https://stackoverflow.com/questions/20180543/how-to-check-version-of-python-modules
pip3 freeze
asn1crypto==0.24.0 attrs==17.4.0 bcrypt==3.1.4 beautifulsoup4==4.6.0 bleach==2.1.3 ... pandas==0.22.0 pandocfilters==1.4.2 paramiko==2.4.0 patsy==0.5.0 pexpect==4.2.1 ... traitlets==4.3.2 tzlocal==1.5.1 urllib3==1.22 wcwidth==0.1.7 webencodings==0.5
pip3 show pandas echo " " pip3 show statsmodels
Name: pandas
Version: 0.22.0
Summary: Powerful data structures for data analysis, time series,and statistics
Home-page: http://pandas.pydata.org
Author: None
Author-email: None
License: BSD
Location: /usr/lib/python3/dist-packages
Requires:
Name: statsmodels
Version: 0.9.0
Summary: Statistical computations and models for Python
Home-page: http://www.statsmodels.org/
Author: None
Author-email: None
License: BSD License
Location: /home/alegrand/.local/lib/python3.6/site-packages
Requires: patsy, pandas
Inspiring from StackOverflow, here is a simple function that lists
loaded package (that have a __version__ attribute, which is
unfortunately not completely standard).
def print_imported_modules(): import sys for name,val in sorted(sys.modules.items()): if(hasattr(val, '__version__')): print(val.__name__, val.__version__) print("**** Package list in the beginning ****"); print_imported_modules() print("**** Package list after loading pandas ****"); import pandas print_imported_modules()
**** Package list in the beginning **** **** Package list after loading pandas **** _csv 1.0 _ctypes 1.1.0 decimal 1.70 argparse 1.1 csv 1.0 ctypes 1.1.0 cycler 0.10.0 dateutil 2.7.3 decimal 1.70 distutils 3.6.5rc1 ipaddress 1.0 json 2.0.9 logging 0.5.1.2 matplotlib 2.1.1 numpy 1.14.5 numpy.core 1.14.5 numpy.core.multiarray 3.1 numpy.core.umath b'0.4.0' numpy.lib 1.14.5 numpy.linalg._umath_linalg b'0.1.5' pandas 0.22.0 _libjson 1.33 platform 1.0.8 pyparsing 2.2.0 pytz 2018.5 re 2.2.1 six 1.11.0 urllib.request 3.6 zlib 1.0
The easiest way to go is as follows:
pip3 freeze > requirements.txt # to obtain the list of packages with their version pip3 install -r requirements.txt # to install the previous list of packages, possibly on an other machine
If you want to have several installed python environments, you may want to use Pipenv. I doubt it allows to track correctly FORTRAN or C dynamic libraries that are wrapped by Python.
The best way seems to be to rely on the devtools package.
sessionInfo() devtools::session_info()
R version 3.5.1 (2018-07-02) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux buster/sid Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.8.0 locale: [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8 [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8 [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base loaded via a namespace (and not attached): [1] compiler_3.5.1 Session info ------------------------------------------------------------------ setting value version R version 3.5.1 (2018-07-02) system x86_64, linux-gnu ui X11 language (EN) collate fr_FR.UTF-8 tz Europe/Paris date 2018-08-01 Packages ---------------------------------------------------------------------- package * version date source base * 3.5.1 2018-07-02 local compiler 3.5.1 2018-07-02 local datasets * 3.5.1 2018-07-02 local devtools 1.13.6 2018-06-27 CRAN (R 3.5.1) digest 0.6.15 2018-01-28 CRAN (R 3.5.0) graphics * 3.5.1 2018-07-02 local grDevices * 3.5.1 2018-07-02 local memoise 1.1.0 2017-04-21 CRAN (R 3.5.1) methods * 3.5.1 2018-07-02 local stats * 3.5.1 2018-07-02 local utils * 3.5.1 2018-07-02 local withr 2.1.2 2018-03-15 CRAN (R 3.5.0)
Some actually advocate that writing a reproducible research compendium can be done by writing an R package. Those of you willing to have a clean R dependency management should thus have a look at Packrat.