427 lines
10 KiB
Python
427 lines
10 KiB
Python
from __future__ import annotations
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__docformat__ = "restructuredtext"
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# Let users know if they're missing any of our hard dependencies
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_hard_dependencies = ("numpy", "pytz", "dateutil")
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_missing_dependencies = []
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for _dependency in _hard_dependencies:
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try:
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__import__(_dependency)
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except ImportError as _e:
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_missing_dependencies.append(f"{_dependency}: {_e}")
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if _missing_dependencies:
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raise ImportError(
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"Unable to import required dependencies:\n" + "\n".join(_missing_dependencies)
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)
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del _hard_dependencies, _dependency, _missing_dependencies
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# numpy compat
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from pandas.compat import is_numpy_dev as _is_numpy_dev # pyright: ignore # noqa:F401
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try:
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from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
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except ImportError as _err: # pragma: no cover
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_module = _err.name
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raise ImportError(
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f"C extension: {_module} not built. If you want to import "
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"pandas from the source directory, you may need to run "
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"'python setup.py build_ext --force' to build the C extensions first."
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) from _err
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else:
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del _tslib, _lib, _hashtable
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from pandas._config import (
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get_option,
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set_option,
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reset_option,
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describe_option,
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option_context,
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options,
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)
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# let init-time option registration happen
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import pandas.core.config_init # pyright: ignore # noqa:F401
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from pandas.core.api import (
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# dtype
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ArrowDtype,
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Int8Dtype,
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Int16Dtype,
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Int32Dtype,
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Int64Dtype,
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UInt8Dtype,
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UInt16Dtype,
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UInt32Dtype,
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UInt64Dtype,
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Float32Dtype,
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Float64Dtype,
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CategoricalDtype,
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PeriodDtype,
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IntervalDtype,
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DatetimeTZDtype,
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StringDtype,
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BooleanDtype,
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# missing
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NA,
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isna,
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isnull,
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notna,
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notnull,
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# indexes
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Index,
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CategoricalIndex,
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RangeIndex,
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MultiIndex,
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IntervalIndex,
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TimedeltaIndex,
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DatetimeIndex,
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PeriodIndex,
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IndexSlice,
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# tseries
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NaT,
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Period,
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period_range,
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Timedelta,
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timedelta_range,
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Timestamp,
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date_range,
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bdate_range,
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Interval,
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interval_range,
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DateOffset,
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# conversion
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to_numeric,
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to_datetime,
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to_timedelta,
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# misc
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Flags,
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Grouper,
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factorize,
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unique,
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value_counts,
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NamedAgg,
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array,
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Categorical,
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set_eng_float_format,
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Series,
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DataFrame,
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)
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from pandas.core.arrays.sparse import SparseDtype
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from pandas.tseries.api import infer_freq
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from pandas.tseries import offsets
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from pandas.core.computation.api import eval
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from pandas.core.reshape.api import (
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concat,
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lreshape,
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melt,
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wide_to_long,
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merge,
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merge_asof,
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merge_ordered,
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crosstab,
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pivot,
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pivot_table,
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get_dummies,
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from_dummies,
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cut,
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qcut,
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)
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from pandas import api, arrays, errors, io, plotting, tseries
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from pandas import testing # noqa:PDF015
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from pandas.util._print_versions import show_versions
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from pandas.io.api import (
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# excel
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ExcelFile,
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ExcelWriter,
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read_excel,
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# parsers
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read_csv,
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read_fwf,
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read_table,
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# pickle
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read_pickle,
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to_pickle,
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# pytables
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HDFStore,
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read_hdf,
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# sql
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read_sql,
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read_sql_query,
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read_sql_table,
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# misc
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read_clipboard,
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read_parquet,
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read_orc,
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read_feather,
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read_gbq,
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read_html,
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read_xml,
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read_json,
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read_stata,
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read_sas,
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read_spss,
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)
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from pandas.io.json import _json_normalize as json_normalize
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from pandas.util._tester import test
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# use the closest tagged version if possible
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from pandas._version import get_versions
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v = get_versions()
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__version__ = v.get("closest-tag", v["version"])
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__git_version__ = v.get("full-revisionid")
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del get_versions, v
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# GH 27101
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__deprecated_num_index_names = ["Float64Index", "Int64Index", "UInt64Index"]
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def __dir__() -> list[str]:
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# GH43028
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# Int64Index etc. are deprecated, but we still want them to be available in the dir.
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# Remove in Pandas 2.0, when we remove Int64Index etc. from the code base.
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return list(globals().keys()) + __deprecated_num_index_names
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def __getattr__(name):
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import warnings
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if name in __deprecated_num_index_names:
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warnings.warn(
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f"pandas.{name} is deprecated "
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"and will be removed from pandas in a future version. "
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"Use pandas.Index with the appropriate dtype instead.",
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FutureWarning,
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stacklevel=2,
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)
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from pandas.core.api import Float64Index, Int64Index, UInt64Index
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return {
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"Float64Index": Float64Index,
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"Int64Index": Int64Index,
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"UInt64Index": UInt64Index,
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}[name]
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elif name == "datetime":
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warnings.warn(
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"The pandas.datetime class is deprecated "
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"and will be removed from pandas in a future version. "
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"Import from datetime module instead.",
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FutureWarning,
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stacklevel=2,
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)
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from datetime import datetime as dt
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return dt
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elif name == "np":
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warnings.warn(
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"The pandas.np module is deprecated "
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"and will be removed from pandas in a future version. "
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"Import numpy directly instead.",
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FutureWarning,
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stacklevel=2,
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)
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import numpy as np
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return np
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elif name in {"SparseSeries", "SparseDataFrame"}:
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warnings.warn(
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f"The {name} class is removed from pandas. Accessing it from "
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"the top-level namespace will also be removed in the next version.",
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FutureWarning,
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stacklevel=2,
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)
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return type(name, (), {})
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elif name == "SparseArray":
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warnings.warn(
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"The pandas.SparseArray class is deprecated "
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"and will be removed from pandas in a future version. "
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"Use pandas.arrays.SparseArray instead.",
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FutureWarning,
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stacklevel=2,
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)
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from pandas.core.arrays.sparse import SparseArray as _SparseArray
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return _SparseArray
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raise AttributeError(f"module 'pandas' has no attribute '{name}'")
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# module level doc-string
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__doc__ = """
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pandas - a powerful data analysis and manipulation library for Python
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=====================================================================
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**pandas** is a Python package providing fast, flexible, and expressive data
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structures designed to make working with "relational" or "labeled" data both
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easy and intuitive. It aims to be the fundamental high-level building block for
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doing practical, **real world** data analysis in Python. Additionally, it has
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the broader goal of becoming **the most powerful and flexible open source data
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analysis / manipulation tool available in any language**. It is already well on
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its way toward this goal.
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Main Features
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-------------
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Here are just a few of the things that pandas does well:
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- Easy handling of missing data in floating point as well as non-floating
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point data.
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- Size mutability: columns can be inserted and deleted from DataFrame and
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higher dimensional objects
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- Automatic and explicit data alignment: objects can be explicitly aligned
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to a set of labels, or the user can simply ignore the labels and let
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`Series`, `DataFrame`, etc. automatically align the data for you in
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computations.
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- Powerful, flexible group by functionality to perform split-apply-combine
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operations on data sets, for both aggregating and transforming data.
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- Make it easy to convert ragged, differently-indexed data in other Python
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and NumPy data structures into DataFrame objects.
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- Intelligent label-based slicing, fancy indexing, and subsetting of large
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data sets.
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- Intuitive merging and joining data sets.
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- Flexible reshaping and pivoting of data sets.
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- Hierarchical labeling of axes (possible to have multiple labels per tick).
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- Robust IO tools for loading data from flat files (CSV and delimited),
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Excel files, databases, and saving/loading data from the ultrafast HDF5
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format.
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- Time series-specific functionality: date range generation and frequency
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conversion, moving window statistics, date shifting and lagging.
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"""
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# Use __all__ to let type checkers know what is part of the public API.
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# Pandas is not (yet) a py.typed library: the public API is determined
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# based on the documentation.
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__all__ = [
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"ArrowDtype",
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"BooleanDtype",
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"Categorical",
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"CategoricalDtype",
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"CategoricalIndex",
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"DataFrame",
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"DateOffset",
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"DatetimeIndex",
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"DatetimeTZDtype",
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"ExcelFile",
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"ExcelWriter",
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"Flags",
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"Float32Dtype",
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"Float64Dtype",
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"Grouper",
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"HDFStore",
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"Index",
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"IndexSlice",
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"Int16Dtype",
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"Int32Dtype",
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"Int64Dtype",
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"Int8Dtype",
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"Interval",
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"IntervalDtype",
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"IntervalIndex",
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"MultiIndex",
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"NA",
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"NaT",
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"NamedAgg",
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"Period",
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"PeriodDtype",
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"PeriodIndex",
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"RangeIndex",
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"Series",
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"SparseDtype",
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"StringDtype",
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"Timedelta",
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"TimedeltaIndex",
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"Timestamp",
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"UInt16Dtype",
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"UInt32Dtype",
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"UInt64Dtype",
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"UInt8Dtype",
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"api",
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"array",
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"arrays",
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"bdate_range",
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"concat",
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"crosstab",
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"cut",
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"date_range",
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"describe_option",
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"errors",
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"eval",
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"factorize",
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"get_dummies",
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"from_dummies",
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"get_option",
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"infer_freq",
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"interval_range",
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"io",
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"isna",
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"isnull",
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"json_normalize",
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"lreshape",
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"melt",
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"merge",
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"merge_asof",
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"merge_ordered",
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"notna",
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"notnull",
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"offsets",
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"option_context",
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"options",
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"period_range",
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"pivot",
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"pivot_table",
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"plotting",
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"qcut",
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"read_clipboard",
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"read_csv",
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"read_excel",
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"read_feather",
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"read_fwf",
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"read_gbq",
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"read_hdf",
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"read_html",
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"read_json",
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"read_orc",
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"read_parquet",
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"read_pickle",
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"read_sas",
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"read_spss",
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"read_sql",
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"read_sql_query",
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"read_sql_table",
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"read_stata",
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"read_table",
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"read_xml",
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"reset_option",
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"set_eng_float_format",
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"set_option",
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"show_versions",
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"test",
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"testing",
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"timedelta_range",
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"to_datetime",
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"to_numeric",
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"to_pickle",
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"to_timedelta",
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"tseries",
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"unique",
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"value_counts",
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"wide_to_long",
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]
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