I wanted to install numpy in python 2.7 without setting environment path. I do not know if that is possible or not but my Professor wants it like so please any advice would be appreciated.
I am not sure I understand your question correctly. You can simply delete python from your environment path. But normally this is not desirable since you then cannot call python from any directory. Better is to create a virtual environment. Or better use: anaconda. This will allow you to use various version of pythons in separate environments without any confusion or clashes between versions. You then install the respective numpy version within a specific environment. See: https://conda.io/docs/user-guide/tasks/manage-python.html
If you mean want to install numpy but you do not have the previleges then your answer can be found here: (Python) Use a library locally instead of installing it
I hope this helps. If not, then please clarify your question.
Related
I'm new to miniconda and anaconda. I just wanted to get an opinion on anaconda vs miniconda in the hope of finding out what's better for my needs.
Currently i've got miniconda installed and everytime I want to work on a project I have to create a new environment inside a project folder.
Before I download and intall it I wanted to know whether if I install it, whether I would have to create an environment for each project, or will they all work at a system level.
If I have to create an environment even in anaconda, then there's no point me installing it as well.
Thanks.
Currently i've got miniconda installed and everytime I want to work on a project I have to create a new environment inside a project folder. Yes, that's basically one of the two functions of Conda, it's a good thing.
Before I download and intall it I wanted to know whether if I install it, whether I would have to create an environment for each project, or will they all work at a system level. That sounds like a bad idea!
If I have to create an environment even in anaconda, then there's no point me installing it as well. What is the point of using Conda, rather?
Take a look at some of the questions with the anaconda and conda tags, there are mountains of people encountering issues which stem from using a single environment for everything.
As for the question of Miniconda vs Anaconda, I stick to using the former, and get all my packages from the popular conda-forge channel.
I am new to Python and starting work on a large project that will be distributed to users. I am also the first in my company to be using, and I wanted to get recommendations on the best way to install Python & packages, so that I don't head off in the wrong direction.
I require data analysis frameworks (pandas, numpy, scipy, matplotlib, statsmodels, pymongo) and my initial approach was to install Python 3.5 directly, and then use pip install on each package.
I ran into similar problems that others have found [Unable to find vcvarsall], and resolved. Next problem was with BLAS and LAPACK missing when installing scipy. At this point I decided Anaconda was the way to go, rather than individual pip installs, and was easily able to set everything up.
One problem with Anaconda is that it installs a lot of packages which I will never use, and may not have some which I would like to use in future, e.g. TensorFlow (presumably can do pip install to get extra ones that are not included?).
An in-between solution seems to be Miniconda, which I believe would have fixed the BLAS/LAPACK problem with scipy.
So my question is: can someone with experience of developing data analysis projects in Python, that will be deployed to users' Windows desktops, and with server-side components running on Linux, provide recommendation of what they would do if starting from scratch at new organization?
(I'm currently in favour of heading down the Anaconda route.)
Personally, I think Anaconda(conda) is better. First of all, conda is cross-platform package manager, and it is easy to install and use. Second, conda has functionality of virtualenv, and you can use conda create to create environment. Finally, there is Anaconda cloud and condo-forge, those community can help you solve conda issue, build packages, and share ideas.
Moreover, Anaconda(conda) indeed install a lot of packages, but those are all dependencies. For example, when you "conda install scikit-learn", conda will automatically help you install the dependency, numpy and spicy.
I am new in the world of OpenMDAO (and also on Python) and I am having some problems to understand the use of the software. I have already installed Anaconda (pyth v2.7) and the OpenMDAO, but I don't know how to run it. I am following this tutorial but I am not sure if I am doing it properly. I write the .py files in notepad++, and I try to run on the IPython but when I use the command : from paraboloid import Paraboloid it appears an error : No module named.api. I think that maybe I am not using the correct path (I'm in the folder where I have the .py files). Probably it's an stupid error, so sorry for the question.
Thank you all, Jose M O
If your tutorial link above is correct, I see that you are using a tutorial for OpenMDAO 0.1.0. That version is 5.5 years old at this time, and is no longer supported. We will be happy to help with your questions, but to get a better foundation, and a much more useful tool, please consider:
Install OpenMDAO 1.5.0 (pip install openmdao or read these installation docs)
Try this paraboloid tutorial instead.
Good luck,
Keith
NOTE: If you installed OpenMDAO 1.x.x and are using the tutorial from 0.1.0, you would have many problems with api imports, as many things have changed since 0.1.0.
Is there a way to have an easily shareable python install? I have a python install on my PC, and sometimes coworkers want to use and modify my code. To do so, they often need to replicate my python config. Is there a way I could easily share my config with them? From another angle, is there a way I could send my code so it can be run with a clean install of Python?
Have you tried using virtualenv ? I think it will meet your need to share your python environment after you have customized it to run your code.
As #Jeff Mercado mentionned, Portable Python is the way to go for me. I can zip my whole python install into one file and share it. It contains all the libraries I need.
I'm trying to install Flagmatic on Mac OS X Yosemite, a software package used to solve problems in extremal graph theory using the "flag algebras" developed by Razborov. I'd like to use Flagmatic to help with a research problem I'm working on, but I'm finding it very difficult to install. I'm a mathematician and while I've had some experience using Sage and Python through the Terminal I don't really know how to fix the sort of problems that I'm running into here. Here's the most up-to-date user guide so you can follow what I'm trying to do. I can broadly break down the problem as follows:
First, there is a link in the user guide to download the semidefinite programming package CSDP which Flagmatic needs in order to do some calculations. You then need to move CSDP to somewhere Flagmatic can find it, so I typed
sudo cp ~/Downloads/csdp/usr/local/bin/
into Terminal, which seemed to work. Then the user guide says by typing
csdp
into Terminal it should launch CSDP. This doesn't work - I get a permission denied message even though I typed sudo. Can anyone explain why this is happening and how to remedy it?
Secondly, when I run Sage (maths software) in the Terminal, I then want to be able to change directory to the pkg folder where there is a script called setup.py. When I run sage and type
python setup.py install
or
-python setup.py install
while in the pkg directory, I get an invalid syntax message with a pointer to the particular phrase setup.py. This is what the user guide tells you to do. If I instead try to install outside of Sage I get an error message The environment variable SAGE_ROOT must be defined. Can anyone provide some explanation and help with how to fix this?
Wow, this is somewhat dated information that they provide, since for well over a year csdp is an experimental Sage package. You should be able to just do
sage -i csdp
to install it (if you have compiler tools, which you probably do in this case). I guess the old instructions would still work but are a little brittle w.r.t. Sage.
Anyway, to the actual problem. You need to do the exact command given in the instructions:
sage -python setup.py install
However, this assumes (!) that you have a sage command in your PATH. Which you may not!
In this case, I recommend you use whatever command you use to run Sage, but instead of just typing /commmand/for/sage do /command/for/sage -python setup.py install in that directory. It will have all the right environment variables set up so that it installs in the right place, I am guessing.
Now, I'm not sure why flagmatic isn't just having themselves become an upstream package on the Sage servers, but that is a question that is harder to answer.
I might udpate the documentation in the near future. For now, however, you might want to visit this: https://github.com/jsliacan/flagmatic-2.0.git. I keep Emil's copy of Flagmatic 2.0 with updated install info. If you follow the README file, you should be fine.
I just built sage on Yosemite from source (worked fine) and then installed Flagmatic 2.0 (or Flagmatic-dev) without difficulties. It should work for you too.
I hope this helps.