129 lines
5.3 KiB
Python
129 lines
5.3 KiB
Python
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# pylint: disable-msg=W0614,W0401,W0611,W0622
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# flake8: noqa
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__docformat__ = 'restructuredtext'
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# Let users know if they're missing any of our hard dependencies
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hard_dependencies = ("numpy", "pytz", "dateutil")
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missing_dependencies = []
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for dependency in hard_dependencies:
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try:
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__import__(dependency)
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except ImportError as e:
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missing_dependencies.append(dependency)
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if missing_dependencies:
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raise ImportError(
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"Missing required dependencies {0}".format(missing_dependencies))
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del hard_dependencies, dependency, missing_dependencies
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# numpy compat
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from pandas.compat.numpy import *
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try:
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from pandas._libs import (hashtable as _hashtable,
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lib as _lib,
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tslib as _tslib)
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except ImportError as e: # pragma: no cover
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# hack but overkill to use re
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module = str(e).replace('cannot import name ', '')
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raise ImportError("C extension: {0} not built. If you want to import "
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"pandas from the source directory, you may need to run "
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"'python setup.py build_ext --inplace --force' to build "
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"the C extensions first.".format(module))
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from datetime import datetime
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# let init-time option registration happen
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import pandas.core.config_init
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from pandas.core.api import *
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from pandas.core.sparse.api import *
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from pandas.tseries.api import *
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from pandas.core.computation.api import *
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from pandas.core.reshape.api import *
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# deprecate tools.plotting, plot_params and scatter_matrix on the top namespace
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import pandas.tools.plotting
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plot_params = pandas.plotting._style._Options(deprecated=True)
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# do not import deprecate to top namespace
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scatter_matrix = pandas.util._decorators.deprecate(
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'pandas.scatter_matrix', pandas.plotting.scatter_matrix, '0.20.0',
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'pandas.plotting.scatter_matrix')
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from pandas.util._print_versions import show_versions
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from pandas.io.api import *
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from pandas.util._tester import test
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import pandas.testing
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# extension module deprecations
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from pandas.util._depr_module import _DeprecatedModule
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json = _DeprecatedModule(deprmod='pandas.json',
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moved={'dumps': 'pandas.io.json.dumps',
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'loads': 'pandas.io.json.loads'})
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parser = _DeprecatedModule(deprmod='pandas.parser',
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removals=['na_values'],
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moved={'CParserError': 'pandas.errors.ParserError'})
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lib = _DeprecatedModule(deprmod='pandas.lib', deprmodto=False,
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moved={'Timestamp': 'pandas.Timestamp',
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'Timedelta': 'pandas.Timedelta',
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'NaT': 'pandas.NaT',
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'infer_dtype': 'pandas.api.types.infer_dtype'})
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tslib = _DeprecatedModule(deprmod='pandas.tslib',
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moved={'Timestamp': 'pandas.Timestamp',
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'Timedelta': 'pandas.Timedelta',
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'NaT': 'pandas.NaT',
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'NaTType': 'type(pandas.NaT)',
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'OutOfBoundsDatetime': 'pandas.errors.OutOfBoundsDatetime'})
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# use the closest tagged version if possible
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from ._version import get_versions
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v = get_versions()
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__version__ = v.get('closest-tag', v['version'])
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del get_versions, v
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# module level doc-string
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__doc__ = """
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pandas - a powerful data analysis and manipulation library for Python
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=====================================================================
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**pandas** is a Python package providing fast, flexible, and expressive data
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structures designed to make working with "relational" or "labeled" data both
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easy and intuitive. It aims to be the fundamental high-level building block for
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doing practical, **real world** data analysis in Python. Additionally, it has
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the broader goal of becoming **the most powerful and flexible open source data
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analysis / manipulation tool available in any language**. It is already well on
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its way toward this goal.
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Main Features
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-------------
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Here are just a few of the things that pandas does well:
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- Easy handling of missing data in floating point as well as non-floating
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point data.
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- Size mutability: columns can be inserted and deleted from DataFrame and
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higher dimensional objects
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- Automatic and explicit data alignment: objects can be explicitly aligned
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to a set of labels, or the user can simply ignore the labels and let
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`Series`, `DataFrame`, etc. automatically align the data for you in
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computations.
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- Powerful, flexible group by functionality to perform split-apply-combine
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operations on data sets, for both aggregating and transforming data.
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- Make it easy to convert ragged, differently-indexed data in other Python
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and NumPy data structures into DataFrame objects.
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- Intelligent label-based slicing, fancy indexing, and subsetting of large
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data sets.
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- Intuitive merging and joining data sets.
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- Flexible reshaping and pivoting of data sets.
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- Hierarchical labeling of axes (possible to have multiple labels per tick).
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- Robust IO tools for loading data from flat files (CSV and delimited),
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Excel files, databases, and saving/loading data from the ultrafast HDF5
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format.
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- Time series-specific functionality: date range generation and frequency
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conversion, moving window statistics, moving window linear regressions,
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date shifting and lagging, etc.
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"""
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