read_csv dtype argument not working when there is a footer

[socheon]

xref #7141

Datafile test.csv

col1|col2
a|438087272980
b|399432587827
c|592706116147
d|1584843561523
footer 1

Command

print pd.read_csv('test.csv', sep='|', skipfooter=1, dtype={'col2':'object'}).dtypes

Output

col1 object
col2 int64
dtype: object

Expected Output

col1 object
col2 object
dtype: object

Platform

Windows XP, Python 2.7, Pandas version 0.11.0

[jreback]

is present in master, thanks for the report

work-around is to coerce after

In [11]: df
Out[11]: 
  col1           col2
0    a   438087272980
1    b   399432587827
2    c   592706116147
3    d  1584843561523

In [12]: df.dtypes
Out[12]: 
col1    object
col2     int64
dtype: object

In [13]: df['col2'].astype(object)
Out[13]: 
0     438087272980
1     399432587827
2     592706116147
3    1584843561523
Name: col2, dtype: object

[guyrt]

dtype is only supported in c parser, but
setfooter is only supported in the python parser, which is what this example is implicitly using.

To fix this bug, we have to either implement dtype in python parser or setfooter in c parser (or both!)

However, there's another problem here. If you explicitly set engine to python, you'll get an error:

ValueError: The 'dtype' option is not supported with the 'python' engine

However, that check happens before we implicitly switch parser to python.

I think it would be best to (a) Issue a warning when we switch converters automatically, and (b) move the engine switch before we validate options against the eventual engine. Probably should be a different ticket though.

[michaelaye]

As I am regularly parsing Gigabytes of text files, I definitely support the idea of having a warning when I'm switched to a slower parsing engine.

[michaelaye]

Just realized that the dtype argument does not work for me in master at all using the 'c engine?

dtypes_dic

{'af': 'int',
 'c': 'int',
 'date': 'int',
 'det': 'int',
 'hour': 'int',
 'minute': 'int',
 'month': 'int',
 'orbit': 'int',
 'year': 'int'}

df = pd.read_csv(fname, delim_whitespace=True, sep='\s*',
                           dtype=dtypes_dic, engine='c')

df.dtypes

date     float64
month    float64
year     float64
...
qual     float64
sppsx    float64
sppsy    float64
Length: 40, dtype: object

[jreback]

FYI, delim_whitespace and sep just uses sep (e.g. delim_whitespace implies what you are doing wtih sep)

[jreback]

@michaelaye dtype works, maybe post an explicit example (this issue is when both skip_footer and dtype is specified with python parser)

[michaelaye]

import StringIO

s = """\ta\tb\tc\td
\t1.0\t4.2\t2\t6
\t6.0\t2.1\t3\t6
"""
s_in = StringIO.StringIO(s)
s_in.seek(0)
df = pd.read_csv(s_in, sep='\s*', dtype={'a':np.int32})
df.dtypes

a    float64
b    float64
c      int64
d      int64
dtype: object

I was using sep='\s*' because delim_whitespace could not cope with initial whitespace as you can try out with this example. Also skipinitialspace did not help. But this sep regex supersedes what delim_whitespace does, so thanks for letting me know that they overlap.

Maybe a down coercion is not allowed due to potential data loss?

[jreback]

@guyrt want to take this? (I guess implement dtype in python parser and footer?)