# flake8: noqa __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: missing_dependencies.append(f"{dependency}: {e}") if missing_dependencies: raise ImportError( "Unable to import required dependencies:\n" + "\n".join(missing_dependencies) ) del hard_dependencies, dependency, missing_dependencies # numpy compat from pandas.compat import ( np_version_under1p18 as _np_version_under1p18, is_numpy_dev as _is_numpy_dev, ) try: from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib except ImportError as e: # pragma: no cover # hack but overkill to use re module = str(e).replace("cannot import 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 --force' to build the C extensions first." ) from e 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 from pandas.core.api import ( # dtype 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, Int64Index, UInt64Index, RangeIndex, Float64Index, 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.arrays.sparse 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, cut, qcut, ) import pandas.api 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 import _json_normalize as json_normalize from pandas.util._tester import test import pandas.testing import pandas.arrays # use the closest tagged version if possible 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 # GH 27101 def __getattr__(name): import warnings if name == "datetime": warnings.warn( "The pandas.datetime class is deprecated " "and will be removed from pandas in a future version. " "Import from datetime module instead.", FutureWarning, stacklevel=2, ) from datetime import datetime as dt return dt elif name == "np": warnings.warn( "The pandas.np module is deprecated " "and will be removed from pandas in a future version. " "Import numpy directly instead", FutureWarning, stacklevel=2, ) import numpy as np return np elif name in {"SparseSeries", "SparseDataFrame"}: warnings.warn( f"The {name} class is removed from pandas. Accessing it from " "the top-level namespace will also be removed in the next version", FutureWarning, stacklevel=2, ) return type(name, (), {}) elif name == "SparseArray": warnings.warn( "The pandas.SparseArray class is deprecated " "and will be removed from pandas in a future version. " "Use pandas.arrays.SparseArray instead.", FutureWarning, stacklevel=2, ) from pandas.core.arrays.sparse import SparseArray as _SparseArray return _SparseArray raise AttributeError(f"module 'pandas' has no attribute '{name}'") # 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. """