I installed pyswarm package using pip (pip install pyswarm).
The problem is that this upgrades my numpy to version 1.14.xx which is something that I really don't want.
Is there anyway to install python package without letting it manipulate other already installed packages?
Actually, there is nothing much you can do because the pyswarm is dependent on the specific version of numpy.
One Solution is you can use virtualenv to create separate python environment
You can use virutalenv to create separate python environments for you projects so that it versions of the libraries won't conflict between the project. I recommend using pipenv which is the combination of pip and virtualenv. It is very easy to easy and has powerful features.
Related
I am relatively new to Python and I was told to use Anaconda and create a virtual environment (as I am mainly using pytorch for neural networks). But I start losing track about all my libraries.
First of all, I am sometimes not sure, how to install a library - should I use conda install (if so, I have difficulties finding the exact command) or should I use pip install. Also there is a difference between pip3 install and pip install, but for some applications it matters and for others it doesn't. I also start getting conflicts between packages and I don't know how to fix it.
In short: Do you guys have ideas for efficiently organizing your libraries with or without Anaconda? I am happy for any advice.
if you want to see what library and modules you are using you can check it with pip3 if you are using python3 and pip if you are using python2. so pip3 freeze gives you a list of installed libraries and also pip3 list. is giving you installed modules.
but for organize your library and modules. i recommend you install virtualenv on desktop and then you can install Django near that folder in desktop so you will control all your modules easily and when you will upload it on sever you will upload with all modules what you need.
I've got python 3.7 installed on Windows 10. The recommended way to upgrade to 3.8 appears to be to do a new installation, which means I will have both versions installed. I don't need both versions, but I would like to keep all the packages I installed for version 3.7.
How do I achieve this please? Also will new new path variable for 3.8 replace the one for 3.7?
The process for such a common use case seems strangely complex. Am I missing something?
Simple solution would be in CMD to do
pip freeze > packages.txt
This will write all your current packages to the text file 'packages.txt'
Then uninstall Python 3.7 as you would any Windows program then install Python 3.8 and in CMD do
pip install -r packages.txt
This will install all the packages that you had before.
Though I would recommend using conda as that handless Python versions and packages for you, along with environments.
One way to do this is to run:
python3.7 -m pip freeze > installed.txt
Then, after installing the new Python version you can install the packages with:
python3.8 -m pip install -r installed.txt
There is a chance that the packages you installed for your old Python installation are not compatible with the new version. For that reason it is safer to keep both Python installations and then use virtual environments for each of your projects.
You can create a virtualenv for each of your projects, using the Python version you need for that project, and install your dependencies only in the virtualenv for that specific project. This way you can avoid the situation where project A requires an old version of a certain package but project B requires a newer one. If you install all your packages globally you run into problems in this case.
See also What is a virtualenv, and why should I use one?
I would recommend moving over to conda to manage your environments.
https://docs.conda.io/projects/conda/en/latest/user-guide/install/windows.html
The current thinking for most of the development projects that I've worked on involving python is that the version and libraries are specified on a per project basis. Conda allows you to freeze the environment so that it's more portable. You can generate an environment.yml file that allows someone to recreate your environment from scratch, and you can maintain only the packages needed for a given project.
As per your original question, you can set the PYTHONPATH to point to the old and new directories. I can't guarantee that the libraries will work though since there could be version compatibility issues.
I'm a beginner trying to play around with machine learning. I downloaded python, and used pip to download libraries like TensorFlow, Pandas, Numpy, etc.
Now, I find that Anaconda is a better package manager to use for machine learning. I'm not sure what I'm supposed to do. Do I have to download all the libraries with Anaconda (which I tried to do with Pandas, and it said the library is already downloaded)?
Could you guys explain to me how I can move from using pip to using anaconda? I really don't understand environments, and this package manager stuff, so please help me!
In principle there is no need to change your package manager. Simply switch to do conda install the next time you would do pip install. Think of it like this: Do you have to re-download everything when switching from internet-explorer to firefox? Probably, some things work a little different between conda and pip but for a basic beginner, these differences should be neglectable.
You could freeze your pip packages and re-install them inside a conda environment to have everything (e.g. package dependencies) neatly managed by Anaconda, which is imho good practice. Pip packages will be available in every subsequent created conda environment, so if you want to use different packages in different environments, better re-install those using conda.
There is some non-trivial difference between conda and pip, mentioned here and here.
Best practices are to use different environment for different purposes. On a conda environment, download or re-download all requirement packages for that environment. Also always install a conda package only after you are done with pip install. Using both two environment, be sure not use the "--user" on pip as conda have user priviledge issues connecting to packages installed by pip.
You can check this link for more information
Title basically states it all. I upgraded my version of Python in order to hopefully play more nicely with Mac OS 10.9, but am now unable to use some modules I need for my work (NumPy, Pandas, SciPy, Scikit-Learn, etc.) Does this upgrade automatically wipe out any previously installed modules? Do I just need to install them again? Thanks in advance.
When you upgraded, it created a new sitepackages directory structure. Your packages are not installed any more, so yes you need to reinstall them into the new version.
Before you do that, take a good look at virtual environments rather than install the modules and packages globally.
http://docs.python-guide.org/en/latest/dev/virtualenvs will get you started, then google virtualenvwrapper.
I would recommend you try out the anaconda python distribution. It comes with all of these packages pre-installed, and its free. Also, in addition to pip, you can use the conda package manager which is much better for scientific packages. See http://technicaldiscovery.blogspot.com/2013/12/why-i-promote-conda.html for an explanation.
With conda, you can install numpy/scipy/pandas/etc with conda install numpy scipy pandas and it just works, and takes about 10 seconds. No compilation necessary (OTOH pip install scipy can take over 15 minutes, requires a fortran compiler, and is generally very tricky).
link: http://continuum.io/downloads
I don't know what I have did but now when use pip to install a package it install it for python 3 (python3.3 folder) not for python2.7.
Another problem I installed django_debug_toolbar and now my django version is 1.6.4 not 1.3 I installed.
Now I can't remove django 1.6.4 with pip. Do you have a solution?
Learn using virtualenv. It allows you to have different environments with isolated version of Python and set of installed packages. Each created virtual environment is by default having pip installed.
You messed up things (as you know very well) as you probably reinstalled pip for another version of Python.
You might find more versions of pip in your system. Check the version of python they use (on Linux watch the shebang on first line). Use explicit path to proper pip to manage packages for related Python.
Often people install pip and rename it or give an alias - names like pip33 or pip27.
Note, that virtualenv allows creation of different environments (with different Python versions) without need to install virutalenv for each of these Pythons.
With virtualenv I would also highly recommend using virtualenvwrapper which adds a few very handy commands.
My problem came when I installed django_debug_toolbar. when I pip-installed django-debug-toolbar, the latest version of Django was installed automatically.