Remote Data Access

Functions from pandas_datareader.data and pandas_datareader.wb extract data from various Internet sources into a pandas DataFrame. Currently the following sources are supported:

It should be noted, that various sources support different kinds of data, so not all sources implement the same methods and the data elements returned might also differ.

Yahoo! Finance

Historical stock prices from Yahoo! Finance.

In [1]: import pandas_datareader.data as web

In [2]: import datetime

In [3]: start = datetime.datetime(2010, 1, 1)

In [4]: end = datetime.datetime(2013, 1, 27)

In [5]: f = web.DataReader("F", 'yahoo', start, end)

In [6]: f.ix['2010-01-04']
Out[6]: 
Open               10.170000
High               10.280000
Low                10.050000
Close              10.280000
Adj Close           8.201456
Volume       60855800.000000
Name: 2010-01-04 00:00:00, dtype: float64

Historical corporate actions (Dividends and Stock Splits) with ex-dates from Yahoo! Finance.

In [7]: import pandas_datareader.data as web

In [8]: import datetime

In [9]: start = datetime.datetime(2010, 1, 1)

In [10]: end = datetime.datetime(2015, 5, 9)

In [11]: web.DataReader('AAPL', 'yahoo-actions', start, end)
Out[11]: 
              value    action
Date                         
2015-05-07  0.52000  DIVIDEND
2015-02-05  0.47000  DIVIDEND
2014-11-06  0.47000  DIVIDEND
2014-08-07  0.47000  DIVIDEND
2014-06-09  0.00000     SPLIT
2014-05-08  0.47000  DIVIDEND
2014-02-06  0.43571  DIVIDEND
2013-11-06  0.43571  DIVIDEND
2013-08-08  0.43571  DIVIDEND
2013-05-09  0.43571  DIVIDEND
2013-02-07  0.37857  DIVIDEND
2012-11-07  0.37857  DIVIDEND
2012-08-09  0.37857  DIVIDEND

Historical dividends from Yahoo! Finance.

In [12]: import pandas_datareader.data as web

In [13]: import datetime

In [14]: start = datetime.datetime(2010, 1, 1)

In [15]: end = datetime.datetime(2013, 1, 27)

In [16]: f = web.DataReader("F", 'yahoo-dividends', start, end)

In [17]: f
Out[17]: 
            Dividends
Date                 
2012-01-27       0.05
2012-04-30       0.05
2012-08-01       0.05
2012-10-31       0.05

Yahoo! Finance Quotes

*Experimental*

The YahooQuotesReader class allows to get quotes data from Yahoo! Finance.

In [18]: import pandas_datareader.data as web

In [19]: amzn = web.get_quote_yahoo('AMZN')

In [20]: amzn
Out[20]: 
          PE change_pct     last  short_ratio    time
AMZN  195.83     +0.09%  1039.87         1.14  4:00pm

Yahoo! Finance Options

*Experimental*

The Options class allows the download of options data from Yahoo! Finance.

The get_all_data method downloads and caches option data for all expiry months and provides a formatted DataFrame with a hierarchical index, so its easy to get to the specific option you want.

In [21]: from pandas_datareader.data import Options

In [22]: aapl = Options('aapl', 'yahoo')

In [23]: data = aapl.get_all_data()

In [24]: data.iloc[0:5, 0:5]
Out[24]: 
                                              Last     Bid     Ask       Chg  \
Strike Expiry     Type Symbol                                                  
2.5    2017-08-18 call AAPL170818C00002500  147.70  147.45  148.05  0.000000   
                  put  AAPL170818P00002500    0.02    0.00    0.02  0.000000   
       2018-01-19 call AAPL180119C00002500  150.18  152.50  153.20 -2.130005   
5.0    2017-08-18 call AAPL170818C00005000  145.80  144.95  145.50  0.000000   
       2018-01-19 call AAPL180119C00005000  147.98  150.05  150.70  0.000000   

                                              PctChg  
Strike Expiry     Type Symbol                         
2.5    2017-08-18 call AAPL170818C00002500  0.000000  
                  put  AAPL170818P00002500  0.000000  
       2018-01-19 call AAPL180119C00002500 -1.390978  
5.0    2017-08-18 call AAPL170818C00005000  0.000000  
       2018-01-19 call AAPL180119C00005000  0.000000  

#Show the $100 strike puts at all expiry dates:
In [25]: data.loc[(100, slice(None), 'put'),:].iloc[0:5, 0:5]
Out[25]: 
                                            Last   Bid   Ask  Chg  PctChg
Strike Expiry     Type Symbol                                            
100    2017-07-28 put  AAPL170728P00100000  0.01  0.00  0.02    0       0
       2017-08-18 put  AAPL170818P00100000  0.01  0.00  0.01    0       0
       2017-08-25 put  AAPL170825P00100000  0.02  0.00  0.02    0       0
       2017-09-01 put  AAPL170901P00100000  0.04  0.00  0.03    0       0
       2017-09-15 put  AAPL170915P00100000  0.02  0.01  0.02    0       0

#Show the volume traded of $100 strike puts at all expiry dates:
In [26]: data.loc[(100, slice(None), 'put'),'Vol'].head()
Out[26]: 
Strike  Expiry      Type  Symbol             
100     2017-07-28  put   AAPL170728P00100000      1
        2017-08-18  put   AAPL170818P00100000    620
        2017-08-25  put   AAPL170825P00100000      2
        2017-09-01  put   AAPL170901P00100000      1
        2017-09-15  put   AAPL170915P00100000      3
Name: Vol, dtype: float64

If you don’t want to download all the data, more specific requests can be made.

In [27]: import datetime

In [28]: expiry = datetime.date(2016, 1, 1)

In [29]: data = aapl.get_call_data(expiry=expiry)

In [30]: data.iloc[0:5:, 0:5]
Out[30]: 
                                             Last    Bid    Ask       Chg  \
Strike Expiry     Type Symbol                                               
95     2017-07-28 call AAPL170728C00095000  58.55  57.50  58.15  0.000000   
100    2017-07-28 call AAPL170728C00100000  53.55  52.50  53.15  0.000000   
105    2017-07-28 call AAPL170728C00105000  48.55  47.50  48.15  0.000000   
120    2017-07-28 call AAPL170728C00120000  30.18  30.00  30.45 -0.670000   
130    2017-07-28 call AAPL170728C00130000  23.15  22.65  22.95  0.799999   

                                              PctChg  
Strike Expiry     Type Symbol                         
95     2017-07-28 call AAPL170728C00095000  0.000000  
100    2017-07-28 call AAPL170728C00100000  0.000000  
105    2017-07-28 call AAPL170728C00105000  0.000000  
120    2017-07-28 call AAPL170728C00120000 -2.171799  
130    2017-07-28 call AAPL170728C00130000  3.579415  

Note that if you call get_all_data first, this second call will happen much faster, as the data is cached.

If a given expiry date is not available, data for the next available expiry will be returned (January 15, 2015 in the above example).

Available expiry dates can be accessed from the expiry_dates property.

In [31]: aapl.expiry_dates
Out[31]: 
[datetime.date(2017, 7, 28),
 datetime.date(2017, 8, 4),
 datetime.date(2017, 8, 11),
 datetime.date(2017, 8, 18),
 datetime.date(2017, 8, 25),
 datetime.date(2017, 9, 1),
 datetime.date(2017, 9, 15),
 datetime.date(2017, 10, 20),
 datetime.date(2017, 11, 17),
 datetime.date(2017, 12, 15),
 datetime.date(2018, 1, 19),
 datetime.date(2018, 2, 16),
 datetime.date(2018, 4, 20),
 datetime.date(2018, 6, 15),
 datetime.date(2018, 9, 21),
 datetime.date(2019, 1, 18)]

In [32]: data = aapl.get_call_data(expiry=aapl.expiry_dates[0])

In [33]: data.iloc[0:5:, 0:5]
Out[33]: 
                                             Last    Bid    Ask       Chg  \
Strike Expiry     Type Symbol                                               
95     2017-07-28 call AAPL170728C00095000  58.55  57.50  58.15  0.000000   
100    2017-07-28 call AAPL170728C00100000  53.55  52.50  53.15  0.000000   
105    2017-07-28 call AAPL170728C00105000  48.55  47.50  48.15  0.000000   
120    2017-07-28 call AAPL170728C00120000  30.18  30.00  30.45 -0.670000   
130    2017-07-28 call AAPL170728C00130000  23.15  22.65  22.95  0.799999   

                                              PctChg  
Strike Expiry     Type Symbol                         
95     2017-07-28 call AAPL170728C00095000  0.000000  
100    2017-07-28 call AAPL170728C00100000  0.000000  
105    2017-07-28 call AAPL170728C00105000  0.000000  
120    2017-07-28 call AAPL170728C00120000 -2.171799  
130    2017-07-28 call AAPL170728C00130000  3.579415  

A list-like object containing dates can also be passed to the expiry parameter, returning options data for all expiry dates in the list.

In [34]: data = aapl.get_near_stock_price(expiry=aapl.expiry_dates[0:3])

In [35]: data.iloc[0:5:, 0:5]
Out[35]: 
                                            Last   Bid   Ask   Chg    PctChg
Strike Expiry     Type Symbol                                               
152.5  2017-08-04 call AAPL170804C00152500  3.19  3.10  3.20  0.18  5.980069
       2017-08-11 call AAPL170811C00152500  3.50  3.45  3.55  0.15  4.477615
155.0  2017-07-28 call AAPL170728C00155000  0.31  0.28  0.30 -0.01 -3.124997
       2017-08-04 call AAPL170804C00155000  2.04  1.95  2.00  0.13  6.806283
       2017-08-11 call AAPL170811C00155000  2.37  2.34  2.39  0.03  1.282050

The month and year parameters can be used to get all options data for a given month.

Google Finance

In [36]: import pandas_datareader.data as web

In [37]: import datetime

In [38]: start = datetime.datetime(2010, 1, 1)

In [39]: end = datetime.datetime(2013, 1, 27)

In [40]: f = web.DataReader("F", 'google', start, end)

In [41]: f.ix['2010-01-04']
Out[41]: 
Open            10.17
High            10.28
Low             10.05
Close           10.28
Volume    60855796.00
Name: 2010-01-04 00:00:00, dtype: float64

Google Finance Quotes

*Experimental*

The GoogleQuotesReader class allows to get quotes data from Google Finance.

In [42]: import pandas_datareader.data as web

In [43]: q = web.get_quote_google(['AMZN', 'GOOG'])

In [44]: q
Out[44]: 
      change_pct     last                time
AMZN        0.09  1039.87 2017-07-25 16:00:00
GOOG       -3.02   950.70 2017-07-25 16:00:00

Google Finance Options

*Experimental*

The Options class allows the download of options data from Google Finance.

The get_options_data method downloads options data for specified expiry date and provides a formatted DataFrame with a hierarchical index, so its easy to get to the specific option you want.

Available expiry dates can be accessed from the expiry_dates property.

In [45]: from pandas_datareader.data import Options

In [46]: goog = Options('goog', 'google')

In [47]: data = goog.get_options_data(expiry=goog.expiry_dates[0])

In [48]: data.iloc[0:5, 0:5]
Out[48]: 
                                              Last     Bid     Ask    Chg  \
Strike Expiry     Type Symbol                                               
340    2018-01-19 call GOOG180119C00340000  578.00  611.20  615.50   0.00   
                  put  GOOG180119P00340000    0.05     NaN    0.05   0.00   
350    2018-01-19 call GOOG180119C00350000  604.50  601.30  604.20 -30.80   
                  put  GOOG180119P00350000    0.05    0.05    0.10  -0.05   
360    2018-01-19 call GOOG180119C00360000  612.40  591.30  595.50   0.00   

                                            PctChg  
Strike Expiry     Type Symbol                       
340    2018-01-19 call GOOG180119C00340000    0.00  
                  put  GOOG180119P00340000    0.00  
350    2018-01-19 call GOOG180119C00350000   -4.85  
                  put  GOOG180119P00350000  -50.00  
360    2018-01-19 call GOOG180119C00360000    0.00  

Enigma

Access datasets from Enigma, the world’s largest repository of structured public data.

In [49]: import os

In [50]: import pandas_datareader as pdr

In [51]: df = pdr.get_data_enigma('enigma.trade.ams.toxic.2015', os.getenv('ENIGMA_API_KEY'))

ValueErrorTraceback (most recent call last)
<ipython-input-51-8b19d4dc1932> in <module>()
----> 1 df = pdr.get_data_enigma('enigma.trade.ams.toxic.2015', os.getenv('ENIGMA_API_KEY'))

/home/docs/checkouts/readthedocs.org/user_builds/pandas-datareader/envs/latest/local/lib/python2.7/site-packages/pandas_datareader-0.5.0-py2.7.egg/pandas_datareader/data.pyc in get_data_enigma(*args, **kwargs)
     42 
     43 def get_data_enigma(*args, **kwargs):
---> 44     return EnigmaReader(*args, **kwargs).read()
     45 
     46 

/home/docs/checkouts/readthedocs.org/user_builds/pandas-datareader/envs/latest/local/lib/python2.7/site-packages/pandas_datareader-0.5.0-py2.7.egg/pandas_datareader/enigma.pyc in __init__(self, datapath, api_key, retry_count, pause, session)
     47             self._api_key = os.getenv('ENIGMA_API_KEY')
     48             if self._api_key is None:
---> 49                 raise ValueError("Please provide an Enigma API key or set "
     50                                  "the ENIGMA_API_KEY environment variable\n"
     51                                  "If you do not have an API key, you can get "

ValueError: Please provide an Enigma API key or set the ENIGMA_API_KEY environment variable
If you do not have an API key, you can get one here: https://app.enigma.io/signup

In [52]: df.columns

NameErrorTraceback (most recent call last)
<ipython-input-52-6a4642092433> in <module>()
----> 1 df.columns

NameError: name 'df' is not defined

Quandl

Daily financial data (prices of stocks, ETFs etc.) from Quandl. The symbol names consist of two parts: DB name and symbol name. DB names can be all the free ones listed on the Quandl website <https://blog.quandl.com/free-data-on-quandl>__. Symbol names vary with DB name; for WIKI (US stocks), they are the common ticker symbols, in some other cases (such as FSE) they can be a bit strange. Some sources are also mapped to suitable ISO country codes in the dot suffix style shown above, currently available for `BE, CN, DE, FR, IN, JP, NL, PT, UK, US.

As of June 2017, each DB has a different data schema, the coverage in terms of time range is sometimes surprisingly small, and the data quality is not always good.

In [53]: import pandas_datareader.data as web

In [54]: symbol = 'WIKI/AAPL'  # or 'AAPL.US'

In [55]: df = web.DataReader(symbol, 'quandl', "2015-01-01", "2015-01-05")

RemoteDataErrorTraceback (most recent call last)
<ipython-input-55-d584ffd028d3> in <module>()
----> 1 df = web.DataReader(symbol, 'quandl', "2015-01-01", "2015-01-05")

/home/docs/checkouts/readthedocs.org/user_builds/pandas-datareader/envs/latest/local/lib/python2.7/site-packages/pandas_datareader-0.5.0-py2.7.egg/pandas_datareader/data.pyc in DataReader(name, data_source, start, end, retry_count, pause, session, access_key)
    170         return QuandlReader(symbols=name, start=start, end=end,
    171                             retry_count=retry_count, pause=pause,
--> 172                             session=session).read()
    173     else:
    174         msg = "data_source=%r is not implemented" % data_source

/home/docs/checkouts/readthedocs.org/user_builds/pandas-datareader/envs/latest/local/lib/python2.7/site-packages/pandas_datareader-0.5.0-py2.7.egg/pandas_datareader/quandl.pyc in read(self)
    103 
    104     def read(self):
--> 105         df = super(QuandlReader, self).read()
    106         df.rename(columns=lambda n: n.replace(' ', '')
    107                                      .replace('.', '')

/home/docs/checkouts/readthedocs.org/user_builds/pandas-datareader/envs/latest/local/lib/python2.7/site-packages/pandas_datareader-0.5.0-py2.7.egg/pandas_datareader/base.pyc in read(self)
    179         if isinstance(self.symbols, (compat.string_types, int)):
    180             df = self._read_one_data(self.url,
--> 181                                      params=self._get_params(self.symbols))
    182         # Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
    183         elif isinstance(self.symbols, DataFrame):

/home/docs/checkouts/readthedocs.org/user_builds/pandas-datareader/envs/latest/local/lib/python2.7/site-packages/pandas_datareader-0.5.0-py2.7.egg/pandas_datareader/base.pyc in _read_one_data(self, url, params)
     77         """ read one data from specified URL """
     78         if self._format == 'string':
---> 79             out = self._read_url_as_StringIO(url, params=params)
     80         elif self._format == 'json':
     81             out = self._get_response(url, params=params).json()

/home/docs/checkouts/readthedocs.org/user_builds/pandas-datareader/envs/latest/local/lib/python2.7/site-packages/pandas_datareader-0.5.0-py2.7.egg/pandas_datareader/base.pyc in _read_url_as_StringIO(self, url, params)
     88         Open url (and retry)
     89         """
---> 90         response = self._get_response(url, params=params)
     91         text = self._sanitize_response(response)
     92         out = StringIO()

/home/docs/checkouts/readthedocs.org/user_builds/pandas-datareader/envs/latest/local/lib/python2.7/site-packages/pandas_datareader-0.5.0-py2.7.egg/pandas_datareader/base.pyc in _get_response(self, url, params, headers)
    137         if params is not None and len(params) > 0:
    138             url = url + "?" + urlencode(params)
--> 139         raise RemoteDataError('Unable to read URL: {0}'.format(url))
    140 
    141     def _get_crumb(self, *args):

RemoteDataError: Unable to read URL: https://www.quandl.com/api/v3/datasets/WIKI/AAPL.csv?start_date=2015-01-01&end_date=2015-01-05&order=asc

In [56]: df.loc['2015-01-02']

NameErrorTraceback (most recent call last)
<ipython-input-56-51b84fe95f16> in <module>()
----> 1 df.loc['2015-01-02']

NameError: name 'df' is not defined

FRED

In [57]: import pandas_datareader.data as web

In [58]: import datetime

In [59]: start = datetime.datetime(2010, 1, 1)

In [60]: end = datetime.datetime(2013, 1, 27)

In [61]: gdp = web.DataReader("GDP", "fred", start, end)

In [62]: gdp.ix['2013-01-01']
Out[62]: 
GDP    16475.4
Name: 2013-01-01 00:00:00, dtype: float64

# Multiple series:
In [63]: inflation = web.DataReader(["CPIAUCSL", "CPILFESL"], "fred", start, end)

In [64]: inflation.head()
Out[64]: 
            CPIAUCSL  CPILFESL
DATE                          
2010-01-01   217.488   220.633
2010-02-01   217.281   220.731
2010-03-01   217.353   220.783
2010-04-01   217.403   220.822
2010-05-01   217.290   220.962

Fama/French

Access datasets from the Fama/French Data Library. The get_available_datasets function returns a list of all available datasets.

In [65]: from pandas_datareader.famafrench import get_available_datasets

In [66]: import pandas_datareader.data as web

In [67]: len(get_available_datasets())
Out[67]: 262

In [68]: ds = web.DataReader("5_Industry_Portfolios", "famafrench")

In [69]: print(ds['DESCR'])
5 Industry Portfolios
---------------------

This file was created by CMPT_IND_RETS using the 201705 CRSP database. It contains value- and equal-weighted returns for 5 industry portfolios. The portfolios are constructed at the end of June. The annual returns are from January to December. Missing data are indicated by -99.99 or -999. Copyright 2017 Kenneth R. French

  0 : Average Value Weighted Returns -- Monthly (89 rows x 5 cols)
  1 : Average Equal Weighted Returns -- Monthly (89 rows x 5 cols)
  2 : Average Value Weighted Returns -- Annual (7 rows x 5 cols)
  3 : Average Equal Weighted Returns -- Annual (7 rows x 5 cols)
  4 : Number of Firms in Portfolios (89 rows x 5 cols)
  5 : Average Firm Size (89 rows x 5 cols)
  6 : Sum of BE / Sum of ME (7 rows x 5 cols)
  7 : Value-Weighted Average of BE/ME (7 rows x 5 cols)

In [70]: ds[4].ix['1926-07']

KeyErrorTraceback (most recent call last)
<ipython-input-70-79093f940e41> in <module>()
----> 1 ds[4].ix['1926-07']

/usr/lib/python2.7/dist-packages/pandas/core/indexing.pyc in __getitem__(self, key)
     68             return self._getitem_tuple(key)
     69         else:
---> 70             return self._getitem_axis(key, axis=0)
     71 
     72     def _get_label(self, label, axis=0):

/usr/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
    965                     return self._get_loc(key, axis=axis)
    966 
--> 967             return self._get_label(key, axis=axis)
    968 
    969     def _getitem_iterable(self, key, axis=0):

/usr/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _get_label(self, label, axis)
     84             raise IndexingError('no slices here, handle elsewhere')
     85 
---> 86         return self.obj._xs(label, axis=axis)
     87 
     88     def _get_loc(self, key, axis=0):

/usr/lib/python2.7/dist-packages/pandas/core/generic.pyc in xs(self, key, axis, level, copy, drop_level)
   1484                                                       drop_level=drop_level)
   1485         else:
-> 1486             loc = self.index.get_loc(key)
   1487 
   1488             if isinstance(loc, np.ndarray):

/usr/lib/python2.7/dist-packages/pandas/tseries/period.pyc in get_loc(self, key, method, tolerance)
    667                 return Index.get_loc(self, key.ordinal, method, tolerance)
    668             except KeyError:
--> 669                 raise KeyError(key)
    670 
    671     def _maybe_cast_slice_bound(self, label, side, kind):

KeyError: Period('1926-07', 'M')

World Bank

pandas users can easily access thousands of panel data series from the World Bank’s World Development Indicators by using the wb I/O functions.

Indicators

Either from exploring the World Bank site, or using the search function included, every world bank indicator is accessible.

For example, if you wanted to compare the Gross Domestic Products per capita in constant dollars in North America, you would use the search function:

In [1]: from pandas_datareader import wb

In [2]: wb.search('gdp.*capita.*const').iloc[:,:2]
Out[2]:
                     id                                               name
3242            GDPPCKD             GDP per Capita, constant US$, millions
5143     NY.GDP.PCAP.KD                 GDP per capita (constant 2005 US$)
5145     NY.GDP.PCAP.KN                      GDP per capita (constant LCU)
5147  NY.GDP.PCAP.PP.KD  GDP per capita, PPP (constant 2005 internation...

Then you would use the download function to acquire the data from the World Bank’s servers:

In [3]: dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'CA', 'MX'], start=2005, end=2008)

In [4]: print(dat)
                      NY.GDP.PCAP.KD
country       year
Canada        2008  36005.5004978584
              2007  36182.9138439757
              2006  35785.9698172849
              2005  35087.8925933298
Mexico        2008  8113.10219480083
              2007  8119.21298908649
              2006  7961.96818458178
              2005  7666.69796097264
United States 2008  43069.5819857208
              2007  43635.5852068142
              2006   43228.111147107
              2005  42516.3934699993

The resulting dataset is a properly formatted DataFrame with a hierarchical index, so it is easy to apply .groupby transformations to it:

In [6]: dat['NY.GDP.PCAP.KD'].groupby(level=0).mean()
Out[6]:
country
Canada           35765.569188
Mexico            7965.245332
United States    43112.417952
dtype: float64

Now imagine you want to compare GDP to the share of people with cellphone contracts around the world.

In [7]: wb.search('cell.*%').iloc[:,:2]
Out[7]:
                     id                                               name
3990  IT.CEL.SETS.FE.ZS  Mobile cellular telephone users, female (% of ...
3991  IT.CEL.SETS.MA.ZS  Mobile cellular telephone users, male (% of po...
4027      IT.MOB.COV.ZS  Population coverage of mobile cellular telepho...

Notice that this second search was much faster than the first one because pandas now has a cached list of available data series.

In [13]: ind = ['NY.GDP.PCAP.KD', 'IT.MOB.COV.ZS']
In [14]: dat = wb.download(indicator=ind, country='all', start=2011, end=2011).dropna()
In [15]: dat.columns = ['gdp', 'cellphone']
In [16]: print(dat.tail())
                        gdp  cellphone
country   year
Swaziland 2011  2413.952853       94.9
Tunisia   2011  3687.340170      100.0
Uganda    2011   405.332501      100.0
Zambia    2011   767.911290       62.0
Zimbabwe  2011   419.236086       72.4

Finally, we use the statsmodels package to assess the relationship between our two variables using ordinary least squares regression. Unsurprisingly, populations in rich countries tend to use cellphones at a higher rate:

In [17]: import numpy as np
In [18]: import statsmodels.formula.api as smf
In [19]: mod = smf.ols("cellphone ~ np.log(gdp)", dat).fit()
In [20]: print(mod.summary())
                            OLS Regression Results
==============================================================================
Dep. Variable:              cellphone   R-squared:                       0.297
Model:                            OLS   Adj. R-squared:                  0.274
Method:                 Least Squares   F-statistic:                     13.08
Date:                Thu, 25 Jul 2013   Prob (F-statistic):            0.00105
Time:                        15:24:42   Log-Likelihood:                -139.16
No. Observations:                  33   AIC:                             282.3
Df Residuals:                      31   BIC:                             285.3
Df Model:                           1
===============================================================================
                  coef    std err          t      P>|t|      [95.0% Conf. Int.]
-------------------------------------------------------------------------------
Intercept      16.5110     19.071      0.866      0.393       -22.384    55.406
np.log(gdp)     9.9333      2.747      3.616      0.001         4.331    15.535
==============================================================================
Omnibus:                       36.054   Durbin-Watson:                   2.071
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              119.133
Skew:                          -2.314   Prob(JB):                     1.35e-26
Kurtosis:                      11.077   Cond. No.                         45.8
==============================================================================

Country Codes

The country argument accepts a string or list of mixed two or three character ISO country codes, as well as dynamic World Bank exceptions to the ISO standards.

For a list of the the hard-coded country codes (used solely for error handling logic) see pandas_datareader.wb.country_codes.

Problematic Country Codes & Indicators

Note

The World Bank’s country list and indicators are dynamic. As of 0.15.1, wb.download() is more flexible. To achieve this, the warning and exception logic changed.

The world bank converts some country codes, in their response, which makes error checking by pandas difficult. Retired indicators still persist in the search.

Given the new flexibility of 0.15.1, improved error handling by the user may be necessary for fringe cases.

To help identify issues:

There are at least 4 kinds of country codes:

  1. Standard (2/3 digit ISO) - returns data, will warn and error properly.
  2. Non-standard (WB Exceptions) - returns data, but will falsely warn.
  3. Blank - silently missing from the response.
  4. Bad - causes the entire response from WB to fail, always exception inducing.

There are at least 3 kinds of indicators:

  1. Current - Returns data.
  2. Retired - Appears in search results, yet won’t return data.
  3. Bad - Will not return data.

Use the errors argument to control warnings and exceptions. Setting errors to ignore or warn, won’t stop failed responses. (ie, 100% bad indicators, or a single “bad” (#4 above) country code).

See docstrings for more info.

OECD

OECD Statistics are avaliable via DataReader. You have to specify OECD’s data set code.

To confirm data set code, access to each data -> Export -> SDMX Query. Following example is to download “Trade Union Density” data which set code is “UN_DEN”.

In [71]: import pandas_datareader.data as web

In [72]: import datetime

In [73]: df = web.DataReader('UN_DEN', 'oecd', end=datetime.datetime(2012, 1, 1))

In [74]: df.columns
Out[74]: 
Index([u'Australia', u'Austria', u'Belgium', u'Canada', u'Czech Republic',
       u'Denmark', u'Finland', u'France', u'Germany', u'Greece', u'Hungary',
       u'Iceland', u'Ireland', u'Italy', u'Japan', u'Korea', u'Luxembourg',
       u'Mexico', u'Netherlands', u'New Zealand', u'Norway', u'Poland',
       u'Portugal', u'Slovak Republic', u'Spain', u'Sweden', u'Switzerland',
       u'Turkey', u'United Kingdom', u'United States', u'OECD countries',
       u'Chile', u'Slovenia', u'Estonia', u'Israel'],
      dtype='object', name=u'Country')

In [75]: df[['Japan', 'United States']]
Out[75]: 
Country         Japan  United States
Time                                
2010-01-01  18.403807      11.383460
2011-01-01  18.995042      11.329488
2012-01-01  17.972384      10.815352

Eurostat

Eurostat are avaliable via DataReader.

Get Rail accidents by type of accident (ERA data) data. The result will be a DataFrame which has DatetimeIndex as index and MultiIndex of attributes or countries as column. The target URL is:

You can specify dataset ID “tran_sf_railac” to get corresponding data via DataReader.

In [76]: import pandas_datareader.data as web

In [77]: df = web.DataReader("tran_sf_railac", 'eurostat')

In [78]: df
Out[78]: 
ACCIDENT    Collisions of trains, including collisions with obstacles within the clearance gauge  \
UNIT                                                                                      Number   
GEO                                                                                      Austria   
FREQ                                                                                      Annual   
TIME_PERIOD                                                                                        
2010-01-01                                                   3                                     
2011-01-01                                                   2                                     
2012-01-01                                                   1                                     
2013-01-01                                                   4                                     
2014-01-01                                                   1                                     
2015-01-01                                                   7                                     

ACCIDENT                                                                \
UNIT                                                                     
GEO         Belgium Bulgaria Switzerland Channel Tunnel Czech Republic   
FREQ         Annual   Annual      Annual         Annual         Annual   
TIME_PERIOD                                                              
2010-01-01        5        2           5              0              3   
2011-01-01        0        0           4              0              6   
2012-01-01        3        3           4              0              6   
2013-01-01        1        2           6              0              5   
2014-01-01        3        4           0              0             13   
2015-01-01        0        3           3              0             14   

ACCIDENT                                                                      \
UNIT                                                                           
GEO         Germany (until 1990 former territory of the FRG) Denmark Estonia   
FREQ                                                  Annual  Annual  Annual   
TIME_PERIOD                                                                    
2010-01-01                                                13       0       1   
2011-01-01                                                18       1       0   
2012-01-01                                                23       1       3   
2013-01-01                                                29       0       0   
2014-01-01                                                32       0       0   
2015-01-01                                                40       3       0   

ACCIDENT                ...        Total                                     \
UNIT                    ...       Number                                      
GEO         Greece      ...       Latvia Netherlands Norway Poland Portugal   
FREQ        Annual      ...       Annual      Annual Annual Annual   Annual   
TIME_PERIOD             ...                                                   
2010-01-01       4      ...           41          24     20    449       42   
2011-01-01       1      ...           35          29     36    488       27   
2012-01-01       2      ...           25          30     19    379       36   
2013-01-01       2      ...           26          36     30    328       48   
2014-01-01       1      ...           22          20     28    313       50   
2015-01-01       1      ...           25          31     19    307       23   

ACCIDENT                                                     
UNIT                                                         
GEO         Romania Sweden Slovenia Slovakia United Kingdom  
FREQ         Annual Annual   Annual   Annual         Annual  
TIME_PERIOD                                                  
2010-01-01      271     69       21       85             62  
2011-01-01      217     54       11       84             78  
2012-01-01      215     47       14       96             75  
2013-01-01      180     43       13       94             84  
2014-01-01      185     53       15      113             54  
2015-01-01      141     40       14       87             40  

[6 rows x 210 columns]

EDGAR Index

** As of December 31st, the SEC disabled access via FTP. EDGAR support currently broken until re-write to use HTTPS. **

Company filing index from EDGAR (SEC).

The daily indices get large quickly (i.e. the set of daily indices from 1994 to 2015 is 1.5GB), and the FTP server will close the connection past some downloading threshold . In testing, pulling one year at a time works well. If the FTP server starts refusing your connections, you should be able to reconnect after waiting a few minutes.

TSP Fund Data

Download mutual fund index prices for the TSP.

In [79]: import pandas_datareader.tsp as tsp

In [80]: tspreader = tsp.TSPReader(start='2015-10-1', end='2015-12-31')

In [81]: tspreader.read()
Out[81]: 
            L Income   L 2020   L 2030   L 2040   L 2050   G Fund   F Fund  \
date                                                                         
2015-10-01   17.5164  22.5789  24.2159  25.5690  14.4009  14.8380  17.0467   
2015-10-02   17.5707  22.7413  24.4472  25.8518  14.5805  14.8388  17.0924   
2015-10-05   17.6395  22.9582  24.7571  26.2306  14.8233  14.8413  17.0531   
2015-10-06   17.6338  22.9390  24.7268  26.1898  14.7979  14.8421  17.0790   
2015-10-07   17.6639  23.0324  24.8629  26.3598  14.9063  14.8429  17.0725   
2015-10-08   17.6957  23.1364  25.0122  26.5422  15.0240  14.8437  17.0363   
2015-10-09   17.7048  23.1646  25.0521  26.5903  15.0554  14.8445  17.0511   
...              ...      ...      ...      ...      ...      ...      ...   
2015-12-22   17.7493  23.1452  24.9775  26.4695  14.9611  14.9076  16.9607   
2015-12-23   17.8015  23.3149  25.2208  26.7663  15.1527  14.9084  16.9421   
2015-12-24   17.7991  23.3039  25.2052  26.7481  15.1407  14.9093  16.9596   
2015-12-28   17.7950  23.2811  25.1691  26.7015  15.1101  14.9128  16.9799   
2015-12-29   17.8270  23.3871  25.3226  26.8905  15.2319  14.9137  16.9150   
2015-12-30   17.8066  23.3216  25.2267  26.7707  15.1556  14.9146  16.9249   
2015-12-31   17.7733  23.2085  25.0635  26.5715  15.0263  14.9154  16.9549   

             C Fund   S Fund   I Fund       
date                                        
2015-10-01  25.7953  34.0993  23.3202  NaN  
2015-10-02  26.1669  34.6504  23.6367       
2015-10-05  26.6467  35.3565  24.1475       
2015-10-06  26.5513  35.1320  24.2294       
2015-10-07  26.7751  35.6035  24.3671       
2015-10-08  27.0115  35.9016  24.6406       
2015-10-09  27.0320  35.9772  24.7723       
...             ...      ...      ...  ...  
2015-12-22  27.4848  35.0903  23.8679       
2015-12-23  27.8272  35.5749  24.3623       
2015-12-24  27.7831  35.6084  24.3272       
2015-12-28  27.7230  35.4625  24.2816       
2015-12-29  28.0236  35.8047  24.4757       
2015-12-30  27.8239  35.5126  24.4184       
2015-12-31  27.5622  35.2356  24.0952       

[62 rows x 11 columns]

Nasdaq Trader Symbol Definitions

Download the latest symbols from Nasdaq.

Note that Nasdaq updates this file daily, and historical versions are not available. More information on the field definitions.

In [12]: from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
In [13]: symbols = get_nasdaq_symbols()
In [14]: print(symbols.ix['IBM'])
    Nasdaq Traded                                                    True
    Security Name       International Business Machines Corporation Co...
    Listing Exchange                                                    N
    Market Category
    ETF                                                             False
    Round Lot Size                                                    100
    Test Issue                                                      False
    Financial Status                                                  NaN
    CQS Symbol                                                        IBM
    NASDAQ Symbol                                                     IBM
    NextShares                                                      False
    Name: IBM, dtype: object