY", 3.5
2011, "Lexington, KY", 4.0


the code

np.genfromtxt('t.csv', delimiter=',')


produces the error:


ValueError: Some errors were detected !
Line #2 (got 4 columns instead of 3)


The data structure I am looking for is:

array([['2012', 'Louisville KY', '3.5'],
['2011', 'Lexington, KY', '4.0']],
dtype='|S13')


Looking over the documentation, I don't see any options to deal with this. Is there a way do to it with numpy, or do I just need to read in the data with the
csv
module and then convert it to a numpy array?

Answer Source

You can use pandas (the becoming default library for working with dataframes (heterogeneous data) in scientific python) for this. It's read_csv can handle this. From the docs:

quotechar : string

The character to used to denote the start and end of a quoted item. Quoted items 
can include the delimiter and it will be ignored.

The default value is ". An example:

In [1]: import pandas as pd

In [2]: from StringIO import StringIO

In [3]: s="""year, city, value
   ...: 2012, "Louisville KY", 3.5
   ...: 2011, "Lexington, KY", 4.0"""

In [4]: pd.read_csv(StringIO(s), quotechar='"', skipinitialspace=True)
Out[4]:
   year           city  value
0  2012  Louisville KY    3.5
1  2011  Lexington, KY    4.0

The trick here is that you also have to use skipinitialspace=True to deal with the spaces after the comma-delimiter.

Apart from a powerful csv reader, I can also strongly advice to use pandas with the heterogeneous data you have (the example output in numpy you give are all strings, although you could use structured arrays).

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