You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
354 lines
8.0 KiB
354 lines
8.0 KiB
1 year ago
|
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",
|
||
|
|
]
|