import sys

 sys.path.append('sub1')

 import mytest

 print(mytest.sub(2,3))

 # pylint: disable-msg=W0614,W0401,W0611,W0622

 # flake8: noqa

 __docformat__ = 'restructuredtext'

 # Let users know if they're missing any of our hard dependencies

 hard_dependencies = ("numpy", "pytz", "dateutil")

 missing_dependencies = []

 for dependency in hard_dependencies:

     try:

         __import__(dependency)

     except ImportError as e:

         missing_dependencies.append(dependency)

 if missing_dependencies:

     raise ImportError(

         "Missing required dependencies {0}".format(missing_dependencies))

 del hard_dependencies, dependency, missing_dependencies

 # numpy compat

 from pandas.compat.numpy import *

 try:

     from pandas._libs import (hashtable as _hashtable,

                              lib as _lib,

                              tslib as _tslib)

 except ImportError as e:  # pragma: no cover

     # hack but overkill to use re

     module = str(e).replace('cannot import name ', '')

     raise ImportError("C extension: {0} not built. If you want to import "

                       "pandas from the source directory, you may need to run "

                       "'python setup.py build_ext --inplace --force' to build "

                       "the C extensions first.".format(module))

 from datetime import datetime

 # let init-time option registration happen

 import pandas.core.config_init

 from pandas.core.api import *

 from pandas.core.sparse.api import *

 from pandas.tseries.api import *

 from pandas.core.computation.api import *

 from pandas.core.reshape.api import *

 # deprecate tools.plotting, plot_params and scatter_matrix on the top namespace

 import pandas.tools.plotting

 plot_params = pandas.plotting._style._Options(deprecated=True)

 # do not import deprecate to top namespace

 scatter_matrix = pandas.util._decorators.deprecate(

     'pandas.scatter_matrix', pandas.plotting.scatter_matrix, '0.20.0',

     'pandas.plotting.scatter_matrix')

 from pandas.util._print_versions import show_versions

 from pandas.io.api import *

 from pandas.util._tester import test

 import pandas.testing

 # extension module deprecations

 from pandas.util._depr_module import _DeprecatedModule

 json = _DeprecatedModule(deprmod='pandas.json',

                          moved={'dumps': 'pandas.io.json.dumps',

                                 'loads': 'pandas.io.json.loads'})

 parser = _DeprecatedModule(deprmod='pandas.parser',

                            removals=['na_values'],

                            moved={'CParserError': 'pandas.errors.ParserError'})

 lib = _DeprecatedModule(deprmod='pandas.lib', deprmodto=False,

                         moved={'Timestamp': 'pandas.Timestamp',

                                'Timedelta': 'pandas.Timedelta',

                                'NaT': 'pandas.NaT',

                                'infer_dtype': 'pandas.api.types.infer_dtype'})

 tslib = _DeprecatedModule(deprmod='pandas.tslib',

                           moved={'Timestamp': 'pandas.Timestamp',

                                  'Timedelta': 'pandas.Timedelta',

                                  'NaT': 'pandas.NaT',

                                  'NaTType': 'type(pandas.NaT)',

                                  'OutOfBoundsDatetime': 'pandas.errors.OutOfBoundsDatetime'})

 # use the closest tagged version if possible

 from ._version import get_versions

 v = get_versions()

 __version__ = v.get('closest-tag', v['version'])

 del get_versions, v

 # module level doc-string

 __doc__ = """

 pandas - a powerful data analysis and manipulation library for Python

 =====================================================================

 **pandas** is a Python package providing fast, flexible, and expressive data

 structures designed to make working with "relational" or "labeled" data both

 easy and intuitive. It aims to be the fundamental high-level building block for

 doing practical, **real world** data analysis in Python. Additionally, it has

 the broader goal of becoming **the most powerful and flexible open source data

 analysis / manipulation tool available in any language**. It is already well on

 its way toward this goal.

 Main Features

 -------------

 Here are just a few of the things that pandas does well:

   - Easy handling of missing data in floating point as well as non-floating

     point data.

   - Size mutability: columns can be inserted and deleted from DataFrame and

     higher dimensional objects

   - Automatic and explicit data alignment: objects can be explicitly aligned

     to a set of labels, or the user can simply ignore the labels and let

     `Series`, `DataFrame`, etc. automatically align the data for you in

     computations.

   - Powerful, flexible group by functionality to perform split-apply-combine

     operations on data sets, for both aggregating and transforming data.

   - Make it easy to convert ragged, differently-indexed data in other Python

     and NumPy data structures into DataFrame objects.

   - Intelligent label-based slicing, fancy indexing, and subsetting of large

     data sets.

   - Intuitive merging and joining data sets.

   - Flexible reshaping and pivoting of data sets.

   - Hierarchical labeling of axes (possible to have multiple labels per tick).

   - Robust IO tools for loading data from flat files (CSV and delimited),

     Excel files, databases, and saving/loading data from the ultrafast HDF5

     format.

   - Time series-specific functionality: date range generation and frequency

     conversion, moving window statistics, moving window linear regressions,

     date shifting and lagging, etc.

 """

 __all__ = ['file1','file2'] #package1下有file1.py,file2.py

 __all__= ['file1','file2']