It is not possible for me to install the python library 'traffic' and get it running.
I'm trying to install the traffic library for python. So far, nothing has worked out.
When using pip install traffic the error "Could not build wheels for Cartopy, which is required to install pyproject.toml-based projects" is generated.
The command conda create -n traffic -c conda-forge python=3.9.7 traffic the installation is running several hours and evetually terminated.
I already downloaded Microsoft Visual Studio and ran conda create ... in there. The same error message came up.
Does anyone know how to install the traffic library
I am trying to install mmcv python package, but Ubuntu terminal crashes every time during installaction (application simply closes without any errors). I tried to install this package both using standard Linux terminal and via VS Code - the result is always the same. It causes no errors when I install other python packages, but when I try to install mmcv - terminal crushes.
I am using this code for installation:
pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.1/index.html
I tried to install mmcv using this code in Kaggle Kernel (it is Jupyter Notebook-like development environment, which works on Linux too) - and package was installed correctly.
It seems to me that my terminal crashes because mmcv is quite "heavy" python package, but I do not know what to do with it. How can I solve this problem and install mmcv?
It seems like my version of Ubuntu is not compatible with old versions of mmcv.
This code worked for me:
pip install -U openmim
mim install mmcv-full==1.5.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
Recently I started using Spyder for my python programming. One of the Code required QT version greater than 5.12 so I installed standalone version of Spyder. To install additional packages I am using Anaconda. Despite of creating virtual environment I am not able to use Spyder as kernel throws error. " The Python environment or installation whose interpreter is located at
/Users/ugowda/opt/anaconda3/bin/python
doesn't have the spyder‑kernels module or the right version of it installed (>= 2.2.1 and < 2.3.0). Without this module is not possible for Spyder to create a console for you.
You can install it by activating your environment (if necessary) and then running in a system terminal:
conda install spyder‑kernels=2.2
or
pip install spyder‑kernels==2.2.*"
Tried several online suggestions,didn't work. Did anyone face similar problem ? Do anyone has solution ?
I used this command after I activated my env:
pip install spyder-kernels==2.2.1
I first installed Spyder and then afterwards Python on a server (with Windows Server 2019) all on the directory "C:\Users\wi932\ .spyder-py3" and the Python folder "C:\Users\wi932\Python\Python38". Then I installed many packages (like tensorflow, matplotlib, scikitlearn) by using the command prompt of windows and pip from the directory "C:\Users\wi932\Python\Python38\Scripts" and it was okay.
However, when running Spyder I can't use the packages that I installed using pip. Whenever I run a Python programm with those modules I get an error message "ModuleNotFoundError: No module named 'tensorflow'". So the question is how can I build a "connection" between the pip and Spyder?
Does anyone have an idea? I'll appreciate every comment.
Have you considered installing anaconda?
Spyder comes as a part of this and is easy to install from the anaconda client. You can also create your base environment in the anaconda client and then switch to pip to install packages from there.
I am new to Python. I am a very old R programmer. One of my PhD courses is performed via Python.
So, I installed Jupyter Notebook; I wrote simple "Hello World" in Jupyter Notebook. I wonder whether I must install Anaconda or not?
I ask this because: I looked at Anaconda's IDE in youtube, and it shows RStudio, Jupyter Notebook, etc. in a bundled manner. In RStudio, one can perform all the package handlings within RStudio.
So, I wondered is there a way to install python packages within Jupyter Notebook, or should I really install Anaconda? What is the benefit of installing Anaconda (besides Jupyter Notebook)?
The packages in python are generally installed with "pip" (or "conda") in the terminal and are then available regardless where you run your script from. Assuming you don't have multiple python versions set up on your PC, they should then all be available in your jupyter-notebook also.
If you don't want to open another window to do this, you can also run BASH code from Jupyter itself, just start the line with an exclamation mark (!)
i.e.
!pip install pandas
The benefits of Anaconda are that it bundles everything you need to at least start your more basic projects (python release, basic packages, IDE) and that you can set-up project-specific environments that do not interfere with your system-wide package installations.
Python packages are not installed with python functions, like it would be the case in R with install.packages("package name"). Instead, an external package manager usually is used to install and possibly compile the package files to a directory where python can import it from.
Anaconda offers (among others) the package manager conda. Most popular is pip. Some Linux distributions also offer python packages via their package manager (e.g. apt on Debian/Ubuntu). All these package managers download packages from their own repositories, so conda install numpy, pip install numpy and apt install python3-numpy all install a package numpy, but from different sources and in possibly different versions.
Jupyter Notebook is a programming environment, where you can execute shell commands with !command, so depending on the system where the Jupyter server is running, you can use !pip install numpy, !conda install numpy or other commands as cells in the Jupyter Notebook you are working in. This will run the command in a shell.
That graphical menu with Jupyter, RStudio etc. you describe is the program "Anaconda Navigator", which is installed with Anaconda. Jupyter is just a Python library, which is pre-installed with Anaconda, but can also be installed via pip, apt and other package managers.
Like Virtualenv, Anaconda also uses the concept of creating environments so as to isolate different libraries and versions. Anaconda also introduces its own package manager, called conda, from where you can install libraries.
Additionally, Anaconda still has the useful interaction with pip that allows you to install any additional libraries which are not available in the Anaconda package manager.
It is a good option for setting up of a better environment for working with jupyter.