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Merge pull request #690 from bashtage/fix-docs
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DOC: Fix doc build
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@bashtage
bashtage committed Sep 17, 2019
2 parents 149f905 + 3808ccb commit 1d2fe8d
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9 changes: 8 additions & 1 deletion docs/source/conf.py
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Expand Up @@ -43,6 +43,7 @@
"sphinx.ext.intersphinx",
"sphinx.ext.extlinks",
"sphinx.ext.napoleon",
"sphinx.ext.todo",
"IPython.sphinxext.ipython_directive",
"IPython.sphinxext.ipython_console_highlighting",
]
Expand Down Expand Up @@ -70,6 +71,12 @@
#
# The short X.Y version.
version = pdr.__version__.split("+")[0]
if "+" in pdr.__version__:
commit = pdr.__version__.split("+")[1]
commits_since_tag, commit_hash = commit.split(".")[:2]
commit_hash = commit_hash[1:]
commit = " (+" + commits_since_tag + ", " + commit_hash + ")"
version += commit
# The full version, including alpha/beta/rc tags.
release = pdr.__version__

Expand All @@ -86,7 +93,7 @@
exclude_patterns = []

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = "sphinx"
pygments_style = "default"

# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True
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12 changes: 0 additions & 12 deletions docs/source/readers/robinhood.rst
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This file was deleted.

211 changes: 157 additions & 54 deletions docs/source/remote_data.rst
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Expand Up @@ -56,43 +56,68 @@ Free registration is required to get an API key. Free accounts are rate
limited and can access a limited number of symbols (500 at the time of
writing).

.. ipython:: python
import os
import pandas_datareader as pdr
.. code-block:: ipython
df = pdr.get_data_tiingo('GOOG', api_key=os.getenv('TIINGO_API_KEY'))
df.head()
In [1]: import os
In [2]: import pandas_datareader as pdr
In [3]: df = pdr.get_data_tiingo('GOOG', api_key=os.getenv('TIINGO_API_KEY'))
In [4]: df.head()
close high low open volume adjClose adjHigh adjLow adjOpen adjVolume divCash splitFactor
symbol date
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
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
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
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
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
.. _remote_data.iex:

IEX
===

.. warning:: Usage of all IEX readers now requries an API key. See
.. warning:: Usage of all IEX readers now requires an API key. See
below for additional information.

The Investors Exchange (IEX) provides a wide range of data through an
`API <https://iexcloud.io/api/docs/>`__. Historical stock
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.

.. ipython:: python
.. code-block:: ipython
os.environ["IEX_API_KEY"] = "pk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
import pandas_datareader.data as web
from datetime import datetime
start = datetime(2016, 9, 1)
end = datetime(2018, 9, 1)
f = web.DataReader('F', 'iex', start, end)
f.loc['2018-08-31']
In [1]: import pandas_datareader.data as web
In [2]: from datetime import datetime
In [3]: start = datetime(2016, 9, 1)
In [4]: end = datetime(2018, 9, 1)
In [5]: f = web.DataReader('F', 'iex', start, end)
In [6]: f.loc['2018-08-31']
Out[6]:
open 9.64
high 9.68
low 9.40
close 9.48
volume 76424884.00
Name: 2018-08-31, dtype: float64
.. note::

You must provide an API Key when using IEX. You can do this using
``os.environ["IEX_API_KEY"] = "pk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"``
or by exporting the key before starting the IPython session.

There are additional interfaces to this API that are
directly exposed: tops (`'iex-tops'`) and last (`'iex-lasts'`).
A third interface to the deep API is exposed through
`Deep` class or the `get_iex_book` function.

.. ipython:: python
.. todo:: Execute block when markets are open

.. code-block:: ipython
import pandas_datareader.data as web
f = web.DataReader('gs', 'iex-tops')
Expand Down Expand Up @@ -123,16 +148,27 @@ symbols. The following endpoints are available:
* ``av-monthly`` - Monthly Time Series
* ``av-monthly-adjusted`` - Monthly Time Series (Adjusted)

.. ipython:: python
.. code-block:: ipython
import os
from datetime import datetime
import pandas_datareader.data as web
In [1]: import os
In [2]: from datetime import datetime
In [3]: import pandas_datareader.data as web
In [4]: f = web.DataReader("AAPL", "av-daily", start=datetime(2017, 2, 9),
...: end=datetime(2017, 5, 24),
...: access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
In [5]: f.loc["2017-02-09"]
Out[5]:
open 1.316500e+02
high 1.324450e+02
low 1.311200e+02
close 1.324200e+02
volume 2.834990e+07
Name: 2017-02-09, dtype: float64
f = web.DataReader("AAPL", "av-daily", start=datetime(2017, 2, 9),
end=datetime(2017, 5, 24),
access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
f.loc["2017-02-09"]
The top-level function ``get_data_alphavantage`` is also provided. This
function will
Expand All @@ -147,13 +183,21 @@ endpoint allows the retrieval of realtime stock quotes for up to 100 symbols at
once. These quotes are accessible through the top-level function
``get_quote_av``.

.. ipython:: python
.. code-block:: ipython
import os
from datetime import datetime
import pandas_datareader.data as web
In [1]: import os
In [2]: from datetime import datetime
In [3]: import pandas_datareader.data as web
In [4]: web.get_quote_av(["AAPL", "TSLA"])
Out[4]:
price volume timestamp
symbol
AAPL 219.87 NaN 2019-09-16 15:59:59
TSLA 242.80 NaN 2019-09-16 15:59:57
web.get_quote_av(["AAPL", "TSLA"])
.. note:: Most quotes are only available during market hours.
Expand All @@ -167,23 +211,54 @@ currency exchange rates (for physical and digital currencies).
To request the exchange rate of physical or digital currencies, simply format
as "FROM/TO" as in "USD/JPY".

.. ipython:: python
.. code-block:: ipython
import os
import pandas_datareader.data as web
In [1]: import os
In [2]: import pandas_datareader.data as web
In [3]: f = web.DataReader("USD/JPY", "av-forex",
...: access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
In [4]: f
Out[4]:
USD/JPY
From_Currency Code USD
From_Currency Name United States Dollar
To_Currency Code JPY
To_Currency Name Japanese Yen
Exchange Rate 108.17000000
Last Refreshed 2019-09-17 10:43:36
Time Zone UTC
Bid Price 108.17000000
Ask Price 108.17000000
f = web.DataReader("USD/JPY", "av-forex",
access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
Multiple pairs are are allowable:

.. ipython:: python
.. code-block:: ipython
import os
import pandas_datareader.data as web
In [1]: import os
In [2]: import pandas_datareader.data as web
In [3]: f = web.DataReader(["USD/JPY", "BTC/CNY"], "av-forex",
...: access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
In [4]: f
Out[4]:
USD/JPY BTC/CNY
From_Currency Code USD BTC
From_Currency Name United States Dollar Bitcoin
To_Currency Code JPY CNY
To_Currency Name Japanese Yen Chinese Yuan
Exchange Rate 108.17000000 72230.38039500
Last Refreshed 2019-09-17 10:44:35 2019-09-17 10:44:01
Time Zone UTC UTC
Bid Price 108.17000000 72226.26407700
Ask Price 108.17000000 72230.02554000
f = web.DataReader(["USD/JPY", "BTC/CNY"], "av-forex",
access_key=os.getenv('ALPHAVANTAGE_API_KEY'))
Sector Performance
Expand All @@ -192,12 +267,21 @@ Sector Performance
`Alpha Vantage <https://www.alphavantage.co/documentation>`__ provides sector
performances through the top-level function ``get_sector_performance_av``.

.. ipython:: python
.. code-block:: ipython
import os
import pandas_datareader.data as web
In [1]: import os
In [2]: import pandas_datareader.data as web
In [3]: web.get_sector_performance_av().head()
Out[4]:
RT 1D 5D 1M 3M YTD 1Y 3Y 5Y 10Y
Energy 3.29% 3.29% 4.82% 11.69% 3.37% 9.07% -15.26% -7.69% -32.31% 12.15%
Real Estate 1.02% 1.02% -1.39% 1.26% 3.49% 24.95% 16.55% NaN NaN NaN
Utilities 0.08% 0.08% 0.72% 2.77% 3.72% 18.16% 16.09% 27.95% 48.41% 113.09%
Industrials -0.15% -0.15% 2.42% 8.59% 5.10% 22.70% 0.50% 34.50% 43.53% 183.47%
Health Care -0.23% -0.23% 0.88% 1.91% 0.09% 5.20% -2.38% 26.37% 43.43% 216.01%
web.get_sector_performance_av().head()
.. _remote_data.enigma:
Expand All @@ -214,7 +298,7 @@ Econdb database of time series aggregated into datasets.
import os
import pandas_datareader.data as web
f = web.DataReader('ticker=RGDPQNO', 'econdb')
f = web.DataReader('ticker=RGDPUS', 'econdb')
f.head()
.. _remote_data.econdb:
Expand All @@ -230,13 +314,24 @@ URL has changed from `app.enigma.io <https://app.enigma.io>`__ as of release
Datasets are unique identified by the ``uuid4`` at the end of a dataset's web address.
For example, the following code downloads from `USDA Food Recalls 1996 Data <https://public.enigma.com/datasets/292129b0-1275-44c8-a6a3-2a0881f24fe1>`__.

.. ipython:: python
.. code-block:: ipython
import os
import pandas_datareader as pdr
In [1]: import os
In [2]: import pandas_datareader as pdr
In [3]: df = pdr.get_data_enigma('292129b0-1275-44c8-a6a3-2a0881f24fe1', os.getenv('ENIGMA_API_KEY'))
In [4]: df.columns
Out[4]:
Index(['case_number', 'recall_notification_report_number',
'recall_notification_report_url', 'date_opened', 'date_closed',
'recall_class', 'press_release', 'domestic_est_number', 'company_name',
'imported_product', 'foreign_estab_number', 'city', 'state', 'country',
'product', 'problem', 'description', 'total_pounds_recalled',
'pounds_recovered'],
dtype='object')
df = pdr.get_data_enigma('292129b0-1275-44c8-a6a3-2a0881f24fe1', os.getenv('ENIGMA_API_KEY'))
df.columns
.. _remote_data.quandl:

Expand All @@ -258,12 +353,20 @@ 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.

.. ipython:: python
.. code-block:: ipython
In [1]: import pandas_datareader.data as web
In [2]: symbol = 'WIKI/AAPL' # or 'AAPL.US'
In [3]: df = web.DataReader(symbol, 'quandl', '2015-01-01', '2015-01-05')
In [4]: df.loc['2015-01-02']
Out[4]:
Open High Low Close Volume ... AdjOpen AdjHigh AdjLow AdjClose AdjVolume
Date ...
2015-01-02 111.39 111.44 107.35 109.33 53204626.0 ... 105.820966 105.868466 101.982949 103.863957 53204626.0
import pandas_datareader.data as web
symbol = 'WIKI/AAPL' # or 'AAPL.US'
df = web.DataReader(symbol, 'quandl', '2015-01-01', '2015-01-05')
df.loc['2015-01-02']
.. _remote_data.fred:

Expand Down
3 changes: 2 additions & 1 deletion pandas_datareader/econdb.py
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Expand Up @@ -50,7 +50,8 @@ def show_func(x):
df = df.join(series, how="outer")
else:
df = series
df.index = pd.to_datetime(df.index, errors="ignore")
if df.shape[0] > 0:
df.index = pd.to_datetime(df.index, errors="ignore")
df.index.name = "TIME_PERIOD"
df = df.truncate(self.start, self.end)
return df

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