Implement Google quote functionality

[gliptak]

No description provided.

[femtotrader]

Thanks @gliptak but there is some PEP8 errors

$ flake8 --ignore E501 pandas_datareader
pandas_datareader/google/quotes.py:6:1: E302 expected 2 blank lines, found 1
pandas_datareader/google/quotes.py:26:45: E226 missing whitespace around arithmetic operator
pandas_datareader/google/quotes.py:28:69: W292 no newline at end of file

Could you fix them ? That will be very kind.

[gliptak]

@femtotrader Corrected.

[femtotrader]

TSGTM. Thanks @gliptak

[femtotrader]

I noticed (too lately before merging) that

In [5]: pdr.get_quote_google(['MSFT', 'F', 'GOOG'], '2016-02-01', '2016-02-02')
Out[5]:
           0
MSFT   51.30
F      12.47
GOOG  705.07

I wonder why not just using dict... or at least a Series (not a DataFrame) !

[femtotrader]

#184

[gliptak]

@femtotrader Could you describe the format you would like to see? Thanks

[femtotrader]

A DataFrame with only one column (named "0") is not a good idea.
A Pandas Series with key being ticker might be a better idea.
You might also define name of this Series (maybe "quote" or "quotes")

[femtotrader]

When there is only one ticker, only quote should be returned.

For now we are having

In [18]: pdr.get_quote_google('GOOG')
Out[18]:
           0
GOOG  705.07

it should be

In [18]: pdr.get_quote_google('GOOG')
Out[18]: 705.07

same for

pdr.get_quote_google(['GOOG'])

Zipline get_pricing function is working in a similar way.

[gliptak]

get_quote_yahoo('GOOG') returns:

        PE change_pct    last  short_ratio    time
 GOOG  30.47     +3.02%  718.81         1.38  4:00pm

get_quote_yahoo((['GOOG', 'AAPL', 'GOOG'])) returns:

         PE change_pct    last  short_ratio    time
GOOG  30.47     +3.02%  718.81         1.38  4:00pm
AAPL  10.70     +3.97%  100.53         1.02  4:00pm
GOOG  30.47     +3.02%  718.81         1.38  4:00pm

[femtotrader]

Ok, so that's a DataFrame because there was more data than just last price.
But you might ensure that data have correct type.
You should display here df.dtypes and also if there is object type, display type of each data of first row (for example)
change_pct seems to be a string so you might convert to float (or decimal.Decimal).
Something like

In [56]: s = '+3.02%'
In [57]: float(s[:-1])
Out[57]: 3.02
In [58]: s = '-3.02%'
In [59]: float(s[:-1])
Out[59]: -3.02

time seems to also be a string... you might convert to datetime.time
pd.to_datetime('08:00PM').time() seems to do it.