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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.r) stacklevela 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:rJrnrErQrqrcrVrHrrrgrCrDrhrrPrXr<r=r>r;rarGrTrSrKrYrlrZrFrWrRrprrrIr\rUr^r@rArBr?rrmrr`rvr}rr_r7rrurirrr4rsrbrrLrMrrwrxrzr{r|rNrOrtr8r9r[r~rrrrrrrrrrrrrrrrrrrrrrrr6ror5rrrr]rerdrrfrrjrkry) __future__rr/r warnings __docformat__Z_hard_dependenciesZ_missing_dependenciesZ _dependency __import__ ImportError_eappendrZ pandas.compatr3Z _is_numpy_devZ_errname_moduleZpandas._configr4r5r6r7r8r9Zpandas.core.config_initZpandasZpandas.core.apir:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarbrcrdrerfrgrhrirjrkrlrmrnrorprqZpandas.core.dtypes.dtypesrrZpandas.tseries.apirsZpandas.tseriesrtZpandas.core.computation.apiruZpandas.core.reshape.apirvrwrxryrzr{r|r}r~rrrrrrrrrrrrZpandas.util._print_versionsrZ pandas.io.apirrrrrrrrrrrrrrrrrrrrrrrrZpandas.io.json._normalizerZpandas.util._testerrZ_built_with_mesonZpandas._version_mesonrrZpandas._versionrvgetenvironwarn FutureWarning__doc____all__r-r-r-r.sx  ,   A    @   h!      +