ó šxŠ\c@sÆdZdZgZx?eD]7ZyeeƒWqek rOZejeƒqXqWerredjeƒƒ‚n[[[ddl Ty ddl m Z m ZmZWn@ek rçZeeƒjdd ƒZed jeƒƒ‚nXdd lmZdd lZddlTddlTddlTddlTddlTdd lmZddlTddlm Z dd l!Zdd l"Zddl#m$Z$e$ƒZ%e%j&de%dƒZ'e%j&dƒZ([$[%dZ)d S(trestructuredtexttnumpytpytztdateutils!Missing required dependencies {0}iÿÿÿÿ(t*(t hashtabletlibttslibscannot import name ts¶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.(tdatetimeN(t show_versions(ttesti(t get_versionss closest-tagtversionsfull-revisionids 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. (RRR(*t __docformat__thard_dependenciestmissing_dependenciest dependencyt __import__t ImportErrortetappendtformattpandas.compat.numpyt pandas._libsRt _hashtableRt_libRt_tslibtstrtreplacetmoduleR tpandas.core.config_inittpandastpandas.core.apitpandas.core.sparse.apitpandas.tseries.apitpandas.core.computation.apitpandas.core.reshape.apitpandas.util._print_versionsR t pandas.io.apitpandas.util._testerR tpandas.testingt pandas.arrayst_versionR tvtgett __version__t__git_version__t__doc__(((s.lib/python2.7/site-packages/pandas/__init__.pytsH               *