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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, date shifting and lagging.
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   rI   r8   r]   r"   r#   Újson_normalizerM   rN   rP   rQ   rR   r$   r%   rJ   r   r   r1   rT   rU   r^   rY   ro   re   rd   rr   rf   rs   rk   rt   rv   rq   rp   rh   rx   ry   rl   rm   rn   rw   rg   ru   r   rE   r   ra   r{   r`   r3   r;   r:   ri   r<   r_   r@   rA   rO   )£Ú
__future__r   Ú__docformat__Z_hard_dependenciesZ_missing_dependenciesZ_dependencyÚ
__import__ÚImportErrorÚ_eÚappendÚjoinZpandas.compatr   Z_is_numpy_devZpandas._libsr   Z
_hashtabler   Z_libr	   Z_tslibZ_errr˜   Ú_moduleZpandas._configr
   r   r   r   r   r   Zpandas.core.config_initZpandasr”   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r    r!   r"   r#   r$   r%   r&   r'   r(   r)   r*   r+   r,   r-   r.   r/   r0   r1   r2   r3   r4   r5   r6   r7   r8   r9   r:   r;   r<   r=   r>   r?   r@   rA   rB   rC   rD   rE   rF   rG   r–   rH   Zpandas.tseries.apirI   Zpandas.tseriesrJ   Zpandas.core.computation.apirK   Zpandas.core.reshape.apirL   rM   rN   rO   rP   rQ   rR   rS   rT   rU   rV   rW   rX   rY   rZ   r[   r\   r]   r^   r_   r`   Zpandas.util._print_versionsra   Zpandas.io.apirb   rc   rd   re   rf   rg   rh   ri   rj   rk   rl   rm   rn   ro   rp   rq   rr   rs   rt   ru   rv   rw   rx   ry   Zpandas.io.jsonrz   r›   Zpandas.util._testerr{   Zpandas._versionr|   ÚvÚgetÚ__version__Z__git_version__r…   rˆ   rš   Ú__doc__Ú__all__r†   r†   r†   r‡   Ú<module>   sD  (ÿ
ÿü 
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
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