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Not a 100% crypto tie in immediately, but I think it would be an awesome foundation, is the MIT Python courses. You could start immediately as all the material is freely available online if I'm not mistaken.
Python Anaconda is a system that downloads, maintains, tweaks, a Python installation. You'll have to do quite a bit of reading to get up to spead on Anaconda, but there is documentation, youtube videos, etc. Once you learn your way around it, you can write code so that you can follow the MIT course or any course.
https://www.anaconda.com/products/individual
Later you can take online courses that you can follow, work assignments, get your assignments graded. Places like EDX, Coursera, Udacity offer online courses.
Might not be technically correct, but: Anaconda is a distribution of Python that contains everything you need to get started, and its own package manager (like pip). You can easily create virtual environments and install packages, and it will take care of all/most of the "behind the scenes" dependency management. It's great for people like me who just want a Python with mostly generic packages that "works", and one which I can recreate on multiple machines.
I typed this response to the deleted comment, so I'll just post here:
Saying "Anaconda is not FOSS" is also misleading because anaconda refers to multiple things.
The anaconda distribution is a set of binary packages that have been built for different operating systems by Anaconda (the company). They share these packages via their webservice anaconda.org. If you are a heavy commercial user of the webservice, then they ask you to pay for their service (pay for massive downloading from their website). See: https://www.anaconda.com/blog/sustaining-our-stewardship-of-the-open-source-data-science-community
But every binary on anaconda.org has its own license. Some packages are built by Anaconda (the company) and many more are built by other people and organizations (just like PyPi). When you install packages using conda for use or distribution you must abide by the packages' licenses. Some packages are probably considered more FOSS than others. If you want to redistribute those packages you should carefully examine the licenses. All the packages are still distributed under FOSS licenses, even those build by anaconda (the company).
I do not know how to fix the existing problem.
But once you do, please do yourself a favour and use something like Anaconda (install 3.7 version).
Anaconda lets you create "containers" of python installations that are independent of each other and the native python installation on your Mac. Kind of like a VM for python!
So, if you are working on multiple projects which use multiple versions of python, you can easily switch back and forth between different "python environments" using a terminal command.
See this to get an idea of how it works: https://conda.io/docs/user-guide/tasks/manage-environments.html
I highly recommend learning the Python language. Start by downloading the <strong>Anaconda distribution</strong> of Python 3 and installing it on your system. Once it's finished installing, run the Anaconda launcher and wait for it to load (it will take a minute), then start up qtPython or JupyterLab to get a Python interpreter.
Finally go to <strong>Automate the Boring Stuff With Python</strong> by Al Sweigart, and work your way through each chapter. In total it should take roughly 40 hours, maybe longer if this is your first programming language.
Anaconda the software is not FOSS though (whether it's the "Individual Edition", "Commercial Edition", or "Enterprise Edition").
Further all the repositories (main, R, msys2, etc.) maintained by Anaconda the company are also not FOSS. They all carry an implicit commercial terms of service for downloading from, independent of the license the binary itself has.
See: https://www.anaconda.com/terms-of-service
And I can assure you from Anaconda's perspective this isn't just legalize they put there to protect themselves, they are actively enforcing it and blocking access to their repository for companies they don't feel are in compliance with these terms of service.
I've had the "pleasure" of being on calls with Anaconda's sales reps this year as our company was blocked from accessing Anaconda. Even despite what is written in the blog post you linked, we are most certainly not in the category of "heavy commercial usage", in fact we cache everything locally so our bandwidth costs on Anaconda themselves are extremely minimal as we pull a package at most once.
We have been migrating to miniforge/conda-forge where it makes sense so we can better identify what are our actual commercial requirements.
If you download the Anaconda distribution, it comes with Spyder/PyCharm and the most popular 3rd party libraries, so it works "out of the box" like MATLAB.
Nice idea. I really like it. A repl for playing with Fortran will make it more approachable. It is actually quite a modern programming language, people just think Fortran is F77.
I would include the installation instructions from https://docs.lfortran.org/installation/ in the actual README.md. Also, don't assume people have things like anaconda installed. A step-by-step installation for macOS / Linux, assuming a fresh install of both, would be a good first step.
I had to:
- download anaconda installer from https://www.anaconda.com/distribution/ (this is a 637MB file and takes 2.3G for installation!)
Note: anaconda makes an entry into your ~/.profile and changes your prompt! That's a code smell. I removed the entry that the anaconda made into ~/.profile and just added:
PATH=$PATH:~/anaconda3/bin
- install antlr (followed directions at https://www.antlr.org/)
You want to make it as easy and straight forward as possible for people to dive right in.
The dlib shape predictor does not seem to be good enough. I suggest to look into this for proper alignment, I think it's what /u/deepfakes uses: https://github.com/1adrianb/face-alignment
To install on Windows: * Download https://www.anaconda.com/download/ * When installing, make sure to check python replacement * Open anaconda prompt and install pytorch this way: https://github.com/pytorch/pytorch/issues/494#issuecomment-304439658
Installing all of the libraries can be a pain if you're using MS Windows, Anaconda (https://www.anaconda.com/) or one of the other pre-packaged Python distributions can make it a lot less painful. Anaconda includes all of the libraries you listed and Spyder, a fairly solid IDE.
Good luck!
They updated TOS last year to more aggressively define commercial use. Everyone on our team has a license now, even though we are small (<5).
“ We clarified our definition of commercial usage in our Terms of Service in an update on Sept. 30, 2020. The new language states that use by individual hobbyists, students, universities, non-profit organizations, or businesses with less than 200 employees is allowed, and all other usage is considered commercial and thus requires a business relationship with Anaconda.”
https://www.anaconda.com/blog/anaconda-commercial-edition-faq
Yep, we plan to report out the results, just as we do with our annual State of Data Science survey: https://www.anaconda.com/blog/2020-anaconda-state-of-data-science-report-moving-from-hype-toward-maturity
I don't recommend to use rstudio for python. If it's your first rodeo with python for data analysis and you don't have any experience in venv and pip and IDE like PyCharm then Anaconda is your swiss knife for the job.
It provides: - Jupiter notebook, JupiterLabs, spiders, etc. - conda package manager - user-friendly UI
Just Google some tutorial about anaconda just to learn the basics
Hope this helps
Maybe you could try to install the Anaconda distribution. It contains Python and other useful packages and I remember it being easy to install and manage.
See Sustaining our stewardship of the open-source data science community.
The relevant paragraph is
> This does not apply to non-commercial users (e.g. students, academics, hobbyists). Even if you are using Anaconda for commercial purposes, if you are just doing small one-off research projects or just learning data science, this is no real burden to our systems, and these changes do not apply to you either.
tl;dr: Unless you are hammering the Anaconda repository, you should be fine.
This has nothing to do with machine learning, so I would suggest asking in (one or more of) the following sub-reddits:
- r/Jupyter
- r/JupyterNotebooks
- r/IPython
- r/vscode
Also, there is nothing called Anaconda Jupyter. Jupyter comes from Project Jupyter and Anaconda is a Python distribution.
A year ago there was a survey done. 70% of students and professors thought they needed to know matlab 30% python. when industry was asked what they wanted 70% said python and 30% said matlab.
Matlab is just too expensive and limited, while python as juypiter or anaconda are just so much more powerful.
Edit: matlab also indexes arrays starting from one, and I have never forgiven them
Minha dica é instalar o Anaconda (distribuição do python)
https://www.anaconda.com/download/
Você está planejando fazer o que com o python?
A melhor maneira de aprender é utilizar o conhecimento para criar algo que seja prático no seu dia-a-dia... Um site, mod para jogos, automatação de tarefas, programa desktop, etc.
Packages like matplotlib and others in the scipy stack are notoriously difficult to install due to the high number of dependencies and C extensions. I don't know what error you are getting, but if you are new to programming / Python I typically recommend installing one of the scientific Python distributions listed on that same page. They usually "Just Work" and can save you a ton of headache.
I prefer Anaconda.
Most libraries have the installation instructions on their websites or somewhere on the internet. As with compatibility with the M1 Mac, I think a lot of libraries are yet to be usable on the newer Macs. I found this StackOverflow post that has more information.
On a side note, I actually like using anaconda as my Python distribution which makes it easy to install packages like numpy, pandas etc and also organise virtual environments. However, you will have to see if it works with your Mac but I think the SO post mentioned using miniconda to install ML packages so I think it might work.
> I genuinely have no idea where to start in that regard even if I know 500 python textbooks back to back.
Sorry, but that is not the truth.
Any even half decent textbook will have you set up your development environment in the very first chapter(s).
What you need:
- An editor - simplest form: Notepad on Windows (wouldn't use that, though), Sublime, Visual Studio Code, IDLE (which is integrated when you download a Python interpreter), PyCharm, JuPyter Notebooks, and countless others.This is where you type your program.
- A Python interpreter - this is where the source code runs.
When you download Python, from Python.org or Anaconda you will get a place to edit your code (IDLE, JuPyter Notebooks) and a place to run your code - the Python runtime (either Python.exe, Python3.exe, or JuPyter).
You should really start with some decent tutorial, like Automate the Boring Stuff with Python.
You can try Octave if you want something similar to MATLAB.
You might consider using the Anaconda Distribution, namely Python, for doing what you're trying to do. There are a lot of built-in modules that do useful things. Specifically, you can use Scipy to perform rotations without having to design your own algorithm.
no man, you're in the right sub. It's not sometimes as easy as you might think.
If you do the MIT Edx course for beginners (and you should, it started this week) they recommend Anaconda. As you develop your skills, you might not need it, but for getting started out of the box it's great. You just install it, open Spyder (the IDE included) and it just works like you might think. All the issues you are having go away, which is just what beginners need.
In your terminal, type echo $PATH. This will print out a list of all the directories your terminal looks for installed programs in. Python is installed in one or more of these. The python installations in /usr/bin are there by default with MacOS. I'd recommend not deleting them, but you can with sudo rm -rf py* (check that you don't have any other programs starting with py that aren't python-related first). In any of the other directories in your PATH, you can safely delete python.
​
Next install some kind of virtual environment manager. I prefer Anaconda, which handles both managing virtual environments and module installation. The download page is here on the Anaconda website. You can use Anaconda to create python environments (installations) in the Anaconda installation directory with specific versions of python and different modules.
I highly recommend learning the Python language. Start by downloading the <strong>Anaconda distribution</strong> of Python 3 and installing it on your system. Once it's finished installing, run the Anaconda launcher and wait for it to load (it will take a minute), then start up qtPython or JupyterLab to get a Python interpreter.
Finally go to <strong>Automate the Boring Stuff With Python</strong> by Al Sweigart, and work your way through each chapter. In total it should take roughly 40 hours, maybe longer if this is your first programming language.
I also suggest cross-posting to /r/cscareerquestions
You may wish to read this from anaconda.
A Python Data Scientist’s Guide to the Apple Silicon Transition
​
The AVX bit was interesting. My guess is that rather early into this 3--4 year cycle, most of the python projects will natively support M1. It probably will take a bit longer to get ML apps to support the "Neural Engine"
I don't know for your particular issue, but maybe you can go with anaconda, it is a package manager such as pip, but also an environment manager (for python but also non-python stuff) and developing into virtual environments is a good practice.
And I think it will be good for you since astropy is shipped with the anaconda installation by default.
What do you currently do in the health care field, and what IT job are you interested in? What attracts you to IT? I ask because IT is a wide field.
If you're stilling to work in IT in healthcare field, then your current experience in healthcare can be an asset, because you already know the jargon and how to talk to other healthcare professionals.
You have lots of options. While basic IT is a good skill set, IT paired with another subject is a killer skill set, because IT, specially programming, is a force multiplier that gives you a set of tools that can supercharge many other skills.
If you like programming and statistics, then you can look into health care analytics < https://en.wikipedia.org/wiki/Health_care_analytics >. You would need to know some programming, SQL, and statistics. To go this route, learn SQL, a few statistics courses, and either Python or R. I suggest the anaconda python distribution < https://www.anaconda.com/ >
Yes, as a beginner (if I may presume) it is a little hazardous on Mac because of the potential reliance on the pre-existing python environment. Even if things work now if you keep using the system version of python and end up updating or overwriting packages things could get messy.
You're trying to install and use a seperate python 3 installation which is better and will leave the system python alone as long as you are actually using the python 3. Speaking as someone that was once a beginner and also went down this route, I think chances are high you sometimes won't be using the python 3 simply by accident, and this will cause you confusion.
I would strongly advise you look into python environments. These are like little sandboxes with their own installations of python.
The more python native approach is with VirtualEnv
There is also Anaconda which is similar but a different package manager ecosystem. It can be easier to get along with because it has a GUI for managing the environments but you may get sidetracked when following instructions in textbooks.
That's not the right way to look at it.
Jupyter is a specific tool for a specific use case: creating narrative analyses. It happens to be a simple way to use python, which is why you've heard that.
The short answer to "how to download python" is go to python.org and download python.
However, there are plenty of setup traps for beginners, and you will see plenty of evidence for that in this sub.
This is why if you do the incredibly popular mit edx course (see sidebar), they start you off on anaconda / spyder. It is a pain free way to install python 3, a nice but simple ide (spyder), many popular libraries, and just have it work out of the box.
That's my recommendation. When you learn more you can switch to something else.
Two things, first how to (hopefully solve your problem), secondly how to install python 3 in a secure way.
How to (hopefully) solve your problem
I don't know if it would work but you could try to reinstall python 2.7 after first completely removing python 3.7
If you cannot get to the terminal you can switch tty to get to a terminal:
Ctrl+Alt+F2 (new tty) Ctrl+Alt+F1 (back to the main window again)
Once in the terminal, search for installed 3.7 packages (note *e*grep and backslash before period)
apt list --installed | egrep "python3.7"
for each one do (some packages may be removed as a consequence of the the first you remove because of dependency)
sudo apt purge <package name>
After the packages are removed, reinstall the python2.7 packages. First search for them
apt list --installed | egrep "python2"
On my machine (18.04) they are
libpython2.7 libpython2.7-dev libpython2.7-minimal libpython2.7-stdlib python2.7 python2.7-dev python2.7-minimal
For each one
sudo apt reinstall <package name>
There is a good chance this will solve your problem.
How to install python 3.
got the Anaconda website, download the anaconda distribution and follow the instructions there
https://www.anaconda.com/distribution/
With anaconda you create virtual environments (look at the help for this). After you have created an venv you activate it (also see help). Each venv is an independent environment and it doesn't interfere with the OS.
There are lots of online help on Anaconda.
> the Python 2.7 files are STILL on my computer, but not where they belong. They are now currently sitting in my Documents folder lol
Put them back, those files are there for a reason. You generally don't want to mess with your system Python installation at all. Hopefully you didn't permanently mess up your installation.
Personal recommendation: uninstall any other versions of Python you have installed. Download and install Anaconda. Read the user guide, including how to use conda. Do not skip the last step.
>surprise, surprise, pip won't work without administrator permission
Before reinventing the wheel and changing your code, I would just use Anaconda, which doesn't require admin rights to use.
Sure!
Download an IDE like Anaconda's Spyder to play around with
Here is a couple in suggested order:
- Siraj Raval
- this is absolutely the best resource for whatever you want to learn python/ML related.
- He is funny and smart and explains things really well. Suggest slowing down to .75 or something if he is talking too fast for you ( I definitely did in a couple vids...)
- Sololearn (download at app store for your phone)
- Kaggle.com
- check a competition your interested in, and look at people's EDA, then try and replicate what they are doing on your own
- Codeacademy.com (not sure if still free though..)
Anaconda bundles python libs (a lot of them, including pyqt5) and qtdesigner.
You can install it on Mac. To keep using PyCharm, just point PyCharm's python interpreter to the Anaconda's interpreter.
​
Source : creating python apps in Qt
Like the other commenters are saying, Python is probably going to be your best bet. It's currently the 'real programming language' used most for math and statistics (unless you need crazy high performance, in which case you might use FORTRAN or C or something).
I'd recommend installing Python via Anaconda, which is a distribution of Python specifically for data science. You want version 3.6. The Matplotlib library (libraries provide premade code to make developing your programs easier) is good for drawing your graphs. I'm also a big fan of developing programs of using Jupyter Notebooks, which are a way of writing Python code and immediately seeing its output right there next to it. Jupyter comes with Anaconda. You could even do the whole project in Jupyter and do something like this to make it interactive.
The only caveat here is if the assignment requires you to turn in one single '.exe' file and have your teacher just be able to run it with no setup. Python is not very good at this. In this case, you might consider writing the program in C# and using Windows Forms for the interface, since I assume most schools run Windows.
Download Python 3 from Anaconda and start with their suite of tools (Jupyter notebooks, Spyder, etc). Then as you get more comfortable you can expand to other ides or text editors
Edit: Here’s the link to anaconda
This is probably overkill, but if you want to be one the safe side, just download and install (ana)conda:
https://www.anaconda.com/download/
Chances are, if you're using Python, you'll want all those juicy Data Science packages anyway, at some point.
You may want to consider Anaconda, which is a widely used tool to manage your Python installation. You can quickly create/destroy virtual Python environments of any version with their own isolated libraries.
The most common way to manage multiple Python installations on your local machine is through Anaconda. Anaconda allows for a quick installation of Python, then you can create any number of virtual environments, each with their own packages and running any Python version.
For example, with Anaconda installed, I can create a new Python3.6 environment with:
conda create -n py36 python=3.6
Then activate the environment with
source activate py36
From here, you can install all you packages with pip normally. Read more at: https://www.anaconda.com/download/
I have experimented with numerous ways of installing python. I find the best, the easiest and the most complete way to do that is using the Anaconda distribution. You just basically have to go to https://www.anaconda.com/download/ and choose which distribution to download. I recommend downloading Python 3. Using anaconda you will get a lot of necessary libraries with it, if you find something missing, it can be easily added to the distribution.
Last year I took Microsoft's data science curriculum (which I would rate highly, and is free unless you want a certificate). The curriculum offered 2 pathways: Python and R. I followed the Python pathway and I found Python to be relatively straight forward to learn, and very efficient to write - a few lines of code go along way in Python (e.g. generators, data shaping, tuples).
C# is my favorite language, but I like having python around now for scripting tasks when I don't want to create an entire c# project.
I use Anaconda for data science as well as general small python tasks. Anaconda is free, and a great option.
In my experience Civil uses coding as a tool, so the focus is making the resulting program work for you, not others.
With that in mind a language that is open and popular (so it has tools and is in constant update) with also being easy to use is Python.
University students are frequently taught Matlab, but you need a license for it and in some ways it is more limiting than Python, although maybe more user friendly (debatable).
Anaconda is a program you can install in a Windows machine to start programming in Python right away. You can even programming from a browser with Jupyter.
The best way to avoid getting them mixed up in the first place is to install Anaconda, which manages different versions of Python for you in an isolated environment.
Well, fair enough. Python is an excellent choice of language, not least because of it's strict formatting. It also has a vast library of libraries for doing all sorts of matters financial. You might want to take a look at Anaconda https://www.anaconda.com/ distribution, as it provides all the scaffolding to let you get straight to the problem rather than learning to be a systems administrator.
Also, open an Amazon AWS account and get their free t2-micro instance if you want your code to be permanently connected to the Internet. You can scale up on AWS to quite dramatically large amounts of storage, CPU cores and GPUs fairly painlessly.
Also check out https://www.quantopian.com and https://www.quantconnect.com/ who provide a browser-based programming and back-testing environments with built-in data feeds.
<strong>Anaconda</strong> is all you need for scientific development, and comes with a MATLAB-like IDE called Spyder. It's not as nice as other IDEs when it comes to auditing and development, but it's good for learning Python with the command/variable explorer and gets the job done.
Outside of that, I recommend <strong>Sublime Text</strong> over PyCharm any day of the week. It has a plugin library which makes gives it the feature richness of PyCharm, minus all the fluff you don't need or want. The biggest plus IMO is that while both have free trial versions, a permanent license of Sublime is only $80, whereas PyCharm is a subscription product for the professional version.
Anaconda users should prefer to use conda as much as much as possible, since mixing conda and pip can (not always, but sometimes) cause problems. On a production server it would definitely be pointless to use Ana/conda but it makes sense for a development environment.
Personally I feel like the biggest hurdle for learning a language is setting up your environment in the first place. Make sure you set up your coding workspace first before you go deep into Python because reading up on coding without entering the commands yourself means you lose out on the learning experience
The most user-friendly environment for you to use is Spyder editor on Anaconda (https://www.anaconda.com/products/individual). It may take some time to set up, but this is what I started out with and it’s 100% worth it
Good overview here: https://www.anaconda.com/blog/apple-silicon-transition
Precompiled support for m1 won't happen until there are free build farms for it.
Personally I'd stay off it unless you enjoy compiling stuff.
PMs are fine, sir.
Also, choose one or two IDEs for your coding; these are the platforms that you code in. I recommend:
- Anaconda - https://www.anaconda.com - this is a complete data science platform available for Windows, Mac, and Linux. When you install it, you'll get an array of IDEs to work from. I recommend Jupyter book or jupyter lab. They run from within your browser and allow you to code fragmentarily (as in, run one line at a time instead of an entire block of code). Secondly, the other program within Anaconda that's great for data science is Spyder. It's just a great IDE, overall, and very versatile.
- Microsoft's Visual Studio Code - it's quite new still, but seems to be very user-friendly unlike ones like PyCharm which can seem cluttered.
Anyway, most people new to data science will use Jupyter and that's fine.
Re: portfolio, open a Github account and learn how to use Git from the command line. There's really only 3 or 4 actual commands you'll need, as you'll see down the line.
https://docs.python.org/3/tutorial/venv.html
they work like a VM. like a brand new virtual machine just opened for that specific interpreter. so u need to install all the 'modules' u need.
but running programs /code when that enviroment/vm is active will only use the modules installed for that so u can ..for example make a program that only uses python 2. and a special version of this module. etc.
​
either prompt or a web based prompt (jupyter notebook) are mandatory for code testing. your boss should know.
​
it s a framework/packet manager. or a super VM (using the same analogy).
if your code uses conda features then ..your code will need to be run on conda .
and anything BUT free individual package comes with a hefty price.
so make sure whomever u write conda code for has ..the ability to run this code.
definitely figure out how environments work.
​
Sorry for spamming your post.
Just one last thing, I believe the easiest way you could install Prophet is with conda. You can install with the command here (after installing conda, of course).
You will also see that lots of scientific packages are more easily installable with conda then with pip.
Anaconda (conda with lots of packages): https://www.anaconda.com/
Miniconda (conda, without the packages): https://conda.io/miniconda.html
I'm curious did you have issues with Anaconda's new commercial licensing? https://www.anaconda.com/blog/anaconda-commercial-edition-faq
This has caused a bit of an upset at our company and we've migrated towards using miniforge with conda-forge where we can.
If you don't have it already, get Python + Numpy + Scipy + Matplotlib. If you like you can get them all in big Scientific Python package with a lot of other libraries, tools and editors, called Anaconda.
Start with plotting graphs of functions, then after that you can progress to more advanced stuff like writing your own differential equation solver, and linear algebra (vectors, matrices, eigenvalue problems).
> "I downloaded the program I just don’t know where to begin."
What did you download? I recommend installing anaconda. It comes with python, sypder ide, jupyter notebook, and common libraries (numpy, pandas, matplotlib, scipy).
You should be using the Spyder IDE when you first start out.
I would suggest downloading Anaconda, it comes with a python version out of the box. After you have installed it, just download (if you havent already) the code editor of your choice (I use VSCode, Pycharm is also very widely used).
open the editor, if you have chosen VSCode, go to the VScode marketplace and install the python extension.
Then change the Interpreter setting in the IDE to the python.exe file of your python installation if it isnt added automatically (path should be similar to this: "C:\Users\__yourUser__\anaconda3\python.exe"). In VSCode this is as easy as pressing CTRL+SHIFT+P and searching for "interpreter" then just paste your path.
Done! Now you can just create files with the .py or .pyw extension and edit them in your code editor.
When you are more advanced you can use Virtual Envs with anaconda aswell. For documentation on that go here.
If you have any questions please feel free to ask.
Install anaconda (python +required packeges + editor) so you don't have to worry about anything. Use jupyter notebook for learning (editor for python included in anaconda). anaconda download page
https://www.anaconda.com/products/individual
Using virtual environments is pretty handy with Python because it can get messy if you try having multiple Python versions installed on your computer. With virtual environments you don't run the risk of screwing it up as you can run multiple virtual environments and creating a virtual env with a specific Python version is as easy as:
conda create -n myenv python=3.6
Then you activate that environment with:
conda activate myenv
and do as you would in Windows CMD. It's also useful to have a virtual env for each project, so you can easily delete the packages you don't need after you're done with it. Keeps the clutter minimal.
Here's a little guide.
In addition Anaconda is a package manager as well, and comes with some very useful packages.
I only run Python from virtual envs after having had some bad experiences with having multiple versions of Python on my PC.
There should be an installer on the site and you can check a box to add it to your PATH automatically during installation. I would delete any version of Python you have beforehand. Then whenever you need to run some Python open the Anaconda CLI.
Numpy is a rather complex library, and has particular build requirements and options for different systems. Python strives to be fairly simple to build and to work on for the core Python developers.
Having Numpy be an independent library frees the core Python developers from the support complexities, and also lets the Numpy developers ship on a release cycle that can be much more responsive to their own needs rather than a fixed release-the-world cycle of Python core.
If someone needs a Python that includes Numpy by default, consider the Anaconda distribution, which makes installation and upgrades quite easy for many of the complex scientific libraries.
You are in good company! The Anaconda 2020 State of Data Science report says,
"fewer than half (48%) of respondents feel they can demonstrate the impact of data science on business outcomes."
The issue is usually not about the quality of your data science. It's endemic across organizations.
It sounds like you're doing more analytics than trying to deploy models? In that case, stakeholders have all sorts of reasons for not acting on good insights. Real change is hard for a lot of organizations. Also, in my experience, leaders have A LOT of trouble being told there isn't a statistically sound way to answer question X about policy Y. It's possible that there would not be a reasonable or valid way to interrogate policy ideas with the datasets you have.
In either case: difficulty affecting business outcomes is a systemic problems that you as an individual data scientist aren't responsible for. You're not alone, and as the field matures, I expect companies will get better at turning data science into value. For now, we have a long way to go :)
My suggestion might be, if you can, to figure out what dataset(s) and methods in theory would help you make an actionable suggestion to stakeholders. And then you can ask if that dataset can be found. Then, you can be confident you've done the most you could.
Go and download anaconda python, it comes with all the scientific python packages installed, and all the code editing tools you need.
https://www.anaconda.com/products/individual
​
The path is something like understanding methods to:
- getting data into python, probably as a pandas data frame, maybe your data is a csv file, maybe it's a database etc.
- data cleaning and normalization
- transformations and running some functions on some data to derive some insights.
- visualisation - do you need to plot / tabulate data
Impressive work, but if you want to do the same with even more utility and work with the data and geometry: http://geopandas.org
As you went to the length build your own command line tool, I'm assuming you are not familiar with Python, so here is what would get you running in no time:
- Anaconda
- Check out Jupyter Notebooks, easy to visualize and explore geodata (comes with Anaconda, too)
- geopandas for 90% of the work with shapefiles, shapely for advanced geometry manipulation
geopandas uses the famous Python pandas package syntax and extends it for shapefiles.
If you do the incredibly popular MIT EDX course , they recommend you use Anaconda (and Spyder, the included IDE) (https://www.anaconda.com/distribution/). The reason is, you don't have to worry about any of that stuff you're struggling with. The things you are trying to do are absolutely useful to know later, but for a beginner just wanting to get familiar with the basics of the langauge, you don't need to worry about them (yet).
Start with Anaconda, see how you like it and go from there.
Yes, you're going to need at least Python 3.6 to get it working. A tip: install Anaconda (https://www.anaconda.com/distribution/) if you have the space (it takes up 3.5 GB), it will give you the latest Python version and tonnes of additional packages.
You both should look into using anaconda, it manages environments and packages really well. Basically it installs python3 and you run vs code or juypter notebook thru that, as a semi noob i definitely recommend.
install anaconda
https://www.anaconda.com/distribution/
go there , scroll down, select your operating system, select python 3.7
if you're on MS Windows select graphical installer
during installation it will ask you if you want to add python to the path
say no
I suggest you install Anaconda Python 3 for your development objectives.
As a rule of thumb, it is a good idea to let the packaged python interpreter be used as-is by the OS, and to install and maintain a separate python interpreter with its own virtual environments for development.
Because Anaconda Python is totally isolated, it has its own configuration and pip and all that which is easier to manage.
I would highly recommend installing from a distribution such as Anaconda, which will come with Scipy among many other libraries preinstalled.
Sorry if this is not the advice you are looking for. Good luck.
As for IDE you can use Spyder. It's pretty cool. Download Anaconda, since it will allow you to download and use packages seamlessly and switch quickly between environments and it comes with Spyder.
Anaconda Download Link : https://www.anaconda.com/distribution/#download-section
For webscraping there is the BeautifulSoup package, it's really great. Easy to use and understand.
BeautifulSoup installation: https://pypi.org/project/beautifulsoup4/
(If you are using Anaconda, you can directly install the package from the Anaconda navigator)
Documentation : https://www.crummy.com/software/BeautifulSoup/bs4/doc/
Hope this answers your question. :)
- For IDE (not interpreter) I use VS Code
- Go for python 3 definetely
- I like to code python on Linux better, but once you set it up on windows and get the hang of it it's no big difference
- If you want to use numpy and many other libraries check out the Anaconda distribution, will help ypu install VS code as well
>So the bash Terminal is a unix OS?
The underpinnings of MacOS (formally OS X, formally NeXTSTEP) is "Darwin", which is based on BSD, which in turn is based on Unix. It's more detailed than that, but that's my succinct explanation. When you open Terminal, you get a virtual console that runs a shell, which by default is Bash. You don't have to use Bash, but Bash is pretty ubiquitous. (Other popular options include, for example, zsh and fish.) There are also other terminal applications out there, iTerm2 comes to mind. Sorry if this is overload, but I love this stuff.
>It shows the location as /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages and so when I go to the Go bar and type in ~/Library/Frameworks it makes a "duup" sound like when you make a mistake and sends me to /Library/ folder with no folders named "Frameworks" in it.
Keep in mind that /Library is different than ~/Library! This is taken from another Unix convention, where you can install software system-wide, or per-user. (This was because *nix systems were originally multi-user, and users might want to install software only they can use, or use different versions, etc.) So you have a system Library folder, and a Library folder that pertains just to you. In this case, numpy is installed to the system Python library.
Now, if you'll indulge me just a little more, using the system Python can be dicey. This is because some system applications may rely on Python using very specific versions of packages. So it's in your best interest to install Python (version 3 is now mainstream) for yourself instead. I highly recommend using Anaconda.
Good luck with everything!
> Anaconda
Anaconda uses its own isolated environment, so messing with your system's has no effect (or should have no effect). See their own instructions on how to update Anaconda's own environment to 3.7.
How did you install conda, did you activate the environment?
The correct steps are:
- Install conda from https://www.anaconda.com/download/#linux
- Create new environment with `conda create -n test python=3`
- Activate the environment with `conda activate test`
- Install PyTorch from pytorch.org
- Test with `python -c 'import torch; print(torch.cuda.is_available())'`
last step should print `True` if it is successful.
>Does no one else have this problem?
Yes, installing libraries like matplotliband kiwisolver is notoriously difficult for a variety of reasons (like dependencies on C or Fortran code or various low-level mathematics libraries). The simplest solution is usually to use a 3rd-party Python distribution which takes care of all the issues for you. I personally recommend Anaconda, which also includes the conda package manager.
Anaconda's SOTU report picks GCP: https://www.anaconda.com/blog/company-blog/anaconda-2018-state-of-data-science-report-released/
+1 for Anaconda. Download and install Anaconda, which installs Python and a number of other useful packages, and it's much easier to maintain environments and different versions of software down the road
Interesting that that professor chooses a different distribution. I would still go with Anaconda's Python 3.6 distribution and then use the Spyder IDE as most professors still use these, and that's what's installed on the university computers. However if you get comfortable enough with enthought's Canopy IDE you should be able to pick up Spyder pretty easily.
As for intro to University, it kind of depends on the person, I never took it so I can't comment too much on it. If you can take it this summer before the fall term starts I suppose it could benefit you but again, having never taken it I'm not totally sure how useful it is.
This fairly simple Python script will take all sheets in all workbooks, and make one single sheet excel file, with two new columns, what workbook and what sheet the original data is from.
If you go down this path, I recommend installing Anaconda
import os import pandas as pd
lst = list() #list to hold data for file in os.listdir(): #loops every file in folder script is placed If file.endswith(".xlsx"): #skips non-xlsx files xlsx = pd.ExcelFile(file) #pandas Excel handling for sheet in xlsx.sheet_names: #loops over every sheet data = xlsx.parse(sheet) #retrieves data data["Workbook"] = file #new column with file name data["sheet_name"] = sheet #new column with sheet name lst.append(data) #save data
df = pd.concat(lst) #collect data df.to_excel("The one.xlsx") #write new excel file
>Install an older version of python that supports PySide and then copy my code into these older python documents.
Might be easier if you just download the Python 2.7 version of Anaconda and have it taking care of packages including PySide.
It's important because containers effectively decouple apps from operating systems. It seems like most Chromebook users (myself included) say they can use a Chromebook for between 90% and 99% of what they want to use a computer for --- but that remaining 1% to 10% involves one or two apps that must run in a separate OS. (Video editing software gets mentioned a lot; for me, it's Anaconda.)
If Crostini works out to be what I hope it will be, it will mean you can pick apps a la carte to run on ChromeOS without having to install a complete separate OS to do one thing.
(Correct me if I'm wrong on the technical side.)
Anaconda will probably meet your needs. It's a distribution of Python and a bunch of libraries intended for data science. Comes with a package manager, and has Enterprise solutions if that's where you're headed. And it's only one installer.
To find the paths of local packages you can use
import site site.getsitepackages()
Source stack overflow ^^heh
I'd recommend Anaconda for managing virtual environments. Since you're on windows your OS doesn't depend on python, so you probably won't break anything by pip installing packages directly.
However, I think you should get used to using a new virtual environment for each project early on because you'll definitely need it later. For example, say you start a project with a large number of requirements. If you use a virtual environment:
1. You can use pip freeze > requirements.txt to export all your dependencies to a text file. Then if anyone wants to run your project all they need is pip install -r requirements.txt and they have all the packages they need.
2. Your project may need pyperclip v1.0, but another project you're working on needs v2.0. If you have a virtual environment for each project, you can have both versions, one in each env. Without virtual environments, you'll have to pick one.
(ana)Conda is a python "distribution" that includes a bunch of the packages people might want and the low level dependencies (libjpeg, libtiff, etc) that the python packages need. I just makes all of the package installation type stuff easier. Wheels have solved much of these problems though so sticking with pip and your local python is fine. It kinda just depends whether you think its worth it to invest time in solving dependency problems (use pip) or if you just want to get to using pillow as fast as possible (use conda).
Have you just tried doing
pip install Pillow
as recommended in the Pillow docs?
Even though Excel should be in your toolkit, I would focus on Python since it is the language of choice for GIS and really common for scripts.
Anaconda is a great data science platform for Python and you can find different Python tutorials done in Jupyter.
R is also really easy to get started in as well, check out Swirl which offers interactive R lessons.
Does anyone have any idea how to get Anaconda to work from cmdr? Right now, whenever I have to work in Windows, I have to switch between separate consoles: the dedicated one for Anaconda for any python-related work, and cmder for everything else.
Could you tell us what's going wrong? What OS you're using? List out the steps you tried, and where you believe the error was? This couldn't be more vague... Also, you just need the installer. No need to tamper with command prompt at all. I never did that when I installed. Use this link, but delete your Anaconda3 folder first for a fresh install, or 'uninstall' from programs and features
I had a little time and wrote a scraper in python to get the fan data. If you can manage to install python (https://www.anaconda.com/download/), you'll just need to download this .py script, open it to change the output directory, and run it (at the command line, type python 'the path to the script.py').
With ~13k pages it will take about 8 hours to run!
https://gist.github.com/seanchrismurphy/b5c2904bcea1efd2c228d2657ca38326
If you install anaconda, you don't need to worry about any version of python, nympy, and pandas. It will install all the libraries you need. 1) In your vm, open browser and connect to https://www.anaconda.com/download/ and download the correct version of anaconda. 2) https://docs.continuum.io/anaconda/install/ you can find the installation instruction in the link above. Select correct machine you want to use and follow the instruction. It is fairly easy.
I installed it in my Ubuntu 17 and it works fine.
Once you get it working again, I highly recommend that you start your Python journey with Anaconda. It will sandbox everything for you so you can make environments for playing with packages and versions, etc. Once you get some experience you can look into other options but this will give you a predictable (and safe) installation.
It is a package-type deal with python and programs to work with it. https://www.anaconda.com/
Personally, I much prefer to manage my venvs through the terminal and code with VS Code but that's just me.
Dude, don't install from scratch on the command line. DL something to manage that part for you until you get used to the language.
Anaconda should take care of most of the path and environment stuff (and give you a ton of options for IDEs to do the same. In that download there should be links to installs for jupyter, pycharm, and a bunch of other tools that should manage all that for you.
You can also just sign up for binder or google collab or databricks community edition and have all of the path, environment, and compute resources all pushed off 100% to the cloud. Very useful especially when starting to learn.
https://colab.research.google.com/#scrollTo=gJr_9dXGpJ05
Start by installing anaconda though, as I think that will solve most of your problems. https://www.anaconda.com/products/individual
You will likely install spyder bundled in the big anaconda distribution.
The advantage of anaconda over pycharm is that it sets up an environment with the most popular python packages right out of the box. Pycharm is more flexible, but as a beginner, you will be spending a long time setting it up, and you'll be doing it ad-hoc, it will be like putting out fires as they come up vs. using a non-flammable distribution (spyder in anaconda). This means you're spending less time figuring out what you need to set up and more time actually writing python code.
As a beginner, I suggest you stay off PyCharm until you feel ready to open the hood of the python car and look inside. Spyder is ideal for that kind of thing. Plus, all of its features are self-contained and easily available.
It looks less safe than the AUR to me, and I don't use the AUR because I don't want to review PKGBUILD files and verify that they're benign.
An example of what I would consider as the right way to do something like this is what Anaconda is doing with curated repositories by default and conda-forge to provide a fast track to official developers to provide packages to users.
Want to do this and are semi tech savvy?
Here's how for Windows users:
Download Anaconda https://www.anaconda.com/products/individual
Download the code for this program https://github.com/facebookresearch/demucs Click 'Code' then 'Download as Zip'.
Extract the zip somewhere, like your Documents folder.
After install, open the new program "Anaconda Powershell Prompt".
Type in
python.exe -m pip install -U demucsWait until it finishes and the last few lines say success.
Type in
pip install PySoundFileOpen Windows Explorer and find the file you want to de-music. It has to be in .wav. If you have an MP3 you want to use, download Audacity, open the file, and do Export as Wav.
Hold left shift and right click the file.
Click on 'copy as path'.
Type in
demucs [[paste exactly what it copied]]Wait, it'll tell you where it's storing the outputted file.
You're done
The steps are pretty simple, but I wouldn't recommend doing this unless you have an okay foundation in what computer code is and can navigate a site like Github.
Not a problem. You can download the software here https://jupyter.org/. You can also install Anaconda, which comes with other tools. https://www.anaconda.com/products/individual#
The Spyder IDE also lets you work with them pretty well. https://docs.spyder-ide.org/5/plugins/notebook.html
Re: How is Anaconda different than Base Python:
> Conda and pip are often considered as being nearly identical. Although some of the functionality of these two tools overlap, they were designed and should be used for different purposes. Pip is the Python Packaging Authority’s recommended tool for installing packages from the Python Package Index, PyPI. Pip installs Python software packaged as wheels or source distributions. The latter may require that the system have compatible compilers, and possibly libraries, installed before invoking pip to succeed. > >Conda is a cross platform package and environment manager that installs and manages conda packages from the Anaconda repository as well as from the Anaconda Cloud. Conda packages are binaries. There is never a need to have compilers available to install them. Additionally conda packages are not limited to Python software. They may also contain C or C++ libraries, R packages or any other software.
https://www.anaconda.com/blog/understanding-conda-and-pip
Re:can I extend python to be compatible with Anaconda
There's nothing incompatible between Python and Anaconda, they're two different things with different uses and goals (see above).
To your point, if all that your teacher needs you to work with are Python packages and you're willing to manage things manually (or even just prefer to) then sticking with your regular install of Python is just fine.
The only thing to keep in mind is that Anaconda makes things like virtual environments and package installation easier (especially for packages that have build requirements that are more complex or technical). So if you find yourself stuck trying to compile a C library that your Python package needs, it's possible that Anaconda would handle all of that for you much easier.
Is that maybe because anaconda isn't free for use in the workplace (any more)?
https://www.anaconda.com/pricing
Our IT folks sent around a "do you really need it, because we are about to delete it" email a while back...