o eI! @s~ddlmZddZe[dZdZgZeD]$ZzeeWqey:Z ze ede WYdZ [ qdZ [ wwerFedd e[[[zdd l m ZWneyiZz ejZed ed edZ[wwdd lmZmZmZmZmZmZddlZddlmZmZmZmZm Z m!Z!m"Z"m#Z#m$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@mAZAmBZBmCZCmDZDmEZEmFZFmGZGmHZHmIZImJZJmKZKmLZLmMZMmNZNmOZOmPZPmQZQmRZRmSZSddlTmUZUddlVmWZWddlXmYZYddlZm[Z[ddl\m]Z]m^Z^m_Z_m`Z`maZambZbmcZcmdZdmeZemfZfmgZgmhZhmiZimjZjddlmkZkmlZlmmZmmnZnmoZompZpddlmqZqddlrmsZsddltmuZumvZvmwZwmxZxmyZymzZzm{Z{m|Z|m}Z}m~Z~mZmZmZmZmZmZmZmZmZmZmZmZmZmZddlmZddlmZdZz ddlmZmZdZWn"eyddlmZeZededZed Z[[Ynwd!Zgd"ZdS)#) annotationscCsHddl}|j|j|jt|jd}|j|r"||dSdS)Nrz pandas.libs) ospathabspathjoindirname__file__pardirisdiradd_dll_directory)rZlibs_dirr ?D:\Projects\ConvertPro\env\Lib\site-packages\pandas/__init__.py_delvewheel_patch_1_5_0s " rrestructuredtext)numpyZpytzZdateutilz: Nz(Unable to import required dependencies:  ) is_numpy_devz C extension: z not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext' to build the C extensions first.) get_option set_option reset_optiondescribe_optionoption_contextoptions)8 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 factorizeunique value_countsNamedAggarray Categoricalset_eng_float_formatSeries DataFrame) 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_featherread_gbq read_htmlread_xml read_json read_stataread_sas read_spss)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. )rrr)rMr$r0rPrBr5r'rkrlrFr"r#rGrsr/r7rrrrr@r&r3r2r*r8rKr9r%r6r1rOrQr(r;r4r=rr r!rrcrLrdr?rUr\rar>rrerTrHr_r`rrRrArfr+r,rrVrWrYrZr[r-r.rSrrr:r]r^rgrbrxrnrmr{ror|rtr}rrzryrqrrrurvrwrrpr~rrNrrjrrir<rDrCrrrErhrIrJrX) __future__rr __docformat__Z_hard_dependenciesZ_missing_dependenciesZ _dependency __import__ ImportError_eappendrZ pandas.compatrZ _is_numpy_devZ_errname_moduleZpandas._configrrrrrrZpandas.core.config_initZpandasZpandas.core.apirrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPZpandas.core.dtypes.dtypesrQZpandas.tseries.apirRZpandas.tseriesrSZpandas.core.computation.apirTZpandas.core.reshape.apirUrVrWrXrYrZr[r\r]r^r_r`rarbrcrdrerfrgrhriZpandas.util._print_versionsrjZ pandas.io.apirkrlrmrnrorprqrrrsrtrurvrwrxryrzr{r|r}r~rrrrZpandas.io.json._normalizerZpandas.util._testerrZ_built_with_mesonZpandas._version_mesonrrZpandas._versionrvget__doc____all__r r r r sr        A   @   h !    +