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UInt8DtypeUInt16DtypeUInt32DtypeUInt64DtypeFloat32DtypeFloat64DtypeCategoricalDtypePeriodDtypeIntervalDtypeDatetimeTZDtypeStringDtypeBooleanDtypeNAisnaisnullnotnanotnullIndexCategoricalIndex
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to_numericto_datetimeto_timedeltaFlagsGrouper	factorizeuniquevalue_countsNamedAggarrayCategoricalset_eng_float_formatSeries	DataFrame)SparseDtype)
infer_freq)offsets)eval)concatlreshapemeltwide_to_longmerge
merge_asofmerge_orderedcrosstabpivotpivot_tableget_dummiesfrom_dummiescutqcut)apiarrayserrorsioplottingtseries)testing)show_versions)	ExcelFileExcelWriter
read_excelread_csvread_fwf
read_tableread_pickle	to_pickleHDFStoreread_hdfread_sqlread_sql_queryread_sql_tableread_clipboardread_parquetread_orcread_featherread_gbq	read_htmlread_xml	read_json
read_stataread_sas	read_spss)json_normalize)testF)__version____git_version__T)get_versionszclosest-tagversionzfull-revisionidPANDAS_DATA_MANAGERzThe env variable PANDAS_DATA_MANAGER is set. The data_manager option is deprecated and will be removed in a future version. Only the BlockManager will be available. Unset this environment variable to silence this warning.   )
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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#   rz   rM   rN   rP   rQ   rR   r$   r%   rJ   r   r   r1   rT   rU   r^   rY   ro   re   rd   rr   rf   rs   rk   rt   rv   rq   rp   rh   rx   ry   rl   rm   rn   rw   rg   ru   r   rE   r   ra   r{   r`   r3   r;   r:   ri   r<   r_   r@   rA   rO   )
__future__r   oswarnings__docformat___hard_dependencies_missing_dependencies_dependency
__import__ImportError_eappendjoinpandas.compatr	   _is_numpy_dev_errname_modulepandas._configr
   r   r   r   r   r   pandas.core.config_initpandaspandas.core.apir   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r    r!   r"   r#   r$   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   pandas.core.dtypes.dtypesrH   pandas.tseries.apirI   pandas.tseriesrJ   pandas.core.computation.apirK   pandas.core.reshape.apirL   rM   rN   rO   rP   rQ   rR   rS   rT   rU   rV   rW   rX   rY   rZ   r[   r\   r]   r^   r_   r`   pandas.util._print_versionsra   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   pandas.io.json._normalizerz   pandas.util._testerr{   _built_with_mesonpandas._version_mesonr|   r}   pandas._versionr~   vgetenvironwarnFutureWarning__doc____all__     A/var/www/html/env/lib/python3.13/site-packages/pandas/__init__.py<module>r      s1   " 	 " 3  %K=; & 
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