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ArrowDtypeÚ	Int8DtypeÚ
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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.
)rr   r.   rR   r)   r5   rU   rG   r:   r,   rp   rq   rK   r'   r(   rL   rx   r4   r<   r    r!   r"   r   rE   r+   r8   r7   r/   r=   rP   r>   r*   r;   r6   rT   rV   r-   r@   r9   rB   r$   r%   r&   r#   rh   rQ   ri   rD   rZ   ra   rf   rC   r   rj   rY   rM   rd   re   r   rW   rF   rk   r0   r1   rˆ   r[   r\   r^   r_   r`   r2   r3   rX   r   r   r?   rb   rc   rl   rg   r}   rs   rr   r€   rt   r�   ry   r‚   r„   r   r~   rv   r†   r‡   rz   r{   r|   r…   ru   rƒ   r   rS   r   ro   r‰   rn   rA   rI   rH   rw   rJ   rm   rN   rO   r]   ) Ú
__future__r   r   r   ÚwarningsÚ__docformat__Ú_hard_dependenciesÚ_missing_dependenciesÚ_dependencyÚ
__import__ÚImportErrorÚ_eÚappendr   Úpandas.compatr   Ú_is_numpy_devÚ_errÚnameÚ_moduleÚpandas._configr   r   r   r   r   r   Úpandas.core.config_initÚpandasÚpandas.core.apir   r   r    r!   r"   r#   r$   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   rH   rI   rJ   rK   rL   rM   rN   rO   rP   rQ   rR   rS   rT   rU   Úpandas.core.dtypes.dtypesrV   Úpandas.tseries.apirW   Úpandas.tseriesrX   Úpandas.core.computation.apirY   Úpandas.core.reshape.apirZ   r[   r\   r]   r^   r_   r`   ra   rb   rc   rd   re   rf   rg   rh   ri   rj   rk   rl   rm   rn   Úpandas.util._print_versionsro   Úpandas.io.apirp   rq   rr   rs   rt   ru   rv   rw   rx   ry   rz   r{   r|   r}   r~   r   r€   r�   r‚   rƒ   r„   r…   r†   r‡   Úpandas.io.json._normalizerˆ   Úpandas.util._testerr‰   Ú_built_with_mesonÚpandas._version_mesonrŠ   r‹   Úpandas._versionrŒ   ÚvÚgetÚenvironÚwarnÚFutureWarningÚ__doc__Ú__all__© r   r   ú<module>r¸      s¯  ðØ "Ð "Ð "Ð "Ð "Ð "ð'ð 'ð 'ð Ð Ñ Ô Ð Øð 
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�;ÑÔÐÐøØð =ð =ð =Ø×$Ò$¨Ð%;Ð%;°rÐ%;Ð%;Ñ<Ô<Ð<Ð<Ð<Ð<Ð<Ð<øøøøð=øøøð ð Ø
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ˆ+ð	G˜ð 	Gð 	Gð 	Gñô ð ð	øøøøðøøøðð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð Ð Ð Ð ð?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ð ?ðB 2Ð 1Ð 1Ð 1Ð 1Ð 1à )Ð )Ð )Ð )Ð )Ð )Ø "Ð "Ð "Ð "Ð "Ð "à ,Ð ,Ð ,Ð ,Ð ,Ð ,ðð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð" >Ð =Ð =Ð =Ð =Ð =Ð =Ð =Ð =Ð =Ð =Ð =Ð =Ð =Ð =Ð =Ø Ð Ð Ð Ð Ð Ø 5Ð 5Ð 5Ð 5Ð 5Ð 5ðð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ð ðB 5Ð 4Ð 4Ð 4Ð 4Ð 4à $Ð $Ð $Ð $Ð $Ð $ð Ð ððð ð ð ð ð ð ð ð
 ÐÐøØð ð ð Ø,Ð,Ð,Ð,Ð,Ð,àˆ‰Œ€AØ—%’%˜ q¨¤|Ñ4Ô4€KØ—e’eÐ-Ñ.Ô.€OØ�a�a�aðøøøð ˜BœJÐ&Ð&Ø€H„Mð	Vð 	Øðñ ô ð ð ˆbð&€ðVsð sð s€€€s?   ¨4´A¹AÁAÂB ÂB/ÂB*Â*B/Ç
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