The Pandas library is under constant development and new features are added regularly. This means that code you may read about online may not work if you are running an older version of Pandas. Similarly, some of Pandas’ dependencies - the other Python packages it uses internally - get upgraded and have different functionality.
In this quick and easy tutorial, I’ll show you how you can use the
pd.__version__ dunder method and the
pd.show_versions() method to view the current version of Pandas installed in your data science environment, as well as the names and version numbers for all the Pandas dependencies currently in use.
To get started, open a Jupyter notebook and import the Pandas library using the
import pandas as pd naming convention. Once loaded, you can use the
pd.__version__ dunder method (short for double underscore method) to find the version of Pandas loaded in your environment. I’m running version 1.5.2.
import pandas as pd
pd.show_versions() method can be used to return some very useful information about your Python environment, including the architecture of your machine, the processor, language, operating system, and a full list of the Pandas package dependencies and their version numbers.
If you encounter a Pandas issue that needs to be raised with the Pandas development team, it’s a good idea to provide this with your bug report so they get a better understanding of the issue in order to attempt to replicate it.
INSTALLED VERSIONS ------------------ commit : 8dab54d6573f7186ff0c3b6364d5e4dd635ff3e7 python : 3.8.10.final.0 python-bits : 64 OS : Linux OS-release : 5.15.0-56-generic Version : #62~20.04.1-Ubuntu SMP Tue Nov 22 21:24:20 UTC 2022 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 1.5.2 numpy : 1.21.6 pytz : 2022.6 dateutil : 2.8.2 setuptools : 59.8.0 pip : 22.3.1 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.3 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : None jinja2 : 3.0.3 IPython : 7.20.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None brotli : 1.0.9 fastparquet : None fsspec : 2022.11.0 gcsfs : None matplotlib : 3.4.1 numba : 0.56.2 numexpr : None odfpy : None openpyxl : 3.0.10 pandas_gbq : None pyarrow : 10.0.0 pyreadstat : None pyxlsb : None s3fs : None scipy : 1.7.3 snappy : None sqlalchemy : 1.4.41 tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None tzdata : None
Matt Clarke, Thursday, January 05, 2023