from __future__ import annotations
__docformat__ = "restructuredtext"
# Let users know if they're missing any of our hard dependencies
_hard_dependencies = ("numpy", "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
)
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 -m pip install -ve . --no-build-isolation -Ceditable-verbose=true' "
"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,
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_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_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",
"wide_to_long",
]