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pytest-xdist ; extra == 'test' +Requires-Dist: hypothesis (>=3.58) ; extra == 'test' + + +**pandas** is a Python package providing fast, flexible, and expressive data +structures designed to make working with structured (tabular, multidimensional, +potentially heterogeneous) and time series 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. + +pandas is well suited for many different kinds of data: + + - Tabular data with heterogeneously-typed columns, as in an SQL table or + Excel spreadsheet + - Ordered and unordered (not necessarily fixed-frequency) time series data. + - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and + column labels + - Any other form of observational / statistical data sets. The data actually + need not be labeled at all to be placed into a pandas data structure + +The two primary data structures of pandas, Series (1-dimensional) and DataFrame +(2-dimensional), handle the vast majority of typical use cases in finance, +statistics, social science, and many areas of engineering. For R users, +DataFrame provides everything that R's ``data.frame`` provides and much +more. pandas is built on top of `NumPy `__ and is +intended to integrate well within a scientific computing environment with many +other 3rd party libraries. + +Here are just a few of the things that pandas does well: + + - Easy handling of **missing data** (represented as NaN) 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. + +Many of these principles are here to address the shortcomings frequently +experienced using other languages / scientific research environments. 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+++ b/venv/Lib/site-packages/pandas-1.0.0.dist-info/entry_points.txt @@ -0,0 +1,3 @@ +[pandas_plotting_backends] +matplotlib = pandas:plotting._matplotlib + diff --git a/venv/Lib/site-packages/pandas-1.0.0.dist-info/top_level.txt b/venv/Lib/site-packages/pandas-1.0.0.dist-info/top_level.txt new file mode 100644 index 0000000..fb6c7ed --- /dev/null +++ b/venv/Lib/site-packages/pandas-1.0.0.dist-info/top_level.txt @@ -0,0 +1 @@ +pandas diff --git a/venv/Lib/site-packages/pandas/__init__.py b/venv/Lib/site-packages/pandas/__init__.py new file mode 100644 index 0000000..491bcb2 --- /dev/null +++ b/venv/Lib/site-packages/pandas/__init__.py @@ -0,0 +1,406 @@ +# 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.numpy import ( + _np_version_under1p14, + _np_version_under1p15, + _np_version_under1p16, + _np_version_under1p17, + _np_version_under1p18, +) + +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 --inplace --force' to build " + "the C extensions first." + ) + +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, + 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 + 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_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 ._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 +# TODO: remove Panel compat in 1.0 +if pandas.compat.PY37: + + def __getattr__(name): + import warnings + + if name == "Panel": + + warnings.warn( + "The Panel class is removed from pandas. Accessing it " + "from the top-level namespace will also be removed in " + "the next version", + FutureWarning, + stacklevel=2, + ) + + class Panel: + pass + + return Panel + + elif 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}'") + + +else: + + class Panel: + pass + + class SparseDataFrame: + pass + + class SparseSeries: + pass + + class __numpy: + def __init__(self): + import numpy as np + import warnings + + self.np = np + self.warnings = warnings + + def __getattr__(self, item): + self.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, + ) + + try: + return getattr(self.np, item) + except AttributeError: + raise AttributeError(f"module numpy has no attribute {item}") + + np = __numpy() + + class __Datetime(type): + + from datetime import datetime as dt + + datetime = dt + + def __getattr__(cls, item): + cls.emit_warning() + + try: + return getattr(cls.datetime, item) + except AttributeError: + raise AttributeError(f"module datetime has no attribute {item}") + + def __instancecheck__(cls, other): + return isinstance(other, cls.datetime) + + class __DatetimeSub(metaclass=__Datetime): + def emit_warning(dummy=0): + import warnings + + warnings.warn( + "The pandas.datetime class is deprecated " + "and will be removed from pandas in a future version. " + "Import from datetime instead.", + FutureWarning, + stacklevel=3, + ) + + def __new__(cls, *args, **kwargs): + cls.emit_warning() + from datetime import datetime as dt + + return dt(*args, **kwargs) + + datetime = __DatetimeSub + + class __SparseArray(type): + + from pandas.core.arrays.sparse import SparseArray as sa + + SparseArray = sa + + def __instancecheck__(cls, other): + return isinstance(other, cls.SparseArray) + + class __SparseArraySub(metaclass=__SparseArray): + def emit_warning(dummy=0): + import warnings + + 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=3, + ) + + def __new__(cls, *args, **kwargs): + cls.emit_warning() + from pandas.core.arrays.sparse import SparseArray as sa + + return sa(*args, **kwargs) + + SparseArray = __SparseArraySub + + +# 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. +""" diff --git a/venv/Lib/site-packages/pandas/_config/__init__.py b/venv/Lib/site-packages/pandas/_config/__init__.py new file mode 100644 index 0000000..65936a9 --- /dev/null +++ b/venv/Lib/site-packages/pandas/_config/__init__.py @@ -0,0 +1,28 @@ +""" +pandas._config is considered explicitly upstream of everything else in pandas, +should have no intra-pandas dependencies. + +importing `dates` and `display` ensures that keys needed by _libs +are initialized. +""" +__all__ = [ + "config", + "detect_console_encoding", + "get_option", + "set_option", + "reset_option", + "describe_option", + "option_context", + "options", +] +from pandas._config import config +from pandas._config import dates # noqa:F401 +from pandas._config.config import ( + describe_option, + get_option, + option_context, + options, + reset_option, + set_option, +) +from pandas._config.display import detect_console_encoding diff --git a/venv/Lib/site-packages/pandas/_config/config.py b/venv/Lib/site-packages/pandas/_config/config.py new file mode 100644 index 0000000..0a3009f --- /dev/null +++ b/venv/Lib/site-packages/pandas/_config/config.py @@ -0,0 +1,871 @@ +""" +The config module holds package-wide configurables and provides +a uniform API for working with them. + +Overview +======== + +This module supports the following requirements: +- options are referenced using keys in dot.notation, e.g. "x.y.option - z". +- keys are case-insensitive. +- functions should accept partial/regex keys, when unambiguous. +- options can be registered by modules at import time. +- options can be registered at init-time (via core.config_init) +- options have a default value, and (optionally) a description and + validation function associated with them. +- options can be deprecated, in which case referencing them + should produce a warning. +- deprecated options can optionally be rerouted to a replacement + so that accessing a deprecated option reroutes to a differently + named option. +- options can be reset to their default value. +- all option can be reset to their default value at once. +- all options in a certain sub - namespace can be reset at once. +- the user can set / get / reset or ask for the description of an option. +- a developer can register and mark an option as deprecated. +- you can register a callback to be invoked when the option value + is set or reset. Changing the stored value is considered misuse, but + is not verboten. + +Implementation +============== + +- Data is stored using nested dictionaries, and should be accessed + through the provided API. + +- "Registered options" and "Deprecated options" have metadata associated + with them, which are stored in auxiliary dictionaries keyed on the + fully-qualified key, e.g. "x.y.z.option". + +- the config_init module is imported by the package's __init__.py file. + placing any register_option() calls there will ensure those options + are available as soon as pandas is loaded. If you use register_option + in a module, it will only be available after that module is imported, + which you should be aware of. + +- `config_prefix` is a context_manager (for use with the `with` keyword) + which can save developers some typing, see the docstring. + +""" + +from collections import namedtuple +from contextlib import contextmanager +import re +from typing import Any, Dict, Iterable, List +import warnings + +DeprecatedOption = namedtuple("DeprecatedOption", "key msg rkey removal_ver") +RegisteredOption = namedtuple("RegisteredOption", "key defval doc validator cb") + +# holds deprecated option metadata +_deprecated_options: Dict[str, DeprecatedOption] = {} + +# holds registered option metadata +_registered_options: Dict[str, RegisteredOption] = {} + +# holds the current values for registered options +_global_config: Dict[str, Any] = {} + +# keys which have a special meaning +_reserved_keys: List[str] = ["all"] + + +class OptionError(AttributeError, KeyError): + """Exception for pandas.options, backwards compatible with KeyError + checks + """ + + +# +# User API + + +def _get_single_key(pat, silent): + keys = _select_options(pat) + if len(keys) == 0: + if not silent: + _warn_if_deprecated(pat) + raise OptionError(f"No such keys(s): {repr(pat)}") + if len(keys) > 1: + raise OptionError("Pattern matched multiple keys") + key = keys[0] + + if not silent: + _warn_if_deprecated(key) + + key = _translate_key(key) + + return key + + +def _get_option(pat, silent=False): + key = _get_single_key(pat, silent) + + # walk the nested dict + root, k = _get_root(key) + return root[k] + + +def _set_option(*args, **kwargs): + # must at least 1 arg deal with constraints later + nargs = len(args) + if not nargs or nargs % 2 != 0: + raise ValueError("Must provide an even number of non-keyword arguments") + + # default to false + silent = kwargs.pop("silent", False) + + if kwargs: + kwarg = list(kwargs.keys())[0] + raise TypeError(f'_set_option() got an unexpected keyword argument "{kwarg}"') + + for k, v in zip(args[::2], args[1::2]): + key = _get_single_key(k, silent) + + o = _get_registered_option(key) + if o and o.validator: + o.validator(v) + + # walk the nested dict + root, k = _get_root(key) + root[k] = v + + if o.cb: + if silent: + with warnings.catch_warnings(record=True): + o.cb(key) + else: + o.cb(key) + + +def _describe_option(pat="", _print_desc=True): + + keys = _select_options(pat) + if len(keys) == 0: + raise OptionError("No such keys(s)") + + s = "" + for k in keys: # filter by pat + s += _build_option_description(k) + + if _print_desc: + print(s) + else: + return s + + +def _reset_option(pat, silent=False): + + keys = _select_options(pat) + + if len(keys) == 0: + raise OptionError("No such keys(s)") + + if len(keys) > 1 and len(pat) < 4 and pat != "all": + raise ValueError( + "You must specify at least 4 characters when " + "resetting multiple keys, use the special keyword " + '"all" to reset all the options to their default ' + "value" + ) + + for k in keys: + _set_option(k, _registered_options[k].defval, silent=silent) + + +def get_default_val(pat): + key = _get_single_key(pat, silent=True) + return _get_registered_option(key).defval + + +class DictWrapper: + """ provide attribute-style access to a nested dict""" + + def __init__(self, d, prefix=""): + object.__setattr__(self, "d", d) + object.__setattr__(self, "prefix", prefix) + + def __setattr__(self, key, val): + prefix = object.__getattribute__(self, "prefix") + if prefix: + prefix += "." + prefix += key + # you can't set new keys + # can you can't overwrite subtrees + if key in self.d and not isinstance(self.d[key], dict): + _set_option(prefix, val) + else: + raise OptionError("You can only set the value of existing options") + + def __getattr__(self, key: str): + prefix = object.__getattribute__(self, "prefix") + if prefix: + prefix += "." + prefix += key + try: + v = object.__getattribute__(self, "d")[key] + except KeyError: + raise OptionError("No such option") + if isinstance(v, dict): + return DictWrapper(v, prefix) + else: + return _get_option(prefix) + + def __dir__(self): + return list(self.d.keys()) + + +# For user convenience, we'd like to have the available options described +# in the docstring. For dev convenience we'd like to generate the docstrings +# dynamically instead of maintaining them by hand. To this, we use the +# class below which wraps functions inside a callable, and converts +# __doc__ into a property function. The doctsrings below are templates +# using the py2.6+ advanced formatting syntax to plug in a concise list +# of options, and option descriptions. + + +class CallableDynamicDoc: + def __init__(self, func, doc_tmpl): + self.__doc_tmpl__ = doc_tmpl + self.__func__ = func + + def __call__(self, *args, **kwds): + return self.__func__(*args, **kwds) + + @property + def __doc__(self): + opts_desc = _describe_option("all", _print_desc=False) + opts_list = pp_options_list(list(_registered_options.keys())) + return self.__doc_tmpl__.format(opts_desc=opts_desc, opts_list=opts_list) + + +_get_option_tmpl = """ +get_option(pat) + +Retrieves the value of the specified option. + +Available options: + +{opts_list} + +Parameters +---------- +pat : str + Regexp which should match a single option. + Note: partial matches are supported for convenience, but unless you use the + full option name (e.g. x.y.z.option_name), your code may break in future + versions if new options with similar names are introduced. + +Returns +------- +result : the value of the option + +Raises +------ +OptionError : if no such option exists + +Notes +----- +The available options with its descriptions: + +{opts_desc} +""" + +_set_option_tmpl = """ +set_option(pat, value) + +Sets the value of the specified option. + +Available options: + +{opts_list} + +Parameters +---------- +pat : str + Regexp which should match a single option. + Note: partial matches are supported for convenience, but unless you use the + full option name (e.g. x.y.z.option_name), your code may break in future + versions if new options with similar names are introduced. +value : object + New value of option. + +Returns +------- +None + +Raises +------ +OptionError if no such option exists + +Notes +----- +The available options with its descriptions: + +{opts_desc} +""" + +_describe_option_tmpl = """ +describe_option(pat, _print_desc=False) + +Prints the description for one or more registered options. + +Call with not arguments to get a listing for all registered options. + +Available options: + +{opts_list} + +Parameters +---------- +pat : str + Regexp pattern. All matching keys will have their description displayed. +_print_desc : bool, default True + If True (default) the description(s) will be printed to stdout. + Otherwise, the description(s) will be returned as a unicode string + (for testing). + +Returns +------- +None by default, the description(s) as a unicode string if _print_desc +is False + +Notes +----- +The available options with its descriptions: + +{opts_desc} +""" + +_reset_option_tmpl = """ +reset_option(pat) + +Reset one or more options to their default value. + +Pass "all" as argument to reset all options. + +Available options: + +{opts_list} + +Parameters +---------- +pat : str/regex + If specified only options matching `prefix*` will be reset. + Note: partial matches are supported for convenience, but unless you + use the full option name (e.g. x.y.z.option_name), your code may break + in future versions if new options with similar names are introduced. + +Returns +------- +None + +Notes +----- +The available options with its descriptions: + +{opts_desc} +""" + +# bind the functions with their docstrings into a Callable +# and use that as the functions exposed in pd.api +get_option = CallableDynamicDoc(_get_option, _get_option_tmpl) +set_option = CallableDynamicDoc(_set_option, _set_option_tmpl) +reset_option = CallableDynamicDoc(_reset_option, _reset_option_tmpl) +describe_option = CallableDynamicDoc(_describe_option, _describe_option_tmpl) +options = DictWrapper(_global_config) + +# +# Functions for use by pandas developers, in addition to User - api + + +class option_context: + """ + Context manager to temporarily set options in the `with` statement context. + + You need to invoke as ``option_context(pat, val, [(pat, val), ...])``. + + Examples + -------- + + >>> with option_context('display.max_rows', 10, 'display.max_columns', 5): + ... ... + """ + + def __init__(self, *args): + if not (len(args) % 2 == 0 and len(args) >= 2): + raise ValueError( + "Need to invoke as option_context(pat, val, [(pat, val), ...])." + ) + + self.ops = list(zip(args[::2], args[1::2])) + + def __enter__(self): + self.undo = [(pat, _get_option(pat, silent=True)) for pat, val in self.ops] + + for pat, val in self.ops: + _set_option(pat, val, silent=True) + + def __exit__(self, *args): + if self.undo: + for pat, val in self.undo: + _set_option(pat, val, silent=True) + + +def register_option(key: str, defval: object, doc="", validator=None, cb=None): + """Register an option in the package-wide pandas config object + + Parameters + ---------- + key - a fully-qualified key, e.g. "x.y.option - z". + defval - the default value of the option + doc - a string description of the option + validator - a function of a single argument, should raise `ValueError` if + called with a value which is not a legal value for the option. + cb - a function of a single argument "key", which is called + immediately after an option value is set/reset. key is + the full name of the option. + + Returns + ------- + Nothing. + + Raises + ------ + ValueError if `validator` is specified and `defval` is not a valid value. + + """ + import tokenize + import keyword + + key = key.lower() + + if key in _registered_options: + raise OptionError(f"Option '{key}' has already been registered") + if key in _reserved_keys: + raise OptionError(f"Option '{key}' is a reserved key") + + # the default value should be legal + if validator: + validator(defval) + + # walk the nested dict, creating dicts as needed along the path + path = key.split(".") + + for k in path: + # NOTE: tokenize.Name is not a public constant + # error: Module has no attribute "Name" [attr-defined] + if not re.match("^" + tokenize.Name + "$", k): # type: ignore + raise ValueError(f"{k} is not a valid identifier") + if keyword.iskeyword(k): + raise ValueError(f"{k} is a python keyword") + + cursor = _global_config + msg = "Path prefix to option '{option}' is already an option" + + for i, p in enumerate(path[:-1]): + if not isinstance(cursor, dict): + raise OptionError(msg.format(option=".".join(path[:i]))) + if p not in cursor: + cursor[p] = {} + cursor = cursor[p] + + if not isinstance(cursor, dict): + raise OptionError(msg.format(option=".".join(path[:-1]))) + + cursor[path[-1]] = defval # initialize + + # save the option metadata + _registered_options[key] = RegisteredOption( + key=key, defval=defval, doc=doc, validator=validator, cb=cb + ) + + +def deprecate_option(key, msg=None, rkey=None, removal_ver=None): + """ + Mark option `key` as deprecated, if code attempts to access this option, + a warning will be produced, using `msg` if given, or a default message + if not. + if `rkey` is given, any access to the key will be re-routed to `rkey`. + + Neither the existence of `key` nor that if `rkey` is checked. If they + do not exist, any subsequence access will fail as usual, after the + deprecation warning is given. + + Parameters + ---------- + key - the name of the option to be deprecated. must be a fully-qualified + option name (e.g "x.y.z.rkey"). + + msg - (Optional) a warning message to output when the key is referenced. + if no message is given a default message will be emitted. + + rkey - (Optional) the name of an option to reroute access to. + If specified, any referenced `key` will be re-routed to `rkey` + including set/get/reset. + rkey must be a fully-qualified option name (e.g "x.y.z.rkey"). + used by the default message if no `msg` is specified. + + removal_ver - (Optional) specifies the version in which this option will + be removed. used by the default message if no `msg` + is specified. + + Returns + ------- + Nothing + + Raises + ------ + OptionError - if key has already been deprecated. + + """ + + key = key.lower() + + if key in _deprecated_options: + raise OptionError(f"Option '{key}' has already been defined as deprecated.") + + _deprecated_options[key] = DeprecatedOption(key, msg, rkey, removal_ver) + + +# +# functions internal to the module + + +def _select_options(pat): + """returns a list of keys matching `pat` + + if pat=="all", returns all registered options + """ + + # short-circuit for exact key + if pat in _registered_options: + return [pat] + + # else look through all of them + keys = sorted(_registered_options.keys()) + if pat == "all": # reserved key + return keys + + return [k for k in keys if re.search(pat, k, re.I)] + + +def _get_root(key): + path = key.split(".") + cursor = _global_config + for p in path[:-1]: + cursor = cursor[p] + return cursor, path[-1] + + +def _is_deprecated(key): + """ Returns True if the given option has been deprecated """ + + key = key.lower() + return key in _deprecated_options + + +def _get_deprecated_option(key): + """ + Retrieves the metadata for a deprecated option, if `key` is deprecated. + + Returns + ------- + DeprecatedOption (namedtuple) if key is deprecated, None otherwise + """ + + try: + d = _deprecated_options[key] + except KeyError: + return None + else: + return d + + +def _get_registered_option(key): + """ + Retrieves the option metadata if `key` is a registered option. + + Returns + ------- + RegisteredOption (namedtuple) if key is deprecated, None otherwise + """ + return _registered_options.get(key) + + +def _translate_key(key): + """ + if key id deprecated and a replacement key defined, will return the + replacement key, otherwise returns `key` as - is + """ + + d = _get_deprecated_option(key) + if d: + return d.rkey or key + else: + return key + + +def _warn_if_deprecated(key): + """ + Checks if `key` is a deprecated option and if so, prints a warning. + + Returns + ------- + bool - True if `key` is deprecated, False otherwise. + """ + + d = _get_deprecated_option(key) + if d: + if d.msg: + print(d.msg) + warnings.warn(d.msg, FutureWarning) + else: + msg = f"'{key}' is deprecated" + if d.removal_ver: + msg += f" and will be removed in {d.removal_ver}" + if d.rkey: + msg += f", please use '{d.rkey}' instead." + else: + msg += ", please refrain from using it." + + warnings.warn(msg, FutureWarning) + return True + return False + + +def _build_option_description(k): + """ Builds a formatted description of a registered option and prints it """ + + o = _get_registered_option(k) + d = _get_deprecated_option(k) + + s = f"{k} " + + if o.doc: + s += "\n".join(o.doc.strip().split("\n")) + else: + s += "No description available." + + if o: + s += f"\n [default: {o.defval}] [currently: {_get_option(k, True)}]" + + if d: + rkey = d.rkey if d.rkey else "" + s += "\n (Deprecated" + s += f", use `{rkey}` instead." + s += ")" + + return s + + +def pp_options_list(keys, width=80, _print=False): + """ Builds a concise listing of available options, grouped by prefix """ + + from textwrap import wrap + from itertools import groupby + + def pp(name: str, ks: Iterable[str]) -> List[str]: + pfx = "- " + name + ".[" if name else "" + ls = wrap( + ", ".join(ks), + width, + initial_indent=pfx, + subsequent_indent=" ", + break_long_words=False, + ) + if ls and ls[-1] and name: + ls[-1] = ls[-1] + "]" + return ls + + ls: List[str] = [] + singles = [x for x in sorted(keys) if x.find(".") < 0] + if singles: + ls += pp("", singles) + keys = [x for x in keys if x.find(".") >= 0] + + for k, g in groupby(sorted(keys), lambda x: x[: x.rfind(".")]): + ks = [x[len(k) + 1 :] for x in list(g)] + ls += pp(k, ks) + s = "\n".join(ls) + if _print: + print(s) + else: + return s + + +# +# helpers + + +@contextmanager +def config_prefix(prefix): + """contextmanager for multiple invocations of API with a common prefix + + supported API functions: (register / get / set )__option + + Warning: This is not thread - safe, and won't work properly if you import + the API functions into your module using the "from x import y" construct. + + Example: + + import pandas._config.config as cf + with cf.config_prefix("display.font"): + cf.register_option("color", "red") + cf.register_option("size", " 5 pt") + cf.set_option(size, " 6 pt") + cf.get_option(size) + ... + + etc' + + will register options "display.font.color", "display.font.size", set the + value of "display.font.size"... and so on. + """ + + # Note: reset_option relies on set_option, and on key directly + # it does not fit in to this monkey-patching scheme + + global register_option, get_option, set_option, reset_option + + def wrap(func): + def inner(key, *args, **kwds): + pkey = f"{prefix}.{key}" + return func(pkey, *args, **kwds) + + return inner + + _register_option = register_option + _get_option = get_option + _set_option = set_option + set_option = wrap(set_option) + get_option = wrap(get_option) + register_option = wrap(register_option) + yield None + set_option = _set_option + get_option = _get_option + register_option = _register_option + + +# These factories and methods are handy for use as the validator +# arg in register_option + + +def is_type_factory(_type): + """ + + Parameters + ---------- + `_type` - a type to be compared against (e.g. type(x) == `_type`) + + Returns + ------- + validator - a function of a single argument x , which raises + ValueError if type(x) is not equal to `_type` + + """ + + def inner(x): + if type(x) != _type: + raise ValueError(f"Value must have type '{_type}'") + + return inner + + +def is_instance_factory(_type): + """ + + Parameters + ---------- + `_type` - the type to be checked against + + Returns + ------- + validator - a function of a single argument x , which raises + ValueError if x is not an instance of `_type` + + """ + + if isinstance(_type, (tuple, list)): + _type = tuple(_type) + type_repr = "|".join(map(str, _type)) + else: + type_repr = f"'{_type}'" + + def inner(x): + if not isinstance(x, _type): + raise ValueError(f"Value must be an instance of {type_repr}") + + return inner + + +def is_one_of_factory(legal_values): + + callables = [c for c in legal_values if callable(c)] + legal_values = [c for c in legal_values if not callable(c)] + + def inner(x): + if x not in legal_values: + + if not any(c(x) for c in callables): + uvals = [str(lval) for lval in legal_values] + pp_values = "|".join(uvals) + msg = f"Value must be one of {pp_values}" + if len(callables): + msg += " or a callable" + raise ValueError(msg) + + return inner + + +def is_nonnegative_int(value): + """ + Verify that value is None or a positive int. + + Parameters + ---------- + value : None or int + The `value` to be checked. + + Raises + ------ + ValueError + When the value is not None or is a negative integer + """ + + if value is None: + return + + elif isinstance(value, int): + if value >= 0: + return + + msg = "Value must be a nonnegative integer or None" + raise ValueError(msg) + + +# common type validators, for convenience +# usage: register_option(... , validator = is_int) +is_int = is_type_factory(int) +is_bool = is_type_factory(bool) +is_float = is_type_factory(float) +is_str = is_type_factory(str) +is_text = is_instance_factory((str, bytes)) + + +def is_callable(obj): + """ + + Parameters + ---------- + `obj` - the object to be checked + + Returns + ------- + validator - returns True if object is callable + raises ValueError otherwise. + + """ + if not callable(obj): + raise ValueError("Value must be a callable") + return True diff --git a/venv/Lib/site-packages/pandas/_config/dates.py b/venv/Lib/site-packages/pandas/_config/dates.py new file mode 100644 index 0000000..5bf2b49 --- /dev/null +++ b/venv/Lib/site-packages/pandas/_config/dates.py @@ -0,0 +1,23 @@ +""" +config for datetime formatting +""" +from pandas._config import config as cf + +pc_date_dayfirst_doc = """ +: boolean + When True, prints and parses dates with the day first, eg 20/01/2005 +""" + +pc_date_yearfirst_doc = """ +: boolean + When True, prints and parses dates with the year first, eg 2005/01/20 +""" + +with cf.config_prefix("display"): + # Needed upstream of `_libs` because these are used in tslibs.parsing + cf.register_option( + "date_dayfirst", False, pc_date_dayfirst_doc, validator=cf.is_bool + ) + cf.register_option( + "date_yearfirst", False, pc_date_yearfirst_doc, validator=cf.is_bool + ) diff --git a/venv/Lib/site-packages/pandas/_config/display.py b/venv/Lib/site-packages/pandas/_config/display.py new file mode 100644 index 0000000..067b7c5 --- /dev/null +++ b/venv/Lib/site-packages/pandas/_config/display.py @@ -0,0 +1,59 @@ +""" +Unopinionated display configuration. +""" +import locale +import sys + +from pandas._config import config as cf + +# ----------------------------------------------------------------------------- +# Global formatting options +_initial_defencoding = None + + +def detect_console_encoding(): + """ + Try to find the most capable encoding supported by the console. + slightly modified from the way IPython handles the same issue. + """ + global _initial_defencoding + + encoding = None + try: + encoding = sys.stdout.encoding or sys.stdin.encoding + except (AttributeError, IOError): + pass + + # try again for something better + if not encoding or "ascii" in encoding.lower(): + try: + encoding = locale.getpreferredencoding() + except locale.Error: + # can be raised by locale.setlocale(), which is + # called by getpreferredencoding + # (on some systems, see stdlib locale docs) + pass + + # when all else fails. this will usually be "ascii" + if not encoding or "ascii" in encoding.lower(): + encoding = sys.getdefaultencoding() + + # GH#3360, save the reported defencoding at import time + # MPL backends may change it. Make available for debugging. + if not _initial_defencoding: + _initial_defencoding = sys.getdefaultencoding() + + return encoding + + +pc_encoding_doc = """ +: str/unicode + Defaults to the detected encoding of the console. + Specifies the encoding to be used for strings returned by to_string, + these are generally strings meant to be displayed on the console. +""" + +with cf.config_prefix("display"): + cf.register_option( + "encoding", detect_console_encoding(), pc_encoding_doc, validator=cf.is_text + ) diff --git a/venv/Lib/site-packages/pandas/_config/localization.py b/venv/Lib/site-packages/pandas/_config/localization.py new file mode 100644 index 0000000..dd1d494 --- /dev/null +++ b/venv/Lib/site-packages/pandas/_config/localization.py @@ -0,0 +1,166 @@ +""" +Helpers for configuring locale settings. + +Name `localization` is chosen to avoid overlap with builtin `locale` module. +""" +from contextlib import contextmanager +import locale +import re +import subprocess + +from pandas._config.config import options + + +@contextmanager +def set_locale(new_locale, lc_var=locale.LC_ALL): + """ + Context manager for temporarily setting a locale. + + Parameters + ---------- + new_locale : str or tuple + A string of the form .. For example to set + the current locale to US English with a UTF8 encoding, you would pass + "en_US.UTF-8". + lc_var : int, default `locale.LC_ALL` + The category of the locale being set. + + Notes + ----- + This is useful when you want to run a particular block of code under a + particular locale, without globally setting the locale. This probably isn't + thread-safe. + """ + current_locale = locale.getlocale() + + try: + locale.setlocale(lc_var, new_locale) + normalized_locale = locale.getlocale() + if all(x is not None for x in normalized_locale): + yield ".".join(normalized_locale) + else: + yield new_locale + finally: + locale.setlocale(lc_var, current_locale) + + +def can_set_locale(lc, lc_var=locale.LC_ALL): + """ + Check to see if we can set a locale, and subsequently get the locale, + without raising an Exception. + + Parameters + ---------- + lc : str + The locale to attempt to set. + lc_var : int, default `locale.LC_ALL` + The category of the locale being set. + + Returns + ------- + is_valid : bool + Whether the passed locale can be set + """ + + try: + with set_locale(lc, lc_var=lc_var): + pass + except (ValueError, locale.Error): + # horrible name for a Exception subclass + return False + else: + return True + + +def _valid_locales(locales, normalize): + """ + Return a list of normalized locales that do not throw an ``Exception`` + when set. + + Parameters + ---------- + locales : str + A string where each locale is separated by a newline. + normalize : bool + Whether to call ``locale.normalize`` on each locale. + + Returns + ------- + valid_locales : list + A list of valid locales. + """ + if normalize: + normalizer = lambda x: locale.normalize(x.strip()) + else: + normalizer = lambda x: x.strip() + + return list(filter(can_set_locale, map(normalizer, locales))) + + +def _default_locale_getter(): + raw_locales = subprocess.check_output(["locale -a"], shell=True) + return raw_locales + + +def get_locales(prefix=None, normalize=True, locale_getter=_default_locale_getter): + """ + Get all the locales that are available on the system. + + Parameters + ---------- + prefix : str + If not ``None`` then return only those locales with the prefix + provided. For example to get all English language locales (those that + start with ``"en"``), pass ``prefix="en"``. + normalize : bool + Call ``locale.normalize`` on the resulting list of available locales. + If ``True``, only locales that can be set without throwing an + ``Exception`` are returned. + locale_getter : callable + The function to use to retrieve the current locales. This should return + a string with each locale separated by a newline character. + + Returns + ------- + locales : list of strings + A list of locale strings that can be set with ``locale.setlocale()``. + For example:: + + locale.setlocale(locale.LC_ALL, locale_string) + + On error will return None (no locale available, e.g. Windows) + + """ + try: + raw_locales = locale_getter() + except subprocess.CalledProcessError: + # Raised on (some? all?) Windows platforms because Note: "locale -a" + # is not defined + return None + + try: + # raw_locales is "\n" separated list of locales + # it may contain non-decodable parts, so split + # extract what we can and then rejoin. + raw_locales = raw_locales.split(b"\n") + out_locales = [] + for x in raw_locales: + try: + out_locales.append(str(x, encoding=options.display.encoding)) + except UnicodeError: + # 'locale -a' is used to populated 'raw_locales' and on + # Redhat 7 Linux (and maybe others) prints locale names + # using windows-1252 encoding. Bug only triggered by + # a few special characters and when there is an + # extensive list of installed locales. + out_locales.append(str(x, encoding="windows-1252")) + + except TypeError: + pass + + if prefix is None: + return _valid_locales(out_locales, normalize) + + pattern = re.compile(f"{prefix}.*") + found = pattern.findall("\n".join(out_locales)) + return _valid_locales(found, normalize) diff --git a/venv/Lib/site-packages/pandas/_libs/__init__.py b/venv/Lib/site-packages/pandas/_libs/__init__.py new file mode 100644 index 0000000..af67cb3 --- /dev/null +++ b/venv/Lib/site-packages/pandas/_libs/__init__.py @@ -0,0 +1,11 @@ +# flake8: noqa + +from .tslibs import ( + NaT, + NaTType, + OutOfBoundsDatetime, + Period, + Timedelta, + Timestamp, + iNaT, +) diff --git a/venv/Lib/site-packages/pandas/_libs/algos.cp37-win_amd64.pyd b/venv/Lib/site-packages/pandas/_libs/algos.cp37-win_amd64.pyd new file mode 100644 index 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