I wrote pip install ppaquette-gym-doom
and it gives me a
error: legacy:install-failure
I tried pip install gensim pip install dlib --pre python -m pip install --upgrade pillow
python -m pip install --upgrade pip and python -m pip isntall --upgrade wheel
those codes didnt solved my problem
I am not familiar with gym-doom but based on some quick research it looks like you are getting this error because "ppaquette-gym-doom" is an obsolete version. From the github page "Note: This environment is not maintained anymore, and uses an old version of VizDoom."
It seems like the new version is simply "pip install gym-doom". This will also require something called Vizdoom.
Again this is based on quick research. I have never used these libraries.
I am using python 3.6.0 within a venv. I would like to "pip install" matplotlib==2.0.0, however when I do this, pip seems to automatically grab the newest versions of all other required supporting packages for matplotlib. i.e. cycler 0.11.0, pyparsing==3.0.7, etc. These latest supporting package versions do not seem to work with the older version of matplotlib and it throws errors when attempting to import matplotlib.
How do I install matplotlib without pip attempting to install all its supporting packages automatically?
My current temporary solution is to go back and manually install each package before installing matplotlib but I'm sure I will run into this issue again so would like to find a better solution.
Pip has a built-in feature:
pip install matplotlib --no-dependencies
To exclude specific, you can put it in requirements file and pass it:
pip install --no-deps -r requirements.txt
This error raised while installing geopandas. I've looking for its solution on the web, but none of them really explain what happened and how to solve it..
This is the full error:
Collecting geopandas
Using cached https://files.pythonhosted.org/packages/24/11/d77c157c16909bd77557d00798b05a5b6615ed60acb5900fbe6a65d35e93/geopandas-0.4.0-py2.py3-none-any.whl
Requirement already satisfied: shapely in c:\users\alvaro\anaconda3\envs\tfdeeplearning\lib\site-packages (from geopandas) (1.6.4.post2)
Requirement already satisfied: pandas in c:\users\alvaro\anaconda3\envs\tfdeeplearning\lib\site-packages (from geopandas) (0.20.3)
Collecting fiona (from geopandas)
Using cached https://files.pythonhosted.org/packages/3a/16/84960540e9fce61d767fd2f0f1d95f4c63e99ab5d8fddc308e8b51b059b8/Fiona-1.8.4.tar.gz
Complete output from command python setup.py egg_info:
A GDAL API version must be specified. Provide a path to gdal-config using a GDAL_CONFIG environment variable or use a GDAL_VERSION environment variable.
----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in C:\Users\Alvaro\AppData\Local\Temp\pip-install-oxgkjg8l\fiona\
pip install wheel
pip install pipwin
pipwin install numpy
pipwin install pandas
pipwin install shapely
pipwin install gdal
pipwin install fiona
pipwin install pyproj
pipwin install six
pipwin install rtree
pipwin install geopandas
here are the source links:
http://geopandas.org/install.html#installation
https://pip.pypa.io/en/latest/user_guide/#installing-from-wheels
https://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
If you still have problems, consider uninstalling the above (pip uninstall) and reinstalling.
I solved this problem by running the following commands:
pip install pipwin
pipwin install gdal
pipwin install fiona
pip install geopandas
Works successfully on Windows.
Geospatial Data Abstraction Library (GDAL) is a library designed for vector geospatial data formats. It's a prerequisite for installing Fiona, the Python API for OGR (which doesn't really stand for anything), which is in turn a prerequisite for Geopandas. On UNIX-like systems the gdal-config script tells Fiona stuff about your particular gdal installation.
It seems that your gdal-config is not in one of the usual places on your PATH, so Fiona was unable to find it.
If you're using Anaconda, best is to remove gdal with conda remove gdal and then do a fresh conda install geopandas.
As a general rule, if you're using Conda you should never use pip to install something inside it unless you're absolutely sure conda offers no support for it. (Many package can be found on conda by specifying the right channel - -c argument.) And specifically in the case of geopandas, the maintainers recommend using conda over pip, since pip requires you to install the dependencies correctly.
I had a lot of issues myself installing geopandas, mostly showing error when downloading fiona and gdal. I did every step above and did a conda install geopandas but failed. The only thing worked for me is to install fiona and gdal wheel separately.
go to the link by Christoph: gohlke:https://www.lfd.uci.edu/~gohlke/pythonlibs/#fiona
You can search for fiona and gdal wheel files. Make sure you choose the file as per your python version, if it is 3.7 then there would be cp37.
Download the file
go to command prompt, put cd and then pip install , install GDAL wheel file, then fiona, then just do pip install geopandas.
This solution worked for me.
To install gdal, I followed the following steps:
downloaded the version that satisfies my computer (64 bit) from
https://www.lfd.uci.edu/~gohlke/pythonlibs/ . The file was GDAL-3.1.4-cp37-cp37m-win_amd64.whl
Put the file in a folder on the desktop.
From cmd, i moved to that directory and executed python -m pip install GDAL-3.1.4-cp37-cp37m-win_amd64.whl
This is followed by installing fiona the same way: python -m pip install Fiona-1.8.18-cp37-cp37m-win_amd64.whl
For shapely, i executed conda install -c conda-forge shapely
After that, i was able to install keplergl as usual: pip install keplergl
install descartes: conda install -c conda-forge descartes (or python -m pip install descartes).
In this way, i didn't have to play around with the 'Environmental Variables' as this may affect other programs
Cheers..
Installing geopandas
Geopandas has very complex multi-language dependencies, some of which need to be built with consistent compiler versions across packages. Because of this, the geopandas docs recommend installing using conda in a new environment using conda-forge only. Here are some general best practices to keep in mind:
conda is the recommended installation method. You can install geopandas from pip or source, but it's going to be a bumpy ride and it's not recommended. If you're installing conda for the first time, I recommend you start with miniconda (or better yet miniforge, a conda-forge-first miniconda variant), not anaconda, to keep your base env lean.
When using conda, you should not mix and match conda channels.
When installing geopandas, try creating a fresh environment rather than installing into your base environment. If you have anaconda installed, it comes with a large number of packages from the "defaults" channel installed in your base environment. I recommend deleting anaconda and installing miniconda, then installing into a new environment.
Try to create a new environment with everything you plan to use all at once rather than iteratively modifying the environment. In other words, if you want to use geopandas with scikit_learn, folium, and rasterio, install them together with a single conda create command
As a last resort, delete your conda installation and re-install miniconda. Desperate times call for desperate measures, and this usually resolves gnarly installation nightmares.
To create a fresh conda environment in which you install all necessary dependencies at the same time, using the conda-forge channel:
conda create -n my-geopandas-env -c conda-forge geopandas [all other packages you need]
For example, I might set up an environment with something along the lines of...
conda create -n my-geopandas-env -c conda-forge python=3.9 \
ipython ipykernel geopandas scipy seaborn fiona matplotlib cartopy
Bundling your installations into a single environment creation step like this reduces the chance of packages falling out of sync. To speed this process up, you could first install mamba or mambaforge, a faster drop-in replacement for conda, into your base environment and then run the above commands with mamba instead of conda.
Generally, it's best to avoid installing much of anything in your base environment (cross-environment system utilities like mamba are some of the few exceptions). If you already have a complex base environment (maybe you started with anaconda rather than miniconda) this may be the time to delete your entire conda installation and start from scratch (I know that's terrifying... sorry! but it'll save you heartache in the future). mamba is great for speeding this process up.
Connecting your editor to the conda environment
Once you have installed all of the packages you need, activate your environment with conda activate my-geopandas-env. See the conda guide to managing environments for more info.
Jupyter/ipython
Some editors/IDEs including jupyter require additional packages - jupyter requires that ipython and ipykernel be installed in order to load the environment within the notebook or editor - that's why I included ipykernel in my list above. See the ipykernel docs for more info.
Other IDES
To link this environment to an IDE such as VSCODE, spider, etc., find the location of this python version with conda run -n my-geopandas-env which python then point your editor to this python executable. Check the docs of your specific editor to get more targeted info about how to set up a conda environment for use with your editor:
Spider: FAQ on using an existing environment and Spider wiki guide to working with packages and environments
VSCode: Using python environments in vscode
PyCharm: Configure a conda virtual environment
I don't have conda installed, then using just pip I followed these steps:
Download GDAL and Fiona wheels directly on:
GDAL: https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal
FIONA: https://www.lfd.uci.edu/~gohlke/pythonlibs/#fiona
Then:
pip install <gdal.whl>
pip install <fiona.whl>
In my case I did pip install GDAL-3.4.1-cp38-cp38-win_amd64.whl and Fiona-1.8.21-cp38-cp38-win_amd64.whl. Where cp38 stands for python 3.8.
After that you are able to install geopandas with pip as well.
pip install geo pandas
For me, the only solution was to install the ready binaries from here
https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal
Then just install locally
pip install GDAL-3.1.4-cp38-cp38-win_amd64.whl
One way in which I could install geopandas was through the Anaconda Navigator. Get into the environment and install the package 'geopandas'. After that I could import the geopandas package in spyder
I will add
!pip install descartes
to #JDOaktown list.
I started with pip install geopandas and got the error, but later tried with conda install --channel conda-forge geopandas and the error disappeared.
Successfully installed in RHEL 7.8.
It automatically downloaded the required packages. This might be helpful
Installing collected packages: certifi, pyproj, shapely, attrs, click, click-plugins, munch, cligj, fiona, geopandas
Successfully installed attrs-20.3.0 certifi-2020.11.8 click-7.1.2 click-plugins-1.1.1 cligj-0.7.0 fiona-1.8.17 geopandas-0.8.1 munch-2.5.0 pyproj-3.0.0.post1 shapely-1.7.1
If you want to install GDAL, Geopandas, Shapely, Fiona etc in a windows Virtual Environment download .whl files for all of them and first install GDAL using
pip install gdal-.whl
Following this command edit the activate.bat file in you venv\Scripts folder and add
GDAL_CONFIG = \venv\Lib\site-packages\osgeo
Then you can install rest using pip install
I started off with a clean environment gdal_test in Conda environments, but made the mistake of using the old activate gdal_test instead of conda activate gdal_test. This made Conda Environment resolving take forever, which is why I resolved to other methods at first.
Takeaway: let conda handle it, with a proper new environment.
I ran into this problem not with anaconda/windows, but with python:3.6 Docker image. Google search always led me to this question, so I think I will share how I resolve my issue in case others also end up here.
Based on here, you need to install system relevant packages in the Dockerfile before running pip install geopandas or pip install requirements.txt:
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
libatlas-base-dev \
libgdal-dev \
gfortran
The following worked on macOS:
brew install gdal --HEAD
Verify the installation by running gdal-config --version
Following that pip installation as normal worked without a problem.
I am trying to install the OpenCV-python on my mac and i have used the following:
$pip install opencv-python
which gave me the following error:
$pip install opencv-python
Collecting opencv-python
Using cached opencv_python-3.4.0.12-cp27-cp27m macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Collecting numpy>=1.11.1 (from opencv-python)
Using cached numpy-1.14.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
matplotlib 1.3.1 requires nose, which is not installed.
matplotlib 1.3.1 requires tornado, which is not installed.
Installing collected packages: numpy, opencv-python
Found existing installation: numpy 1.8.0rc1
Cannot uninstall 'numpy'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
Then i did try the pip install --upgrade matplotlib which didnot change anything. It just show me:
matplotlib 2.2.2 requires backports.functools-lru-cache, which is not installed.
matplotlib 2.2.2 has requirement numpy>=1.7.1, but you'll have numpy 1.8.0rc1 which is incompatible.
As I found many ways to install the openCV-python in the internet like:
https://www.pyimagesearch.com/2015/06/15/install-opencv-3-0-and-python-2-7-on-osx/
and I installed on my other mac but i got import cv2 problem alot in my codes.
I will be more than happy if anyone have a good solution or recommendation to install the openCV-python.
Thanks
In summary, macOS comes with the Python preinstalled and you should not mess with the packages installed as some system utilities depend on them.
https://docs.python.org/3.7/using/mac.html
The Apple-provided build of Python is installed in /System/Library/Frameworks/Python.framework and /usr/bin/python, respectively. You should never modify or delete these, as they are Apple-controlled and are used by Apple- or third-party software. Remember that if you choose to install a newer Python version from python.org, you will have two different but functional Python installations on your computer, so it will be important that your paths and usages are consistent with what you want to do.
You should take a look on either venv or virtualenv.
You can read this answer: https://stackoverflow.com/a/41972262/4796844 that will get you through the basics.
In a nutshell, to solve your problem:
$ python3 -m venv ./project-name
$ . ./project-name/bin/activate
$ pip install opencv-python
And to leave the virtual environment, simply:
$ deactivate
Today I decided to install python and the scipy stack manually, instead of using Anaconda (or Canopy) as I had previously done. I use homebrew on my mac and have python2 and python3 (2.7 and 3.6) installed via homebrew. But reading through the documentation, there are multiple ways to install the scipy stack and I want to know what the differences are. I have tested they independently and they all work.
From the Homebrew documentation:
python2 -m pip install numpy scipy matplotlib
python3 -m pip install numpy scipy matplotlib
These are the same two commands that the Matplotlib installation docuentation lists for how to install matplotlib through homebrew. Why does this use pip (the system Python 2.7.x's pip) instead of pip2 and pip3 respectively? Is it because you call python2/python3 first?
However, the SciPy documentation for installing these modules when using homebrew is different:
brew tap homebrew/science && brew install numpy scipy matplotlib
(NOTE: the matplotlib formula is located in the homebrew/science repository, which is why you need to use brew tap.)
Finally, from the command line readout when installing python2 and python3 via homebrew:
pip2 install numpy scipy matplotlib
pip3 install numpy scipy matplotlib
which are based on the following readouts:
Pip and setuptools have been installed. To update them
pip2 install --upgrade pip setuptools
You can install Python packages with
pip2 install <package>
They will install into the site-package directory
/usr/local/lib/python2.7/site-packages
See: https://docs.brew.sh/Homebrew-and-Python.html
...
Pip, setuptools, and wheel have been installed. To update them
pip3 install --upgrade pip setuptools wheel
You can install Python packages with
pip3 install <package>
They will install into the site-package directory
/usr/local/lib/python3.6/site-packages
See: https://docs.brew.sh/Homebrew-and-Python.html
So between four sources of documentation, there are three different ways to install scipy when using homebrew and they all work; but how is each different and should one be preferred?
From what I can tell, the first and third methods, which both invoke pip (pip2/pip3), are functionally equivalent - both invoke Homebrew's Python X.X.X's pip - but one implicitly, the other explicitly. I assume this means both methods install the pre-built binary packages from pip in the form of wheels. For the second method, I think it installs homebrew's own formulae for these packages (i.e. maintained separately by homebrew in it's repository).
If this is true, then I assume one should use the second method if you are using a version of python which is maintained by homebrew (i.e. installed via brew install python or python3). My reasoning is that if you later decide to install another formula via homebrew that has any of the scipy stack as a dependency, it will install those modules again from homebrew's repository if you installed them using pip previously.
As mentioned, I am not sure if my understanding is correct and I have not been able to find any answers, so any insights or confirmations would be appreciated.
Your analysis seems correct: variants 1 and 3 will install numpy/scipy from the python package index (PyPI) and will use pre-built wheels (if available for your platform, which they most likely are).
Variant 2 installs the brew formula.
As mentioned by #Evhz, the conda packages for numpy and scipy use the Intel Math Kernel library, which can provide significant speedups (not just on Intel processors) versus the packages installed from PyPI or brew, both of which are linked against OpenBLAS.
Concerning which method to prefer: it's not entirely straightforward.
Yes, on the surface, using brew to manage both the python interpreter and the python packages would seem consistent.
However, homebrew only provides formulae for a handful of python packages, so you'll end up needing to mix with pip in any case.
If you want performance, you go with conda, which will be managing both the interpreter and python packages.
However, also anaconda / conda-forge still have some catching up to do with PyPI, so you'll likely need to mix with pip again.
In the end, there is no perfect solution but as long as you knowingly decide for one, you're unlikely to run into issues.