import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: C:\\Users\\Afia Jahan\\anaconda3\\envs\\py312; pyv: 3.12.11 | packaged by Anaconda, Inc. | (main, Jun  5 2025, 12:58:53) [MSC v.1929 64 bit (AMD64)]'

print(pyu.ps2("pandas"))

Name: pandas
Version: 2.3.0
Summary: Powerful data structures for data analysis, time series, and statistics
Home-page: https://pandas.pydata.org
Author: 
Author-email: The Pandas Development Team <pandas-dev@python.org>
License: BSD 3-Clause License

 Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
 All rights reserved.

 Copyright (c) 2011-2023, Open source contributors.

 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
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Location: C:\Users\Afia Jahan\anaconda3\envs\py312\Lib\site-packages
Requires: numpy, python-dateutil, pytz, tzdata
Required-by: farm-haystack, seaborn, yfinance

from functools import reduce
product = reduce((lambda x, y: x * y), [1, 2, 3, 4])

product

24

import numpy as np

sizes = np.full((10), 7, dtype=int)
sizes

array([7, 7, 7, 7, 7, 7, 7, 7, 7, 7])