from __future__ import annotations __docformat__ = "restructuredtext" # Let users know if they're missing any of our hard dependencies _hard_dependencies = ("numpy", "pytz", "dateutil") _missing_dependencies = [] for _dependency in _hard_dependencies: try: __import__(_dependency) except ImportError as _e: # pragma: no cover _missing_dependencies.append(f"{_dependency}: {_e}") if _missing_dependencies: # pragma: no cover raise ImportError( "Unable to import required dependencies:\n" + "\n".join(_missing_dependencies) ) del _hard_dependencies, _dependency, _missing_dependencies try: # numpy compat from pandas.compat import ( is_numpy_dev as _is_numpy_dev, # pyright: ignore[reportUnusedImport] # noqa: F401,E501 ) except ImportError as _err: # pragma: no cover _module = _err.name raise ImportError( f"C extension: {_module} 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." ) from _err from pandas._config import ( get_option, set_option, reset_option, describe_option, option_context, options, ) # let init-time option registration happen import pandas.core.config_init # pyright: ignore[reportUnusedImport] # noqa: F401 from pandas.core.api import ( # dtype ArrowDtype, Int8Dtype, Int16Dtype, Int32Dtype, Int64Dtype, UInt8Dtype, UInt16Dtype, UInt32Dtype, UInt64Dtype, Float32Dtype, Float64Dtype, CategoricalDtype, PeriodDtype, IntervalDtype, DatetimeTZDtype, StringDtype, BooleanDtype, # missing NA, isna, isnull, notna, notnull, # indexes Index, CategoricalIndex, RangeIndex, MultiIndex, IntervalIndex, TimedeltaIndex, DatetimeIndex, PeriodIndex, IndexSlice, # tseries NaT, Period, period_range, Timedelta, timedelta_range, Timestamp, date_range, bdate_range, Interval, interval_range, DateOffset, # conversion to_numeric, to_datetime, to_timedelta, # misc Flags, Grouper, factorize, unique, value_counts, NamedAgg, array, Categorical, set_eng_float_format, Series, DataFrame, ) from pandas.core.dtypes.dtypes import SparseDtype from pandas.tseries.api import infer_freq from pandas.tseries import offsets from pandas.core.computation.api import eval from pandas.core.reshape.api import ( concat, lreshape, melt, wide_to_long, merge, merge_asof, merge_ordered, crosstab, pivot, pivot_table, get_dummies, from_dummies, cut, qcut, ) from pandas import api, arrays, errors, io, plotting, tseries from pandas import testing from pandas.util._print_versions import show_versions from pandas.io.api import ( # excel ExcelFile, ExcelWriter, read_excel, # parsers read_csv, read_fwf, read_table, # pickle read_pickle, to_pickle, # pytables HDFStore, read_hdf, # sql read_sql, read_sql_query, read_sql_table, # misc read_clipboard, read_parquet, read_orc, read_feather, read_gbq, read_html, read_xml, read_json, read_stata, read_sas, read_spss, ) from pandas.io.json._normalize import json_normalize from pandas.util._tester import test # use the closest tagged version if possible _built_with_meson = False try: from pandas._version_meson import ( # pyright: ignore [reportMissingImports] __version__, __git_version__, ) _built_with_meson = True except ImportError: from pandas._version import get_versions v = get_versions() __version__ = v.get("closest-tag", v["version"]) __git_version__ = v.get("full-revisionid") del get_versions, v # module level doc-string __doc__ = """ 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. """ # Use __all__ to let type checkers know what is part of the public API. # Pandas is not (yet) a py.typed library: the public API is determined # based on the documentation. __all__ = [ "ArrowDtype", "BooleanDtype", "Categorical", "CategoricalDtype", "CategoricalIndex", "DataFrame", "DateOffset", "DatetimeIndex", "DatetimeTZDtype", "ExcelFile", "ExcelWriter", "Flags", "Float32Dtype", "Float64Dtype", "Grouper", "HDFStore", "Index", "IndexSlice", "Int16Dtype", "Int32Dtype", "Int64Dtype", "Int8Dtype", "Interval", "IntervalDtype", "IntervalIndex", "MultiIndex", "NA", "NaT", "NamedAgg", "Period", "PeriodDtype", "PeriodIndex", "RangeIndex", "Series", "SparseDtype", "StringDtype", "Timedelta", "TimedeltaIndex", "Timestamp", "UInt16Dtype", "UInt32Dtype", "UInt64Dtype", "UInt8Dtype", "api", "array", "arrays", "bdate_range", "concat", "crosstab", "cut", "date_range", "describe_option", "errors", "eval", "factorize", "get_dummies", "from_dummies", "get_option", "infer_freq", "interval_range", "io", "isna", "isnull", "json_normalize", "lreshape", "melt", "merge", "merge_asof", "merge_ordered", "notna", "notnull", "offsets", "option_context", "options", "period_range", "pivot", "pivot_table", "plotting", "qcut", "read_clipboard", "read_csv", "read_excel", "read_feather", "read_fwf", "read_gbq", "read_hdf", "read_html", "read_json", "read_orc", "read_parquet", "read_pickle", "read_sas", "read_spss", "read_sql", "read_sql_query", "read_sql_table", "read_stata", "read_table", "read_xml", "reset_option", "set_eng_float_format", "set_option", "show_versions", "test", "testing", "timedelta_range", "to_datetime", "to_numeric", "to_pickle", "to_timedelta", "tseries", "unique", "value_counts", "wide_to_long", ]