Data('file.csv') no longer infers fields with string values

[bittlingmayer]

On 0.8.0 this worked. But since I updated, instantiating Data from a .csv is broken in cases like the following:

x,tl,z
Be careful driving.,hy,en
Be careful.,hy,en
Can you translate this for me?,hy,en
Chicago is very different from Boston.,hy,en
Don't worry.,hy,en

Actual vs expected output:

Data('data/broken.csv').fields
['0', '1', '2']
Data('data/works.csv').fields
[u'x', u'tl', u'z']

If the values are numbers, then it is inferred correctly:

x,tl,z
Be careful driving.,1,en
Be careful.,12,en
Can you translate this for me?,2,en
Chicago is very different from Boston.,2,en
Don't worry.,2,en

If the column name is 'y', then it is also inferred as expected:

x,y,z
Be careful driving.,hy,en
Be careful.,hy,en
Can you translate this for me?,hy,en
Chicago is very different from Boston.,hy,en
Don't worry.,hy,en

So it's very sensitive.

[bittlingmayer]

I will solve it for now by passing an argument to force the first row to be interpreted as column names.

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html

[bittlingmayer]

http://blaze.pydata.org/en/latest/csv.html#correcting-csv-dialects states: In the first case of incorrect guessing of CSV dialect (e.g. delimiter) Blaze respects and passes through all keyword arguments to pandas.read_csv.

However

Data('data/broken.csv', header=0).fields
['0', '1', '2']

Moreover, pandas infers the fields as expected:

pandas.read_csv('data/broken.csv').columns
Index([u'x', u'tl', u'z'], dtype='object')

So for now the workaround is Data(pandas.read_csv('data/broken.csv')).

[cpcloud]

@bittlingmayer Pass has_header=True to the Data constructor:

d = Data('foo.csv', has_header=True)

[cpcloud]

I'm going to add a test and a bit of documentation for this issue and then close it

[bittlingmayer]

Documentation would be good, although I also think the original ie pandas behaviour is a bit smarter.

[cpcloud]

@bittlingmayer Yes, it's smarter, however this is a compromise between being explicit and convenience