Packages
Packages contain code, data, and documentation in a standardized collection format that you can install to perform specific tasks or operations within your code.
Python Packages
Python is a general-purpose programming language. Install packages to perform specific tasks or operations within your code. For example, the Python package NumPy, or numpy in commands, can be used to perform operations on arrays. There are multiple methods you can use to install packages in Jupyter Notebooks.
How to Install Packages
Note
After installing packages in a notebook or console, restart the kernel to ensure the changes take effect. To restart the kernel, click on the Kernel menu and select Restart Kernel….
pip
Install packages directly within a Jupyter Notebook by running the pip install command in a code cell. To install the package numpy:
pip install --user numpy
conda
If you are using the Anaconda distribution of Python, you can use conda install to install packages. Conda is a package manager that simplifies the installation and management of packages and environments. To install the package numpy:
conda install numpy
Magic Commands
Magic commands are built-in Jupyter/IPython commands, you can learn more about magic commands in the IPython documentation. Use the %pip and %conda magic commands in a code cell to install packages. To install the package numpy with %pip or %conda:
%pip install --user numpy
%conda install numpy
Requirements File
If you have a requirements.txt file that lists all the packages you want to install, you can use the %pip magic command with the -r flag to install all the packages at once.
%pip install -r requirements.txt --user
Terminal
JupyterLab provides a terminal within the interface. Open a terminal in the File menu by selecting New and then Terminal. From the terminal, you can use pip install or conda install commands to install packages just like you would in a regular command prompt or terminal.
How to Install Custom Packages
Note
Before installing a custom package, ensure that you have access to the package source. Packages can be obtained from various sources such as Python Package Index (PyPI), GitHub repositories, or local files.
Ensure that any custom packages you install are compatible with your Python version and dependencies to avoid any conflicts or compatibility issues.
Follow these steps to install custom packages in Jupyter Notebooks:
Install a custom package using one of the three methods below:
pip: The most common method for installing custom packages is using pip, the package installer for Python. In a Jupyter Notebook code cell, you can run the following command to install a custom package, such as
numpy, from PyPI:pip install --user numpy
GitHub: If the package is hosted on a GitHub repository, you can use pip to install it directly from the repository. In the code cell, run the following command, replacing
https://github.com/author/package.git@branchwith the actual GitHub repository URL:pip install --user git+https://github.com/author/package.git@branch
local files: If you have the package stored locally, you can install it using pip by specifying the path to the package file. In the code cell, run the following command, replacing
/path/to/package.whlwith the actual path to the package file:pip install --user /path/to/package.whl
Click on the Kernel menu and select Restart Kernel…, to ensure the changes take effect.
Import the package into your Jupyter Notebook code cells using the standard Python import statement. The below example imports
numpy:import numpy as np
List of Installed Packages
To view the list of installed packages, run the pip list command in a notebook, console, or terminal.
pip list Example (click to expand/collapse)
lhelms2@jupyter-lhelms2:~$ pip list
Package Version
----------------------------- ------------
alembic 1.12.0
anyio 4.0.0
argon2-cffi 23.1.0
argon2-cffi-bindings 21.2.0
arrow 1.3.0
asttokens 2.4.0
async-generator 1.10
async-lru 2.0.4
attrs 23.1.0
Babel 2.12.1
backcall 0.2.0
backports.functools-lru-cache 1.6.5
beautifulsoup4 4.12.2
bleach 6.0.0
blinker 1.6.2
boltons 23.0.0
Brotli 1.1.0
cached-property 1.5.2
certifi 2023.7.22
certipy 0.1.3
cffi 1.16.0
charset-normalizer 3.2.0
colorama 0.4.6
comm 0.1.4
conda 23.7.4
conda-package-handling 2.2.0
conda_package_streaming 0.9.0
cryptography 41.0.4
debugpy 1.8.0
decorator 5.1.1
defusedxml 0.7.1
entrypoints 0.4
exceptiongroup 1.1.3
executing 1.2.0
fastjsonschema 2.18.1
fqdn 1.5.1
greenlet 2.0.2
idna 3.4
importlib-metadata 6.8.0
importlib-resources 6.1.0
ipykernel 6.25.2
ipython 8.16.0
ipython-genutils 0.2.0
isoduration 20.11.0
jedi 0.19.0
Jinja2 3.1.2
json5 0.9.14
jsonpatch 1.33
jsonpointer 2.4
jsonschema 4.19.1
jsonschema-specifications 2023.7.1
jupyter_client 8.3.1
jupyter_core 5.3.2
jupyter-events 0.7.0
jupyter-lsp 2.2.0
jupyter_server 2.7.3
jupyter_server_terminals 0.4.4
jupyter-telemetry 0.1.0
jupyterhub 4.0.2
jupyterlab 4.0.6
jupyterlab-pygments 0.2.2
jupyterlab_server 2.25.0
libmambapy 1.5.1
Mako 1.2.4
mamba 1.5.1
MarkupSafe 2.1.3
matplotlib-inline 0.1.6
mistune 3.0.1
nbclassic 1.0.0
nbclient 0.8.0
nbconvert 7.8.0
nbformat 5.9.2
nest-asyncio 1.5.6
notebook 7.0.4
notebook_shim 0.2.3
oauthlib 3.2.2
overrides 7.4.0
packaging 23.2
pamela 1.1.0
pandocfilters 1.5.0
parso 0.8.3
pexpect 4.8.0
pickleshare 0.7.5
pip 23.2.1
pkgutil_resolve_name 1.3.10
platformdirs 3.10.0
pluggy 1.3.0
prometheus-client 0.17.1
prompt-toolkit 3.0.39
psutil 5.9.5
ptyprocess 0.7.0
pure-eval 0.2.2
pycosat 0.6.4
pycparser 2.21
pycurl 7.45.1
Pygments 2.16.1
PyJWT 2.8.0
pyOpenSSL 23.2.0
PySocks 1.7.1
python-dateutil 2.8.2
python-json-logger 2.0.7
pytz 2023.3.post1
PyYAML 6.0.1
pyzmq 25.1.1
referencing 0.30.2
requests 2.31.0
rfc3339-validator 0.1.4
rfc3986-validator 0.1.1
rpds-py 0.10.3
ruamel.yaml 0.17.33
ruamel.yaml.clib 0.2.7
Send2Trash 1.8.2
setuptools 68.2.2
six 1.16.0
sniffio 1.3.0
soupsieve 2.5
SQLAlchemy 2.0.21
stack-data 0.6.2
terminado 0.17.1
tinycss2 1.2.1
tomli 2.0.1
toolz 0.12.0
tornado 6.3.3
tqdm 4.66.1
traitlets 5.10.1
types-python-dateutil 2.8.19.14
typing_extensions 4.8.0
typing-utils 0.1.0
uri-template 1.3.0
urllib3 2.0.5
wcwidth 0.2.7
webcolors 1.13
webencodings 0.5.1
websocket-client 1.6.3
wheel 0.41.2
zipp 3.17.0
zstandard 0.21.0
R Packages
R is a programming language and software environment for statistical computing and graphics. R and its libraries implement a wide variety of statistical and graphical techniques, such as linear and non-linear modeling, classical statistical tests, time-series analysis, classification, and clustering.
R is easily extensible through functions and extensions. The R community is noted for its active contributions to developing R packages. R packages contain code, data, and documentation in a standardized collection format that R users can install. R and R packages are available via the Comprehensive R Archive Network (CRAN), a collection of sites that carry the R distribution(s), the contributed extensions, documentation for R, and binaries.
Available Packages
A list of available packages in CRAN can be found in the CRAN package repository. From the CRAN package repository, you can click on a package and learn more about it and access its documentation.
You can also view a list of available packages from your JupyterLab environment. To view the list of available packages in CRAN, run the available.packages() command in a notebook or console. If you want to view a subset of the list, you can append [first-row-number:last-row-number,] to the command. For example, to view the first 100 available packages, the command is:
available.packages()[1:100,]
How to Install Packages from CRAN
To install an available package from CRAN, use the install.packages('PACKAGE_NAME') command, replacing PACKAGE_NAME with the name of the package you want to install. For example, to install the package abc, the command is:
install.packages('abc')
How to Install Packages from GitHub
To install a package from GitHub that is not available in CRAN, you can use the CRAN githubinstall package. See the githubinstall reference manual for more details on how to use this package.
Installed Packages
To check if a single package is installed, use the below code, replacing PACKAGE_NAME with the name of the package you want to check. If the package is installed, the code will return TRUE; if it is not installed, it will return FALSE.
a<-installed.packages() packages<-a[,1] is.element('PACKAGE_NAME', packages)Reference: UCLA OARC Stats
To view a list of installed packages, run the installed.packages() command in a notebook or console. If you want to view a subset of the list, you can append [first-row-number:last-row-number,], to the command. For example, to view the first 100 installed packages, the command is:
installed.packages()[1:100,]