Merge pull request #101 from sinhrks/oecd

ENH: support OECD datasource

Author: davidastephens

.travis.yml

@@ -37,10 +37,12 @@ install:
   - conda update -q conda
   # Useful for debugging any issues with conda
   - conda info -a
-
   - conda create -q -n test-environment python=$PYTHON pandas=$PANDAS nose coverage setuptools html5lib lxml
   - source activate test-environment
   - pip install beautifulsoup4
+  - if [[ "$PYTHON" == "2.6" ]]; then  
+      pip install simplejson;
+    fi
   - pip install coveralls --quiet
   - conda list
   - python setup.py install

docs/source/remote_data.rst

@@ -28,6 +28,7 @@ Currently the following sources are supported:
     - :ref:`St.Louis FED (FRED)<remote_data.fred>`
     - :ref:`Kenneth French's data library<remote_data.ff>`
     - :ref:`World Bank<remote_data.wb>`
+    - :ref:`OECD<remote_data.oecd>`
 
 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.
 
@@ -340,3 +341,27 @@ 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.
+
+.. _remote_data.oecd:
+
+OECD
+====
+
+`OECD Statistics <http://stats.oecd.org/>`__ 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".
+
+
+.. ipython:: python
+
+    import pandas_datareader.data as web
+    import datetime
+
+    df = web.DataReader('UN_DEN', 'oecd', end=datetime.datetime(2012, 1, 1))
+
+    df.columns
+
+    df[['Japan', 'United States']]
+

docs/source/whatsnew/v0.2.0.txt

@@ -20,6 +20,7 @@ New features
 - Added latitude and longitude to output of wb.get_countries (:issue:`47`).
 - Extended DataReader to fetch dividends and stock splits from Yahoo (:issue:`45`).
 - Added get_available_datasets to famafrench (:issue:`56`).
+- ``DataReader`` now supports OECD data sources, see :ref:`here<remote_data.oecd>` (:issue:`101`).
 
 .. _whatsnew_0170.api:
 

pandas_datareader/data.py

@@ -19,6 +19,8 @@
 
 from pandas_datareader.fred import _get_data as get_data_fred
 from pandas_datareader.famafrench import _get_data as get_data_famafrench
+from pandas_datareader.oecd import _get_data as get_data_oecd
+
 
 def DataReader(name, data_source=None, start=None, end=None,
                retry_count=3, pause=0.001):
@@ -82,6 +84,8 @@ def DataReader(name, data_source=None, start=None, end=None,
         return get_data_fred(name, start, end)
     elif data_source == "famafrench":
         return get_data_famafrench(name)
+    elif data_source == "oecd":
+        return get_data_oecd(name, start, end)
     else:
         raise NotImplementedError(
                 "data_source=%r is not implemented" % data_source)

pandas_datareader/io/__init__.py

@@ -0,0 +1 @@
+from pandas_datareader.io.jsdmx import read_jsdmx

pandas_datareader/io/jsdmx.py

@@ -0,0 +1,103 @@
+# pylint: disable-msg=E1101,W0613,W0603
+
+from __future__ import unicode_literals
+
+import itertools
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+import pandas.compat as compat
+
+from pandas_datareader.io.util import _read_content
+
+
+def read_jsdmx(path_or_buf):
+    """
+    Convert a SDMX-JSON string to pandas object
+
+    Parameters
+    ----------
+    filepath_or_buffer : a valid SDMX-JSON string or file-like
+        http://sdmx.org/wp-content/uploads/2014/07/sdmx-json-data-message.pdf
+
+    Returns
+    -------
+    results : Series, DataFrame, or dictionaly of Series or DataFrame.
+    """
+
+    jdata = _read_content(path_or_buf)
+
+    try:
+        import simplejson as json
+    except ImportError:
+        if sys.version_info[:2] < (2, 7):
+            raise ImportError('simplejson is required in python 2.6')
+        import json
+
+    if isinstance(jdata, dict):
+        data = jdata
+    else:
+        data = json.loads(jdata, object_pairs_hook=compat.OrderedDict)
+
+    structure = data['structure']
+    index = _parse_dimensions(structure['dimensions']['observation'])
+    columns = _parse_dimensions(structure['dimensions']['series'])
+
+    dataset = data['dataSets']
+    if len(dataset) != 1:
+        raise ValueError("length of 'dataSets' must be 1")
+    dataset = dataset[0]
+    values = _parse_values(dataset, index=index, columns=columns)
+
+    df = pd.DataFrame(values, columns=columns, index=index)
+    return df
+
+
+def _get_indexer(index):
+    if index.nlevels == 1:
+        return [str(i) for i in compat.range(len(index))]
+    else:
+        it = itertools.product(*[compat.range(len(level)) for level in index.levels])
+        return [':'.join(map(str, i)) for i in it]
+
+
+def _parse_values(dataset, index, columns):
+    size = len(index)
+    series = dataset['series']
+
+    values = []
+    # for s_key, s_value in compat.iteritems(series):
+    for s_key in _get_indexer(columns):
+        try:
+            observations = series[s_key]['observations']
+            observed = []
+            for o_key in _get_indexer(index):
+                try:
+                    observed.append(observations[o_key][0])
+                except KeyError:
+                    observed.append(np.nan)
+        except KeyError:
+            observed = [np.nan] * size
+
+        values.append(observed)
+
+    return np.transpose(np.array(values))
+
+
+def _parse_dimensions(dimensions):
+    arrays = []
+    names = []
+    for key in dimensions:
+        values = [v['name'] for v in key['values']]
+
+        role = key.get('role', None)
+        if role == 'time':
+            values = pd.DatetimeIndex(values)
+
+        arrays.append(values)
+        names.append(key['name'])
+    midx = pd.MultiIndex.from_product(arrays, names=names)
+    return midx
+

pandas_datareader/io/tests/__init__.py

@@ -0,0 +1 @@
+{"header":{"id":"aef8b006-de08-4263-9a26-0612a984c7c6","test":false,"prepared":"2014-08-30T09:50:48Z","sender":{"id":"OECD","name":"OECD"},"request":{"uri":"http://stats.oecd.org/SDMX-JSON/data/LAND_USE/JPN+USA/OECD"}},"dataSets":[{"action":"Informational","series":{"0:0":{"attributes":[0],"observations":{"0":[54610],"1":[54420],"2":[54260],"3":[54110],"4":[53960],"5":[53790],"6":[53580],"7":[53400],"8":[53170],"9":[52790],"10":[52430],"11":[52040],"12":[51650],"13":[51240],"14":[50830],"15":[50380],"16":[49940],"17":[49490],"18":[49050],"19":[48660],"20":[48300],"21":[47930],"22":[47630],"23":[47360],"24":[47140],"25":[46920],"26":[46710],"27":[46500],"28":[46280],"29":[46090],"30":[45930],"31":[45610]}},"0:1":{"attributes":[0],"observations":{"0":[14.909],"1":[14.853],"2":[14.817],"3":[14.776],"4":[14.796],"5":[14.753],"6":[14.692],"7":[14.646],"8":[14.583],"9":[14.479],"10":[14.38],"11":[14.273],"12":[14.166],"13":[14.054],"14":[13.941],"15":[13.818],"16":[13.701],"17":[13.578],"18":[13.457],"19":[13.35],"20":[13.251],"21":[13.15],"22":[13.067],"23":[12.993],"24":[12.933],"25":[12.872],"26":[12.815],"27":[12.757],"28":[12.697],"29":[12.645],"30":[12.601],"31":[12.513]}},"0:2":{"attributes":[0],"observations":{"0":[377800],"1":[377800],"2":[377800],"3":[377800],"4":[377800],"5":[377800],"6":[377800],"7":[377800],"8":[377800],"9":[377800],"10":[377800],"11":[377800],"12":[377800],"13":[377800],"14":[377800],"15":[377800],"16":[377800],"17":[377800],"18":[377800],"19":[377800],"20":[377800],"21":[377880],"22":[377890],"23":[377900],"24":[377910],"25":[377910],"26":[377920],"27":[377930],"28":[377940],"29":[377947],"30":[377950],"31":[377955]}},"0:3":{"attributes":[0],"observations":{"10":[249500],"11":[249426],"12":[249352],"13":[249278],"14":[249204],"15":[249130],"16":[249056],"17":[248982],"18":[248908],"19":[248834],"20":[248760],"21":[248878],"22":[248996],"23":[249114],"24":[249232],"25":[249350],"26":[249438],"27":[249526],"28":[249614],"29":[249702],"30":[249790],"31":[249878]}},"0:4":{"attributes":[0],"observations":{"10":[68.431],"11":[68.411],"12":[68.391],"13":[68.37],"14":[68.35],"15":[68.33],"16":[68.328],"17":[68.308],"18":[68.288],"19":[68.267],"20":[68.247],"21":[68.279],"22":[68.312],"23":[68.344],"24":[68.376],"25":[68.409],"26":[68.433],"27":[68.457],"28":[68.481],"29":[68.505],"30":[68.529],"31":[68.554]}},"0:5":{"attributes":[0],"observations":{"0":[366300],"1":[366400],"2":[366200],"3":[366200],"4":[364700],"5":[364600],"6":[364700],"7":[364600],"8":[364600],"9":[364600],"10":[364600],"11":[364600],"12":[364600],"13":[364600],"14":[364600],"15":[364600],"16":[364500],"17":[364500],"18":[364500],"19":[364500],"20":[364500],"21":[364500],"22":[364500],"23":[364500],"24":[364500],"25":[364500],"26":[364500],"27":[364500],"28":[364500],"29":[364500],"30":[364500],"31":[364500]}},"0:6":{"attributes":[0],"observations":{"0":[6000],"1":[6000],"2":[6000],"3":[6000],"4":[6000],"5":[5000],"6":[5000],"7":[5000],"8":[5000],"9":[4500],"10":[4500],"11":[4500],"12":[4500],"13":[4500],"14":[4500],"15":[4050],"16":[4050],"17":[4050],"18":[4050],"19":[4050],"20":[4280]}},"0:7":{"attributes":[0],"observations":{"0":[1.638],"1":[1.638],"2":[1.638],"3":[1.638],"4":[1.645],"5":[1.371],"6":[1.371],"7":[1.371],"8":[1.371],"9":[1.234],"10":[1.234],"11":[1.234],"12":[1.234],"13":[1.234],"14":[1.234],"15":[1.111],"16":[1.111],"17":[1.111],"18":[1.111],"19":[1.111],"20":[1.174]}},"0:8":{"attributes":[0],"observations":{"10":[58170],"11":[58634],"12":[59098],"13":[59582],"14":[60066],"15":[61040],"16":[61454],"17":[61978],"18":[62492],"19":[62956],"20":[63160],"21":[67692],"22":[67874],"23":[68026],"24":[68128],"25":[68230],"26":[68352],"27":[68474],"28":[68606],"29":[68708],"30":[68780],"31":[69012]}},"0:9":{"attributes":[0],"observations":{"10":[15.954],"11":[16.082],"12":[16.209],"13":[16.342],"14":[16.474],"15":[16.742],"16":[16.86],"17":[17.004],"18":[17.145],"19":[17.272],"20":[17.328],"21":[18.571],"22":[18.621],"23":[18.663],"24":[18.691],"25":[18.719],"26":[18.752],"27":[18.786],"28":[18.822],"29":[18.85],"30":[18.87],"31":[18.933]}},"1:0":{"attributes":[0],"observations":{"0":[1906240],"1":[1906240],"2":[1897990],"3":[1897990],"4":[1897990],"5":[1897990],"6":[1897990],"7":[1877760],"8":[1877760],"9":[1877760],"10":[1877760],"11":[1877760],"12":[1861800],"13":[1849480],"14":[1841390],"15":[1841390],"16":[1813060],"17":[1800920],"18":[1792820],"19":[1780680],"20":[1780680],"21":[1781000],"22":[1757070],"23":[1743640],"24":[1697560],"25":[1678150],"26":[1630413],"27":[1644800],"28":[1656635],"29":[1631396],"30":[1624330],"31":[1627625]}},"1:1":{"attributes":[0],"observations":{"0":[20.813],"1":[20.813],"2":[20.723],"3":[20.723],"4":[20.723],"5":[20.723],"6":[20.723],"7":[20.502],"8":[20.502],"9":[20.502],"10":[20.502],"11":[20.502],"12":[20.328],"13":[20.193],"14":[20.105],"15":[20.105],"16":[19.795],"17":[19.663],"18":[19.574],"19":[19.442],"20":[19.436],"21":[19.439],"22":[19.178],"23":[19.031],"24":[18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pandas_datareader/io/tests/data/jsdmx/land_use.json

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