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Merge pull request #690 from bashtage/fix-docs
DOC: Fix doc build
2 parents 149f905 + 3808ccb commit 1d2fe8d

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‎docs/source/conf.py

Lines changed: 8 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -43,6 +43,7 @@
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"sphinx.ext.intersphinx",
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"sphinx.ext.extlinks",
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"sphinx.ext.napoleon",
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"sphinx.ext.todo",
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"IPython.sphinxext.ipython_directive",
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"IPython.sphinxext.ipython_console_highlighting",
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]
@@ -70,6 +71,12 @@
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#
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# The short X.Y version.
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version = pdr.__version__.split("+")[0]
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if "+" in pdr.__version__:
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commit = pdr.__version__.split("+")[1]
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commits_since_tag, commit_hash = commit.split(".")[:2]
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commit_hash = commit_hash[1:]
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commit = " (+" + commits_since_tag + ", " + commit_hash + ")"
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version += commit
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# The full version, including alpha/beta/rc tags.
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release = pdr.__version__
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@@ -86,7 +93,7 @@
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exclude_patterns = []
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# The name of the Pygments (syntax highlighting) style to use.
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pygments_style = "sphinx"
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pygments_style = "default"
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# If true, `todo` and `todoList` produce output, else they produce nothing.
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todo_include_todos = True

‎docs/source/readers/robinhood.rst

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This file was deleted.

‎docs/source/remote_data.rst

Lines changed: 157 additions & 54 deletions
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@@ -56,43 +56,68 @@ Free registration is required to get an API key. Free accounts are rate
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limited and can access a limited number of symbols (500 at the time of
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writing).
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.. ipython:: python
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import os
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import pandas_datareader as pdr
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.. code-block:: ipython
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df = pdr.get_data_tiingo('GOOG', api_key=os.getenv('TIINGO_API_KEY'))
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df.head()
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In [1]: import os
62+
In [2]: import pandas_datareader as pdr
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In [3]: df = pdr.get_data_tiingo('GOOG', api_key=os.getenv('TIINGO_API_KEY'))
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In [4]: df.head()
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close high low open volume adjClose adjHigh adjLow adjOpen adjVolume divCash splitFactor
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symbol date
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GOOG 2014-03-27 00:00:00+00:00 558.46 568.00 552.92 568.000 13100 558.46 568.00 552.92 568.000 13100 0.0 1.0
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2014-03-28 00:00:00+00:00 559.99 566.43 558.67 561.200 41100 559.99 566.43 558.67 561.200 41100 0.0 1.0
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2014-03-31 00:00:00+00:00 556.97 567.00 556.93 566.890 10800 556.97 567.00 556.93 566.890 10800 0.0 1.0
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2014-04-01 00:00:00+00:00 567.16 568.45 558.71 558.710 7900 567.16 568.45 558.71 558.710 7900 0.0 1.0
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2014-04-02 00:00:00+00:00 567.00 604.83 562.19 565.106 146700 567.00 604.83 562.19 565.106 146700 0.0 1.0
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.. _remote_data.iex:
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IEX
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===
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73-
.. warning:: Usage of all IEX readers now requries an API key. See
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.. warning:: Usage of all IEX readers now requires an API key. See
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below for additional information.
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The Investors Exchange (IEX) provides a wide range of data through an
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`API <https://iexcloud.io/api/docs/>`__. Historical stock
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prices are available for up to 15 years. The usage of these readers requires the publishable API key from IEX Cloud Console, which can be stored in the ``IEX_API_KEY`` environment variable.
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.. ipython:: python
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.. code-block:: ipython
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os.environ["IEX_API_KEY"] = "pk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
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import pandas_datareader.data as web
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from datetime import datetime
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start = datetime(2016, 9, 1)
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end = datetime(2018, 9, 1)
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f = web.DataReader('F', 'iex', start, end)
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f.loc['2018-08-31']
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In [1]: import pandas_datareader.data as web
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90+
In [2]: from datetime import datetime
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In [3]: start = datetime(2016, 9, 1)
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In [4]: end = datetime(2018, 9, 1)
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In [5]: f = web.DataReader('F', 'iex', start, end)
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In [6]: f.loc['2018-08-31']
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Out[6]:
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open 9.64
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high 9.68
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low 9.40
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close 9.48
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volume 76424884.00
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Name: 2018-08-31, dtype: float64
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.. note::
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You must provide an API Key when using IEX. You can do this using
110+
``os.environ["IEX_API_KEY"] = "pk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"``
111+
or by exporting the key before starting the IPython session.
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There are additional interfaces to this API that are
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directly exposed: tops (`'iex-tops'`) and last (`'iex-lasts'`).
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A third interface to the deep API is exposed through
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`Deep` class or the `get_iex_book` function.
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.. ipython:: python
118+
.. todo:: Execute block when markets are open
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.. code-block:: ipython
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import pandas_datareader.data as web
98123
f = web.DataReader('gs', 'iex-tops')
@@ -123,16 +148,27 @@ symbols. The following endpoints are available:
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* ``av-monthly`` - Monthly Time Series
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* ``av-monthly-adjusted`` - Monthly Time Series (Adjusted)
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126-
.. ipython:: python
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.. code-block:: ipython
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128-
import os
129-
from datetime import datetime
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import pandas_datareader.data as web
153+
In [1]: import os
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155+
In [2]: from datetime import datetime
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157+
In [3]: import pandas_datareader.data as web
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In [4]: f = web.DataReader("AAPL", "av-daily", start=datetime(2017, 2, 9),
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...: end=datetime(2017, 5, 24),
161+
...: access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
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163+
In [5]: f.loc["2017-02-09"]
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Out[5]:
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open 1.316500e+02
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high 1.324450e+02
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low 1.311200e+02
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close 1.324200e+02
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volume 2.834990e+07
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Name: 2017-02-09, dtype: float64
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132-
f = web.DataReader("AAPL", "av-daily", start=datetime(2017, 2, 9),
133-
end=datetime(2017, 5, 24),
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access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
135-
f.loc["2017-02-09"]
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The top-level function ``get_data_alphavantage`` is also provided. This
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function will
@@ -147,13 +183,21 @@ endpoint allows the retrieval of realtime stock quotes for up to 100 symbols at
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once. These quotes are accessible through the top-level function
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``get_quote_av``.
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.. ipython:: python
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.. code-block:: ipython
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152-
import os
153-
from datetime import datetime
154-
import pandas_datareader.data as web
188+
In [1]: import os
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190+
In [2]: from datetime import datetime
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In [3]: import pandas_datareader.data as web
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In [4]: web.get_quote_av(["AAPL", "TSLA"])
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Out[4]:
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price volume timestamp
197+
symbol
198+
AAPL 219.87 NaN 2019-09-16 15:59:59
199+
TSLA 242.80 NaN 2019-09-16 15:59:57
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156-
web.get_quote_av(["AAPL", "TSLA"])
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.. note:: Most quotes are only available during market hours.
@@ -167,23 +211,54 @@ currency exchange rates (for physical and digital currencies).
167211
To request the exchange rate of physical or digital currencies, simply format
168212
as "FROM/TO" as in "USD/JPY".
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170-
.. ipython:: python
214+
.. code-block:: ipython
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172-
import os
173-
import pandas_datareader.data as web
216+
In [1]: import os
217+
218+
In [2]: import pandas_datareader.data as web
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220+
In [3]: f = web.DataReader("USD/JPY", "av-forex",
221+
...: access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
222+
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In [4]: f
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Out[4]:
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USD/JPY
226+
From_Currency Code USD
227+
From_Currency Name United States Dollar
228+
To_Currency Code JPY
229+
To_Currency Name Japanese Yen
230+
Exchange Rate 108.17000000
231+
Last Refreshed 2019-09-17 10:43:36
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Time Zone UTC
233+
Bid Price 108.17000000
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Ask Price 108.17000000
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175-
f = web.DataReader("USD/JPY", "av-forex",
176-
access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
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178237
Multiple pairs are are allowable:
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180-
.. ipython:: python
239+
.. code-block:: ipython
181240
182-
import os
183-
import pandas_datareader.data as web
241+
In [1]: import os
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243+
In [2]: import pandas_datareader.data as web
244+
245+
In [3]: f = web.DataReader(["USD/JPY", "BTC/CNY"], "av-forex",
246+
...: access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
247+
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In [4]: f
250+
Out[4]:
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USD/JPY BTC/CNY
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From_Currency Code USD BTC
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From_Currency Name United States Dollar Bitcoin
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To_Currency Code JPY CNY
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To_Currency Name Japanese Yen Chinese Yuan
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Exchange Rate 108.17000000 72230.38039500
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Last Refreshed 2019-09-17 10:44:35 2019-09-17 10:44:01
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Time Zone UTC UTC
259+
Bid Price 108.17000000 72226.26407700
260+
Ask Price 108.17000000 72230.02554000
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185-
f = web.DataReader(["USD/JPY", "BTC/CNY"], "av-forex",
186-
access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
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Sector Performance
@@ -192,12 +267,21 @@ Sector Performance
192267
`Alpha Vantage <https://www.alphavantage.co/documentation>`__ provides sector
193268
performances through the top-level function ``get_sector_performance_av``.
194269

195-
.. ipython:: python
270+
.. code-block:: ipython
196271
197-
import os
198-
import pandas_datareader.data as web
272+
In [1]: import os
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274+
In [2]: import pandas_datareader.data as web
275+
276+
In [3]: web.get_sector_performance_av().head()
277+
Out[4]:
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RT 1D 5D 1M 3M YTD 1Y 3Y 5Y 10Y
279+
Energy 3.29% 3.29% 4.82% 11.69% 3.37% 9.07% -15.26% -7.69% -32.31% 12.15%
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Real Estate 1.02% 1.02% -1.39% 1.26% 3.49% 24.95% 16.55% NaN NaN NaN
281+
Utilities 0.08% 0.08% 0.72% 2.77% 3.72% 18.16% 16.09% 27.95% 48.41% 113.09%
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Industrials -0.15% -0.15% 2.42% 8.59% 5.10% 22.70% 0.50% 34.50% 43.53% 183.47%
283+
Health Care -0.23% -0.23% 0.88% 1.91% 0.09% 5.20% -2.38% 26.37% 43.43% 216.01%
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web.get_sector_performance_av().head()
201285
202286
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.. _remote_data.enigma:
@@ -214,7 +298,7 @@ Econdb database of time series aggregated into datasets.
214298
import os
215299
import pandas_datareader.data as web
216300
217-
f = web.DataReader('ticker=RGDPQNO', 'econdb')
301+
f = web.DataReader('ticker=RGDPUS', 'econdb')
218302
f.head()
219303
220304
.. _remote_data.econdb:
@@ -230,13 +314,24 @@ URL has changed from `app.enigma.io <https://app.enigma.io>`__ as of release
230314
Datasets are unique identified by the ``uuid4`` at the end of a dataset's web address.
231315
For example, the following code downloads from `USDA Food Recalls 1996 Data <https://public.enigma.com/datasets/292129b0-1275-44c8-a6a3-2a0881f24fe1>`__.
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233-
.. ipython:: python
317+
.. code-block:: ipython
234318
235-
import os
236-
import pandas_datareader as pdr
319+
In [1]: import os
320+
321+
In [2]: import pandas_datareader as pdr
322+
323+
In [3]: df = pdr.get_data_enigma('292129b0-1275-44c8-a6a3-2a0881f24fe1', os.getenv('ENIGMA_API_KEY'))
324+
325+
In [4]: df.columns
326+
Out[4]:
327+
Index(['case_number', 'recall_notification_report_number',
328+
'recall_notification_report_url', 'date_opened', 'date_closed',
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'recall_class', 'press_release', 'domestic_est_number', 'company_name',
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'imported_product', 'foreign_estab_number', 'city', 'state', 'country',
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'product', 'problem', 'description', 'total_pounds_recalled',
332+
'pounds_recovered'],
333+
dtype='object')
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df = pdr.get_data_enigma('292129b0-1275-44c8-a6a3-2a0881f24fe1', os.getenv('ENIGMA_API_KEY'))
239-
df.columns
240335
241336
.. _remote_data.quandl:
242337

@@ -258,12 +353,20 @@ As of June 2017, each DB has a different data schema,
258353
the coverage in terms of time range is sometimes surprisingly small, and
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the data quality is not always good.
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261-
.. ipython:: python
356+
.. code-block:: ipython
357+
358+
In [1]: import pandas_datareader.data as web
359+
360+
In [2]: symbol = 'WIKI/AAPL' # or 'AAPL.US'
361+
362+
In [3]: df = web.DataReader(symbol, 'quandl', '2015-01-01', '2015-01-05')
363+
364+
In [4]: df.loc['2015-01-02']
365+
Out[4]:
366+
Open High Low Close Volume ... AdjOpen AdjHigh AdjLow AdjClose AdjVolume
367+
Date ...
368+
2015-01-02 111.39 111.44 107.35 109.33 53204626.0 ... 105.820966 105.868466 101.982949 103.863957 53204626.0
262369
263-
import pandas_datareader.data as web
264-
symbol = 'WIKI/AAPL' # or 'AAPL.US'
265-
df = web.DataReader(symbol, 'quandl', '2015-01-01', '2015-01-05')
266-
df.loc['2015-01-02']
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.. _remote_data.fred:
269372

‎pandas_datareader/econdb.py

Lines changed: 2 additions & 1 deletion
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@@ -50,7 +50,8 @@ def show_func(x):
5050
df = df.join(series, how="outer")
5151
else:
5252
df = series
53-
df.index = pd.to_datetime(df.index, errors="ignore")
53+
if df.shape[0] > 0:
54+
df.index = pd.to_datetime(df.index, errors="ignore")
5455
df.index.name = "TIME_PERIOD"
5556
df = df.truncate(self.start, self.end)
5657
return df

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