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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-rr,rOr'r3rRrEr8r*rnrorIr%r&rJrvr2r:rrrr rCr)r6r5r;rMr<r(r9r4rQrTr+r>r7r@r!r"r#r$rfrNrgrBrSrXr_rdrArrhrWrKrcrbrrUrDrir.r/rrYrZr\r]r^r0r1rVrrr=r`rarjrer{rqrpr~rrrwrrrr}r|rtrrrxryrzrrsrrrPrrmrrlr?rGrFrurHrkrLr[) __future__rr __docformat___hard_dependencies _dependency __import__ ImportError_e pandas.compatr _is_numpy_dev_errname_modulepandas._configrrrrrrpandas.core.config_initpandaspandas.core.apirrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQrRpandas.core.colrSpandas.core.dtypes.dtypesrTpandas.tseries.apirUpandas.tseriesrVpandas.core.computation.apirWpandas.core.reshape.apirXrYrZr[r\r]r^r_r`rarbrcrdrerfrgrhrirjrkrlpandas.util._print_versionsrm pandas.io.apirnrorprqrrrsrtrurvrwrxryrzr{r|r}r~rrrrrrrpandas.io.json._normalizerpandas.util._testerr_built_with_mesonpandas._version_mesonrrpandas._versionrvget__doc____all__rrrs"' # +%K;&  >>>>>>>>>>>>>>>~ 1)",">=5B5$  & Vs E 3K=A4 4  iiG  y!+ +   @,A%% q|4Kee-.Oa s@E#E= F##E:( E55E:=F FF #