add pandas.read_text for complex text/log parsing
Current method is using read_table/read_csv and placing logic in sep field is a bruteforce way to process log files, but isn't clean as you will get two padded columns of NaN at the start and end and does not handle parameter of regex for column names.
#### Code Sample, a copy-pastable example if possible

df = pd.read_table("file.log",
              sep=r'^(?P<datetime>[\d-]+? [\d:]+?) (?P<level>.+?) [(?P<name>.+?)] ((?P<filename>.+?):(?P<lineno>\d+?)) - (?P<message>.*?)$',
             )

df currently would produce...

|  | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | NaN | 2015-01-01 00:00:00 | INFO | main | main.java | 10 | Hello World | NaN |
| 1 | NaN | 2015-01-01 00:00:00 | ERROR | main | main.java | 11 | Ouch! | NaN |
#### Expected Output

|  | datetime | level | name | filename | lineno | message |
| --- | --- | --- | --- | --- | --- | --- |
| 0 | 2015-01-01 00:00:00 | INFO | main | main.java | 10 | Hello World |
| 1 | 2015-01-01 00:00:00 | ERROR | main | main.java | 11 | Ouch! |
#### output of `pd.show_versions()`
## INSTALLED VERSIONS

commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.1
IPython: 4.1.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.2
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.7.1.None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
