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Use pandas.arrays.SparseArray instead.)rz"module 'pandas' has no attribute '')warningsrwarn FutureWarningpandas.core.apirrrrrtypepandas.core.arrays.sparserAttributeError)namerrrrdtr _SparseArrays r __getattr__rsN ++ dVC C    JI)$&        3    ,   -     4 4 4&P P   D"b!!    5    J =dV1E FFra 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. )rrr#rGrr*rJr<r/r!rerfr@rrrArmr)r1rrrrr:r r-r,r$r2rEr3rr0r+rIrKr"r5r.r7rrrrr]rFr^r9rOrVr[r8rr_rNrBrYrZr rLr;r`r%r&json_normalizerPrQrSrTrUr'r(rMrrr4rWrXrar\rrrhrgrurirvrnrwryrtrsrkr{r|rorprqrzrjrxrrHrrdr~rcr6r>r=rlr?rbrCrDrR)returnz list[str]) __future__r __docformat___hard_dependencies_missing_dependencies _dependency __import__ ImportError_eappendjoin pandas.compatr _is_numpy_dev pandas._libsr _hashtabler _libr _tslib_errr_modulepandas._configr rrrrrpandas.core.config_initpandasrrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrrKpandas.tseries.apirLpandas.tseriesrMpandas.core.computation.apirNpandas.core.reshape.apirOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarbrcpandas.util._print_versionsrd pandas.io.apirerfrgrhrirjrkrlrmrnrorprqrrrsrtrurvrwrxryrzr{r|pandas.io.jsonr}rpandas.util._testerr~pandas._versionrvget __version____git_version__rrr__doc____all__rrrrs"" 3%=K=;=  3dii@U6VV  %:8 !RR j???????????????B2)",">=5B=$)NeeM1Y<0 %%)*! 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