import copy
import itertools
import re
import numpy as np
from pandas.core.base import PandasObject
from pandas.core.common import (_possibly_downcast_to_dtype, isnull,
_possibly_infer_to_datetimelike)
from pandas.core.index import Index, MultiIndex, _ensure_index
from pandas.core.indexing import (_maybe_convert_indices, _length_of_indexer)
import pandas.core.common as com
from pandas.sparse.array import _maybe_to_sparse, SparseArray
import pandas.lib as lib
import pandas.tslib as tslib
import pandas.computation.expressions as expressions
import nose
from numpy import nan
import numpy as np
from pandas import Index, isnull, Timestamp
from pandas.util.testing import assert_almost_equal
import pandas.util.testing as tm
from pandas.compat import range, lrange, zip
import pandas.lib as lib
import pandas.algos as algos
def test_series_grouper():
from pandas import Series
def test_series_bin_grouper():
from pandas import Series
def test_generate_bins(self):
from pandas.core.groupby import generate_bins_generic
import statsmodels.base.model as base
from statsmodels.base import data
import statsmodels.base.wrapper as wrap
from statsmodels.tsa.base import datetools
from numpy import arange, asarray
from pandas import Index
from pandas import datetools as pandas_datetools
import datetime
_freq_to_pandas = datetools._freq_to_pandas
dates : array-like of datetime, optional
An array-like object of datetime objects. If a pandas object is given
self._data.predict_end = dtend
# for pandas 0.7.x vs 0.8.x
import numpy as np
import pandas.lib as lib
from pandas.types.common import (is_number,
_ensure_object)
from pandas.types.cast import _possibly_downcast_to_dtype
import pandas as pd
from pandas.compat import reduce
from pandas.core.index import Index
from pandas.core import common as com
"""
Numpy version of itertools.product or pandas.compat.product.
>>> import pandas as pd
import os
import nose
import numpy as np
from pandas.compat import zip
from pandas import DataFrame, Series, unique
import pandas.util.testing as tm
from pandas.util.testing import assertRaisesRegexp
import pandas.core.common as com
from pandas.core.algorithms import quantile
from pandas.tools.tile import cut, qcut
import pandas.tools.tile as tmod
import nose
from datetime import datetime
from numpy.random import randn
import numpy as np
from pandas.core.api import Series, DataFrame, date_range
from pandas.util.testing import assert_almost_equal
import pandas.core.datetools as datetools
import pandas.stats.moments as mom
import pandas.util.testing as tm
import pandas.stats.math as pmath
import pandas.tests.test_series as ts
from pandas import ols
import re
from datetime import datetime, timedelta
import numpy as np
import pandas.compat as compat
import pandas as pd
from pandas.compat import u, StringIO
from pandas.core.base import FrozenList, FrozenNDArray, DatetimeIndexOpsMixin
from pandas.util.testing import assertRaisesRegexp, assert_isinstance
from pandas import Series, Index, Int64Index, DatetimeIndex, PeriodIndex
from pandas import _np_version_under1p7
import pandas.tslib as tslib
import nose
import pandas.util.testing as tm
exp1 = """<class 'pandas.tseries.index.DatetimeIndex'>
Length: 0, Freq: D, Timezone: None"""
import sys
import numpy as np
from pandas import *
import pandas.core.sparse as spm
import pandas.compat as compat
reload(spm)
from pandas.core.sparse import *
if 'linux' in sys.platform:
pth = '/home/wesm/code/pandas/example'
else:
# import hotshot, hotshot.stats
import pandas.stats.plm as plm
import pandas.stats.interface as face
reload(plm)
import nose
import sys
import functools
from datetime import datetime
from numpy.random import randn
from pandas import Series, DataFrame, bdate_range, isnull, notnull
from pandas.util.testing import (
assert_almost_equal, assert_series_equal, assert_frame_equal
)
import pandas.core.datetools as datetools
import pandas.stats.moments as mom
import pandas.util.testing as tm
from pandas.compat import range, zip, PY3, StringIO
import itertools
import functools
import numpy as np
import operator
import pandas.hashtable as _hash
from pandas import compat, lib, algos, tslib
from pandas.types.common import (_ensure_int64, _ensure_object,
_ensure_float64, _get_dtype,
is_int_or_datetime_dtype, is_any_int_dtype)
from pandas.types.cast import _int64_max, _maybe_upcast_putmask
from pandas.types.missing import isnull, notnull
from pandas.core.common import _values_from_object
import sys
import itertools
import functools
import numpy as np
try:
import bottleneck as bn
_USE_BOTTLENECK = True
import pandas.core.common as com
import pandas.hashtable as _hash
from pandas import compat, lib, algos, tslib
from pandas.compat import builtins
from pandas.core.common import (isnull, notnull, _values_from_object,
_maybe_upcast_putmask,
# 1.6.2 bug in that it results dtype of timedelta64[us]
from pandas import Series
import datetime
import numpy as np
from numpy.testing import assert_equal
import pandas as pd
from pandas import DataFrame, Series, Index, MultiIndex, Grouper
from pandas.tools.merge import concat
from pandas.tools.pivot import pivot_table, crosstab
from pandas.compat import range, u, product
import pandas.util.testing as tm
if __name__ == '__main__':
import nose
from pandas import compat
from pandas.compat import u
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, period_range, Index, SparseSeries, SparseDataFrame,
SparsePanel)
import pandas.util.testing as tm
from pandas.util.testing import ensure_clean
from pandas.tests.test_series import assert_series_equal
from pandas.tests.test_frame import assert_frame_equal
from pandas.tests.test_panel import assert_panel_equal
import pandas
from pandas.sparse.tests.test_sparse import assert_sp_series_equal, assert_sp_frame_equal
from pandas import Timestamp, tslib
import nose
import unittest
from pandas.core.daterange import DateRange
from pandas.core.index import Index, MultiIndex
from pandas.core.common import rands, groupby
from pandas.core.frame import DataFrame
from pandas.core.groupby import GroupByError
from pandas.core.series import Series
from pandas.util.testing import (assert_panel_equal, assert_frame_equal,
assert_series_equal, assert_almost_equal)
from pandas.core.panel import Panel
from collections import defaultdict
import pandas._tseries as lib
import pandas.core.datetools as dt
import numpy as np
import nose
import unittest
from pandas.core.daterange import DateRange
from pandas.core.index import Index, MultiIndex
from pandas.core.common import rands, groupby
from pandas.core.frame import DataFrame
from pandas.core.series import Series
from pandas.util.testing import (assert_panel_equal, assert_frame_equal,
assert_series_equal, assert_almost_equal)
from pandas.core.panel import Panel
from collections import defaultdict
import pandas._tseries as lib
import pandas.core.datetools as dt
import numpy as np
import unittest
import nose
from datetime import datetime
from numpy.random import randn
import numpy as np
from pandas.core.api import Series, DataFrame, DateRange
from pandas.util.testing import assert_almost_equal
import pandas.core.datetools as datetools
import pandas.stats.moments as mom
import pandas.util.testing as tm
if __name__ == '__main__':
import nose
import nose
from pandas import tslib
import datetime
from pandas.core.api import Timestamp, Series
from pandas.tslib import period_asfreq, period_ordinal
from pandas.tseries.index import date_range
from pandas.tseries.frequencies import get_freq
import pandas.tseries.offsets as offsets
from pandas import _np_version_under1p7
import pandas.util.testing as tm
from pandas.util.testing import assert_series_equal
import nose
import pandas
from pandas.compat import u
from pandas.util.testing import network
from pandas.util.testing import assert_frame_equal
from numpy.testing.decorators import slow
from pandas.io.wb import search, download, get_countries
import pandas.util.testing as tm
result = search('gdp.*capita.*constant').ix[:, :2]
expected = pandas.DataFrame(expected)
expected = {'GDPPCKN': {(u('United States'), u('2003')): u('40800.0735367688'), (u('Canada'), u('2004')): u('37857.1261134552'), (u('United States'), u('2005')): u('42714.8594790102'), (u('Canada'), u('2003')): u('37081.4575704003'), (u('United States'), u('2004')): u('41826.1728310667'), (u('Mexico'), u('2003')): u('72720.0691255285'), (u('Mexico'), u('2004')): u('74751.6003347038'), (u('Mexico'), u('2005')): u('76200.2154469437'), (u('Canada'), u('2005')): u('38617.4563629611')}, 'GDPPCKD': {(u('United States'), u('2003')): u('40800.0735367688'), (u('Canada'), u('2004')): u('34397.055116118'), (u('United States'), u('2005')): u('42714.8594790102'), (u('Canada'), u('2003')): u('33692.2812368928'), (u('United States'), u('2004')): u('41826.1728310667'), (u('Mexico'), u('2003')): u('7608.43848670658'), (u('Mexico'), u('2004')): u('7820.99026814334'), (u('Mexico'), u('2005')): u('7972.55364129367'), (u('Canada'), u('2005')): u('35087.8925933298')}}
expected = pandas.DataFrame(expected)
expected.index = result.index
assert_frame_equal(result, pandas.DataFrame(expected))