I'm trying to use pip to install a package. I try to run pip install from the Python shell, but I get a SyntaxError. Why do I get this error? How do I use pip to install the package?
>>> pip install selenium
^
SyntaxError: invalid syntax
pip is run from the command line, not the Python interpreter. It is a program that installs modules, so you can use them from Python. Once you have installed the module, then you can open the Python shell and do import selenium.
The Python shell is not a command line, it is an interactive interpreter. You type Python code into it, not commands.
Use the command line, not the Python shell (DOS, PowerShell in Windows).
C:\Program Files\Python2.7\Scripts> pip install XYZ
If you installed Python into your PATH using the latest installers, you don't need to be in that folder to run pip
Terminal in Mac or Linux
$ pip install XYZ
As #sinoroc suggested correct way of installing a package via pip is using separate process since pip may cause closing a thread or may require a restart of interpreter to load new installed package so this is the right way of using the API: subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'SomeProject']) but since Python allows to access internal API and you know what you're using the API for you may want to use internal API anyway eg. if you're building own GUI package manager with alternative resourcess like https://www.lfd.uci.edu/~gohlke/pythonlibs/
Following soulution is OUT OF DATE, instead of downvoting suggest updates. see https://github.com/pypa/pip/issues/7498 for reference.
UPDATE: Since pip version 10.x there is no more get_installed_distributions() or main method under import pip instead use import pip._internal as pip.
UPDATE ca. v.18 get_installed_distributions() has been removed. Instead you may use generator freeze like this:
from pip._internal.operations.freeze import freeze
print([package for package in freeze()])
# eg output ['pip==19.0.3']
If you want to use pip inside the Python interpreter, try this:
import pip
package_names=['selenium', 'requests'] #packages to install
pip.main(['install'] + package_names + ['--upgrade'])
# --upgrade to install or update existing packages
If you need to update every installed package, use following:
import pip
for i in pip.get_installed_distributions():
pip.main(['install', i.key, '--upgrade'])
If you want to stop installing other packages if any installation fails, use it in one single pip.main([]) call:
import pip
package_names = [i.key for i in pip.get_installed_distributions()]
pip.main(['install'] + package_names + ['--upgrade'])
Note: When you install from list in file with -r / --requirement parameter you do NOT need open() function.
pip.main(['install', '-r', 'filename'])
Warning: Some parameters as simple --help may cause python interpreter to stop.
Curiosity: By using pip.exe you actually use python interpreter and pip module anyway. If you unpack pip.exe or pip3.exe regardless it's python 2.x or 3.x, inside is the SAME single file __main__.py:
# -*- coding: utf-8 -*-
import re
import sys
from pip import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(main())
To run pip in Python 3.x, just follow the instructions on Python's page: Installing Python Modules.
python -m pip install SomePackage
Note that this is run from the command line and not the python shell (the reason for syntax error in the original question).
I installed python and when I run pip command it used to throw me an error like shown in pic below.
Make Sure pip path is added in environmental variables. For me, the python and pip installation path is::
Python: C:\Users\fhhz\AppData\Local\Programs\Python\Python38\
pip: C:\Users\fhhz\AppData\Local\Programs\Python\Python38\Scripts
Both these paths were added to path in environmental variables.
Now Open a new cmd window and type pip, you should be seeing a screen as below.
Now type pip install <<package-name>>. Here I'm installing package spyder so my command line statement will be as pip install spyder and here goes my running screen..
and I hope we are done with this!!
you need to type it in cmd not in the IDLE. becuse IDLE is not an command prompt if you want to install something from IDLE type this
>>>from pip.__main__ import _main as main
>>>main(#args splitted by space in list example:['install', 'requests'])
this is calling pip like pip <commands> in terminal. The commands will be seperated by spaces that you are doing there to.
If you are doing it from command line,
try -
python -m pip install selenium
or (for Python3 and above)
python3 -m pip install selenium
I'm trying to run the Pix2Pix tutorial for Tensorflow. I'm using the official docker container for this. This is how I start my container:
docker run --gpus all -it -p 8888:8888 --rm -v $PWD:/tf -w /tmp tensorflow/tensorflow:latest-gpu-py3-jupyter
I'm not able to get pass by this cell
generator = Generator()
tf.keras.utils.plot_model(generator, show_shapes=True, dpi=64)
# output -> Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.
I have tried also installing the pydot and graphviz using pip and also apt-get. Even if this libraries are installed I get the same error.
I had same problem and follow this link
In short:
run these command on command prompt.
pip install pydot
pip install graphviz
From website, download and install graphviz software
Note: in install time, check "add to system path" option to add bin
folder to path variable otherwise you should do it manually. restart
your windows
How can I run the code in this repository without following their steps, I want to run the main.py file, from the Demo, directly.
What are the setup.py and install.sh files used for?
setup.py is the canonical name for the installer of a Python package. For example, when you run pip install x, pip runs the setup.py of the package you installed. In this case, install.sh is just a shortcut for running setup.py.
There's no way to use a package without installing it, since it won't have what it needs to operate properly.
Right now, I have
!wget myPackage
!tar -xvjf myPackage
!cd myPackage && python setup.py install
All of which complete, but then
import myPackage
fails. That seems likely to be because the python which I call with !python setup.py is different than the python which executes each cell. How do I install a package using !python setup.py install such that I'm able to import the package?
I tried !which python which gave me /usr/local/bin/python but /usr/local/bin/python setup.py install had the same issue, which seems reasonable.
Also see: https://colab.research.google.com/notebooks/snippets/importing_libraries.ipynb
Restart your Python runtime after the install using the Runtime -> Restart runtime... menu.
Here's a worked example:
https://colab.research.google.com/drive/1Z1d5IGStWkqGXMk_txdxTngDXMkxcDOh
I've noticed that installing Pandas and Numpy (it's dependency) in a Docker container using the base OS Alpine vs. CentOS or Debian takes much longer. I created a little test below to demonstrate the time difference. Aside from the few seconds Alpine takes to update and download the build dependencies to install Pandas and Numpy, why does the setup.py take around 70x more time than on Debian install?
Is there any way to speed up the install using Alpine as the base image or is there another base image of comparable size to Alpine that is better to use for packages like Pandas and Numpy?
Dockerfile.debian
FROM python:3.6.4-slim-jessie
RUN pip install pandas
Build Debian image with Pandas & Numpy:
[PandasDockerTest] time docker build -t debian-pandas -f Dockerfile.debian . --no-cache
Sending build context to Docker daemon 3.072kB
Step 1/2 : FROM python:3.6.4-slim-jessie
---> 43431c5410f3
Step 2/2 : RUN pip install pandas
---> Running in 2e4c030f8051
Collecting pandas
Downloading pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl (26.2MB)
Collecting numpy>=1.9.0 (from pandas)
Downloading numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl (12.2MB)
Collecting pytz>=2011k (from pandas)
Downloading pytz-2018.3-py2.py3-none-any.whl (509kB)
Collecting python-dateutil>=2 (from pandas)
Downloading python_dateutil-2.6.1-py2.py3-none-any.whl (194kB)
Collecting six>=1.5 (from python-dateutil>=2->pandas)
Downloading six-1.11.0-py2.py3-none-any.whl
Installing collected packages: numpy, pytz, six, python-dateutil, pandas
Successfully installed numpy-1.14.1 pandas-0.22.0 python-dateutil-2.6.1 pytz-2018.3 six-1.11.0
Removing intermediate container 2e4c030f8051
---> a71e1c314897
Successfully built a71e1c314897
Successfully tagged debian-pandas:latest
docker build -t debian-pandas -f Dockerfile.debian . --no-cache 0.07s user 0.06s system 0% cpu 13.605 total
Dockerfile.alpine
FROM python:3.6.4-alpine3.7
RUN apk --update add --no-cache g++
RUN pip install pandas
Build Alpine image with Pandas & Numpy:
[PandasDockerTest] time docker build -t alpine-pandas -f Dockerfile.alpine . --no-cache
Sending build context to Docker daemon 16.9kB
Step 1/3 : FROM python:3.6.4-alpine3.7
---> 4b00a94b6f26
Step 2/3 : RUN apk --update add --no-cache g++
---> Running in 4b0c32551e3f
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/main/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/main/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/community/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/community/x86_64/APKINDEX.tar.gz
(1/17) Upgrading musl (1.1.18-r2 -> 1.1.18-r3)
(2/17) Installing libgcc (6.4.0-r5)
(3/17) Installing libstdc++ (6.4.0-r5)
(4/17) Installing binutils-libs (2.28-r3)
(5/17) Installing binutils (2.28-r3)
(6/17) Installing gmp (6.1.2-r1)
(7/17) Installing isl (0.18-r0)
(8/17) Installing libgomp (6.4.0-r5)
(9/17) Installing libatomic (6.4.0-r5)
(10/17) Installing pkgconf (1.3.10-r0)
(11/17) Installing mpfr3 (3.1.5-r1)
(12/17) Installing mpc1 (1.0.3-r1)
(13/17) Installing gcc (6.4.0-r5)
(14/17) Installing musl-dev (1.1.18-r3)
(15/17) Installing libc-dev (0.7.1-r0)
(16/17) Installing g++ (6.4.0-r5)
(17/17) Upgrading musl-utils (1.1.18-r2 -> 1.1.18-r3)
Executing busybox-1.27.2-r7.trigger
OK: 184 MiB in 50 packages
Removing intermediate container 4b0c32551e3f
---> be26c3bf4e42
Step 3/3 : RUN pip install pandas
---> Running in 36f6024e5e2d
Collecting pandas
Downloading pandas-0.22.0.tar.gz (11.3MB)
Collecting python-dateutil>=2 (from pandas)
Downloading python_dateutil-2.6.1-py2.py3-none-any.whl (194kB)
Collecting pytz>=2011k (from pandas)
Downloading pytz-2018.3-py2.py3-none-any.whl (509kB)
Collecting numpy>=1.9.0 (from pandas)
Downloading numpy-1.14.1.zip (4.9MB)
Collecting six>=1.5 (from python-dateutil>=2->pandas)
Downloading six-1.11.0-py2.py3-none-any.whl
Building wheels for collected packages: pandas, numpy
Running setup.py bdist_wheel for pandas: started
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: still running...
Running setup.py bdist_wheel for pandas: finished with status 'done'
Stored in directory: /root/.cache/pip/wheels/e8/ed/46/0596b51014f3cc49259e52dff9824e1c6fe352048a2656fc92
Running setup.py bdist_wheel for numpy: started
Running setup.py bdist_wheel for numpy: still running...
Running setup.py bdist_wheel for numpy: still running...
Running setup.py bdist_wheel for numpy: still running...
Running setup.py bdist_wheel for numpy: finished with status 'done'
Stored in directory: /root/.cache/pip/wheels/9d/cd/e1/4d418b16ea662e512349ef193ed9d9ff473af715110798c984
Successfully built pandas numpy
Installing collected packages: six, python-dateutil, pytz, numpy, pandas
Successfully installed numpy-1.14.1 pandas-0.22.0 python-dateutil-2.6.1 pytz-2018.3 six-1.11.0
Removing intermediate container 36f6024e5e2d
---> a93c59e6a106
Successfully built a93c59e6a106
Successfully tagged alpine-pandas:latest
docker build -t alpine-pandas -f Dockerfile.alpine . --no-cache 0.54s user 0.33s system 0% cpu 16:08.47 total
Debian based images use only python pip to install packages with .whl format:
Downloading pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl (26.2MB)
Downloading numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl (12.2MB)
WHL format was developed as a quicker and more reliable method of installing Python software than re-building from source code every time. WHL files only have to be moved to the correct location on the target system to be installed, whereas a source distribution requires a build step before installation.
Wheel packages pandas and numpy are not supported in images based on Alpine platform. That's why when we install them using python pip during the building process, we always compile them from the source files in alpine:
Downloading pandas-0.22.0.tar.gz (11.3MB)
Downloading numpy-1.14.1.zip (4.9MB)
and we can see the following inside container during the image building:
/ # ps aux
PID USER TIME COMMAND
1 root 0:00 /bin/sh -c pip install pandas
7 root 0:04 {pip} /usr/local/bin/python /usr/local/bin/pip install pandas
21 root 0:07 /usr/local/bin/python -c import setuptools, tokenize;__file__='/tmp/pip-build-en29h0ak/pandas/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n
496 root 0:00 sh
660 root 0:00 /bin/sh -c gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -DTHREAD_STACK_SIZE=0x100000 -fPIC -Ibuild/src.linux-x86_64-3.6/numpy/core/src/pri
661 root 0:00 gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -DTHREAD_STACK_SIZE=0x100000 -fPIC -Ibuild/src.linux-x86_64-3.6/numpy/core/src/private -Inump
662 root 0:00 /usr/libexec/gcc/x86_64-alpine-linux-musl/6.4.0/cc1 -quiet -I build/src.linux-x86_64-3.6/numpy/core/src/private -I numpy/core/include -I build/src.linux-x86_64-3.6/numpy/core/includ
663 root 0:00 ps aux
If we modify Dockerfile a little:
FROM python:3.6.4-alpine3.7
RUN apk add --no-cache g++ wget
RUN wget https://pypi.python.org/packages/da/c6/0936bc5814b429fddb5d6252566fe73a3e40372e6ceaf87de3dec1326f28/pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl
RUN pip install pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl
we get the following error:
Step 4/4 : RUN pip install pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl
---> Running in 0faea63e2bda
pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl is not a supported wheel on this platform.
The command '/bin/sh -c pip install pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl' returned a non-zero code: 1
Unfortunately, the only way to install pandas on an Alpine image is to wait until build finishes.
Of course if you want to use the Alpine image with pandas in CI for example, the best way to do so is to compile it once, push it to any registry and use it as a base image for your needs.
EDIT:
If you want to use the Alpine image with pandas you can pull my nickgryg/alpine-pandas docker image. It is a python image with pre-compiled pandas on the Alpine platform. It should save your time.
ANSWER: AS OF 3/9/2020, FOR PYTHON 3, IT STILL DOESN'T!
Here is a complete working Dockerfile:
FROM python:3.7-alpine
RUN echo "#testing http://dl-cdn.alpinelinux.org/alpine/edge/testing" >> /etc/apk/repositories
RUN apk add --update --no-cache py3-numpy py3-pandas#testing
The build is very sensitive to the exact python and alpine version numbers - getting these wrong seems to provoke Max Levy's error so:libpython3.7m.so.1.0 (missing) - but the above does now work for me.
My updated Dockerfile is available at https://gist.github.com/jtlz2/b0f4bc07ce2ff04bc193337f2327c13b
[Earlier Update:]
ANSWER: IT DOESN'T!
In any Alpine Dockerfile you can simply do*
RUN apk add py2-numpy#community py2-scipy#community py-pandas#edge
This is because numpy, scipy and now pandas are all available prebuilt on alpine:
https://pkgs.alpinelinux.org/packages?name=*numpy
https://pkgs.alpinelinux.org/packages?name=*scipy&branch=edge
https://pkgs.alpinelinux.org/packages?name=*pandas&branch=edge
One way to avoid rebuilding every time, or using a Docker layer, is to use a prebuilt, native Alpine Linux/.apk package, e.g.
https://github.com/sgerrand/alpine-pkg-py-pandas
https://github.com/nbgallery/apks
You can build these .apks once and use them wherever in your Dockerfile you like :)
This also saves you having to bake everything else into the Docker image before the fact - i.e. the flexibility to pre-build any Docker image you like.
PS I have put a Dockerfile stub at https://gist.github.com/jtlz2/b0f4bc07ce2ff04bc193337f2327c13b that shows roughly how to build the image. These include the important steps (*):
RUN echo "#community http://dl-cdn.alpinelinux.org/alpine/edge/community" >> /etc/apk/repositories
RUN apk update
RUN apk add --update --no-cache libgfortran
Real honest advice here, switch to Debian based image and then all your problems will be gone.
Alpine for python applications doesn't work well.
Here is an example of my dockerfile:
FROM python:3.7.6-buster
RUN pip install pandas==1.0.0
RUN pip install sklearn
RUN pip install Django==3.0.2
RUN pip install cx_Oracle==7.3.0
RUN pip install excel
RUN pip install djangorestframework==3.11.0
The python:3.7.6-buster is more appropriate in this case, in addition, you don't need any extra dependency in the OS.
Follow a usefull and recent article: https://pythonspeed.com/articles/alpine-docker-python/:
Don’t use Alpine Linux for Python images
Unless you want massively slower build times, larger images, more work, and the potential for obscure bugs, you’ll want to avoid Alpine Linux as a base image. For some recommendations on what you should use, see my article on choosing a good base image.
Just going to bring some of these answers together in one answer and add a detail I think was missed. The reason certain python libraries, particularly optimized math and data libraries, take so long to build on alpine is because the pip wheels for these libraries include binaries precompiled from c/c++ and linked against gnu-libc (glibc), a common set of c standard libraries. Debian, Fedora, CentOS all (typically) use glibc, but alpine, in order to stay lightweight, uses musl-libc instead. c/c++ binaries build on a glibc system will not work on a system without glibc and the same goes for musl.
Pip looks first for a wheel with the correct binaries, if it can't find one, it tries to compile the binaries from the c/c++ source and links them against musl. In many cases, this won't even work unless you have the python headers from python3-dev or build tools like make.
Now the silver lining, as others have mentioned, there are apk packages with the proper binaries provided by the community, using these will save you the (sometimes lengthy) process of building the binaries.
You can, in fact, install from a pure python .whl on alpine, but, at the time of this writing, manylinux did not support binary distributions for alpine due to the musl/gnu issue.
Update Oct 2022
Newer versions of python/pip support musl via the package musllinux which, I assume, is a musl impl for manylinux. Still no official 'musl' support for CUDA though.
ATTENTION
Look at the #jtlz2 answer with the latest update
OUTDATED
So, py3-pandas & py3-numpy packages moved to the testing alpine repository, so, you can download it by adding these lines in to the your Dockerfile:
RUN echo "http://dl-8.alpinelinux.org/alpine/edge/testing" >> /etc/apk/repositories \
&& apk update \
&& apk add py3-numpy py3-pandas
Hope it helps someone!
Alpine packages links:
- py3-pandas
- py3-numpy
Alpine repositories docks info.
In this case the alpine not be the best solution change alpine for slim:
FROM python:3.8.3-alpine
Change to that:
FROM python:3.8.3-slim
In my case it was resolved with this small change.
This worked for me:
FROM python:3.8-alpine
RUN echo "#testing http://dl-cdn.alpinelinux.org/alpine/edge/testing" >> /etc/apk/repositories
RUN apk add --update --no-cache py3-numpy py3-pandas#testing
ENV PYTHONPATH=/usr/lib/python3.8/site-packages
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
EXPOSE 5003
ENTRYPOINT [ "python" ]
CMD [ "app.py" ]
Most of the code here is from the answer of jtlz2 from this same thread and Faylixe from another thread.
Turns out the lighter version of pandas is found in the Alpine repository py3-numpy but it doesn't get installed in the same file path from where Python reads the imports by default. Therefore you need to add the ENV. Also be mindful about the alpine version.
I have solved the installation with some additional changes:
Requirements
Migrate from python3.8-alpine to python3.10-alpine:
docker pull python:3.10-alpine
Important!
I had to migrate because when I was installing py3-pandas, it installed the package as python3.10, not in the required version
that I was using python3.8).
To figure out where the libraries of a package were installed, you can check that with the following command:
apk info -L py3-pandas
Not install backports.zoneinfo package since python3.9 (I had to add a condition in the requirements.txt to install the package with versions lower than 3.9).
backports.zoneinfo==0.2.1;python_version<"3.9"
Installation
After the previous changes, I proceed to install panda performing the following:
Add 3 additional repositories to /etc/apk/repositories (the repositories can vary based on the version of your distribution), reference here:
for x in $(echo "main community testing"); \
do echo "https://dl-cdn.alpinelinux.org/alpine/edge/${x}" >> /etc/apk/repositories; \
done
Validate the content of the file /etc/apk/repositories:
$ cat /etc/apk/repositories
https://dl-cdn.alpinelinux.org/alpine/v3.16/main
https://dl-cdn.alpinelinux.org/alpine/v3.16/community
https://dl-cdn.alpinelinux.org/alpine/edge/main
https://dl-cdn.alpinelinux.org/alpine/edge/community
https://dl-cdn.alpinelinux.org/alpine/edge/testing
Perform to install pandas (pynum is installed automatically as a dependency of pandas):
sudo apk update && sudo apk add py3-pandas
Set the environment variable PYTHONPATH:
export PYTHONPATH=/usr/lib/python3.10/site-packages/
Validate the packages can be imported (on my case I tested it with django):
python manage.py shell
import pandas as pd
import numpy as np
technologies = ['Spark','Pandas','Java','Python', 'PHP']
fee = [25000,20000,15000,15000,18000]
duration = ['5o Days','35 Days',np.nan,'30 Days', '30 Days']
discount = [2000,1000,800,500,800]
columns=['Courses','Fee','Duration','Discount']
df = pd.DataFrame(list(zip(technologies,fee,duration,discount)), columns=columns)
print(df)
pandas is considered a community supported package, so the answers pointing to edge/testing are not going to work as Alpine does not officially support pandas as a core package (it still works, it's just not supported by the core Alpine developers).
Try this Dockerfile:
FROM python:3.8-alpine
RUN echo "#community http://dl-cdn.alpinelinux.org/alpine/edge/community" >> /etc/apk/repositories \
&& apk add py3-pandas#community
ENV PYTHONPATH="/usr/lib/python3.8/site-packages"
This works for the vanilla Alpine image too, using FROM alpine:3.12.
Update: thanks to #cegprakash for raising the question about how to work with this setup when you also have a requirements.txt file that must be satisfied inside the container.
I added one line to the Dockerfile snippet to export the PYTHONPATH variable into the container runtime. If you do this, it won't matter whether pandas or numpy are included in the requirements file or not (provided they are pegged to the same version that was installed via apk).
The reason this is needed is that apk installs the py3-pands#community package under /usr/lib, but that location is not on the default PYTHONPATH that pip checks before installing new packages. If we don't include this step to add it, pip and python will not find the package and pip will try to download and install it under /usr/local which is what we're trying to avoid.
And given that we really want to make sure that pip doesn't try to install pandas, I would suggest to not include pandas or numpy in the requirements.txt file if you've already installed them with apk using the above method. It's just a little extra insurance that things will go as intended.
The following Dockerfile worked for me to install pandas, among other dependencies as listed below.
python:3.10-alpine Dockerfile
# syntax=docker/dockerfile:1
FROM python:3.10-alpine as base
RUN apk add --update --no-cache --virtual .tmp-build-deps \
gcc g++ libc-dev linux-headers postgresql-dev build-base \
&& apk add libffi-dev
COPY requirements.txt requirements.txt
RUN pip install --no-cache-dir --upgrade -r requirements.txt
pyproject.toml dependencies
python = "^3.10"
Django = "^3.2.9"
djangorestframework = "^3.12.4"
PyYAML = ">=5.3.0,<6.0.0"
Markdown = "^3.3.6"
uritemplate = "^4.1.1"
install = "^1.3.5"
drf-spectacular = "^0.21.0"
django-extensions = "^3.1.5"
django-filter = "^21.1"
django-cors-headers = "^3.10.1"
httpx = "^0.22.0"
channels = "^3.0.4"
daphne = "^3.0.2"
whitenoise = "^6.2.0"
djoser = "^2.1.0"
channels-redis = "^3.4.0"
pika = "^1.2.1"
backoff = "^2.1.2"
psycopg2-binary = "^2.9.3"
pandas = "^1.5.0"
alpine takes lot of time to install pandas and the image size is also huge. I tried the python:3.8-slim-buster version of python base image. Image build was very fast and size of image was less than half in comparison to alpine python docker image
https://github.com/dguyhasnoname/k8s-cluster-checker/blob/master/Dockerfile