Add files using upload-large-folder tool

.gitattributes

@@ -566,3 +566,4 @@ falcon/lib/python3.10/site-packages/sklearn/cluster/_k_means_minibatch.cpython-3
 evalkit_tf437/lib/python3.10/site-packages/sympy/polys/matrices/__pycache__/domainmatrix.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 falcon/lib/python3.10/site-packages/sklearn/preprocessing/_csr_polynomial_expansion.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 falcon/lib/python3.10/site-packages/PIL/_imagingmath.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+falcon/lib/python3.10/site-packages/pandas/tests/tools/__pycache__/test_to_datetime.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

falcon/lib/python3.10/site-packages/httpcore-0.17.3.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

falcon/lib/python3.10/site-packages/httpcore-0.17.3.dist-info/LICENSE.md

@@ -0,0 +1,27 @@
+Copyright © 2020, [Encode OSS Ltd](https://www.encode.io/).
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

falcon/lib/python3.10/site-packages/httpcore-0.17.3.dist-info/METADATA

@@ -0,0 +1,542 @@
+Metadata-Version: 2.1
+Name: httpcore
+Version: 0.17.3
+Summary: A minimal low-level HTTP client.
+Home-page: https://github.com/encode/httpcore
+Author: Tom Christie
+Author-email: tom@tomchristie.com
+License: BSD
+Project-URL: Documentation, https://www.encode.io/httpcore
+Project-URL: Source, https://github.com/encode/httpcore
+Classifier: Development Status :: 3 - Alpha
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Topic :: Internet :: WWW/HTTP
+Classifier: Framework :: AsyncIO
+Classifier: Framework :: Trio
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+License-File: LICENSE.md
+Requires-Dist: h11 (<0.15,>=0.13)
+Requires-Dist: sniffio (==1.*)
+Requires-Dist: anyio (<5.0,>=3.0)
+Requires-Dist: certifi
+Provides-Extra: http2
+Requires-Dist: h2 (<5,>=3) ; extra == 'http2'
+Provides-Extra: socks
+Requires-Dist: socksio (==1.*) ; extra == 'socks'
+
+# HTTP Core
+
+[![Test Suite](https://github.com/encode/httpcore/workflows/Test%20Suite/badge.svg)](https://github.com/encode/httpcore/actions)
+[![Package version](https://badge.fury.io/py/httpcore.svg)](https://pypi.org/project/httpcore/)
+
+> *Do one thing, and do it well.*
+
+The HTTP Core package provides a minimal low-level HTTP client, which does
+one thing only. Sending HTTP requests.
+
+It does not provide any high level model abstractions over the API,
+does not handle redirects, multipart uploads, building authentication headers,
+transparent HTTP caching, URL parsing, session cookie handling,
+content or charset decoding, handling JSON, environment based configuration
+defaults, or any of that Jazz.
+
+Some things HTTP Core does do:
+
+* Sending HTTP requests.
+* Thread-safe / task-safe connection pooling.
+* HTTP(S) proxy & SOCKS proxy support.
+* Supports HTTP/1.1 and HTTP/2.
+* Provides both sync and async interfaces.
+* Async backend support for `asyncio` and `trio`.
+
+## Requirements
+
+Python 3.7+
+
+## Installation
+
+For HTTP/1.1 only support, install with:
+
+```shell
+$ pip install httpcore
+```
+
+For HTTP/1.1 and HTTP/2 support, install with:
+
+```shell
+$ pip install httpcore[http2]
+```
+
+For SOCKS proxy support, install with:
+
+```shell
+$ pip install httpcore[socks]
+```
+
+# Sending requests
+
+Send an HTTP request:
+
+```python
+import httpcore
+
+response = httpcore.request("GET", "https://www.example.com/")
+
+print(response)
+# <Response [200]>
+print(response.status)
+# 200
+print(response.headers)
+# [(b'Accept-Ranges', b'bytes'), (b'Age', b'557328'), (b'Cache-Control', b'max-age=604800'), ...]
+print(response.content)
+# b'<!doctype html>\n<html>\n<head>\n<title>Example Domain</title>\n\n<meta charset="utf-8"/>\n ...'
+```
+
+The top-level `httpcore.request()` function is provided for convenience. In practice whenever you're working with `httpcore` you'll want to use the connection pooling functionality that it provides.
+
+```python
+import httpcore
+
+http = httpcore.ConnectionPool()
+response = http.request("GET", "https://www.example.com/")
+```
+
+Once you're ready to get going, [head over to the documentation](https://www.encode.io/httpcore/).
+
+## Motivation
+
+You *probably* don't want to be using HTTP Core directly. It might make sense if
+you're writing something like a proxy service in Python, and you just want
+something at the lowest possible level, but more typically you'll want to use
+a higher level client library, such as `httpx`.
+
+The motivation for `httpcore` is:
+
+* To provide a reusable low-level client library, that other packages can then build on top of.
+* To provide a *really clear interface split* between the networking code and client logic,
+  so that each is easier to understand and reason about in isolation.
+
+
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
+
+## 0.17.3 (5th July 2023)
+
+- Support async cancellations, ensuring that the connection pool is left in a clean state when cancellations occur. (#726)
+- The networking backend interface has [been added to the public API](https://www.encode.io/httpcore/network-backends). Some classes which were previously private implementation detail are now part of the top-level public API. (#699)
+- Graceful handling of HTTP/2 GoAway frames, with requests being transparently retried on a new connection. (#730)
+- Add exceptions when a synchronous `trace callback` is passed to an asynchronous request or an asynchronous `trace callback` is passed to a synchronous request. (#717)
+
+## 0.17.2 (May 23th, 2023)
+
+- Add `socket_options` argument to `ConnectionPool` and `HTTProxy` classes. (#668)
+- Improve logging with per-module logger names. (#690)
+- Add `sni_hostname` request extension. (#696)
+- Resolve race condition during import of `anyio` package. (#692)
+- Enable TCP_NODELAY for all synchronous sockets. (#651)
+
+## 0.17.1 (May 17th, 2023)
+
+- If 'retries' is set, then allow retries if an SSL handshake error occurs. (#669)
+- Improve correctness of tracebacks on network exceptions, by raising properly chained exceptions. (#678)
+- Prevent connection-hanging behaviour when HTTP/2 connections are closed by a server-sent 'GoAway' frame. (#679)
+- Fix edge-case exception when removing requests from the connection pool. (#680)
+- Fix pool timeout edge-case. (#688)
+
+## 0.17.0 (March 16th, 2023)
+
+- Add DEBUG level logging. (#648)
+- Respect HTTP/2 max concurrent streams when settings updates are sent by server. (#652)
+- Increase the allowable HTTP header size to 100kB. (#647)
+- Add `retries` option to SOCKS proxy classes. (#643)
+
+## 0.16.3 (December 20th, 2022)
+
+- Allow `ws` and `wss` schemes. Allows us to properly support websocket upgrade connections. (#625)
+- Forwarding HTTP proxies use a connection-per-remote-host. Required by some proxy implementations. (#637)
+- Don't raise `RuntimeError` when closing a connection pool with active connections. Removes some error cases when cancellations are used. (#631)
+- Lazy import `anyio`, so that it's no longer a hard dependancy, and isn't imported if unused. (#639)
+
+## 0.16.2 (November 25th, 2022)
+
+- Revert 'Fix async cancellation behaviour', which introduced race conditions. (#627)
+- Raise `RuntimeError` if attempting to us UNIX domain sockets on Windows. (#619)
+
+## 0.16.1 (November 17th, 2022)
+
+- Fix HTTP/1.1 interim informational responses, such as "100 Continue". (#605)
+
+## 0.16.0 (October 11th, 2022)
+
+- Support HTTP/1.1 informational responses. (#581)
+- Fix async cancellation behaviour. (#580)
+- Support `h11` 0.14. (#579)
+
+## 0.15.0 (May 17th, 2022)
+
+- Drop Python 3.6 support (#535)
+- Ensure HTTP proxy CONNECT requests include `timeout` configuration. (#506)
+- Switch to explicit `typing.Optional` for type hints. (#513)
+- For `trio` map OSError exceptions to `ConnectError`. (#543)
+
+## 0.14.7 (February 4th, 2022)
+
+- Requests which raise a PoolTimeout need to be removed from the pool queue. (#502)
+- Fix AttributeError that happened when Socks5Connection were terminated. (#501)
+
+## 0.14.6 (February 1st, 2022)
+
+- Fix SOCKS support for `http://` URLs. (#492)
+- Resolve race condition around exceptions during streaming a response. (#491)
+
+## 0.14.5 (January 18th, 2022)
+
+- SOCKS proxy support. (#478)
+- Add proxy_auth argument to HTTPProxy. (#481)
+- Improve error message on 'RemoteProtocolError' exception when server disconnects without sending a response. (#479)
+
+## 0.14.4 (January 5th, 2022)
+
+- Support HTTP/2 on HTTPS tunnelling proxies. (#468)
+- Fix proxy headers missing on HTTP forwarding. (#456)
+- Only instantiate SSL context if required. (#457)
+- More robust HTTP/2 handling. (#253, #439, #440, #441)
+
+## 0.14.3 (November 17th, 2021)
+
+- Fix race condition when removing closed connections from the pool. (#437)
+
+## 0.14.2 (November 16th, 2021)
+
+- Failed connections no longer remain in the pool. (Pull #433)
+
+## 0.14.1 (November 12th, 2021)
+
+- `max_connections` becomes optional. (Pull #429)
+- `certifi` is now included in the install dependancies. (Pull #428)
+- `h2` is now strictly optional. (Pull #428)
+
+## 0.14.0 (November 11th, 2021)
+
+The 0.14 release is a complete reworking of `httpcore`, comprehensively addressing some underlying issues in the connection pooling, as well as substantially redesigning the API to be more user friendly.
+
+Some of the lower-level API design also makes the components more easily testable in isolation, and the package now has 100% test coverage.
+
+See [discussion #419](https://github.com/encode/httpcore/discussions/419) for a little more background.
+
+There's some other neat bits in there too, such as the "trace" extension, which gives a hook into inspecting the internal events that occur during the request/response cycle. This extension is needed for the HTTPX cli, in order to...
+
+* Log the point at which the connection is established, and the IP/port on which it is made.
+* Determine if the outgoing request should log as HTTP/1.1 or HTTP/2, rather than having to assume it's HTTP/2 if the --http2 flag was passed. (Which may not actually be true.)
+* Log SSL version info / certificate info.
+
+Note that `curio` support is not currently available in 0.14.0. If you're using `httpcore` with `curio` please get in touch, so we can assess if we ought to prioritize it as a feature or not.
+
+## 0.13.7 (September 13th, 2021)
+
+- Fix broken error messaging when URL scheme is missing, or a non HTTP(S) scheme is used. (Pull #403)
+
+## 0.13.6 (June 15th, 2021)
+
+### Fixed
+
+- Close sockets when read or write timeouts occur. (Pull #365)
+
+## 0.13.5 (June 14th, 2021)
+
+### Fixed
+
+- Resolved niggles with AnyIO EOF behaviours. (Pull #358, #362)
+
+## 0.13.4 (June 9th, 2021)
+
+### Added
+
+- Improved error messaging when URL scheme is missing, or a non HTTP(S) scheme is used. (Pull #354)
+
+### Fixed
+
+- Switched to `anyio` as the default backend implementation when running with `asyncio`. Resolves some awkward [TLS timeout issues](https://github.com/encode/httpx/discussions/1511).
+
+## 0.13.3 (May 6th, 2021)
+
+### Added
+
+- Support HTTP/2 prior knowledge, using `httpcore.SyncConnectionPool(http1=False)`. (Pull #333)
+
+### Fixed
+
+- Handle cases where environment does not provide `select.poll` support. (Pull #331)
+
+## 0.13.2 (April 29th, 2021)
+
+### Added
+
+- Improve error message for specific case of `RemoteProtocolError` where server disconnects without sending a response. (Pull #313)
+
+## 0.13.1 (April 28th, 2021)
+
+### Fixed
+
+- More resiliant testing for closed connections. (Pull #311)
+- Don't raise exceptions on ungraceful connection closes. (Pull #310)
+
+## 0.13.0 (April 21st, 2021)
+
+The 0.13 release updates the core API in order to match the HTTPX Transport API,
+introduced in HTTPX 0.18 onwards.
+
+An example of making requests with the new interface is:
+
+```python
+with httpcore.SyncConnectionPool() as http:
+    status_code, headers, stream, extensions = http.handle_request(
+        method=b'GET',
+        url=(b'https', b'example.org', 443, b'/'),
+        headers=[(b'host', b'example.org'), (b'user-agent', b'httpcore')]
+        stream=httpcore.ByteStream(b''),
+        extensions={}
+    )
+    body = stream.read()
+    print(status_code, body)
+```
+
+### Changed
+
+- The `.request()` method is now `handle_request()`. (Pull #296)
+- The `.arequest()` method is now `.handle_async_request()`. (Pull #296)
+- The `headers` argument is no longer optional. (Pull #296)
+- The `stream` argument is no longer optional. (Pull #296)
+- The `ext` argument is now named `extensions`, and is no longer optional. (Pull #296)
+- The `"reason"` extension keyword is now named `"reason_phrase"`. (Pull #296)
+- The `"reason_phrase"` and `"http_version"` extensions now use byte strings for their values. (Pull #296)
+- The `httpcore.PlainByteStream()` class becomes `httpcore.ByteStream()`. (Pull #296)
+
+### Added
+
+- Streams now support a `.read()` interface. (Pull #296)
+
+### Fixed
+
+- Task cancellation no longer leaks connections from the connection pool. (Pull #305)
+
+## 0.12.3 (December 7th, 2020)
+
+### Fixed
+
+- Abort SSL connections on close rather than waiting for remote EOF when using `asyncio`.  (Pull #167)
+- Fix exception raised in case of connect timeouts when using the `anyio` backend. (Pull #236)
+- Fix `Host` header precedence for `:authority` in HTTP/2. (Pull #241, #243)
+- Handle extra edge case when detecting for socket readability when using `asyncio`. (Pull #242, #244)
+- Fix `asyncio` SSL warning when using proxy tunneling. (Pull #249)
+
+## 0.12.2 (November 20th, 2020)
+
+### Fixed
+
+- Properly wrap connect errors on the asyncio backend. (Pull #235)
+- Fix `ImportError` occurring on Python 3.9 when using the HTTP/1.1 sync client in a multithreaded context. (Pull #237)
+
+## 0.12.1 (November 7th, 2020)
+
+### Added
+
+- Add connect retries. (Pull #221)
+
+### Fixed
+
+- Tweak detection of dropped connections, resolving an issue with open files limits on Linux. (Pull #185)
+- Avoid leaking connections when establishing an HTTP tunnel to a proxy has failed. (Pull #223)
+- Properly wrap OS errors when using `trio`. (Pull #225)
+
+## 0.12.0 (October 6th, 2020)
+
+### Changed
+
+- HTTP header casing is now preserved, rather than always sent in lowercase. (#216 and python-hyper/h11#104)
+
+### Added
+
+- Add Python 3.9 to officially supported versions.
+
+### Fixed
+
+- Gracefully handle a stdlib asyncio bug when a connection is closed while it is in a paused-for-reading state. (#201)
+
+## 0.11.1 (September 28nd, 2020)
+
+### Fixed
+
+- Add await to async semaphore release() coroutine (#197)
+- Drop incorrect curio classifier (#192)
+
+## 0.11.0 (September 22nd, 2020)
+
+The Transport API with 0.11.0 has a couple of significant changes.
+
+Firstly we've moved changed the request interface in order to allow extensions, which will later enable us to support features
+such as trailing headers, HTTP/2 server push, and CONNECT/Upgrade connections.
+
+The interface changes from:
+
+```python
+def request(method, url, headers, stream, timeout):
+    return (http_version, status_code, reason, headers, stream)
+```
+
+To instead including an optional dictionary of extensions on the request and response:
+
+```python
+def request(method, url, headers, stream, ext):
+    return (status_code, headers, stream, ext)
+```
+
+Having an open-ended extensions point will allow us to add later support for various optional features, that wouldn't otherwise be supported without these API changes.
+
+In particular:
+
+* Trailing headers support.
+* HTTP/2 Server Push
+* sendfile.
+* Exposing raw connection on CONNECT, Upgrade, HTTP/2 bi-di streaming.
+* Exposing debug information out of the API, including template name, template context.
+
+Currently extensions are limited to:
+
+* request: `timeout` - Optional. Timeout dictionary.
+* response: `http_version` - Optional. Include the HTTP version used on the response.
+* response: `reason` - Optional. Include the reason phrase used on the response. Only valid with HTTP/1.*.
+
+See https://github.com/encode/httpx/issues/1274#issuecomment-694884553 for the history behind this.
+
+Secondly, the async version of `request` is now namespaced as `arequest`.
+
+This allows concrete transports to support both sync and async implementations on the same class.
+
+### Added
+
+- Add curio support. (Pull #168)
+- Add anyio support, with `backend="anyio"`. (Pull #169)
+
+### Changed
+
+- Update the Transport API to use 'ext' for optional extensions. (Pull #190)
+- Update the Transport API to use `.request` and `.arequest` so implementations can support both sync and async. (Pull #189)
+
+## 0.10.2 (August 20th, 2020)
+
+### Added
+
+- Added Unix Domain Socket support. (Pull #139)
+
+### Fixed
+
+- Always include the port on proxy CONNECT requests. (Pull #154)
+- Fix `max_keepalive_connections` configuration. (Pull #153)
+- Fixes behaviour in HTTP/1.1 where server disconnects can be used to signal the end of the response body. (Pull #164)
+
+## 0.10.1 (August 7th, 2020)
+
+- Include `max_keepalive_connections` on `AsyncHTTPProxy`/`SyncHTTPProxy` classes.
+
+## 0.10.0 (August 7th, 2020)
+
+The most notable change in the 0.10.0 release is that HTTP/2 support is now fully optional.
+
+Use either `pip install httpcore` for HTTP/1.1 support only, or `pip install httpcore[http2]` for HTTP/1.1 and HTTP/2 support.
+
+### Added
+
+- HTTP/2 support becomes optional. (Pull #121, #130)
+- Add `local_address=...` support. (Pull #100, #134)
+- Add `PlainByteStream`, `IteratorByteStream`, `AsyncIteratorByteStream`. The `AsyncByteSteam` and `SyncByteStream` classes are now pure interface classes. (#133)
+- Add `LocalProtocolError`, `RemoteProtocolError` exceptions. (Pull #129)
+- Add `UnsupportedProtocol` exception. (Pull #128)
+- Add `.get_connection_info()` method. (Pull #102, #137)
+- Add better TRACE logs. (Pull #101)
+
+### Changed
+
+- `max_keepalive` is deprecated in favour of `max_keepalive_connections`. (Pull #140)
+
+### Fixed
+
+- Improve handling of server disconnects. (Pull #112)
+
+## 0.9.1 (May 27th, 2020)
+
+### Fixed
+
+- Proper host resolution for sync case, including IPv6 support. (Pull #97)
+- Close outstanding connections when connection pool is closed. (Pull #98)
+
+## 0.9.0 (May 21th, 2020)
+
+### Changed
+
+- URL port becomes an `Optional[int]` instead of `int`. (Pull #92)
+
+### Fixed
+
+- Honor HTTP/2 max concurrent streams settings. (Pull #89, #90)
+- Remove incorrect debug log. (Pull #83)
+
+## 0.8.4 (May 11th, 2020)
+
+### Added
+
+- Logging via HTTPCORE_LOG_LEVEL and HTTPX_LOG_LEVEL environment variables
+and TRACE level logging. (Pull #79)
+
+### Fixed
+
+- Reuse of connections on HTTP/2 in close concurrency situations. (Pull #81)
+
+## 0.8.3 (May 6rd, 2020)
+
+### Fixed
+
+- Include `Host` and `Accept` headers on proxy "CONNECT" requests.
+- De-duplicate any headers also contained in proxy_headers.
+- HTTP/2 flag not being passed down to proxy connections.
+
+## 0.8.2 (May 3rd, 2020)
+
+### Fixed
+
+- Fix connections using proxy forwarding requests not being added to the
+connection pool properly. (Pull #70)
+
+## 0.8.1 (April 30th, 2020)
+
+### Changed
+
+- Allow inherintance of both `httpcore.AsyncByteStream`, `httpcore.SyncByteStream` without type conflicts.
+
+## 0.8.0 (April 30th, 2020)
+
+### Fixed
+
+- Fixed tunnel proxy support.
+
+### Added
+
+- New `TimeoutException` base class.
+
+## 0.7.0 (March 5th, 2020)
+
+- First integration with HTTPX.

falcon/lib/python3.10/site-packages/httpcore-0.17.3.dist-info/top_level.txt

@@ -0,0 +1,4 @@
+httpcore
+httpcore/_async
+httpcore/_backends
+httpcore/_sync

falcon/lib/python3.10/site-packages/pandas/_testing/__init__.py

@@ -0,0 +1,639 @@
+from __future__ import annotations
+
+from decimal import Decimal
+import operator
+import os
+from sys import byteorder
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    ContextManager,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config.localization import (
+    can_set_locale,
+    get_locales,
+    set_locale,
+)
+
+from pandas.compat import pa_version_under10p1
+
+from pandas.core.dtypes.common import is_string_dtype
+
+import pandas as pd
+from pandas import (
+    ArrowDtype,
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+)
+from pandas._testing._io import (
+    round_trip_localpath,
+    round_trip_pathlib,
+    round_trip_pickle,
+    write_to_compressed,
+)
+from pandas._testing._warnings import (
+    assert_produces_warning,
+    maybe_produces_warning,
+)
+from pandas._testing.asserters import (
+    assert_almost_equal,
+    assert_attr_equal,
+    assert_categorical_equal,
+    assert_class_equal,
+    assert_contains_all,
+    assert_copy,
+    assert_datetime_array_equal,
+    assert_dict_equal,
+    assert_equal,
+    assert_extension_array_equal,
+    assert_frame_equal,
+    assert_index_equal,
+    assert_indexing_slices_equivalent,
+    assert_interval_array_equal,
+    assert_is_sorted,
+    assert_is_valid_plot_return_object,
+    assert_metadata_equivalent,
+    assert_numpy_array_equal,
+    assert_period_array_equal,
+    assert_series_equal,
+    assert_sp_array_equal,
+    assert_timedelta_array_equal,
+    raise_assert_detail,
+)
+from pandas._testing.compat import (
+    get_dtype,
+    get_obj,
+)
+from pandas._testing.contexts import (
+    assert_cow_warning,
+    decompress_file,
+    ensure_clean,
+    raises_chained_assignment_error,
+    set_timezone,
+    use_numexpr,
+    with_csv_dialect,
+)
+from pandas.core.arrays import (
+    BaseMaskedArray,
+    ExtensionArray,
+    NumpyExtensionArray,
+)
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+from pandas.core.construction import extract_array
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Dtype,
+        NpDtype,
+    )
+
+    from pandas.core.arrays import ArrowExtensionArray
+
+UNSIGNED_INT_NUMPY_DTYPES: list[NpDtype] = ["uint8", "uint16", "uint32", "uint64"]
+UNSIGNED_INT_EA_DTYPES: list[Dtype] = ["UInt8", "UInt16", "UInt32", "UInt64"]
+SIGNED_INT_NUMPY_DTYPES: list[NpDtype] = [int, "int8", "int16", "int32", "int64"]
+SIGNED_INT_EA_DTYPES: list[Dtype] = ["Int8", "Int16", "Int32", "Int64"]
+ALL_INT_NUMPY_DTYPES = UNSIGNED_INT_NUMPY_DTYPES + SIGNED_INT_NUMPY_DTYPES
+ALL_INT_EA_DTYPES = UNSIGNED_INT_EA_DTYPES + SIGNED_INT_EA_DTYPES
+ALL_INT_DTYPES: list[Dtype] = [*ALL_INT_NUMPY_DTYPES, *ALL_INT_EA_DTYPES]
+
+FLOAT_NUMPY_DTYPES: list[NpDtype] = [float, "float32", "float64"]
+FLOAT_EA_DTYPES: list[Dtype] = ["Float32", "Float64"]
+ALL_FLOAT_DTYPES: list[Dtype] = [*FLOAT_NUMPY_DTYPES, *FLOAT_EA_DTYPES]
+
+COMPLEX_DTYPES: list[Dtype] = [complex, "complex64", "complex128"]
+STRING_DTYPES: list[Dtype] = [str, "str", "U"]
+COMPLEX_FLOAT_DTYPES: list[Dtype] = [*COMPLEX_DTYPES, *FLOAT_NUMPY_DTYPES]
+
+DATETIME64_DTYPES: list[Dtype] = ["datetime64[ns]", "M8[ns]"]
+TIMEDELTA64_DTYPES: list[Dtype] = ["timedelta64[ns]", "m8[ns]"]
+
+BOOL_DTYPES: list[Dtype] = [bool, "bool"]
+BYTES_DTYPES: list[Dtype] = [bytes, "bytes"]
+OBJECT_DTYPES: list[Dtype] = [object, "object"]
+
+ALL_REAL_NUMPY_DTYPES = FLOAT_NUMPY_DTYPES + ALL_INT_NUMPY_DTYPES
+ALL_REAL_EXTENSION_DTYPES = FLOAT_EA_DTYPES + ALL_INT_EA_DTYPES
+ALL_REAL_DTYPES: list[Dtype] = [*ALL_REAL_NUMPY_DTYPES, *ALL_REAL_EXTENSION_DTYPES]
+ALL_NUMERIC_DTYPES: list[Dtype] = [*ALL_REAL_DTYPES, *COMPLEX_DTYPES]
+
+ALL_NUMPY_DTYPES = (
+    ALL_REAL_NUMPY_DTYPES
+    + COMPLEX_DTYPES
+    + STRING_DTYPES
+    + DATETIME64_DTYPES
+    + TIMEDELTA64_DTYPES
+    + BOOL_DTYPES
+    + OBJECT_DTYPES
+    + BYTES_DTYPES
+)
+
+NARROW_NP_DTYPES = [
+    np.float16,
+    np.float32,
+    np.int8,
+    np.int16,
+    np.int32,
+    np.uint8,
+    np.uint16,
+    np.uint32,
+]
+
+PYTHON_DATA_TYPES = [
+    str,
+    int,
+    float,
+    complex,
+    list,
+    tuple,
+    range,
+    dict,
+    set,
+    frozenset,
+    bool,
+    bytes,
+    bytearray,
+    memoryview,
+]
+
+ENDIAN = {"little": "<", "big": ">"}[byteorder]
+
+NULL_OBJECTS = [None, np.nan, pd.NaT, float("nan"), pd.NA, Decimal("NaN")]
+NP_NAT_OBJECTS = [
+    cls("NaT", unit)
+    for cls in [np.datetime64, np.timedelta64]
+    for unit in [
+        "Y",
+        "M",
+        "W",
+        "D",
+        "h",
+        "m",
+        "s",
+        "ms",
+        "us",
+        "ns",
+        "ps",
+        "fs",
+        "as",
+    ]
+]
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+
+    UNSIGNED_INT_PYARROW_DTYPES = [pa.uint8(), pa.uint16(), pa.uint32(), pa.uint64()]
+    SIGNED_INT_PYARROW_DTYPES = [pa.int8(), pa.int16(), pa.int32(), pa.int64()]
+    ALL_INT_PYARROW_DTYPES = UNSIGNED_INT_PYARROW_DTYPES + SIGNED_INT_PYARROW_DTYPES
+    ALL_INT_PYARROW_DTYPES_STR_REPR = [
+        str(ArrowDtype(typ)) for typ in ALL_INT_PYARROW_DTYPES
+    ]
+
+    # pa.float16 doesn't seem supported
+    # https://github.com/apache/arrow/blob/master/python/pyarrow/src/arrow/python/helpers.cc#L86
+    FLOAT_PYARROW_DTYPES = [pa.float32(), pa.float64()]
+    FLOAT_PYARROW_DTYPES_STR_REPR = [
+        str(ArrowDtype(typ)) for typ in FLOAT_PYARROW_DTYPES
+    ]
+    DECIMAL_PYARROW_DTYPES = [pa.decimal128(7, 3)]
+    STRING_PYARROW_DTYPES = [pa.string()]
+    BINARY_PYARROW_DTYPES = [pa.binary()]
+
+    TIME_PYARROW_DTYPES = [
+        pa.time32("s"),
+        pa.time32("ms"),
+        pa.time64("us"),
+        pa.time64("ns"),
+    ]
+    DATE_PYARROW_DTYPES = [pa.date32(), pa.date64()]
+    DATETIME_PYARROW_DTYPES = [
+        pa.timestamp(unit=unit, tz=tz)
+        for unit in ["s", "ms", "us", "ns"]
+        for tz in [None, "UTC", "US/Pacific", "US/Eastern"]
+    ]
+    TIMEDELTA_PYARROW_DTYPES = [pa.duration(unit) for unit in ["s", "ms", "us", "ns"]]
+
+    BOOL_PYARROW_DTYPES = [pa.bool_()]
+
+    # TODO: Add container like pyarrow types:
+    #  https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
+    ALL_PYARROW_DTYPES = (
+        ALL_INT_PYARROW_DTYPES
+        + FLOAT_PYARROW_DTYPES
+        + DECIMAL_PYARROW_DTYPES
+        + STRING_PYARROW_DTYPES
+        + BINARY_PYARROW_DTYPES
+        + TIME_PYARROW_DTYPES
+        + DATE_PYARROW_DTYPES
+        + DATETIME_PYARROW_DTYPES
+        + TIMEDELTA_PYARROW_DTYPES
+        + BOOL_PYARROW_DTYPES
+    )
+    ALL_REAL_PYARROW_DTYPES_STR_REPR = (
+        ALL_INT_PYARROW_DTYPES_STR_REPR + FLOAT_PYARROW_DTYPES_STR_REPR
+    )
+else:
+    FLOAT_PYARROW_DTYPES_STR_REPR = []
+    ALL_INT_PYARROW_DTYPES_STR_REPR = []
+    ALL_PYARROW_DTYPES = []
+    ALL_REAL_PYARROW_DTYPES_STR_REPR = []
+
+ALL_REAL_NULLABLE_DTYPES = (
+    FLOAT_NUMPY_DTYPES + ALL_REAL_EXTENSION_DTYPES + ALL_REAL_PYARROW_DTYPES_STR_REPR
+)
+
+arithmetic_dunder_methods = [
+    "__add__",
+    "__radd__",
+    "__sub__",
+    "__rsub__",
+    "__mul__",
+    "__rmul__",
+    "__floordiv__",
+    "__rfloordiv__",
+    "__truediv__",
+    "__rtruediv__",
+    "__pow__",
+    "__rpow__",
+    "__mod__",
+    "__rmod__",
+]
+
+comparison_dunder_methods = ["__eq__", "__ne__", "__le__", "__lt__", "__ge__", "__gt__"]
+
+
+# -----------------------------------------------------------------------------
+# Comparators
+
+
+def box_expected(expected, box_cls, transpose: bool = True):
+    """
+    Helper function to wrap the expected output of a test in a given box_class.
+
+    Parameters
+    ----------
+    expected : np.ndarray, Index, Series
+    box_cls : {Index, Series, DataFrame}
+
+    Returns
+    -------
+    subclass of box_cls
+    """
+    if box_cls is pd.array:
+        if isinstance(expected, RangeIndex):
+            # pd.array would return an IntegerArray
+            expected = NumpyExtensionArray(np.asarray(expected._values))
+        else:
+            expected = pd.array(expected, copy=False)
+    elif box_cls is Index:
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Dtype inference", category=FutureWarning)
+            expected = Index(expected)
+    elif box_cls is Series:
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Dtype inference", category=FutureWarning)
+            expected = Series(expected)
+    elif box_cls is DataFrame:
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Dtype inference", category=FutureWarning)
+            expected = Series(expected).to_frame()
+        if transpose:
+            # for vector operations, we need a DataFrame to be a single-row,
+            #  not a single-column, in order to operate against non-DataFrame
+            #  vectors of the same length. But convert to two rows to avoid
+            #  single-row special cases in datetime arithmetic
+            expected = expected.T
+            expected = pd.concat([expected] * 2, ignore_index=True)
+    elif box_cls is np.ndarray or box_cls is np.array:
+        expected = np.array(expected)
+    elif box_cls is to_array:
+        expected = to_array(expected)
+    else:
+        raise NotImplementedError(box_cls)
+    return expected
+
+
+def to_array(obj):
+    """
+    Similar to pd.array, but does not cast numpy dtypes to nullable dtypes.
+    """
+    # temporary implementation until we get pd.array in place
+    dtype = getattr(obj, "dtype", None)
+
+    if dtype is None:
+        return np.asarray(obj)
+
+    return extract_array(obj, extract_numpy=True)
+
+
+class SubclassedSeries(Series):
+    _metadata = ["testattr", "name"]
+
+    @property
+    def _constructor(self):
+        # For testing, those properties return a generic callable, and not
+        # the actual class. In this case that is equivalent, but it is to
+        # ensure we don't rely on the property returning a class
+        # See https://github.com/pandas-dev/pandas/pull/46018 and
+        # https://github.com/pandas-dev/pandas/issues/32638 and linked issues
+        return lambda *args, **kwargs: SubclassedSeries(*args, **kwargs)
+
+    @property
+    def _constructor_expanddim(self):
+        return lambda *args, **kwargs: SubclassedDataFrame(*args, **kwargs)
+
+
+class SubclassedDataFrame(DataFrame):
+    _metadata = ["testattr"]
+
+    @property
+    def _constructor(self):
+        return lambda *args, **kwargs: SubclassedDataFrame(*args, **kwargs)
+
+    @property
+    def _constructor_sliced(self):
+        return lambda *args, **kwargs: SubclassedSeries(*args, **kwargs)
+
+
+def convert_rows_list_to_csv_str(rows_list: list[str]) -> str:
+    """
+    Convert list of CSV rows to single CSV-formatted string for current OS.
+
+    This method is used for creating expected value of to_csv() method.
+
+    Parameters
+    ----------
+    rows_list : List[str]
+        Each element represents the row of csv.
+
+    Returns
+    -------
+    str
+        Expected output of to_csv() in current OS.
+    """
+    sep = os.linesep
+    return sep.join(rows_list) + sep
+
+
+def external_error_raised(expected_exception: type[Exception]) -> ContextManager:
+    """
+    Helper function to mark pytest.raises that have an external error message.
+
+    Parameters
+    ----------
+    expected_exception : Exception
+        Expected error to raise.
+
+    Returns
+    -------
+    Callable
+        Regular `pytest.raises` function with `match` equal to `None`.
+    """
+    import pytest
+
+    return pytest.raises(expected_exception, match=None)
+
+
+cython_table = pd.core.common._cython_table.items()
+
+
+def get_cython_table_params(ndframe, func_names_and_expected):
+    """
+    Combine frame, functions from com._cython_table
+    keys and expected result.
+
+    Parameters
+    ----------
+    ndframe : DataFrame or Series
+    func_names_and_expected : Sequence of two items
+        The first item is a name of a NDFrame method ('sum', 'prod') etc.
+        The second item is the expected return value.
+
+    Returns
+    -------
+    list
+        List of three items (DataFrame, function, expected result)
+    """
+    results = []
+    for func_name, expected in func_names_and_expected:
+        results.append((ndframe, func_name, expected))
+        results += [
+            (ndframe, func, expected)
+            for func, name in cython_table
+            if name == func_name
+        ]
+    return results
+
+
+def get_op_from_name(op_name: str) -> Callable:
+    """
+    The operator function for a given op name.
+
+    Parameters
+    ----------
+    op_name : str
+        The op name, in form of "add" or "__add__".
+
+    Returns
+    -------
+    function
+        A function performing the operation.
+    """
+    short_opname = op_name.strip("_")
+    try:
+        op = getattr(operator, short_opname)
+    except AttributeError:
+        # Assume it is the reverse operator
+        rop = getattr(operator, short_opname[1:])
+        op = lambda x, y: rop(y, x)
+
+    return op
+
+
+# -----------------------------------------------------------------------------
+# Indexing test helpers
+
+
+def getitem(x):
+    return x
+
+
+def setitem(x):
+    return x
+
+
+def loc(x):
+    return x.loc
+
+
+def iloc(x):
+    return x.iloc
+
+
+def at(x):
+    return x.at
+
+
+def iat(x):
+    return x.iat
+
+
+# -----------------------------------------------------------------------------
+
+_UNITS = ["s", "ms", "us", "ns"]
+
+
+def get_finest_unit(left: str, right: str):
+    """
+    Find the higher of two datetime64 units.
+    """
+    if _UNITS.index(left) >= _UNITS.index(right):
+        return left
+    return right
+
+
+def shares_memory(left, right) -> bool:
+    """
+    Pandas-compat for np.shares_memory.
+    """
+    if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
+        return np.shares_memory(left, right)
+    elif isinstance(left, np.ndarray):
+        # Call with reversed args to get to unpacking logic below.
+        return shares_memory(right, left)
+
+    if isinstance(left, RangeIndex):
+        return False
+    if isinstance(left, MultiIndex):
+        return shares_memory(left._codes, right)
+    if isinstance(left, (Index, Series)):
+        return shares_memory(left._values, right)
+
+    if isinstance(left, NDArrayBackedExtensionArray):
+        return shares_memory(left._ndarray, right)
+    if isinstance(left, pd.core.arrays.SparseArray):
+        return shares_memory(left.sp_values, right)
+    if isinstance(left, pd.core.arrays.IntervalArray):
+        return shares_memory(left._left, right) or shares_memory(left._right, right)
+
+    if (
+        isinstance(left, ExtensionArray)
+        and is_string_dtype(left.dtype)
+        and left.dtype.storage in ("pyarrow", "pyarrow_numpy")  # type: ignore[attr-defined]
+    ):
+        # https://github.com/pandas-dev/pandas/pull/43930#discussion_r736862669
+        left = cast("ArrowExtensionArray", left)
+        if (
+            isinstance(right, ExtensionArray)
+            and is_string_dtype(right.dtype)
+            and right.dtype.storage in ("pyarrow", "pyarrow_numpy")  # type: ignore[attr-defined]
+        ):
+            right = cast("ArrowExtensionArray", right)
+            left_pa_data = left._pa_array
+            right_pa_data = right._pa_array
+            left_buf1 = left_pa_data.chunk(0).buffers()[1]
+            right_buf1 = right_pa_data.chunk(0).buffers()[1]
+            return left_buf1 == right_buf1
+
+    if isinstance(left, BaseMaskedArray) and isinstance(right, BaseMaskedArray):
+        # By convention, we'll say these share memory if they share *either*
+        #  the _data or the _mask
+        return np.shares_memory(left._data, right._data) or np.shares_memory(
+            left._mask, right._mask
+        )
+
+    if isinstance(left, DataFrame) and len(left._mgr.arrays) == 1:
+        arr = left._mgr.arrays[0]
+        return shares_memory(arr, right)
+
+    raise NotImplementedError(type(left), type(right))
+
+
+__all__ = [
+    "ALL_INT_EA_DTYPES",
+    "ALL_INT_NUMPY_DTYPES",
+    "ALL_NUMPY_DTYPES",
+    "ALL_REAL_NUMPY_DTYPES",
+    "assert_almost_equal",
+    "assert_attr_equal",
+    "assert_categorical_equal",
+    "assert_class_equal",
+    "assert_contains_all",
+    "assert_copy",
+    "assert_datetime_array_equal",
+    "assert_dict_equal",
+    "assert_equal",
+    "assert_extension_array_equal",
+    "assert_frame_equal",
+    "assert_index_equal",
+    "assert_indexing_slices_equivalent",
+    "assert_interval_array_equal",
+    "assert_is_sorted",
+    "assert_is_valid_plot_return_object",
+    "assert_metadata_equivalent",
+    "assert_numpy_array_equal",
+    "assert_period_array_equal",
+    "assert_produces_warning",
+    "assert_series_equal",
+    "assert_sp_array_equal",
+    "assert_timedelta_array_equal",
+    "assert_cow_warning",
+    "at",
+    "BOOL_DTYPES",
+    "box_expected",
+    "BYTES_DTYPES",
+    "can_set_locale",
+    "COMPLEX_DTYPES",
+    "convert_rows_list_to_csv_str",
+    "DATETIME64_DTYPES",
+    "decompress_file",
+    "ENDIAN",
+    "ensure_clean",
+    "external_error_raised",
+    "FLOAT_EA_DTYPES",
+    "FLOAT_NUMPY_DTYPES",
+    "get_cython_table_params",
+    "get_dtype",
+    "getitem",
+    "get_locales",
+    "get_finest_unit",
+    "get_obj",
+    "get_op_from_name",
+    "iat",
+    "iloc",
+    "loc",
+    "maybe_produces_warning",
+    "NARROW_NP_DTYPES",
+    "NP_NAT_OBJECTS",
+    "NULL_OBJECTS",
+    "OBJECT_DTYPES",
+    "raise_assert_detail",
+    "raises_chained_assignment_error",
+    "round_trip_localpath",
+    "round_trip_pathlib",
+    "round_trip_pickle",
+    "setitem",
+    "set_locale",
+    "set_timezone",
+    "shares_memory",
+    "SIGNED_INT_EA_DTYPES",
+    "SIGNED_INT_NUMPY_DTYPES",
+    "STRING_DTYPES",
+    "SubclassedDataFrame",
+    "SubclassedSeries",
+    "TIMEDELTA64_DTYPES",
+    "to_array",
+    "UNSIGNED_INT_EA_DTYPES",
+    "UNSIGNED_INT_NUMPY_DTYPES",
+    "use_numexpr",
+    "with_csv_dialect",
+    "write_to_compressed",
+]

falcon/lib/python3.10/site-packages/pandas/_testing/_hypothesis.py

@@ -0,0 +1,93 @@
+"""
+Hypothesis data generator helpers.
+"""
+from datetime import datetime
+
+from hypothesis import strategies as st
+from hypothesis.extra.dateutil import timezones as dateutil_timezones
+from hypothesis.extra.pytz import timezones as pytz_timezones
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+
+from pandas.tseries.offsets import (
+    BMonthBegin,
+    BMonthEnd,
+    BQuarterBegin,
+    BQuarterEnd,
+    BYearBegin,
+    BYearEnd,
+    MonthBegin,
+    MonthEnd,
+    QuarterBegin,
+    QuarterEnd,
+    YearBegin,
+    YearEnd,
+)
+
+OPTIONAL_INTS = st.lists(st.one_of(st.integers(), st.none()), max_size=10, min_size=3)
+
+OPTIONAL_FLOATS = st.lists(st.one_of(st.floats(), st.none()), max_size=10, min_size=3)
+
+OPTIONAL_TEXT = st.lists(st.one_of(st.none(), st.text()), max_size=10, min_size=3)
+
+OPTIONAL_DICTS = st.lists(
+    st.one_of(st.none(), st.dictionaries(st.text(), st.integers())),
+    max_size=10,
+    min_size=3,
+)
+
+OPTIONAL_LISTS = st.lists(
+    st.one_of(st.none(), st.lists(st.text(), max_size=10, min_size=3)),
+    max_size=10,
+    min_size=3,
+)
+
+OPTIONAL_ONE_OF_ALL = st.one_of(
+    OPTIONAL_DICTS, OPTIONAL_FLOATS, OPTIONAL_INTS, OPTIONAL_LISTS, OPTIONAL_TEXT
+)
+
+if is_platform_windows():
+    DATETIME_NO_TZ = st.datetimes(min_value=datetime(1900, 1, 1))
+else:
+    DATETIME_NO_TZ = st.datetimes()
+
+DATETIME_JAN_1_1900_OPTIONAL_TZ = st.datetimes(
+    min_value=pd.Timestamp(
+        1900, 1, 1
+    ).to_pydatetime(),  # pyright: ignore[reportGeneralTypeIssues]
+    max_value=pd.Timestamp(
+        1900, 1, 1
+    ).to_pydatetime(),  # pyright: ignore[reportGeneralTypeIssues]
+    timezones=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()),
+)
+
+DATETIME_IN_PD_TIMESTAMP_RANGE_NO_TZ = st.datetimes(
+    min_value=pd.Timestamp.min.to_pydatetime(warn=False),
+    max_value=pd.Timestamp.max.to_pydatetime(warn=False),
+)
+
+INT_NEG_999_TO_POS_999 = st.integers(-999, 999)
+
+# The strategy for each type is registered in conftest.py, as they don't carry
+# enough runtime information (e.g. type hints) to infer how to build them.
+YQM_OFFSET = st.one_of(
+    *map(
+        st.from_type,
+        [
+            MonthBegin,
+            MonthEnd,
+            BMonthBegin,
+            BMonthEnd,
+            QuarterBegin,
+            QuarterEnd,
+            BQuarterBegin,
+            BQuarterEnd,
+            YearBegin,
+            YearEnd,
+            BYearBegin,
+            BYearEnd,
+        ],
+    )
+)

falcon/lib/python3.10/site-packages/pandas/_testing/_io.py

@@ -0,0 +1,170 @@
+from __future__ import annotations
+
+import gzip
+import io
+import pathlib
+import tarfile
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+import uuid
+import zipfile
+
+from pandas.compat import (
+    get_bz2_file,
+    get_lzma_file,
+)
+from pandas.compat._optional import import_optional_dependency
+
+import pandas as pd
+from pandas._testing.contexts import ensure_clean
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        FilePath,
+        ReadPickleBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+# ------------------------------------------------------------------
+# File-IO
+
+
+def round_trip_pickle(
+    obj: Any, path: FilePath | ReadPickleBuffer | None = None
+) -> DataFrame | Series:
+    """
+    Pickle an object and then read it again.
+
+    Parameters
+    ----------
+    obj : any object
+        The object to pickle and then re-read.
+    path : str, path object or file-like object, default None
+        The path where the pickled object is written and then read.
+
+    Returns
+    -------
+    pandas object
+        The original object that was pickled and then re-read.
+    """
+    _path = path
+    if _path is None:
+        _path = f"__{uuid.uuid4()}__.pickle"
+    with ensure_clean(_path) as temp_path:
+        pd.to_pickle(obj, temp_path)
+        return pd.read_pickle(temp_path)
+
+
+def round_trip_pathlib(writer, reader, path: str | None = None):
+    """
+    Write an object to file specified by a pathlib.Path and read it back
+
+    Parameters
+    ----------
+    writer : callable bound to pandas object
+        IO writing function (e.g. DataFrame.to_csv )
+    reader : callable
+        IO reading function (e.g. pd.read_csv )
+    path : str, default None
+        The path where the object is written and then read.
+
+    Returns
+    -------
+    pandas object
+        The original object that was serialized and then re-read.
+    """
+    Path = pathlib.Path
+    if path is None:
+        path = "___pathlib___"
+    with ensure_clean(path) as path:
+        writer(Path(path))  # type: ignore[arg-type]
+        obj = reader(Path(path))  # type: ignore[arg-type]
+    return obj
+
+
+def round_trip_localpath(writer, reader, path: str | None = None):
+    """
+    Write an object to file specified by a py.path LocalPath and read it back.
+
+    Parameters
+    ----------
+    writer : callable bound to pandas object
+        IO writing function (e.g. DataFrame.to_csv )
+    reader : callable
+        IO reading function (e.g. pd.read_csv )
+    path : str, default None
+        The path where the object is written and then read.
+
+    Returns
+    -------
+    pandas object
+        The original object that was serialized and then re-read.
+    """
+    import pytest
+
+    LocalPath = pytest.importorskip("py.path").local
+    if path is None:
+        path = "___localpath___"
+    with ensure_clean(path) as path:
+        writer(LocalPath(path))
+        obj = reader(LocalPath(path))
+    return obj
+
+
+def write_to_compressed(compression, path, data, dest: str = "test") -> None:
+    """
+    Write data to a compressed file.
+
+    Parameters
+    ----------
+    compression : {'gzip', 'bz2', 'zip', 'xz', 'zstd'}
+        The compression type to use.
+    path : str
+        The file path to write the data.
+    data : str
+        The data to write.
+    dest : str, default "test"
+        The destination file (for ZIP only)
+
+    Raises
+    ------
+    ValueError : An invalid compression value was passed in.
+    """
+    args: tuple[Any, ...] = (data,)
+    mode = "wb"
+    method = "write"
+    compress_method: Callable
+
+    if compression == "zip":
+        compress_method = zipfile.ZipFile
+        mode = "w"
+        args = (dest, data)
+        method = "writestr"
+    elif compression == "tar":
+        compress_method = tarfile.TarFile
+        mode = "w"
+        file = tarfile.TarInfo(name=dest)
+        bytes = io.BytesIO(data)
+        file.size = len(data)
+        args = (file, bytes)
+        method = "addfile"
+    elif compression == "gzip":
+        compress_method = gzip.GzipFile
+    elif compression == "bz2":
+        compress_method = get_bz2_file()
+    elif compression == "zstd":
+        compress_method = import_optional_dependency("zstandard").open
+    elif compression == "xz":
+        compress_method = get_lzma_file()
+    else:
+        raise ValueError(f"Unrecognized compression type: {compression}")
+
+    with compress_method(path, mode=mode) as f:
+        getattr(f, method)(*args)

falcon/lib/python3.10/site-packages/pandas/_testing/_warnings.py

@@ -0,0 +1,232 @@
+from __future__ import annotations
+
+from contextlib import (
+    contextmanager,
+    nullcontext,
+)
+import inspect
+import re
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+)
+import warnings
+
+from pandas.compat import PY311
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Sequence,
+    )
+
+
+@contextmanager
+def assert_produces_warning(
+    expected_warning: type[Warning] | bool | tuple[type[Warning], ...] | None = Warning,
+    filter_level: Literal[
+        "error", "ignore", "always", "default", "module", "once"
+    ] = "always",
+    check_stacklevel: bool = True,
+    raise_on_extra_warnings: bool = True,
+    match: str | None = None,
+) -> Generator[list[warnings.WarningMessage], None, None]:
+    """
+    Context manager for running code expected to either raise a specific warning,
+    multiple specific warnings, or not raise any warnings. Verifies that the code
+    raises the expected warning(s), and that it does not raise any other unexpected
+    warnings. It is basically a wrapper around ``warnings.catch_warnings``.
+
+    Parameters
+    ----------
+    expected_warning : {Warning, False, tuple[Warning, ...], None}, default Warning
+        The type of Exception raised. ``exception.Warning`` is the base
+        class for all warnings. To raise multiple types of exceptions,
+        pass them as a tuple. To check that no warning is returned,
+        specify ``False`` or ``None``.
+    filter_level : str or None, default "always"
+        Specifies whether warnings are ignored, displayed, or turned
+        into errors.
+        Valid values are:
+
+        * "error" - turns matching warnings into exceptions
+        * "ignore" - discard the warning
+        * "always" - always emit a warning
+        * "default" - print the warning the first time it is generated
+          from each location
+        * "module" - print the warning the first time it is generated
+          from each module
+        * "once" - print the warning the first time it is generated
+
+    check_stacklevel : bool, default True
+        If True, displays the line that called the function containing
+        the warning to show were the function is called. Otherwise, the
+        line that implements the function is displayed.
+    raise_on_extra_warnings : bool, default True
+        Whether extra warnings not of the type `expected_warning` should
+        cause the test to fail.
+    match : str, optional
+        Match warning message.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> with assert_produces_warning():
+    ...     warnings.warn(UserWarning())
+    ...
+    >>> with assert_produces_warning(False):
+    ...     warnings.warn(RuntimeWarning())
+    ...
+    Traceback (most recent call last):
+        ...
+    AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
+    >>> with assert_produces_warning(UserWarning):
+    ...     warnings.warn(RuntimeWarning())
+    Traceback (most recent call last):
+        ...
+    AssertionError: Did not see expected warning of class 'UserWarning'.
+
+    ..warn:: This is *not* thread-safe.
+    """
+    __tracebackhide__ = True
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter(filter_level)
+        try:
+            yield w
+        finally:
+            if expected_warning:
+                expected_warning = cast(type[Warning], expected_warning)
+                _assert_caught_expected_warning(
+                    caught_warnings=w,
+                    expected_warning=expected_warning,
+                    match=match,
+                    check_stacklevel=check_stacklevel,
+                )
+            if raise_on_extra_warnings:
+                _assert_caught_no_extra_warnings(
+                    caught_warnings=w,
+                    expected_warning=expected_warning,
+                )
+
+
+def maybe_produces_warning(warning: type[Warning], condition: bool, **kwargs):
+    """
+    Return a context manager that possibly checks a warning based on the condition
+    """
+    if condition:
+        return assert_produces_warning(warning, **kwargs)
+    else:
+        return nullcontext()
+
+
+def _assert_caught_expected_warning(
+    *,
+    caught_warnings: Sequence[warnings.WarningMessage],
+    expected_warning: type[Warning],
+    match: str | None,
+    check_stacklevel: bool,
+) -> None:
+    """Assert that there was the expected warning among the caught warnings."""
+    saw_warning = False
+    matched_message = False
+    unmatched_messages = []
+
+    for actual_warning in caught_warnings:
+        if issubclass(actual_warning.category, expected_warning):
+            saw_warning = True
+
+            if check_stacklevel:
+                _assert_raised_with_correct_stacklevel(actual_warning)
+
+            if match is not None:
+                if re.search(match, str(actual_warning.message)):
+                    matched_message = True
+                else:
+                    unmatched_messages.append(actual_warning.message)
+
+    if not saw_warning:
+        raise AssertionError(
+            f"Did not see expected warning of class "
+            f"{repr(expected_warning.__name__)}"
+        )
+
+    if match and not matched_message:
+        raise AssertionError(
+            f"Did not see warning {repr(expected_warning.__name__)} "
+            f"matching '{match}'. The emitted warning messages are "
+            f"{unmatched_messages}"
+        )
+
+
+def _assert_caught_no_extra_warnings(
+    *,
+    caught_warnings: Sequence[warnings.WarningMessage],
+    expected_warning: type[Warning] | bool | tuple[type[Warning], ...] | None,
+) -> None:
+    """Assert that no extra warnings apart from the expected ones are caught."""
+    extra_warnings = []
+
+    for actual_warning in caught_warnings:
+        if _is_unexpected_warning(actual_warning, expected_warning):
+            # GH#38630 pytest.filterwarnings does not suppress these.
+            if actual_warning.category == ResourceWarning:
+                # GH 44732: Don't make the CI flaky by filtering SSL-related
+                # ResourceWarning from dependencies
+                if "unclosed <ssl.SSLSocket" in str(actual_warning.message):
+                    continue
+                # GH 44844: Matplotlib leaves font files open during the entire process
+                # upon import. Don't make CI flaky if ResourceWarning raised
+                # due to these open files.
+                if any("matplotlib" in mod for mod in sys.modules):
+                    continue
+            if PY311 and actual_warning.category == EncodingWarning:
+                # EncodingWarnings are checked in the CI
+                # pyproject.toml errors on EncodingWarnings in pandas
+                # Ignore EncodingWarnings from other libraries
+                continue
+            extra_warnings.append(
+                (
+                    actual_warning.category.__name__,
+                    actual_warning.message,
+                    actual_warning.filename,
+                    actual_warning.lineno,
+                )
+            )
+
+    if extra_warnings:
+        raise AssertionError(f"Caused unexpected warning(s): {repr(extra_warnings)}")
+
+
+def _is_unexpected_warning(
+    actual_warning: warnings.WarningMessage,
+    expected_warning: type[Warning] | bool | tuple[type[Warning], ...] | None,
+) -> bool:
+    """Check if the actual warning issued is unexpected."""
+    if actual_warning and not expected_warning:
+        return True
+    expected_warning = cast(type[Warning], expected_warning)
+    return bool(not issubclass(actual_warning.category, expected_warning))
+
+
+def _assert_raised_with_correct_stacklevel(
+    actual_warning: warnings.WarningMessage,
+) -> None:
+    # https://stackoverflow.com/questions/17407119/python-inspect-stack-is-slow
+    frame = inspect.currentframe()
+    for _ in range(4):
+        frame = frame.f_back  # type: ignore[union-attr]
+    try:
+        caller_filename = inspect.getfile(frame)  # type: ignore[arg-type]
+    finally:
+        # See note in
+        # https://docs.python.org/3/library/inspect.html#inspect.Traceback
+        del frame
+    msg = (
+        "Warning not set with correct stacklevel. "
+        f"File where warning is raised: {actual_warning.filename} != "
+        f"{caller_filename}. Warning message: {actual_warning.message}"
+    )
+    assert actual_warning.filename == caller_filename, msg

falcon/lib/python3.10/site-packages/pandas/_testing/asserters.py

@@ -0,0 +1,1435 @@
+from __future__ import annotations
+
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    NoReturn,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.missing import is_matching_na
+from pandas._libs.sparse import SparseIndex
+import pandas._libs.testing as _testing
+from pandas._libs.tslibs.np_datetime import compare_mismatched_resolutions
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_float_dtype,
+    is_integer_dtype,
+    is_number,
+    is_numeric_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    NumpyEADtype,
+)
+from pandas.core.dtypes.missing import array_equivalent
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    IntervalDtype,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+)
+from pandas.core.arrays import (
+    DatetimeArray,
+    ExtensionArray,
+    IntervalArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.indexes.api import safe_sort_index
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeObj
+
+
+def assert_almost_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = "equiv",
+    rtol: float = 1.0e-5,
+    atol: float = 1.0e-8,
+    **kwargs,
+) -> None:
+    """
+    Check that the left and right objects are approximately equal.
+
+    By approximately equal, we refer to objects that are numbers or that
+    contain numbers which may be equivalent to specific levels of precision.
+
+    Parameters
+    ----------
+    left : object
+    right : object
+    check_dtype : bool or {'equiv'}, default 'equiv'
+        Check dtype if both a and b are the same type. If 'equiv' is passed in,
+        then `RangeIndex` and `Index` with int64 dtype are also considered
+        equivalent when doing type checking.
+    rtol : float, default 1e-5
+        Relative tolerance.
+    atol : float, default 1e-8
+        Absolute tolerance.
+    """
+    if isinstance(left, Index):
+        assert_index_equal(
+            left,
+            right,
+            check_exact=False,
+            exact=check_dtype,
+            rtol=rtol,
+            atol=atol,
+            **kwargs,
+        )
+
+    elif isinstance(left, Series):
+        assert_series_equal(
+            left,
+            right,
+            check_exact=False,
+            check_dtype=check_dtype,
+            rtol=rtol,
+            atol=atol,
+            **kwargs,
+        )
+
+    elif isinstance(left, DataFrame):
+        assert_frame_equal(
+            left,
+            right,
+            check_exact=False,
+            check_dtype=check_dtype,
+            rtol=rtol,
+            atol=atol,
+            **kwargs,
+        )
+
+    else:
+        # Other sequences.
+        if check_dtype:
+            if is_number(left) and is_number(right):
+                # Do not compare numeric classes, like np.float64 and float.
+                pass
+            elif is_bool(left) and is_bool(right):
+                # Do not compare bool classes, like np.bool_ and bool.
+                pass
+            else:
+                if isinstance(left, np.ndarray) or isinstance(right, np.ndarray):
+                    obj = "numpy array"
+                else:
+                    obj = "Input"
+                assert_class_equal(left, right, obj=obj)
+
+        # if we have "equiv", this becomes True
+        _testing.assert_almost_equal(
+            left, right, check_dtype=bool(check_dtype), rtol=rtol, atol=atol, **kwargs
+        )
+
+
+def _check_isinstance(left, right, cls) -> None:
+    """
+    Helper method for our assert_* methods that ensures that
+    the two objects being compared have the right type before
+    proceeding with the comparison.
+
+    Parameters
+    ----------
+    left : The first object being compared.
+    right : The second object being compared.
+    cls : The class type to check against.
+
+    Raises
+    ------
+    AssertionError : Either `left` or `right` is not an instance of `cls`.
+    """
+    cls_name = cls.__name__
+
+    if not isinstance(left, cls):
+        raise AssertionError(
+            f"{cls_name} Expected type {cls}, found {type(left)} instead"
+        )
+    if not isinstance(right, cls):
+        raise AssertionError(
+            f"{cls_name} Expected type {cls}, found {type(right)} instead"
+        )
+
+
+def assert_dict_equal(left, right, compare_keys: bool = True) -> None:
+    _check_isinstance(left, right, dict)
+    _testing.assert_dict_equal(left, right, compare_keys=compare_keys)
+
+
+def assert_index_equal(
+    left: Index,
+    right: Index,
+    exact: bool | str = "equiv",
+    check_names: bool = True,
+    check_exact: bool = True,
+    check_categorical: bool = True,
+    check_order: bool = True,
+    rtol: float = 1.0e-5,
+    atol: float = 1.0e-8,
+    obj: str = "Index",
+) -> None:
+    """
+    Check that left and right Index are equal.
+
+    Parameters
+    ----------
+    left : Index
+    right : Index
+    exact : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical. If 'equiv', then RangeIndex can be substituted for
+        Index with an int64 dtype as well.
+    check_names : bool, default True
+        Whether to check the names attribute.
+    check_exact : bool, default True
+        Whether to compare number exactly.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_order : bool, default True
+        Whether to compare the order of index entries as well as their values.
+        If True, both indexes must contain the same elements, in the same order.
+        If False, both indexes must contain the same elements, but in any order.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'Index'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+
+    Examples
+    --------
+    >>> from pandas import testing as tm
+    >>> a = pd.Index([1, 2, 3])
+    >>> b = pd.Index([1, 2, 3])
+    >>> tm.assert_index_equal(a, b)
+    """
+    __tracebackhide__ = True
+
+    def _check_types(left, right, obj: str = "Index") -> None:
+        if not exact:
+            return
+
+        assert_class_equal(left, right, exact=exact, obj=obj)
+        assert_attr_equal("inferred_type", left, right, obj=obj)
+
+        # Skip exact dtype checking when `check_categorical` is False
+        if isinstance(left.dtype, CategoricalDtype) and isinstance(
+            right.dtype, CategoricalDtype
+        ):
+            if check_categorical:
+                assert_attr_equal("dtype", left, right, obj=obj)
+                assert_index_equal(left.categories, right.categories, exact=exact)
+            return
+
+        assert_attr_equal("dtype", left, right, obj=obj)
+
+    # instance validation
+    _check_isinstance(left, right, Index)
+
+    # class / dtype comparison
+    _check_types(left, right, obj=obj)
+
+    # level comparison
+    if left.nlevels != right.nlevels:
+        msg1 = f"{obj} levels are different"
+        msg2 = f"{left.nlevels}, {left}"
+        msg3 = f"{right.nlevels}, {right}"
+        raise_assert_detail(obj, msg1, msg2, msg3)
+
+    # length comparison
+    if len(left) != len(right):
+        msg1 = f"{obj} length are different"
+        msg2 = f"{len(left)}, {left}"
+        msg3 = f"{len(right)}, {right}"
+        raise_assert_detail(obj, msg1, msg2, msg3)
+
+    # If order doesn't matter then sort the index entries
+    if not check_order:
+        left = safe_sort_index(left)
+        right = safe_sort_index(right)
+
+    # MultiIndex special comparison for little-friendly error messages
+    if isinstance(left, MultiIndex):
+        right = cast(MultiIndex, right)
+
+        for level in range(left.nlevels):
+            lobj = f"MultiIndex level [{level}]"
+            try:
+                # try comparison on levels/codes to avoid densifying MultiIndex
+                assert_index_equal(
+                    left.levels[level],
+                    right.levels[level],
+                    exact=exact,
+                    check_names=check_names,
+                    check_exact=check_exact,
+                    check_categorical=check_categorical,
+                    rtol=rtol,
+                    atol=atol,
+                    obj=lobj,
+                )
+                assert_numpy_array_equal(left.codes[level], right.codes[level])
+            except AssertionError:
+                llevel = left.get_level_values(level)
+                rlevel = right.get_level_values(level)
+
+                assert_index_equal(
+                    llevel,
+                    rlevel,
+                    exact=exact,
+                    check_names=check_names,
+                    check_exact=check_exact,
+                    check_categorical=check_categorical,
+                    rtol=rtol,
+                    atol=atol,
+                    obj=lobj,
+                )
+            # get_level_values may change dtype
+            _check_types(left.levels[level], right.levels[level], obj=obj)
+
+    # skip exact index checking when `check_categorical` is False
+    elif check_exact and check_categorical:
+        if not left.equals(right):
+            mismatch = left._values != right._values
+
+            if not isinstance(mismatch, np.ndarray):
+                mismatch = cast("ExtensionArray", mismatch).fillna(True)
+
+            diff = np.sum(mismatch.astype(int)) * 100.0 / len(left)
+            msg = f"{obj} values are different ({np.round(diff, 5)} %)"
+            raise_assert_detail(obj, msg, left, right)
+    else:
+        # if we have "equiv", this becomes True
+        exact_bool = bool(exact)
+        _testing.assert_almost_equal(
+            left.values,
+            right.values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=exact_bool,
+            obj=obj,
+            lobj=left,
+            robj=right,
+        )
+
+    # metadata comparison
+    if check_names:
+        assert_attr_equal("names", left, right, obj=obj)
+    if isinstance(left, PeriodIndex) or isinstance(right, PeriodIndex):
+        assert_attr_equal("dtype", left, right, obj=obj)
+    if isinstance(left, IntervalIndex) or isinstance(right, IntervalIndex):
+        assert_interval_array_equal(left._values, right._values)
+
+    if check_categorical:
+        if isinstance(left.dtype, CategoricalDtype) or isinstance(
+            right.dtype, CategoricalDtype
+        ):
+            assert_categorical_equal(left._values, right._values, obj=f"{obj} category")
+
+
+def assert_class_equal(
+    left, right, exact: bool | str = True, obj: str = "Input"
+) -> None:
+    """
+    Checks classes are equal.
+    """
+    __tracebackhide__ = True
+
+    def repr_class(x):
+        if isinstance(x, Index):
+            # return Index as it is to include values in the error message
+            return x
+
+        return type(x).__name__
+
+    def is_class_equiv(idx: Index) -> bool:
+        """Classes that are a RangeIndex (sub-)instance or exactly an `Index` .
+
+        This only checks class equivalence. There is a separate check that the
+        dtype is int64.
+        """
+        return type(idx) is Index or isinstance(idx, RangeIndex)
+
+    if type(left) == type(right):
+        return
+
+    if exact == "equiv":
+        if is_class_equiv(left) and is_class_equiv(right):
+            return
+
+    msg = f"{obj} classes are different"
+    raise_assert_detail(obj, msg, repr_class(left), repr_class(right))
+
+
+def assert_attr_equal(attr: str, left, right, obj: str = "Attributes") -> None:
+    """
+    Check attributes are equal. Both objects must have attribute.
+
+    Parameters
+    ----------
+    attr : str
+        Attribute name being compared.
+    left : object
+    right : object
+    obj : str, default 'Attributes'
+        Specify object name being compared, internally used to show appropriate
+        assertion message
+    """
+    __tracebackhide__ = True
+
+    left_attr = getattr(left, attr)
+    right_attr = getattr(right, attr)
+
+    if left_attr is right_attr or is_matching_na(left_attr, right_attr):
+        # e.g. both np.nan, both NaT, both pd.NA, ...
+        return None
+
+    try:
+        result = left_attr == right_attr
+    except TypeError:
+        # datetimetz on rhs may raise TypeError
+        result = False
+    if (left_attr is pd.NA) ^ (right_attr is pd.NA):
+        result = False
+    elif not isinstance(result, bool):
+        result = result.all()
+
+    if not result:
+        msg = f'Attribute "{attr}" are different'
+        raise_assert_detail(obj, msg, left_attr, right_attr)
+    return None
+
+
+def assert_is_valid_plot_return_object(objs) -> None:
+    from matplotlib.artist import Artist
+    from matplotlib.axes import Axes
+
+    if isinstance(objs, (Series, np.ndarray)):
+        if isinstance(objs, Series):
+            objs = objs._values
+        for el in objs.ravel():
+            msg = (
+                "one of 'objs' is not a matplotlib Axes instance, "
+                f"type encountered {repr(type(el).__name__)}"
+            )
+            assert isinstance(el, (Axes, dict)), msg
+    else:
+        msg = (
+            "objs is neither an ndarray of Artist instances nor a single "
+            "ArtistArtist instance, tuple, or dict, 'objs' is a "
+            f"{repr(type(objs).__name__)}"
+        )
+        assert isinstance(objs, (Artist, tuple, dict)), msg
+
+
+def assert_is_sorted(seq) -> None:
+    """Assert that the sequence is sorted."""
+    if isinstance(seq, (Index, Series)):
+        seq = seq.values
+    # sorting does not change precisions
+    if isinstance(seq, np.ndarray):
+        assert_numpy_array_equal(seq, np.sort(np.array(seq)))
+    else:
+        assert_extension_array_equal(seq, seq[seq.argsort()])
+
+
+def assert_categorical_equal(
+    left,
+    right,
+    check_dtype: bool = True,
+    check_category_order: bool = True,
+    obj: str = "Categorical",
+) -> None:
+    """
+    Test that Categoricals are equivalent.
+
+    Parameters
+    ----------
+    left : Categorical
+    right : Categorical
+    check_dtype : bool, default True
+        Check that integer dtype of the codes are the same.
+    check_category_order : bool, default True
+        Whether the order of the categories should be compared, which
+        implies identical integer codes.  If False, only the resulting
+        values are compared.  The ordered attribute is
+        checked regardless.
+    obj : str, default 'Categorical'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+    """
+    _check_isinstance(left, right, Categorical)
+
+    exact: bool | str
+    if isinstance(left.categories, RangeIndex) or isinstance(
+        right.categories, RangeIndex
+    ):
+        exact = "equiv"
+    else:
+        # We still want to require exact matches for Index
+        exact = True
+
+    if check_category_order:
+        assert_index_equal(
+            left.categories, right.categories, obj=f"{obj}.categories", exact=exact
+        )
+        assert_numpy_array_equal(
+            left.codes, right.codes, check_dtype=check_dtype, obj=f"{obj}.codes"
+        )
+    else:
+        try:
+            lc = left.categories.sort_values()
+            rc = right.categories.sort_values()
+        except TypeError:
+            # e.g. '<' not supported between instances of 'int' and 'str'
+            lc, rc = left.categories, right.categories
+        assert_index_equal(lc, rc, obj=f"{obj}.categories", exact=exact)
+        assert_index_equal(
+            left.categories.take(left.codes),
+            right.categories.take(right.codes),
+            obj=f"{obj}.values",
+            exact=exact,
+        )
+
+    assert_attr_equal("ordered", left, right, obj=obj)
+
+
+def assert_interval_array_equal(
+    left, right, exact: bool | Literal["equiv"] = "equiv", obj: str = "IntervalArray"
+) -> None:
+    """
+    Test that two IntervalArrays are equivalent.
+
+    Parameters
+    ----------
+    left, right : IntervalArray
+        The IntervalArrays to compare.
+    exact : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical. If 'equiv', then RangeIndex can be substituted for
+        Index with an int64 dtype as well.
+    obj : str, default 'IntervalArray'
+        Specify object name being compared, internally used to show appropriate
+        assertion message
+    """
+    _check_isinstance(left, right, IntervalArray)
+
+    kwargs = {}
+    if left._left.dtype.kind in "mM":
+        # We have a DatetimeArray or TimedeltaArray
+        kwargs["check_freq"] = False
+
+    assert_equal(left._left, right._left, obj=f"{obj}.left", **kwargs)
+    assert_equal(left._right, right._right, obj=f"{obj}.left", **kwargs)
+
+    assert_attr_equal("closed", left, right, obj=obj)
+
+
+def assert_period_array_equal(left, right, obj: str = "PeriodArray") -> None:
+    _check_isinstance(left, right, PeriodArray)
+
+    assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray")
+    assert_attr_equal("dtype", left, right, obj=obj)
+
+
+def assert_datetime_array_equal(
+    left, right, obj: str = "DatetimeArray", check_freq: bool = True
+) -> None:
+    __tracebackhide__ = True
+    _check_isinstance(left, right, DatetimeArray)
+
+    assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray")
+    if check_freq:
+        assert_attr_equal("freq", left, right, obj=obj)
+    assert_attr_equal("tz", left, right, obj=obj)
+
+
+def assert_timedelta_array_equal(
+    left, right, obj: str = "TimedeltaArray", check_freq: bool = True
+) -> None:
+    __tracebackhide__ = True
+    _check_isinstance(left, right, TimedeltaArray)
+    assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray")
+    if check_freq:
+        assert_attr_equal("freq", left, right, obj=obj)
+
+
+def raise_assert_detail(
+    obj, message, left, right, diff=None, first_diff=None, index_values=None
+) -> NoReturn:
+    __tracebackhide__ = True
+
+    msg = f"""{obj} are different
+
+{message}"""
+
+    if isinstance(index_values, Index):
+        index_values = np.asarray(index_values)
+
+    if isinstance(index_values, np.ndarray):
+        msg += f"\n[index]: {pprint_thing(index_values)}"
+
+    if isinstance(left, np.ndarray):
+        left = pprint_thing(left)
+    elif isinstance(left, (CategoricalDtype, NumpyEADtype, StringDtype)):
+        left = repr(left)
+
+    if isinstance(right, np.ndarray):
+        right = pprint_thing(right)
+    elif isinstance(right, (CategoricalDtype, NumpyEADtype, StringDtype)):
+        right = repr(right)
+
+    msg += f"""
+[left]:  {left}
+[right]: {right}"""
+
+    if diff is not None:
+        msg += f"\n[diff]: {diff}"
+
+    if first_diff is not None:
+        msg += f"\n{first_diff}"
+
+    raise AssertionError(msg)
+
+
+def assert_numpy_array_equal(
+    left,
+    right,
+    strict_nan: bool = False,
+    check_dtype: bool | Literal["equiv"] = True,
+    err_msg=None,
+    check_same=None,
+    obj: str = "numpy array",
+    index_values=None,
+) -> None:
+    """
+    Check that 'np.ndarray' is equivalent.
+
+    Parameters
+    ----------
+    left, right : numpy.ndarray or iterable
+        The two arrays to be compared.
+    strict_nan : bool, default False
+        If True, consider NaN and None to be different.
+    check_dtype : bool, default True
+        Check dtype if both a and b are np.ndarray.
+    err_msg : str, default None
+        If provided, used as assertion message.
+    check_same : None|'copy'|'same', default None
+        Ensure left and right refer/do not refer to the same memory area.
+    obj : str, default 'numpy array'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+    index_values : Index | numpy.ndarray, default None
+        optional index (shared by both left and right), used in output.
+    """
+    __tracebackhide__ = True
+
+    # instance validation
+    # Show a detailed error message when classes are different
+    assert_class_equal(left, right, obj=obj)
+    # both classes must be an np.ndarray
+    _check_isinstance(left, right, np.ndarray)
+
+    def _get_base(obj):
+        return obj.base if getattr(obj, "base", None) is not None else obj
+
+    left_base = _get_base(left)
+    right_base = _get_base(right)
+
+    if check_same == "same":
+        if left_base is not right_base:
+            raise AssertionError(f"{repr(left_base)} is not {repr(right_base)}")
+    elif check_same == "copy":
+        if left_base is right_base:
+            raise AssertionError(f"{repr(left_base)} is {repr(right_base)}")
+
+    def _raise(left, right, err_msg) -> NoReturn:
+        if err_msg is None:
+            if left.shape != right.shape:
+                raise_assert_detail(
+                    obj, f"{obj} shapes are different", left.shape, right.shape
+                )
+
+            diff = 0
+            for left_arr, right_arr in zip(left, right):
+                # count up differences
+                if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
+                    diff += 1
+
+            diff = diff * 100.0 / left.size
+            msg = f"{obj} values are different ({np.round(diff, 5)} %)"
+            raise_assert_detail(obj, msg, left, right, index_values=index_values)
+
+        raise AssertionError(err_msg)
+
+    # compare shape and values
+    if not array_equivalent(left, right, strict_nan=strict_nan):
+        _raise(left, right, err_msg)
+
+    if check_dtype:
+        if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
+            assert_attr_equal("dtype", left, right, obj=obj)
+
+
+def assert_extension_array_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = True,
+    index_values=None,
+    check_exact: bool | lib.NoDefault = lib.no_default,
+    rtol: float | lib.NoDefault = lib.no_default,
+    atol: float | lib.NoDefault = lib.no_default,
+    obj: str = "ExtensionArray",
+) -> None:
+    """
+    Check that left and right ExtensionArrays are equal.
+
+    Parameters
+    ----------
+    left, right : ExtensionArray
+        The two arrays to compare.
+    check_dtype : bool, default True
+        Whether to check if the ExtensionArray dtypes are identical.
+    index_values : Index | numpy.ndarray, default None
+        Optional index (shared by both left and right), used in output.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+
+        .. versionchanged:: 2.2.0
+
+            Defaults to True for integer dtypes if none of
+            ``check_exact``, ``rtol`` and ``atol`` are specified.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'ExtensionArray'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+
+        .. versionadded:: 2.0.0
+
+    Notes
+    -----
+    Missing values are checked separately from valid values.
+    A mask of missing values is computed for each and checked to match.
+    The remaining all-valid values are cast to object dtype and checked.
+
+    Examples
+    --------
+    >>> from pandas import testing as tm
+    >>> a = pd.Series([1, 2, 3, 4])
+    >>> b, c = a.array, a.array
+    >>> tm.assert_extension_array_equal(b, c)
+    """
+    if (
+        check_exact is lib.no_default
+        and rtol is lib.no_default
+        and atol is lib.no_default
+    ):
+        check_exact = (
+            is_numeric_dtype(left.dtype)
+            and not is_float_dtype(left.dtype)
+            or is_numeric_dtype(right.dtype)
+            and not is_float_dtype(right.dtype)
+        )
+    elif check_exact is lib.no_default:
+        check_exact = False
+
+    rtol = rtol if rtol is not lib.no_default else 1.0e-5
+    atol = atol if atol is not lib.no_default else 1.0e-8
+
+    assert isinstance(left, ExtensionArray), "left is not an ExtensionArray"
+    assert isinstance(right, ExtensionArray), "right is not an ExtensionArray"
+    if check_dtype:
+        assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}")
+
+    if (
+        isinstance(left, DatetimeLikeArrayMixin)
+        and isinstance(right, DatetimeLikeArrayMixin)
+        and type(right) == type(left)
+    ):
+        # GH 52449
+        if not check_dtype and left.dtype.kind in "mM":
+            if not isinstance(left.dtype, np.dtype):
+                l_unit = cast(DatetimeTZDtype, left.dtype).unit
+            else:
+                l_unit = np.datetime_data(left.dtype)[0]
+            if not isinstance(right.dtype, np.dtype):
+                r_unit = cast(DatetimeTZDtype, right.dtype).unit
+            else:
+                r_unit = np.datetime_data(right.dtype)[0]
+            if (
+                l_unit != r_unit
+                and compare_mismatched_resolutions(
+                    left._ndarray, right._ndarray, operator.eq
+                ).all()
+            ):
+                return
+        # Avoid slow object-dtype comparisons
+        # np.asarray for case where we have a np.MaskedArray
+        assert_numpy_array_equal(
+            np.asarray(left.asi8),
+            np.asarray(right.asi8),
+            index_values=index_values,
+            obj=obj,
+        )
+        return
+
+    left_na = np.asarray(left.isna())
+    right_na = np.asarray(right.isna())
+    assert_numpy_array_equal(
+        left_na, right_na, obj=f"{obj} NA mask", index_values=index_values
+    )
+
+    left_valid = left[~left_na].to_numpy(dtype=object)
+    right_valid = right[~right_na].to_numpy(dtype=object)
+    if check_exact:
+        assert_numpy_array_equal(
+            left_valid, right_valid, obj=obj, index_values=index_values
+        )
+    else:
+        _testing.assert_almost_equal(
+            left_valid,
+            right_valid,
+            check_dtype=bool(check_dtype),
+            rtol=rtol,
+            atol=atol,
+            obj=obj,
+            index_values=index_values,
+        )
+
+
+# This could be refactored to use the NDFrame.equals method
+def assert_series_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = True,
+    check_index_type: bool | Literal["equiv"] = "equiv",
+    check_series_type: bool = True,
+    check_names: bool = True,
+    check_exact: bool | lib.NoDefault = lib.no_default,
+    check_datetimelike_compat: bool = False,
+    check_categorical: bool = True,
+    check_category_order: bool = True,
+    check_freq: bool = True,
+    check_flags: bool = True,
+    rtol: float | lib.NoDefault = lib.no_default,
+    atol: float | lib.NoDefault = lib.no_default,
+    obj: str = "Series",
+    *,
+    check_index: bool = True,
+    check_like: bool = False,
+) -> None:
+    """
+    Check that left and right Series are equal.
+
+    Parameters
+    ----------
+    left : Series
+    right : Series
+    check_dtype : bool, default True
+        Whether to check the Series dtype is identical.
+    check_index_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical.
+    check_series_type : bool, default True
+         Whether to check the Series class is identical.
+    check_names : bool, default True
+        Whether to check the Series and Index names attribute.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+
+        .. versionchanged:: 2.2.0
+
+            Defaults to True for integer dtypes if none of
+            ``check_exact``, ``rtol`` and ``atol`` are specified.
+    check_datetimelike_compat : bool, default False
+        Compare datetime-like which is comparable ignoring dtype.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_category_order : bool, default True
+        Whether to compare category order of internal Categoricals.
+    check_freq : bool, default True
+        Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex.
+    check_flags : bool, default True
+        Whether to check the `flags` attribute.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'Series'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+    check_index : bool, default True
+        Whether to check index equivalence. If False, then compare only values.
+
+        .. versionadded:: 1.3.0
+    check_like : bool, default False
+        If True, ignore the order of the index. Must be False if check_index is False.
+        Note: same labels must be with the same data.
+
+        .. versionadded:: 1.5.0
+
+    Examples
+    --------
+    >>> from pandas import testing as tm
+    >>> a = pd.Series([1, 2, 3, 4])
+    >>> b = pd.Series([1, 2, 3, 4])
+    >>> tm.assert_series_equal(a, b)
+    """
+    __tracebackhide__ = True
+    check_exact_index = False if check_exact is lib.no_default else check_exact
+    if (
+        check_exact is lib.no_default
+        and rtol is lib.no_default
+        and atol is lib.no_default
+    ):
+        check_exact = (
+            is_numeric_dtype(left.dtype)
+            and not is_float_dtype(left.dtype)
+            or is_numeric_dtype(right.dtype)
+            and not is_float_dtype(right.dtype)
+        )
+    elif check_exact is lib.no_default:
+        check_exact = False
+
+    rtol = rtol if rtol is not lib.no_default else 1.0e-5
+    atol = atol if atol is not lib.no_default else 1.0e-8
+
+    if not check_index and check_like:
+        raise ValueError("check_like must be False if check_index is False")
+
+    # instance validation
+    _check_isinstance(left, right, Series)
+
+    if check_series_type:
+        assert_class_equal(left, right, obj=obj)
+
+    # length comparison
+    if len(left) != len(right):
+        msg1 = f"{len(left)}, {left.index}"
+        msg2 = f"{len(right)}, {right.index}"
+        raise_assert_detail(obj, "Series length are different", msg1, msg2)
+
+    if check_flags:
+        assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}"
+
+    if check_index:
+        # GH #38183
+        assert_index_equal(
+            left.index,
+            right.index,
+            exact=check_index_type,
+            check_names=check_names,
+            check_exact=check_exact_index,
+            check_categorical=check_categorical,
+            check_order=not check_like,
+            rtol=rtol,
+            atol=atol,
+            obj=f"{obj}.index",
+        )
+
+    if check_like:
+        left = left.reindex_like(right)
+
+    if check_freq and isinstance(left.index, (DatetimeIndex, TimedeltaIndex)):
+        lidx = left.index
+        ridx = right.index
+        assert lidx.freq == ridx.freq, (lidx.freq, ridx.freq)
+
+    if check_dtype:
+        # We want to skip exact dtype checking when `check_categorical`
+        # is False. We'll still raise if only one is a `Categorical`,
+        # regardless of `check_categorical`
+        if (
+            isinstance(left.dtype, CategoricalDtype)
+            and isinstance(right.dtype, CategoricalDtype)
+            and not check_categorical
+        ):
+            pass
+        else:
+            assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}")
+    if check_exact:
+        left_values = left._values
+        right_values = right._values
+        # Only check exact if dtype is numeric
+        if isinstance(left_values, ExtensionArray) and isinstance(
+            right_values, ExtensionArray
+        ):
+            assert_extension_array_equal(
+                left_values,
+                right_values,
+                check_dtype=check_dtype,
+                index_values=left.index,
+                obj=str(obj),
+            )
+        else:
+            # convert both to NumPy if not, check_dtype would raise earlier
+            lv, rv = left_values, right_values
+            if isinstance(left_values, ExtensionArray):
+                lv = left_values.to_numpy()
+            if isinstance(right_values, ExtensionArray):
+                rv = right_values.to_numpy()
+            assert_numpy_array_equal(
+                lv,
+                rv,
+                check_dtype=check_dtype,
+                obj=str(obj),
+                index_values=left.index,
+            )
+    elif check_datetimelike_compat and (
+        needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype)
+    ):
+        # we want to check only if we have compat dtypes
+        # e.g. integer and M|m are NOT compat, but we can simply check
+        # the values in that case
+
+        # datetimelike may have different objects (e.g. datetime.datetime
+        # vs Timestamp) but will compare equal
+        if not Index(left._values).equals(Index(right._values)):
+            msg = (
+                f"[datetimelike_compat=True] {left._values} "
+                f"is not equal to {right._values}."
+            )
+            raise AssertionError(msg)
+    elif isinstance(left.dtype, IntervalDtype) and isinstance(
+        right.dtype, IntervalDtype
+    ):
+        assert_interval_array_equal(left.array, right.array)
+    elif isinstance(left.dtype, CategoricalDtype) or isinstance(
+        right.dtype, CategoricalDtype
+    ):
+        _testing.assert_almost_equal(
+            left._values,
+            right._values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=bool(check_dtype),
+            obj=str(obj),
+            index_values=left.index,
+        )
+    elif isinstance(left.dtype, ExtensionDtype) and isinstance(
+        right.dtype, ExtensionDtype
+    ):
+        assert_extension_array_equal(
+            left._values,
+            right._values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=check_dtype,
+            index_values=left.index,
+            obj=str(obj),
+        )
+    elif is_extension_array_dtype_and_needs_i8_conversion(
+        left.dtype, right.dtype
+    ) or is_extension_array_dtype_and_needs_i8_conversion(right.dtype, left.dtype):
+        assert_extension_array_equal(
+            left._values,
+            right._values,
+            check_dtype=check_dtype,
+            index_values=left.index,
+            obj=str(obj),
+        )
+    elif needs_i8_conversion(left.dtype) and needs_i8_conversion(right.dtype):
+        # DatetimeArray or TimedeltaArray
+        assert_extension_array_equal(
+            left._values,
+            right._values,
+            check_dtype=check_dtype,
+            index_values=left.index,
+            obj=str(obj),
+        )
+    else:
+        _testing.assert_almost_equal(
+            left._values,
+            right._values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=bool(check_dtype),
+            obj=str(obj),
+            index_values=left.index,
+        )
+
+    # metadata comparison
+    if check_names:
+        assert_attr_equal("name", left, right, obj=obj)
+
+    if check_categorical:
+        if isinstance(left.dtype, CategoricalDtype) or isinstance(
+            right.dtype, CategoricalDtype
+        ):
+            assert_categorical_equal(
+                left._values,
+                right._values,
+                obj=f"{obj} category",
+                check_category_order=check_category_order,
+            )
+
+
+# This could be refactored to use the NDFrame.equals method
+def assert_frame_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = True,
+    check_index_type: bool | Literal["equiv"] = "equiv",
+    check_column_type: bool | Literal["equiv"] = "equiv",
+    check_frame_type: bool = True,
+    check_names: bool = True,
+    by_blocks: bool = False,
+    check_exact: bool | lib.NoDefault = lib.no_default,
+    check_datetimelike_compat: bool = False,
+    check_categorical: bool = True,
+    check_like: bool = False,
+    check_freq: bool = True,
+    check_flags: bool = True,
+    rtol: float | lib.NoDefault = lib.no_default,
+    atol: float | lib.NoDefault = lib.no_default,
+    obj: str = "DataFrame",
+) -> None:
+    """
+    Check that left and right DataFrame are equal.
+
+    This function is intended to compare two DataFrames and output any
+    differences. It is mostly intended for use in unit tests.
+    Additional parameters allow varying the strictness of the
+    equality checks performed.
+
+    Parameters
+    ----------
+    left : DataFrame
+        First DataFrame to compare.
+    right : DataFrame
+        Second DataFrame to compare.
+    check_dtype : bool, default True
+        Whether to check the DataFrame dtype is identical.
+    check_index_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical.
+    check_column_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the columns class, dtype and inferred_type
+        are identical. Is passed as the ``exact`` argument of
+        :func:`assert_index_equal`.
+    check_frame_type : bool, default True
+        Whether to check the DataFrame class is identical.
+    check_names : bool, default True
+        Whether to check that the `names` attribute for both the `index`
+        and `column` attributes of the DataFrame is identical.
+    by_blocks : bool, default False
+        Specify how to compare internal data. If False, compare by columns.
+        If True, compare by blocks.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+
+        .. versionchanged:: 2.2.0
+
+            Defaults to True for integer dtypes if none of
+            ``check_exact``, ``rtol`` and ``atol`` are specified.
+    check_datetimelike_compat : bool, default False
+        Compare datetime-like which is comparable ignoring dtype.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_like : bool, default False
+        If True, ignore the order of index & columns.
+        Note: index labels must match their respective rows
+        (same as in columns) - same labels must be with the same data.
+    check_freq : bool, default True
+        Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex.
+    check_flags : bool, default True
+        Whether to check the `flags` attribute.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'DataFrame'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+
+    See Also
+    --------
+    assert_series_equal : Equivalent method for asserting Series equality.
+    DataFrame.equals : Check DataFrame equality.
+
+    Examples
+    --------
+    This example shows comparing two DataFrames that are equal
+    but with columns of differing dtypes.
+
+    >>> from pandas.testing import assert_frame_equal
+    >>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
+    >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
+
+    df1 equals itself.
+
+    >>> assert_frame_equal(df1, df1)
+
+    df1 differs from df2 as column 'b' is of a different type.
+
+    >>> assert_frame_equal(df1, df2)
+    Traceback (most recent call last):
+    ...
+    AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different
+
+    Attribute "dtype" are different
+    [left]:  int64
+    [right]: float64
+
+    Ignore differing dtypes in columns with check_dtype.
+
+    >>> assert_frame_equal(df1, df2, check_dtype=False)
+    """
+    __tracebackhide__ = True
+    _rtol = rtol if rtol is not lib.no_default else 1.0e-5
+    _atol = atol if atol is not lib.no_default else 1.0e-8
+    _check_exact = check_exact if check_exact is not lib.no_default else False
+
+    # instance validation
+    _check_isinstance(left, right, DataFrame)
+
+    if check_frame_type:
+        assert isinstance(left, type(right))
+        # assert_class_equal(left, right, obj=obj)
+
+    # shape comparison
+    if left.shape != right.shape:
+        raise_assert_detail(
+            obj, f"{obj} shape mismatch", f"{repr(left.shape)}", f"{repr(right.shape)}"
+        )
+
+    if check_flags:
+        assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}"
+
+    # index comparison
+    assert_index_equal(
+        left.index,
+        right.index,
+        exact=check_index_type,
+        check_names=check_names,
+        check_exact=_check_exact,
+        check_categorical=check_categorical,
+        check_order=not check_like,
+        rtol=_rtol,
+        atol=_atol,
+        obj=f"{obj}.index",
+    )
+
+    # column comparison
+    assert_index_equal(
+        left.columns,
+        right.columns,
+        exact=check_column_type,
+        check_names=check_names,
+        check_exact=_check_exact,
+        check_categorical=check_categorical,
+        check_order=not check_like,
+        rtol=_rtol,
+        atol=_atol,
+        obj=f"{obj}.columns",
+    )
+
+    if check_like:
+        left = left.reindex_like(right)
+
+    # compare by blocks
+    if by_blocks:
+        rblocks = right._to_dict_of_blocks()
+        lblocks = left._to_dict_of_blocks()
+        for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))):
+            assert dtype in lblocks
+            assert dtype in rblocks
+            assert_frame_equal(
+                lblocks[dtype], rblocks[dtype], check_dtype=check_dtype, obj=obj
+            )
+
+    # compare by columns
+    else:
+        for i, col in enumerate(left.columns):
+            # We have already checked that columns match, so we can do
+            #  fast location-based lookups
+            lcol = left._ixs(i, axis=1)
+            rcol = right._ixs(i, axis=1)
+
+            # GH #38183
+            # use check_index=False, because we do not want to run
+            # assert_index_equal for each column,
+            # as we already checked it for the whole dataframe before.
+            assert_series_equal(
+                lcol,
+                rcol,
+                check_dtype=check_dtype,
+                check_index_type=check_index_type,
+                check_exact=check_exact,
+                check_names=check_names,
+                check_datetimelike_compat=check_datetimelike_compat,
+                check_categorical=check_categorical,
+                check_freq=check_freq,
+                obj=f'{obj}.iloc[:, {i}] (column name="{col}")',
+                rtol=rtol,
+                atol=atol,
+                check_index=False,
+                check_flags=False,
+            )
+
+
+def assert_equal(left, right, **kwargs) -> None:
+    """
+    Wrapper for tm.assert_*_equal to dispatch to the appropriate test function.
+
+    Parameters
+    ----------
+    left, right : Index, Series, DataFrame, ExtensionArray, or np.ndarray
+        The two items to be compared.
+    **kwargs
+        All keyword arguments are passed through to the underlying assert method.
+    """
+    __tracebackhide__ = True
+
+    if isinstance(left, Index):
+        assert_index_equal(left, right, **kwargs)
+        if isinstance(left, (DatetimeIndex, TimedeltaIndex)):
+            assert left.freq == right.freq, (left.freq, right.freq)
+    elif isinstance(left, Series):
+        assert_series_equal(left, right, **kwargs)
+    elif isinstance(left, DataFrame):
+        assert_frame_equal(left, right, **kwargs)
+    elif isinstance(left, IntervalArray):
+        assert_interval_array_equal(left, right, **kwargs)
+    elif isinstance(left, PeriodArray):
+        assert_period_array_equal(left, right, **kwargs)
+    elif isinstance(left, DatetimeArray):
+        assert_datetime_array_equal(left, right, **kwargs)
+    elif isinstance(left, TimedeltaArray):
+        assert_timedelta_array_equal(left, right, **kwargs)
+    elif isinstance(left, ExtensionArray):
+        assert_extension_array_equal(left, right, **kwargs)
+    elif isinstance(left, np.ndarray):
+        assert_numpy_array_equal(left, right, **kwargs)
+    elif isinstance(left, str):
+        assert kwargs == {}
+        assert left == right
+    else:
+        assert kwargs == {}
+        assert_almost_equal(left, right)
+
+
+def assert_sp_array_equal(left, right) -> None:
+    """
+    Check that the left and right SparseArray are equal.
+
+    Parameters
+    ----------
+    left : SparseArray
+    right : SparseArray
+    """
+    _check_isinstance(left, right, pd.arrays.SparseArray)
+
+    assert_numpy_array_equal(left.sp_values, right.sp_values)
+
+    # SparseIndex comparison
+    assert isinstance(left.sp_index, SparseIndex)
+    assert isinstance(right.sp_index, SparseIndex)
+
+    left_index = left.sp_index
+    right_index = right.sp_index
+
+    if not left_index.equals(right_index):
+        raise_assert_detail(
+            "SparseArray.index", "index are not equal", left_index, right_index
+        )
+    else:
+        # Just ensure a
+        pass
+
+    assert_attr_equal("fill_value", left, right)
+    assert_attr_equal("dtype", left, right)
+    assert_numpy_array_equal(left.to_dense(), right.to_dense())
+
+
+def assert_contains_all(iterable, dic) -> None:
+    for k in iterable:
+        assert k in dic, f"Did not contain item: {repr(k)}"
+
+
+def assert_copy(iter1, iter2, **eql_kwargs) -> None:
+    """
+    iter1, iter2: iterables that produce elements
+    comparable with assert_almost_equal
+
+    Checks that the elements are equal, but not
+    the same object. (Does not check that items
+    in sequences are also not the same object)
+    """
+    for elem1, elem2 in zip(iter1, iter2):
+        assert_almost_equal(elem1, elem2, **eql_kwargs)
+        msg = (
+            f"Expected object {repr(type(elem1))} and object {repr(type(elem2))} to be "
+            "different objects, but they were the same object."
+        )
+        assert elem1 is not elem2, msg
+
+
+def is_extension_array_dtype_and_needs_i8_conversion(
+    left_dtype: DtypeObj, right_dtype: DtypeObj
+) -> bool:
+    """
+    Checks that we have the combination of an ExtensionArraydtype and
+    a dtype that should be converted to int64
+
+    Returns
+    -------
+    bool
+
+    Related to issue #37609
+    """
+    return isinstance(left_dtype, ExtensionDtype) and needs_i8_conversion(right_dtype)
+
+
+def assert_indexing_slices_equivalent(ser: Series, l_slc: slice, i_slc: slice) -> None:
+    """
+    Check that ser.iloc[i_slc] matches ser.loc[l_slc] and, if applicable,
+    ser[l_slc].
+    """
+    expected = ser.iloc[i_slc]
+
+    assert_series_equal(ser.loc[l_slc], expected)
+
+    if not is_integer_dtype(ser.index):
+        # For integer indices, .loc and plain getitem are position-based.
+        assert_series_equal(ser[l_slc], expected)
+
+
+def assert_metadata_equivalent(
+    left: DataFrame | Series, right: DataFrame | Series | None = None
+) -> None:
+    """
+    Check that ._metadata attributes are equivalent.
+    """
+    for attr in left._metadata:
+        val = getattr(left, attr, None)
+        if right is None:
+            assert val is None
+        else:
+            assert val == getattr(right, attr, None)

falcon/lib/python3.10/site-packages/pandas/_testing/contexts.py

@@ -0,0 +1,257 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+import os
+from pathlib import Path
+import tempfile
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+)
+import uuid
+
+from pandas._config import using_copy_on_write
+
+from pandas.compat import PYPY
+from pandas.errors import ChainedAssignmentError
+
+from pandas import set_option
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+    from pandas._typing import (
+        BaseBuffer,
+        CompressionOptions,
+        FilePath,
+    )
+
+
+@contextmanager
+def decompress_file(
+    path: FilePath | BaseBuffer, compression: CompressionOptions
+) -> Generator[IO[bytes], None, None]:
+    """
+    Open a compressed file and return a file object.
+
+    Parameters
+    ----------
+    path : str
+        The path where the file is read from.
+
+    compression : {'gzip', 'bz2', 'zip', 'xz', 'zstd', None}
+        Name of the decompression to use
+
+    Returns
+    -------
+    file object
+    """
+    with get_handle(path, "rb", compression=compression, is_text=False) as handle:
+        yield handle.handle
+
+
+@contextmanager
+def set_timezone(tz: str) -> Generator[None, None, None]:
+    """
+    Context manager for temporarily setting a timezone.
+
+    Parameters
+    ----------
+    tz : str
+        A string representing a valid timezone.
+
+    Examples
+    --------
+    >>> from datetime import datetime
+    >>> from dateutil.tz import tzlocal
+    >>> tzlocal().tzname(datetime(2021, 1, 1))  # doctest: +SKIP
+    'IST'
+
+    >>> with set_timezone('US/Eastern'):
+    ...     tzlocal().tzname(datetime(2021, 1, 1))
+    ...
+    'EST'
+    """
+    import time
+
+    def setTZ(tz) -> None:
+        if tz is None:
+            try:
+                del os.environ["TZ"]
+            except KeyError:
+                pass
+        else:
+            os.environ["TZ"] = tz
+            time.tzset()
+
+    orig_tz = os.environ.get("TZ")
+    setTZ(tz)
+    try:
+        yield
+    finally:
+        setTZ(orig_tz)
+
+
+@contextmanager
+def ensure_clean(
+    filename=None, return_filelike: bool = False, **kwargs: Any
+) -> Generator[Any, None, None]:
+    """
+    Gets a temporary path and agrees to remove on close.
+
+    This implementation does not use tempfile.mkstemp to avoid having a file handle.
+    If the code using the returned path wants to delete the file itself, windows
+    requires that no program has a file handle to it.
+
+    Parameters
+    ----------
+    filename : str (optional)
+        suffix of the created file.
+    return_filelike : bool (default False)
+        if True, returns a file-like which is *always* cleaned. Necessary for
+        savefig and other functions which want to append extensions.
+    **kwargs
+        Additional keywords are passed to open().
+
+    """
+    folder = Path(tempfile.gettempdir())
+
+    if filename is None:
+        filename = ""
+    filename = str(uuid.uuid4()) + filename
+    path = folder / filename
+
+    path.touch()
+
+    handle_or_str: str | IO = str(path)
+    encoding = kwargs.pop("encoding", None)
+    if return_filelike:
+        kwargs.setdefault("mode", "w+b")
+        if encoding is None and "b" not in kwargs["mode"]:
+            encoding = "utf-8"
+        handle_or_str = open(path, encoding=encoding, **kwargs)
+
+    try:
+        yield handle_or_str
+    finally:
+        if not isinstance(handle_or_str, str):
+            handle_or_str.close()
+        if path.is_file():
+            path.unlink()
+
+
+@contextmanager
+def with_csv_dialect(name: str, **kwargs) -> Generator[None, None, None]:
+    """
+    Context manager to temporarily register a CSV dialect for parsing CSV.
+
+    Parameters
+    ----------
+    name : str
+        The name of the dialect.
+    kwargs : mapping
+        The parameters for the dialect.
+
+    Raises
+    ------
+    ValueError : the name of the dialect conflicts with a builtin one.
+
+    See Also
+    --------
+    csv : Python's CSV library.
+    """
+    import csv
+
+    _BUILTIN_DIALECTS = {"excel", "excel-tab", "unix"}
+
+    if name in _BUILTIN_DIALECTS:
+        raise ValueError("Cannot override builtin dialect.")
+
+    csv.register_dialect(name, **kwargs)
+    try:
+        yield
+    finally:
+        csv.unregister_dialect(name)
+
+
+@contextmanager
+def use_numexpr(use, min_elements=None) -> Generator[None, None, None]:
+    from pandas.core.computation import expressions as expr
+
+    if min_elements is None:
+        min_elements = expr._MIN_ELEMENTS
+
+    olduse = expr.USE_NUMEXPR
+    oldmin = expr._MIN_ELEMENTS
+    set_option("compute.use_numexpr", use)
+    expr._MIN_ELEMENTS = min_elements
+    try:
+        yield
+    finally:
+        expr._MIN_ELEMENTS = oldmin
+        set_option("compute.use_numexpr", olduse)
+
+
+def raises_chained_assignment_error(warn=True, extra_warnings=(), extra_match=()):
+    from pandas._testing import assert_produces_warning
+
+    if not warn:
+        from contextlib import nullcontext
+
+        return nullcontext()
+
+    if PYPY and not extra_warnings:
+        from contextlib import nullcontext
+
+        return nullcontext()
+    elif PYPY and extra_warnings:
+        return assert_produces_warning(
+            extra_warnings,
+            match="|".join(extra_match),
+        )
+    else:
+        if using_copy_on_write():
+            warning = ChainedAssignmentError
+            match = (
+                "A value is trying to be set on a copy of a DataFrame or Series "
+                "through chained assignment"
+            )
+        else:
+            warning = FutureWarning  # type: ignore[assignment]
+            # TODO update match
+            match = "ChainedAssignmentError"
+        if extra_warnings:
+            warning = (warning, *extra_warnings)  # type: ignore[assignment]
+        return assert_produces_warning(
+            warning,
+            match="|".join((match, *extra_match)),
+        )
+
+
+def assert_cow_warning(warn=True, match=None, **kwargs):
+    """
+    Assert that a warning is raised in the CoW warning mode.
+
+    Parameters
+    ----------
+    warn : bool, default True
+        By default, check that a warning is raised. Can be turned off by passing False.
+    match : str
+        The warning message to match against, if different from the default.
+    kwargs
+        Passed through to assert_produces_warning
+    """
+    from pandas._testing import assert_produces_warning
+
+    if not warn:
+        from contextlib import nullcontext
+
+        return nullcontext()
+
+    if not match:
+        match = "Setting a value on a view"
+
+    return assert_produces_warning(FutureWarning, match=match, **kwargs)

falcon/lib/python3.10/site-packages/pandas/api/__init__.py

@@ -0,0 +1,16 @@
+""" public toolkit API """
+from pandas.api import (
+    extensions,
+    indexers,
+    interchange,
+    types,
+    typing,
+)
+
+__all__ = [
+    "interchange",
+    "extensions",
+    "indexers",
+    "types",
+    "typing",
+]

falcon/lib/python3.10/site-packages/pandas/api/extensions/__init__.py

@@ -0,0 +1,33 @@
+"""
+Public API for extending pandas objects.
+"""
+
+from pandas._libs.lib import no_default
+
+from pandas.core.dtypes.base import (
+    ExtensionDtype,
+    register_extension_dtype,
+)
+
+from pandas.core.accessor import (
+    register_dataframe_accessor,
+    register_index_accessor,
+    register_series_accessor,
+)
+from pandas.core.algorithms import take
+from pandas.core.arrays import (
+    ExtensionArray,
+    ExtensionScalarOpsMixin,
+)
+
+__all__ = [
+    "no_default",
+    "ExtensionDtype",
+    "register_extension_dtype",
+    "register_dataframe_accessor",
+    "register_index_accessor",
+    "register_series_accessor",
+    "take",
+    "ExtensionArray",
+    "ExtensionScalarOpsMixin",
+]

falcon/lib/python3.10/site-packages/pandas/api/indexers/__init__.py

@@ -0,0 +1,17 @@
+"""
+Public API for Rolling Window Indexers.
+"""
+
+from pandas.core.indexers import check_array_indexer
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    FixedForwardWindowIndexer,
+    VariableOffsetWindowIndexer,
+)
+
+__all__ = [
+    "check_array_indexer",
+    "BaseIndexer",
+    "FixedForwardWindowIndexer",
+    "VariableOffsetWindowIndexer",
+]

falcon/lib/python3.10/site-packages/pandas/api/interchange/__init__.py

@@ -0,0 +1,8 @@
+"""
+Public API for DataFrame interchange protocol.
+"""
+
+from pandas.core.interchange.dataframe_protocol import DataFrame
+from pandas.core.interchange.from_dataframe import from_dataframe
+
+__all__ = ["from_dataframe", "DataFrame"]

falcon/lib/python3.10/site-packages/pandas/api/types/__init__.py

@@ -0,0 +1,23 @@
+"""
+Public toolkit API.
+"""
+
+from pandas._libs.lib import infer_dtype
+
+from pandas.core.dtypes.api import *  # noqa: F403
+from pandas.core.dtypes.concat import union_categoricals
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+
+__all__ = [
+    "infer_dtype",
+    "union_categoricals",
+    "CategoricalDtype",
+    "DatetimeTZDtype",
+    "IntervalDtype",
+    "PeriodDtype",
+]

falcon/lib/python3.10/site-packages/pandas/api/typing/__init__.py

@@ -0,0 +1,55 @@
+"""
+Public API classes that store intermediate results useful for type-hinting.
+"""
+
+from pandas._libs import NaTType
+from pandas._libs.missing import NAType
+
+from pandas.core.groupby import (
+    DataFrameGroupBy,
+    SeriesGroupBy,
+)
+from pandas.core.resample import (
+    DatetimeIndexResamplerGroupby,
+    PeriodIndexResamplerGroupby,
+    Resampler,
+    TimedeltaIndexResamplerGroupby,
+    TimeGrouper,
+)
+from pandas.core.window import (
+    Expanding,
+    ExpandingGroupby,
+    ExponentialMovingWindow,
+    ExponentialMovingWindowGroupby,
+    Rolling,
+    RollingGroupby,
+    Window,
+)
+
+# TODO: Can't import Styler without importing jinja2
+# from pandas.io.formats.style import Styler
+from pandas.io.json._json import JsonReader
+from pandas.io.stata import StataReader
+
+__all__ = [
+    "DataFrameGroupBy",
+    "DatetimeIndexResamplerGroupby",
+    "Expanding",
+    "ExpandingGroupby",
+    "ExponentialMovingWindow",
+    "ExponentialMovingWindowGroupby",
+    "JsonReader",
+    "NaTType",
+    "NAType",
+    "PeriodIndexResamplerGroupby",
+    "Resampler",
+    "Rolling",
+    "RollingGroupby",
+    "SeriesGroupBy",
+    "StataReader",
+    # See TODO above
+    # "Styler",
+    "TimedeltaIndexResamplerGroupby",
+    "TimeGrouper",
+    "Window",
+]

falcon/lib/python3.10/site-packages/pandas/compat/__init__.py

@@ -0,0 +1,199 @@
+"""
+compat
+======
+
+Cross-compatible functions for different versions of Python.
+
+Other items:
+* platform checker
+"""
+from __future__ import annotations
+
+import os
+import platform
+import sys
+from typing import TYPE_CHECKING
+
+from pandas.compat._constants import (
+    IS64,
+    ISMUSL,
+    PY310,
+    PY311,
+    PY312,
+    PYPY,
+)
+import pandas.compat.compressors
+from pandas.compat.numpy import is_numpy_dev
+from pandas.compat.pyarrow import (
+    pa_version_under10p1,
+    pa_version_under11p0,
+    pa_version_under13p0,
+    pa_version_under14p0,
+    pa_version_under14p1,
+    pa_version_under16p0,
+    pa_version_under17p0,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import F
+
+
+def set_function_name(f: F, name: str, cls: type) -> F:
+    """
+    Bind the name/qualname attributes of the function.
+    """
+    f.__name__ = name
+    f.__qualname__ = f"{cls.__name__}.{name}"
+    f.__module__ = cls.__module__
+    return f
+
+
+def is_platform_little_endian() -> bool:
+    """
+    Checking if the running platform is little endian.
+
+    Returns
+    -------
+    bool
+        True if the running platform is little endian.
+    """
+    return sys.byteorder == "little"
+
+
+def is_platform_windows() -> bool:
+    """
+    Checking if the running platform is windows.
+
+    Returns
+    -------
+    bool
+        True if the running platform is windows.
+    """
+    return sys.platform in ["win32", "cygwin"]
+
+
+def is_platform_linux() -> bool:
+    """
+    Checking if the running platform is linux.
+
+    Returns
+    -------
+    bool
+        True if the running platform is linux.
+    """
+    return sys.platform == "linux"
+
+
+def is_platform_mac() -> bool:
+    """
+    Checking if the running platform is mac.
+
+    Returns
+    -------
+    bool
+        True if the running platform is mac.
+    """
+    return sys.platform == "darwin"
+
+
+def is_platform_arm() -> bool:
+    """
+    Checking if the running platform use ARM architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("arm64", "aarch64") or platform.machine().startswith(
+        "armv"
+    )
+
+
+def is_platform_power() -> bool:
+    """
+    Checking if the running platform use Power architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("ppc64", "ppc64le")
+
+
+def is_ci_environment() -> bool:
+    """
+    Checking if running in a continuous integration environment by checking
+    the PANDAS_CI environment variable.
+
+    Returns
+    -------
+    bool
+        True if the running in a continuous integration environment.
+    """
+    return os.environ.get("PANDAS_CI", "0") == "1"
+
+
+def get_lzma_file() -> type[pandas.compat.compressors.LZMAFile]:
+    """
+    Importing the `LZMAFile` class from the `lzma` module.
+
+    Returns
+    -------
+    class
+        The `LZMAFile` class from the `lzma` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `lzma` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_lzma:
+        raise RuntimeError(
+            "lzma module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.LZMAFile
+
+
+def get_bz2_file() -> type[pandas.compat.compressors.BZ2File]:
+    """
+    Importing the `BZ2File` class from the `bz2` module.
+
+    Returns
+    -------
+    class
+        The `BZ2File` class from the `bz2` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `bz2` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_bz2:
+        raise RuntimeError(
+            "bz2 module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.BZ2File
+
+
+__all__ = [
+    "is_numpy_dev",
+    "pa_version_under10p1",
+    "pa_version_under11p0",
+    "pa_version_under13p0",
+    "pa_version_under14p0",
+    "pa_version_under14p1",
+    "pa_version_under16p0",
+    "pa_version_under17p0",
+    "IS64",
+    "ISMUSL",
+    "PY310",
+    "PY311",
+    "PY312",
+    "PYPY",
+]

falcon/lib/python3.10/site-packages/pandas/compat/_constants.py

@@ -0,0 +1,30 @@
+"""
+_constants
+======
+
+Constants relevant for the Python implementation.
+"""
+
+from __future__ import annotations
+
+import platform
+import sys
+import sysconfig
+
+IS64 = sys.maxsize > 2**32
+
+PY310 = sys.version_info >= (3, 10)
+PY311 = sys.version_info >= (3, 11)
+PY312 = sys.version_info >= (3, 12)
+PYPY = platform.python_implementation() == "PyPy"
+ISMUSL = "musl" in (sysconfig.get_config_var("HOST_GNU_TYPE") or "")
+REF_COUNT = 2 if PY311 else 3
+
+__all__ = [
+    "IS64",
+    "ISMUSL",
+    "PY310",
+    "PY311",
+    "PY312",
+    "PYPY",
+]

falcon/lib/python3.10/site-packages/pandas/compat/_optional.py

@@ -0,0 +1,168 @@
+from __future__ import annotations
+
+import importlib
+import sys
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas.util._exceptions import find_stack_level
+
+from pandas.util.version import Version
+
+if TYPE_CHECKING:
+    import types
+
+# Update install.rst & setup.cfg when updating versions!
+
+VERSIONS = {
+    "adbc-driver-postgresql": "0.8.0",
+    "adbc-driver-sqlite": "0.8.0",
+    "bs4": "4.11.2",
+    "blosc": "1.21.3",
+    "bottleneck": "1.3.6",
+    "dataframe-api-compat": "0.1.7",
+    "fastparquet": "2022.12.0",
+    "fsspec": "2022.11.0",
+    "html5lib": "1.1",
+    "hypothesis": "6.46.1",
+    "gcsfs": "2022.11.0",
+    "jinja2": "3.1.2",
+    "lxml.etree": "4.9.2",
+    "matplotlib": "3.6.3",
+    "numba": "0.56.4",
+    "numexpr": "2.8.4",
+    "odfpy": "1.4.1",
+    "openpyxl": "3.1.0",
+    "pandas_gbq": "0.19.0",
+    "psycopg2": "2.9.6",  # (dt dec pq3 ext lo64)
+    "pymysql": "1.0.2",
+    "pyarrow": "10.0.1",
+    "pyreadstat": "1.2.0",
+    "pytest": "7.3.2",
+    "python-calamine": "0.1.7",
+    "pyxlsb": "1.0.10",
+    "s3fs": "2022.11.0",
+    "scipy": "1.10.0",
+    "sqlalchemy": "2.0.0",
+    "tables": "3.8.0",
+    "tabulate": "0.9.0",
+    "xarray": "2022.12.0",
+    "xlrd": "2.0.1",
+    "xlsxwriter": "3.0.5",
+    "zstandard": "0.19.0",
+    "tzdata": "2022.7",
+    "qtpy": "2.3.0",
+    "pyqt5": "5.15.9",
+}
+
+# A mapping from import name to package name (on PyPI) for packages where
+# these two names are different.
+
+INSTALL_MAPPING = {
+    "bs4": "beautifulsoup4",
+    "bottleneck": "Bottleneck",
+    "jinja2": "Jinja2",
+    "lxml.etree": "lxml",
+    "odf": "odfpy",
+    "pandas_gbq": "pandas-gbq",
+    "python_calamine": "python-calamine",
+    "sqlalchemy": "SQLAlchemy",
+    "tables": "pytables",
+}
+
+
+def get_version(module: types.ModuleType) -> str:
+    version = getattr(module, "__version__", None)
+
+    if version is None:
+        raise ImportError(f"Can't determine version for {module.__name__}")
+    if module.__name__ == "psycopg2":
+        # psycopg2 appends " (dt dec pq3 ext lo64)" to it's version
+        version = version.split()[0]
+    return version
+
+
+def import_optional_dependency(
+    name: str,
+    extra: str = "",
+    errors: str = "raise",
+    min_version: str | None = None,
+):
+    """
+    Import an optional dependency.
+
+    By default, if a dependency is missing an ImportError with a nice
+    message will be raised. If a dependency is present, but too old,
+    we raise.
+
+    Parameters
+    ----------
+    name : str
+        The module name.
+    extra : str
+        Additional text to include in the ImportError message.
+    errors : str {'raise', 'warn', 'ignore'}
+        What to do when a dependency is not found or its version is too old.
+
+        * raise : Raise an ImportError
+        * warn : Only applicable when a module's version is to old.
+          Warns that the version is too old and returns None
+        * ignore: If the module is not installed, return None, otherwise,
+          return the module, even if the version is too old.
+          It's expected that users validate the version locally when
+          using ``errors="ignore"`` (see. ``io/html.py``)
+    min_version : str, default None
+        Specify a minimum version that is different from the global pandas
+        minimum version required.
+    Returns
+    -------
+    maybe_module : Optional[ModuleType]
+        The imported module, when found and the version is correct.
+        None is returned when the package is not found and `errors`
+        is False, or when the package's version is too old and `errors`
+        is ``'warn'`` or ``'ignore'``.
+    """
+    assert errors in {"warn", "raise", "ignore"}
+
+    package_name = INSTALL_MAPPING.get(name)
+    install_name = package_name if package_name is not None else name
+
+    msg = (
+        f"Missing optional dependency '{install_name}'. {extra} "
+        f"Use pip or conda to install {install_name}."
+    )
+    try:
+        module = importlib.import_module(name)
+    except ImportError:
+        if errors == "raise":
+            raise ImportError(msg)
+        return None
+
+    # Handle submodules: if we have submodule, grab parent module from sys.modules
+    parent = name.split(".")[0]
+    if parent != name:
+        install_name = parent
+        module_to_get = sys.modules[install_name]
+    else:
+        module_to_get = module
+    minimum_version = min_version if min_version is not None else VERSIONS.get(parent)
+    if minimum_version:
+        version = get_version(module_to_get)
+        if version and Version(version) < Version(minimum_version):
+            msg = (
+                f"Pandas requires version '{minimum_version}' or newer of '{parent}' "
+                f"(version '{version}' currently installed)."
+            )
+            if errors == "warn":
+                warnings.warn(
+                    msg,
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return None
+            elif errors == "raise":
+                raise ImportError(msg)
+            else:
+                return None
+
+    return module

falcon/lib/python3.10/site-packages/pandas/compat/compressors.py

@@ -0,0 +1,77 @@
+"""
+Patched ``BZ2File`` and ``LZMAFile`` to handle pickle protocol 5.
+"""
+
+from __future__ import annotations
+
+from pickle import PickleBuffer
+
+from pandas.compat._constants import PY310
+
+try:
+    import bz2
+
+    has_bz2 = True
+except ImportError:
+    has_bz2 = False
+
+try:
+    import lzma
+
+    has_lzma = True
+except ImportError:
+    has_lzma = False
+
+
+def flatten_buffer(
+    b: bytes | bytearray | memoryview | PickleBuffer,
+) -> bytes | bytearray | memoryview:
+    """
+    Return some 1-D `uint8` typed buffer.
+
+    Coerces anything that does not match that description to one that does
+    without copying if possible (otherwise will copy).
+    """
+
+    if isinstance(b, (bytes, bytearray)):
+        return b
+
+    if not isinstance(b, PickleBuffer):
+        b = PickleBuffer(b)
+
+    try:
+        # coerce to 1-D `uint8` C-contiguous `memoryview` zero-copy
+        return b.raw()
+    except BufferError:
+        # perform in-memory copy if buffer is not contiguous
+        return memoryview(b).tobytes("A")
+
+
+if has_bz2:
+
+    class BZ2File(bz2.BZ2File):
+        if not PY310:
+
+            def write(self, b) -> int:
+                # Workaround issue where `bz2.BZ2File` expects `len`
+                # to return the number of bytes in `b` by converting
+                # `b` into something that meets that constraint with
+                # minimal copying.
+                #
+                # Note: This is fixed in Python 3.10.
+                return super().write(flatten_buffer(b))
+
+
+if has_lzma:
+
+    class LZMAFile(lzma.LZMAFile):
+        if not PY310:
+
+            def write(self, b) -> int:
+                # Workaround issue where `lzma.LZMAFile` expects `len`
+                # to return the number of bytes in `b` by converting
+                # `b` into something that meets that constraint with
+                # minimal copying.
+                #
+                # Note: This is fixed in Python 3.10.
+                return super().write(flatten_buffer(b))

falcon/lib/python3.10/site-packages/pandas/compat/numpy/__init__.py

@@ -0,0 +1,53 @@
+""" support numpy compatibility across versions """
+import warnings
+
+import numpy as np
+
+from pandas.util.version import Version
+
+# numpy versioning
+_np_version = np.__version__
+_nlv = Version(_np_version)
+np_version_lt1p23 = _nlv < Version("1.23")
+np_version_gte1p24 = _nlv >= Version("1.24")
+np_version_gte1p24p3 = _nlv >= Version("1.24.3")
+np_version_gte1p25 = _nlv >= Version("1.25")
+np_version_gt2 = _nlv >= Version("2.0.0")
+is_numpy_dev = _nlv.dev is not None
+_min_numpy_ver = "1.22.4"
+
+
+if _nlv < Version(_min_numpy_ver):
+    raise ImportError(
+        f"this version of pandas is incompatible with numpy < {_min_numpy_ver}\n"
+        f"your numpy version is {_np_version}.\n"
+        f"Please upgrade numpy to >= {_min_numpy_ver} to use this pandas version"
+    )
+
+
+np_long: type
+np_ulong: type
+
+if np_version_gt2:
+    try:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                r".*In the future `np\.long` will be defined as.*",
+                FutureWarning,
+            )
+            np_long = np.long  # type: ignore[attr-defined]
+            np_ulong = np.ulong  # type: ignore[attr-defined]
+    except AttributeError:
+        np_long = np.int_
+        np_ulong = np.uint
+else:
+    np_long = np.int_
+    np_ulong = np.uint
+
+
+__all__ = [
+    "np",
+    "_np_version",
+    "is_numpy_dev",
+]