?iV!ddlmZdZe[dZdZeD]+Z ee#e$rZedededZ[wwxYw[[ ddl m Z n$#e$rZ e j Zed ed e dZ [ wwxYwdd lmZmZmZmZmZmZddlZdd lmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@mAZAmBZBmCZCmDZDmEZEmFZFmGZGmHZHmIZImJZJmKZKmLZLmMZMmNZNmOZOdd lPmQZQddlRmSZSddlTmUZUddlVmWZWddlXmYZYddlZm[Z[m\Z\m]Z]m^Z^m_Z_m`Z`maZambZbmcZcmdZdmeZemfZfmgZgmhZhddlmiZimjZjmkZkmlZlmmZmmnZnddlmoZoddlpmqZqddlrmsZsmtZtmuZumvZvmwZwmxZxmyZymzZzm{Z{m|Z|m}Z}m~Z~mZmZmZmZmZmZmZmZmZmZmZmZddlmZddlmZdZ ddlmZmZdZnN#e$rFddlmZeZededZedZ[[YnwxYwd Zgd!ZdS)") annotationsc ddl}|j|j|j|jt |jdx}r||dSdS)Nrz pandas.libs) ospathisdirabspathjoindirname__file__pardiradd_dll_directory)rlibs_dirs C:\Users\Dell Inspiron 16\Desktop\tws\AgrotaPowerBi\back-agrota-powerbi\mcp-client-agrota\venv\Lib\site-packages\pandas/__init__.py_delvewheel_patch_1_12_0rs} III w}}bgooh>W>WY[Ybdq1r1r!s!ssXtt' X&&&&&''restructuredtext)numpydateutilz%Unable to import required dependency z'. Please see the traceback for details.N) is_numpy_devz C extension: z not built. If you want to import pandas from the source directory, you may need to run 'python -m pip install -ve . --no-build-isolation -Ceditable-verbose=true' to build the C extensions first.) get_option set_option reset_optiondescribe_optionoption_contextoptions)7 ArrowDtype Int8Dtype Int16Dtype Int32Dtype Int64Dtype UInt8Dtype UInt16Dtype UInt32Dtype UInt64Dtype Float32Dtype Float64DtypeCategoricalDtype PeriodDtype IntervalDtypeDatetimeTZDtype StringDtype BooleanDtypeNAisnaisnullnotnanotnullIndexCategoricalIndex RangeIndex MultiIndex IntervalIndexTimedeltaIndex DatetimeIndex PeriodIndex IndexSliceNaTPeriod period_range Timedeltatimedelta_range Timestamp date_range bdate_rangeIntervalinterval_range DateOffset to_numeric to_datetime to_timedeltaFlagsGrouper factorizeuniqueNamedAggarray Categoricalset_eng_float_formatSeries DataFrame)col) SparseDtype) infer_freq)offsets)eval)concatlreshapemelt wide_to_longmerge merge_asof merge_orderedcrosstabpivot pivot_table get_dummies from_dummiescutqcut)apiarrayserrorsioplottingtseries)testing) show_versions) ExcelFile ExcelWriter read_excelread_csvread_fwf read_table read_pickle to_pickleHDFStoreread_hdfread_sqlread_sql_queryread_sql_tableread_clipboard read_parquetread_orc read_feather read_htmlread_xml read_json read_stataread_sas read_spss read_iceberg)json_normalize)testF) __version____git_version__T) get_versionsz closest-tagversionzfull-revisionida 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-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 ; k 4K 4 4 4      iG + + + + +    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>~ 111111))))))"""""",,,,,,">===============555555B544444$$$$$$  ,,,,,, A%% q|44Kee-..Oaaa & Vs s s s= *A?A AA2A--A2 F##AG.-G.