h #SSKJr Sr\"5 CSSKrSSKrSrSr/r\H r\ "\5 M \(a\ "SSR\5-5eCCCSS KJr SS KJrJrJrJrJrJr SSKrSS KJrJrJ r J!r!J"r"J#r#J$r$J%r%J&r&J'r'J(r(J)r)J*r*J+r+J,r,J-r-J.r.J/r/J0r0J1r1J2r2J3r3J4r4J5r5J6r6J7r7J8r8J9r9J:r:J;r;Jr>J?r?J@r@JArAJBrBJCrCJDrDJErEJFrFJGrGJHrHJIrIJJrJJKrKJLrLJMrMJNrNJOrOJPrPJQrQJRrRJSrSJTrTJUrU SSKVJWrW SSKXJYrY SSKZJ[r[ SSK\J]r] SSK^J_r_J`r`JaraJbrbJcrcJdrdJereJfrfJgrgJhrhJiriJjrjJkrkJlrl SSKJmrmJnrnJoroJprpJqrqJrrr SSKJsrs SSKtJuru SSKvJwrwJxrxJyryJzrzJ{r{J|r|J}r}J~r~JrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJr SSKJr SSKJr SrSSKJrJr SrS \GR6;a\GR8"S!\S"S#9 CCS$r/S%Qrg!\ a"r \R\S\ 35 Sr C GMSr C ff=f!\ ar\R$r\ "S \S 35\eSrCff=f!\ a: SSKJr \"5r\GR5S\S5r\GR5S5rCCNf=f)&) annotationsc ,SSKnURRURRURR URR [ 5URS55=n5(aUR"U5 gg)Nrz pandas.libs) ospathisdirabspathjoindirname__file__pardiradd_dll_directory)rlibs_dirs SC:\Users\psant\Downloads\Noiseback\school\venv\Lib\site-packages\pandas/__init__.py_delvewheel_patch_1_10_1rsd  ww}}bggooh>WY[YbYbdq1r!ssXtt X&uNrestructuredtext)numpypytzdateutilz: z(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-revisionidPANDAS_DATA_MANAGERzThe env variable PANDAS_DATA_MANAGER is set. 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.) 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. )rrr.rRr)r5rUrGr:r,rprqrKr'r(rLrxr4r<r r!r"rrEr+r8r7r/r=rPr>r*r;r6rTrVr-r@r9rBr$r%r&r#rhrQrirDrZrarfrCrrjrYrMrdrerrWrFrkr0r1rr[r\r^r_r`r2r3rXrrr?rbrcrlrgr}rsrrrrtrryrrrr~rvrrrzr{r|rrurrrSrrorrnrArIrHrwrJrmrNrOr]) __future__rrrwarnings __docformat___hard_dependencies_missing_dependencies _dependency __import__ ImportError_eappendr pandas.compatr _is_numpy_dev_errname_modulepandas._configrrrrrrpandas.core.config_initpandaspandas.core.apirrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQrRrSrTrUpandas.core.dtypes.dtypesrVpandas.tseries.apirWpandas.tseriesrXpandas.core.computation.apirYpandas.core.reshape.apirZr[r\r]r^r_r`rarbrcrdrerfrgrhrirjrkrlrmrnpandas.util._print_versionsro pandas.io.apirprqrrrsrtrurvrwrxryrzr{r|r}r~rrrrrrrrrpandas.io.json._normalizerpandas.util._testerr_built_with_mesonpandas._version_mesonrrpandas._versionrvgetenvironwarn FutureWarning__doc____all__rrrsC"'  " 3%K=;&  3dii@U6VV  %: ???????????????B2)",">=5B5$  BJJ& MM V   b& Vs [ =$$ }Brd%;<<=iiG  y!G G   ~,A%% q|4Kee-.Oa sAF3G< H3G9GGH$G==H=II