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Merge pull request #474 from bashtage/update-reqs
Update reqs
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‎.coveragerc

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# .coveragerc to control coverage.py
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[run]
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source = pandas_datareader
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branch = True
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include = */pandas_datareader/*
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omit =
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*/_version.py
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*/yahoo/*
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*/google/options.py
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*/google/quotes.py
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*/tests/google/test_options.py
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[report]
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# Regexes for lines to exclude from consideration
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exclude_lines =
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# Have to re-enable the standard pragma
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pragma: no cover
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# Don't complain if tests don't hit defensive assertion code:
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raise NotImplementedError
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raise ImmediateDeprecationError
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# Ignore pass
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pass
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include = */pandas_datareader/*
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omit =
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*/_version.py
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*/yahoo/*
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*/google/options.py
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*/google/quotes.py
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*/tests/google/test_options.py
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ignore_errors = True

‎.travis.yml

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script:
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- export ENIGMA_API_KEY=$ENIGMA_API_KEY
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- pytest -s -r xX --cov=pandas_datareader --cov-report xml:/tmp/cov-datareader.xml --junitxml=/tmp/datareader.xml
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- pytest -s -r xX --cov-config .coveragerc --cov=pandas_datareader --cov-report xml:/tmp/cov-datareader.xml --junitxml=/tmp/datareader.xml
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- flake8 --version
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- flake8 pandas_datareader
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‎README.rst

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@@ -31,31 +31,6 @@ Up to date remote data access for pandas, works for multiple versions of pandas.
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although there are frequent reports of failures. Failure is frequently
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encountered when bulk downloading historical price data.
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Installation
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------------
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Install latest release version via pip
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code-block:: shell
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$ pip install pandas-datareader
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Install latest development version
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code-block:: shell
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$ pip install git+https://github.com/pydata/pandas-datareader.git
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or
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.. code-block:: shell
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$ git clone https://github.com/pydata/pandas-datareader.git
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$ python setup.py install
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Usage
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-----
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`read the docs <https://pandas-datareader.readthedocs.io/>`_ for more details.
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`Development documentation <https://pydata.github.io/pandas-datareader/devel/>`__
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is available for the latest changes in master.
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is available for the latest changes in master.
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Installation
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------------
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Requirements
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~~~~~~~~~~~~
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Using pandas datareader requires the following packages:
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* pandas>=0.19.2
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* lxml
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* requests>=2.3.0
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* requests-file
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* requests-ftp
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* wrapt
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Building the documentation additionally requires:
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* matplotlib
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* ipython
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* sphinx
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* sphinx_rtd_theme
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Testing requires pytest.
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Install latest release version via pip
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code-block:: shell
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$ pip install pandas-datareader
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Install latest development version
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code-block:: shell
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$ pip install git+https://github.com/pydata/pandas-datareader.git
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or
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.. code-block:: shell
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$ git clone https://github.com/pydata/pandas-datareader.git
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$ python setup.py install

‎pandas_datareader/compat/__init__.py

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PANDAS_0190 = (PANDAS_VERSION >= LooseVersion('0.19.0'))
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PANDAS_0200 = (PANDAS_VERSION >= LooseVersion('0.20.0'))
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PANDAS_0210 = (PANDAS_VERSION >= LooseVersion('0.21.0'))
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if PANDAS_0190:
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from pandas.api.types import is_number

‎pandas_datareader/tests/io/data/jsdmx/land_use.json

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‎pandas_datareader/tests/io/data/jsdmx/tourism.json

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‎pandas_datareader/tests/io/test_jsdmx.py

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import pytest
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from pandas_datareader.io import read_jsdmx
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from pandas_datareader.compat import PANDAS_0210
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class TestJSDMX(object):
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def setup_method(self, method):
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self.dirpath = tm.get_data_path()
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@pytest.mark.xfail(reason='Parsing error')
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@pytest.mark.skipif(not PANDAS_0210, reason='Broken on old pandas')
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def test_tourism(self):
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# OECD -> Industry and Services -> Inbound Tourism
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result = read_jsdmx(os.path.join(self.dirpath, 'jsdmx',
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'tourism.json'))
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assert isinstance(result, pd.DataFrame)
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jp = result['Japan']
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visitors = ['China', 'Hong Kong, China',
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'Total international arrivals',
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'Korea', 'Chinese Taipei', 'United States']
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exp_col = pd.MultiIndex.from_product(
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[['Japan'], ['China', 'Hong Kong, China',
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'Total international arrivals',
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'Total international receipts',
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'International passenger transport receipts',
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'International travel receipts',
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'Korea', 'Chinese Taipei', 'United States']],
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names=['Country', 'Variable'])
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exp_idx = pd.DatetimeIndex(['2004', '2005', '2006', '2007',
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'2008', '2009', '2010', '2011',
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'2012'], name='Year')
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values = np.array([
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[616, 300, 6138, 1550, 330, 1220, 1588, 1081, 760],
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[653, 299, 6728, 1710, 340, 1370, 1747, 1275, 822],
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[812, 352, 7334, 1330, 350, 980, 2117, 1309, 817],
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[942, 432, 8347, 1460, 360, 1100, 2601, 1385, 816],
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[1000, 550, 8351, 1430, 310, 1120, 2382, 1390, 768],
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[1006, 450, 6790, 1170, 210, 960, 1587, 1024, 700],
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[1413, 509, 8611, 1350, 190, 1160, 2440, 1268, 727],
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[1043, 365, 6219, 1000, 100, 900, 1658, 994, 566],
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[1430, 482, 8368, 1300, 100, 1200, 2044, 1467, 717]])
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exp_col = pd.Index(
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['China', 'Hong Kong, China', 'Total international arrivals',
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'Korea', 'Chinese Taipei', 'United States'],
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name='Variable')
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exp_idx = pd.DatetimeIndex(['2008-01-01', '2009-01-01', '2010-01-01',
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'2011-01-01', '2012-01-01', '2013-01-01',
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'2014-01-01', '2015-01-01', '2016-01-01'],
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name='Year')
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values = [
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[1000000.0, 550000.0, 8351000.0, 2382000.0, 1390000.0, 768000.0],
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[1006000.0, 450000.0, 6790000.0, 1587000.0, 1024000.0, 700000.0],
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[1413000.0, 509000.0, 8611000.0, 2440000.0, 1268000.0, 727000.0],
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[1043000.0, 365000.0, 6219000.0, 1658000.0, 994000.0, 566000.0],
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[1430000.0, 482000.0, 8368000.0, 2044000.0, 1467000.0, 717000.0],
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[1314000.0, 746000.0, 10364000.0, 2456000.0, 2211000.0, 799000.0],
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[2409000.0, 926000.0, 13413000.0, 2755000.0, 2830000.0, 892000.0],
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[4993689.0, 1524292.0, 19737409.0, 4002095.0, 3677075.0,
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1033258.0],
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[6373564.0, 1839193.0, 24039700.0, 5090302.0, 4167512.0, 1242719.0]
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]
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values = np.array(values, dtype='object')
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expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
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tm.assert_frame_equal(result, expected)
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tm.assert_frame_equal(jp[visitors], expected)
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@pytest.mark.xfail(reason='Parsing error')
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@pytest.mark.skipif(not PANDAS_0210, reason='Broken on old pandas')
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def test_land_use(self):
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# OECD -> Environment -> Resources Land Use
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result = read_jsdmx(os.path.join(self.dirpath, 'jsdmx',
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'land_use.json'))
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assert isinstance(result, pd.DataFrame)
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result = result.loc['2010':'2011']
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cols = ['Arable land and permanent crops',
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'Arable and cropland % land area',
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'Total area', 'Forest', 'Forest % land area',
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'Land area', 'Permanent meadows and pastures',
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'Meadows and pastures % land area', 'Other areas',
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'Other % land area']
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exp_col = pd.MultiIndex.from_product([
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['Japan', 'United States'],
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['Arable land and permanent crops',
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'Arable and cropland % land area',
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'Total area', 'Forest', 'Forest % land area',
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'Land area', 'Permanent meadows and pastures',
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'Meadows and pastures % land area', 'Other areas',
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'Other % land area']], names=['Country', 'Variable'])
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cols], names=['Country', 'Variable'])
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exp_idx = pd.DatetimeIndex(['2010', '2011'], name='Year')
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values = np.array([[45930, 12.601, 377950, 249790, 68.529, 364500,
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np.nan, np.nan, 68780, 18.87, 1624330, 17.757,
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9831510, 3040220, 33.236, 9147420, 2485000,
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27.166, 1997870, 21.841],
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[45610, 12.513, 377955, 249878, 68.554, 364500,
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np.nan, np.nan, 69012, 18.933, 1627625, 17.793,
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9831510, 3044048, 33.278, 9147420, 2485000,
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27.166, 1990747, 21.763]])
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values = [
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[53790.0, 14.753154141525, 377800.0, np.nan, np.nan, 364600.0,
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5000.0, 1.3713658804169, np.nan, np.nan,
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1897990.0, 20.722767650476, 9629090.0, np.nan, np.nan, 9158960.0,
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2416000.0, 26.378540795025, np.nan,
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np.nan],
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[53580.0, 14.691527282698, 377800.0, np.nan, np.nan, 364700.0,
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5000.0, 1.3709898546751, np.nan, np.nan,
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1897990.0, 20.722767650476, 9629090.0, np.nan, np.nan, 9158960.0,
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2416000.0, 26.378540795025, np.nan,
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np.nan]]
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values = np.array(values)
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expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
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tm.assert_frame_equal(result, expected)
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tm.assert_frame_equal(result[exp_col], expected)

‎requirements.txt

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lxml
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numpy
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pandas
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pytest
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requests
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requests_file
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requests_ftp
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pandas>=0.19.2
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pytest>=3
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requests>=2.3.0
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requests-file
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requests-ftp
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wrapt
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# Documentation Only
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matplotlib
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ipython
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sphinx
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sphinx_rtd_theme

‎setup.py

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INSTALL_REQUIRES = (
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['pandas>=0.17.0', 'requests>=2.3.0', 'requests-file', 'requests-ftp']
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['pandas>=0.19.2', 'requests>=2.3.0', 'requests-file', 'requests-ftp', 'wrapt', 'lxml']
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)
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2020
setup(

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