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Python - Blog Posts
Timor Python (Malayopython timoriensis), family Pythonidae, Komodo National Park, Indonesia
photograph by James Hunt Photography
Since there's so many drawings of Azula with dragons I thought why not give a modern azula a ball python
Everybody meet "Terminator, all powerful celestial beast, ender of worlds and destroyer of mankind" or what ty lee calls it "terry" 🫶🏻
I originally wanted to make a ty lee version with her own pet as well but got burnout hallway 😔
oh python, never change
rating = 4.5
WRONG
rating = str(4.5)
Okay thats allowed
"So you know how C is procedural, Haskell is functional, and Java is object-oriented? You may ask: 'what about Python?'
Well, Python is bullshit-oriented."
My friend, teaching me Python.
i love auditing coding classes online (edx my beloved)
cant do graded assignments BUT i get all the notes and videos which gives me some free codes to practice also most assignments have a connected video
Here's all ten of those fuckers that I had to speedrun today, can you tell how much I hate my professor for delivering important information that should've been known from the start of the practice and not three days before the exam?
guys am. am I winning at coding yet .
Eh all programming languages are good for certain use cases (aside from Java - Kotlin is better for android and Go is better for anything else).
Python is good at quick and dirty automation that just needs to get done. It’s very friendly to use and won’t pout at you when you ask it do something. Also once you learn to navigate pandas+numpy combined with Jupyter Notebooks it gets wayyyy faster and easier to use for data wrangling.
For example, I recently used Python to scrape hundreds of thousands of tweets via snscrape without having to use twitter’s API. Once I downloaded all the tweets it took me about 30 minutes to then do some basic analysis/labeling/sorting on said tweets.
Yes pip is terrible. Yes Python has only a hint of types (typescript style type hinting arrived in 3.something). Yes pickle creates so many vulnerabilities. Yes performant Python is basically C in a trench coat.
All that said, there’s a reason Python is many people’s first typed programming language and why I continue to use it whenever I have some data I have to fetch, transform, and analyze or whenever I’m just starting to explore a new field of computer science.
Writing Python is basically like writing pseudo code so I love it for anything that I just need to code up and run once or twice for either a proof of concept before moving to a more “serious” language or just discard the program is for my one-time personal use only.
No one should ever have to maintain more than 1k lines of Python but I will still occasionally write that much Python simply because it lets me explore high level techniques without worrying about being perfectly precise.
Python is not for production but instead for messing around. Python is that goofy ahh language that everyone likes because it doesn’t mind when you affectionately mess with it. Python is the adorable sidekick that makes programming fun again and for that I adore it
Java is a trash language that should burn in the parts of hell where hitler is
Rust on the other hand is a bratty lil language that should burn in the parts of hell where queers party
first chemistry lab this year. currently reading letters to a young poet between breaks. and living off ice tea (obsessed) the weather is so pretty <3
Why Python is the Best Choice for Modern Web Development
Building modern, scalable websites has never been easier thanks to Python. With its powerful frameworks like Django and Flask, Python enables faster development, clean code architecture, and excellent performance. Whether you need an e-commerce platform, a dynamic web application, or a custom enterprise solution, Python provides the flexibility and security businesses need today. If you're looking to bring your idea to life with reliable, cutting-edge technology, partner with a trusted Python web development company.
Building a bot using Twitter API which will like & retweet #100daysofcode tweets. One & half-hour of footage in 6sec looks cool. #timelapse #coding #python #twitter #bot #programming #api #100daysof2020 #developer #lockdown https://www.instagram.com/p/CCbpMsYgU3t/?igshid=ccc7lv2c8en1
Python: Geospatial Environment Setup (Part 2)
Python: Geospatial Environment Setup (Part 2)
Hey again folks! I am here for the second part of Python environmental setup for a geospatial workspace. I published the first part of this post two weeks ago. So if you've not yet read that, I'll catch you up to speed with our checklist:
Install Python ☑
Install Miniconda ☑
Install the basic Python libraries ☑
Create a new environment for your workspace
Install geospatial Python libraries
🗃 Create a new environment for your workspace
Since we have actually manually set up our base environment quite thoroughly with all the basic libraries needed, to make our work easier, we can just clone the base environment and install all the additional essential libraries needed for geospatial analysis. This new environment will be called geopy. Feel free to use a name you identify most with.
Why don't we just create a new environment? Well, it means we have to start installing the Python libraries again from scratch. Although it is no trouble to do so, we want to avoid installing so many libraries all at once. As I mentioned in Part 1, there is always a risk where incomplete dependencies in one library will affect the installation of other libraries that you intend to install in one go. Since we already have a stable and usable base environment, we can proceed to use it as a sort of pre-made skeleton that we will build our geospatial workspace with.
1️⃣ At the Anaconda Command Prompt, type the following:
2️⃣ Press Enter and the environment will be clone for you. Once it is done, you can use the following command to check the availability of your environment 👇🏻
You should be able to see your geopy environment listed along with the base environment.
👩🏻💻 Install geospatial Python libraries
Here we will proceed with the installation of a few geospatial Python libraries that are essential to reading and exploring the vectors and rasters.
🔺 fiona: This library is the core that some of the more updated libraries depend on. It is a simple and straightforward library that reads and writes spatial data in the common Python IOs without relying on the infamous GDAL's OGR classes.
🔺 shapely: shapely library features the capability to manipulate and edit spatial vector data in the planar geometric plane. It is one of the core libraries that recent geospatial Python libraries rely on to enable the reading and editing of vector data.
🔺 pyproj: is the Python interface for the cartographic projections and coordinate system libraries. Another main library that enables the 'location' characteristics in your spatial data to be read.
🔺 rasterio: reads and writes raster formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON.
🔺 geopandas: extends the pandas library to allow spatial operations on the geometric spatial data i.e shapefiles.
💀 As you might have noticed, we won't be doing any direct gdal library installation. It's mainly due to the fact that its installation is a process that seems to be accompanied by misery at every turn and involved workarounds that are pretty inconsistent for different individuals. Does it mean that we won't be using it for our Pythonic geospatial analysis? Heck no. But we will be taking advantage of the automatic dependency installation that comes with all the libraries above. The rasterio library depends on gdal and by installing it, we integrate the gdal library indirectly into our geospatial environment. I found that this method is the most fool-proof. Let's proceed to the installation of these libraries.
1️⃣ At the Anaconda Command Prompt, should you start from the beginning, ensure that your geopy environment is activated. If not, proceed to use the following command to activate geopy.
Once activated, we can install the libraries mentioned one after another. Nevertheless, you also have the option of installing them in one go directly using a single command 👇🏻
💀 geopandas is not included in this line-up NOT because we do not need it. It's another temperamental library that I prefer to isolate and install individually. If gdal is a rabid dog...then geopandas is a feral cat. You never know how-when-why it doesn't like you and forces a single 10-minute installation drag to hours.
3️⃣ Once you're done with installing the first line-up above, proceed with our feral cat below 👇🏻
4️⃣ Use the conda list command again to check if all the libraries have been installed successfully.
🎉Et voilá! Tahniah! You did it!🎉
🎯 The Jupyter Notebook
It should be the end of the road for the helluva task of creating the geospatial environment. But you're going to ask how to start using it anyway. To access this libraries and start analyzing, we can easily use the simple and straight-forward Jupyter Notebook. There are so many IDE choices out there but for data analysis, Jupyter Notebook suffices for me so far and if you are not familiar with Markdown, this tool will ease you into it slowly.
Jupyter Notebook can be installed in your geopy environment as follows:
And proceed to use it by prompting it open via the command prompt
It ain't that bad, right? If you're still having problems with the steps, do check out the real-time video I created to demonstrate the installation. And feel free to share with us what sort of problems you have encountered and the workaround or solutions you implemented! It's almost never a straight line with this, trust me. As mentioned in the previous post, check out the quick demo below 👇🏻
See you guys again for another session on geospatial Python soon!
Python: Geospatial Environment Setup (Part 2)
Hey again folks! I am here for the second part of Python environmental setup for a geospatial workspace. I published the first part of this post two weeks ago. So if you've not yet read that, I'll catch you up to speed with our checklist:
Install Python ☑
Install Miniconda ☑
Install the basic Python libraries ☑
Create a new environment for your workspace
Install geospatial Python libraries
🗃 Create a new environment for your workspace
Since we have actually manually set up our base environment quite thoroughly with all the basic libraries needed, to make our work easier, we can just clone the base environment and install all the additional essential libraries needed for geospatial analysis. This new environment will be called geopy. Feel free to use a name you identify most with.
Why don't we just create a new environment? Well, it means we have to start installing the Python libraries again from scratch. Although it is no trouble to do so, we want to avoid installing so many libraries all at once. As I mentioned in Part 1, there is always a risk where incomplete dependencies in one library will affect the installation of other libraries that you intend to install in one go. Since we already have a stable and usable base environment, we can proceed to use it as a sort of pre-made skeleton that we will build our geospatial workspace with.
1️⃣ At the Anaconda Command Prompt, type the following:
2️⃣ Press Enter and the environment will be clone for you. Once it is done, you can use the following command to check the availability of your environment 👇🏻
You should be able to see your geopy environment listed along with the base environment.
👩🏻💻 Install geospatial Python libraries
Here we will proceed with the installation of a few geospatial Python libraries that are essential to reading and exploring the vectors and rasters.
🔺 fiona: This library is the core that some of the more updated libraries depend on. It is a simple and straightforward library that reads and writes spatial data in the common Python IOs without relying on the infamous GDAL's OGR classes.
🔺 shapely: shapely library features the capability to manipulate and edit spatial vector data in the planar geometric plane. It is one of the core libraries that recent geospatial Python libraries rely on to enable the reading and editing of vector data.
🔺 pyproj: is the Python interface for the cartographic projections and coordinate system libraries. Another main library that enables the 'location' characteristics in your spatial data to be read.
🔺 rasterio: reads and writes raster formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON.
🔺 geopandas: extends the pandas library to allow spatial operations on the geometric spatial data i.e shapefiles.
💀 As you might have noticed, we won't be doing any direct gdal library installation. It's mainly due to the fact that its installation is a process that seems to be accompanied by misery at every turn and involved workarounds that are pretty inconsistent for different individuals. Does it mean that we won't be using it for our Pythonic geospatial analysis? Heck no. But we will be taking advantage of the automatic dependency installation that comes with all the libraries above. The rasterio library depends on gdal and by installing it, we integrate the gdal library indirectly into our geospatial environment. I found that this method is the most fool-proof. Let's proceed to the installation of these libraries.
1️⃣ At the Anaconda Command Prompt, should you start from the beginning, ensure that your geopy environment is activated. If not, proceed to use the following command to activate geopy.
Once activated, we can install the libraries mentioned one after another. Nevertheless, you also have the option of installing them in one go directly using a single command 👇🏻
💀 geopandas is not included in this line-up NOT because we do not need it. It's another temperamental library that I prefer to isolate and install individually. If gdal is a rabid dog...then geopandas is a feral cat. You never know how-when-why it doesn't like you and forces a single 10-minute installation drag to hours.
3️⃣ Once you're done with installing the first line-up above, proceed with our feral cat below 👇🏻
4️⃣ Use the conda list command again to check if all the libraries have been installed successfully.
🎉Et voilá! Tahniah! You did it!🎉
🎯 The Jupyter Notebook
It should be the end of the road for the helluva task of creating the geospatial environment. But you're going to ask how to start using it anyway. To access this libraries and start analyzing, we can easily use the simple and straight-forward Jupyter Notebook. There are so many IDE choices out there but for data analysis, Jupyter Notebook suffices for me so far and if you are not familiar with Markdown, this tool will ease you into it slowly.
Jupyter Notebook can be installed in your geopy environment as follows:
And proceed to use it by prompting it open via the command prompt
It ain't that bad, right? If you're still having problems with the steps, do check out the real-time video I created to demonstrate the installation. And feel free to share with us what sort of problems you have encountered and the workaround or solutions you implemented! It's almost never a straight line with this, trust me. As mentioned in the previous post, check out the quick demo below 👇🏻
See you guys again for another session on geospatial Python soon!
Python: Geospatial Environment Setup (Part 1)
Here’s a quick run down of what you’re supposed to do to prepare yourself to use Python for data analysis.
Install Python ☑
Install Miniconda ☑
Install the basic Python libraries ☑
Create new environment for your workspace
Install geospatial Python libraries
🐍 Installing Python
Let’s cut to the chase. It’s December 14th, 2021. Python 3 is currently at 3.10.1 version. It’s a great milestone for Python 3 but there were heresay of issues concerning 3.10 when it comes to using it with conda. Since we’re using conda for our Python libraries and environment management, we stay safe by installing Python 3.9.5.
Download 👉🏻 Python 3.10.1 if you want to give a hand at some adventurous troubleshooting
Or download 👉🏻 Python 3.9.5 for something quite fuss-free
📌 During installation, don’t forget to ✔ the option Add Python 3.x to PATH. This enables you to access your Python from the command prompt.
Installing Miniconda
As a beginner, you’ll be informed that Anaconda is the easiest Python library manager GUI to implement conda and where it contains all the core and scientific libraries you ever need for your data analysis upon installation. So far, I believe it’s unnecessarily heavy, the GUI isn’t too friendly and I don’t use most of the pre-installed libraries. So after a few years in the darkness about it, I resorted to jump-ship and use the skimped version of conda; Miniconda.
Yes, it does come with the warning that you should have some sort of experience with Python to know what core libraries you need. And that’s the beauty of it. We’ll get to installing those libraries in the next section.
◾ If you’re skeptical about installing libraries from scratch, you can download 👉🏻 Anaconda Individual Edition directly and install it without issues; it takes some time to download due to the big file and a tad bit longer to install.
◾ Download 👉🏻 Miniconda if you’re up to the challenge.
📌 After you’ve installed Miniconda, you will find that it is installed under the Anaconda folder at your Windows Start. By this time, you will already have Python 3 and Anaconda ready in your computer. Next we’ll jump into installing the basic Python libraries necessary for core data analysis and create an environment to house the geospatial libraries.
📚 Installing core Python libraries
Core libraries for data analysis in Python are the followings:
🔺 numpy: a Python library that enables scientific computing by handling multidimensional array objects, or masked objects including matrices and all the mathematical processes involved.
🔺 pandas: enables the handling of ‘relational’ or 'labeled’ data structure in a flexible and intuitive manner. Basically enables the handling of data in a tabular structure similar to what we see in Excel.
🔺matplotlib: a robust library that helps with the visualization of data; static, animated or interactive. It’s a fun library to explore.
🔺 seaborn: another visualization library that is built based on matplotlib which is more high-level and produces more crowd-appealing visualization. Subject to preference though.
🔺 jupyter lab: a web-based user interface for Project Jupyter where you can work with documents, text editors, terminals and or Jupyter Notebooks. We are installing this library to tap into the notebook package that is available with this library installation
To start installing:
1️⃣ At Start, access the Anaconda folder > Select Anaconda Prompt (miniconda3)
2️⃣ An Anaconda Prompt window similar to Windows command prompt will open > Navigate to the folder you would like to keep your analytics workspace using the following common command prompt codes:
◽ To backtrack folder location 👇🏻
◽ Change the current drive, to x drive 👇🏻
◽ Navigate to certain folders of interest e.g deeper from Lea folder i.e Lea\folder_x\folder_y 👇🏻
3️⃣ Once navigated to the folder of choice, you can start installing all of the libraries in a single command as follows:
The command above will enable the simultaneous installation of all the essential Python libraries needed by any data scientists.
💀 Should there be any issues during the installation such as uncharacteristically long installation time; 1 hour is stretching it, press Ctrl + c to cancel any pending processes and proceed to retry by installing the library one by one i.e
Once you manage to go through the installation of the basic Python libraries above, you are half way there! With these packages, you are already set to actually make some pretty serious data analysis. The numpy, pandas and matplotlib libraries are the triple threat for exploratory data analysis (EDA) processes and the jupyter lab library provides the documentation sans coding notebook that is shareable and editable among team mates or colleagues.
Since we’re the folks who like to make ourselves miserable with the spatial details of our data, we will climb up another 2 hurdles to creating a geospatial workspace using conda and installing the libraries needed for geospatial EDA.
If you're issues following the steps here, check out the real-time demonstration of the installations at this link 👇🏻
See you guys in part 2 soon!
Yes peeps. I’ve been studying and on contrary to all my previous attempts to make beautiful notes, I say f it and just work with what helps me clear my head the fastest 🏃🏻♀️. I love writing notes, but I realize, to gather my thoughts properly, I need some sort of way to not waste paper just to arrange and rearrange my ideas or comprehension of things.
What better way of doing that than using a mind map!
So you kiddos out there who are starting out with Python and just can’t wait to get into deep learning or machine learning, I’d say, hold your horses for a minute and have some preview of that pond you’re trying to jump into. And don’t be scared, cause we’re all friends here in the hell-hole of learning plateau. Will it get better? I believe so. I am positive I understand more of the principles of deep learning and the relevance of Python libraries associated with it. Yes...this is a Python bar, darling. 👩🏻💻
There’s no real shortcut if you ask me since we have different way of comprehending things; my pre-existing mold may have harder time grasping the things I am learning right now than you would. So don’t be afraid to doodle while you think. No amount of paper will be enough to help you understand things, so better start being sustainable by using some digital platforms and saving those papers to when you’re truly ready to pen out your understanding of things; not what you read. There’s a difference!
Check out the mind map of some essential Python libraries you can get started with before you start doing some deep learning. It’s worth reviewing all that prior, I promise.
Have fun! 🙆🏻♀️
zero to pandas
There are alot of Python courses out there that we can jump into and get started with. But to a certain extent in that attempt to learn the language, the process becomes unbearably long and frustratingly slow. We all know the feeling of wanting to run before we could learn how to walk; we really wanna get started with some subtantial project but we do not know enough to even call the data into the terminal for viewing.
Back in August, freeCodeCamp in collaboration with Jovian.ai, organized a very interesting 6-week MOOC called Data Analysis with Python: Zero to Pandas and as a self-proclaimed Python groupie, I pledged my allegiance!
If there are any expectation that I've managed to whizz myself through the course and obtained a certificate, nothing of that sort happened; I missed the deadline cause I was busy testing out every single code I found and work had my brain on overdrive. I can't...I just...can't. Even with the extension, I was short of 2 Pythonic answers required to earn the certificate. But don't mistake my blunders for the quality of the content this course has to offer; is worth every gratitude of its graduates!
Zero to Pandas MOOC is a course that spans over 6 weeks with one lecture webinar per week that compacts the basics of Python modules that are relevant in executing data analysis. Like the play on its name, this course assumes no prior knowledge in Python language and aims to teach prospective students the basics on Python language structure AND the steps in analyzing real data. The course does not pretend that data analytics is easy and cut-corners to simplify anything. It is a very 'honest' demonstration that effectively gives overly ambitious future data analysts a flick on the forehead about data analysis. Who are we kidding? Data analysis using programming language requires sturdy knowledge in some nifty codes clean, splice and feature engineer the raw data and real critical thinking on figuring out 'Pythonic' ways to answer analytical questions. What does it even mean by Pythonic ways? Please refer to this article by Robert Clark, How to be Pythonic and Why You Should Care. We can discuss it somewhere down the line, when I am more experienced to understand it better. But for now, Packt Hub has the more comprehensive simple answer; it simply is an adjective coined to describe a way/code/structure of a code that utilizes or take advantage of the Python idioms well and displays the natural fluency in the language.
The bottom line is, we want to be able to fully utilize Python in its context and using its idioms to analyze data.
The course is conducted at Jovian.ai platform by its founder; Aakash and it takes advantage of Jupyter-like notebook format; Binder, in addition to making the synchronization available at Kaggle and Google's Colab. Each webinar in this course spans over close to 2 hours and each week, there are assignments on the lecture given. The assignments are due in a week but given the very disproportionate ratio of students and instructors, there were some extensions on the submission dates that I truly was grateful for. Forum for students is available at Jovian to engage students into discussing their ideas and question and the teaching body also conducts office hours where students can actively ask questions.
The instructor's method of teaching is something I believe to be effective for technical learners. In each lectures, he will be teaching the codes and module requires to execute certain tasks in the thorough procedure of the data analysis task itself. From importing the .csv formatted data into Python to establishing navigation to the data repository...from explaining what the hell loops are to touching base with creating functions. All in the controlled context of two most important module for the real objective of this course; Numpy and Pandas.
My gain from this course is immensely vast and that's why I truly think that freeCodeCamp and Jovian.ai really put the word 'tea' to 'teachers'. Taking advantage of the fact that people are involuntarily quarantined in their house, this course is something that should not be placed aside in the 'LATER' basket. I managed to clear my head to understand what 'loop' is! So I do think it can solve the world's problem!
In conclusion, this is the best course I have ever completed (90%!) on data analysis using Python. I look forward to attending it again and really finish up that last coursework.
Oh. Did I not mention why I got stuck? It was the last coursework. We are required to demonstrate all the steps of data analysis on data of our choice, create 5 questions and answer them using what we've learned throughout the course. Easy eh? Well, I've always had the tendency of digging my own grave everytime I get awesome cool assignments. But I'm not saying I did not do it :). Have a look-see at this notebook and consider the possibilities you can grasp after you've completed the course. And that's just my work...I'm a standard C-grade student.
And the exciting latest news from Jovian.ai is that they have upcoming course at Jovian for Deep Learning called Deep Learning with PyTorch: Zero to GANS! That's actually yesterday's news since they organized it earlier this year...so yeah...this is an impending second cohort! Tentatively, the course will start on Nov 14th. Click the link below to sign-up and get ready to attack the nitty-gritty. Don't say I didn't warn ya.
And that's me, reporting live from the confinement of COVID pandemic somewhere in a developing country at Southeast Asia....
Poker game #sciphy4all #science #physics #programming #computer #computers #pc#perl #python #basic #c #javascript #php #java
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the_bee_movie_but_every_time_they_say_bee_it_gets_randomly_faster_or_slower() by iforgotmylegs https://www.reddit.com/r/ProgrammerHumor/comments/5o144h/the_bee_movie_but_every_time_they_say_bee_it_gets/?utm_source=ifttt
Just did a very easy normal Addition Function hah (⌐■_■)
Please some improvement ideas ;)
Get Hooked on Programming Quick
Highly sought for and praised- the ability to program. Admit it, being comfortable with technology makes you an admirable candidate for your dream job and allows you to navigate the modern world more fluidly. Unfortunately the idea of becoming tech savvy was soiled by the thought of dry lingo and geeky stereotype. For the tech enthused or not, young or wise I have seven ways you can get started with programming in a few hours or much less. (In order of what I thought of first)
1) Codecademy
Best for: Middle School - Adulthood and beyond
Project Time: 30min - As long as you wish
Languages: Web development, Ruby, Python, PHP
Where: http://www.codecademy.com/learn
Friends from internships used codecademy to get exposed to a number of languages relatively quickly and from there decided what to dive into.
2) Code.org
Best for: Elementary - High School
Project Time: 30min - As long as you wish
Languages: Java Script, HTML, basic logic, Lego Robotics, and more
Where: http://code.org/
A really fun way to learn programming by playing games or making your own! President Obama was visited by some aspiring programmers and completed a Disney Frozen themed project.
3) Alice
Best for: Elementary - High School
Project Time: 30min - As long as you wish
Languages: Java
Where: http://www.alice.org/index.php?page=downloads/download_alice3.1
(Younger programmers may need help downloading and getting started)
Alice allows you to program in pseudo Java code and control characters. You can make movies or perform skits with your characters by moving blocks of code around and assigning values. This was my first exposure to programming in high school. As I continued with my computer studies in college found that what I learned from Alice to be extremely useful.
4) Java Seal
Best for: Middle School - Adulthood and beyond
Project Time: 30min - As long as you wish
Languages: Java
Where: https://www.youtube.com/user/JavaSeal
This is my shameless plug; I started this You Tube channel this past summer and recorded some videos about how to get started with Java programming, a basic 10 minute starter program, and a 30min more complex program. Although I haven't downloaded videos recently these are a good start and I expect to make more this summer.
5) Made With Code
Best for: Elementary - Middle School
Project Time: 5min - As long as you wish
Languages: Basic Logic
Where: https://www.madewithcode.com/projects
My Mom's kindergarten class loved to make a Yeti dance and lights shimmer using the Made With Code project. I visited her classroom and had students drag and drop blocks of code on a Smart Board. These projects teach about basic programming logic and assigning values to variables.
6) Khan Academy
Best for: Middle School - Adulthood and beyond
Project Time: 30min - As long as you wish
Languages: Game, Animation, Web Development
Where: https://www.khanacademy.org/computing/computer-programming
Khan Academy has great videos for every subject imaginable in school and now for programming. There are many projects where you can make video games, a website, or animation. There is even a gallery of what other programmers have made while following along with the video.
7) Major League Hacking
Best for: High School - College
Project Time: Few hours to 3 day event
Languages: Any language! Common ones include C++ and Java
Where: Select Colleges https://mlh.io/seasons/f2014/events
This isn't a website to visit but a Hackathon event to go to! Hackathons are events where students are challenged to create a solution to a problem such as an app that can send you texts to keep you on task, a light that can let you know when you need to wear warm clothes, or the next big social media hot spot. Hackathons can run for a few hours to three days. Most of the Hackathons don't require programming experience and are a great place to learn.
(These age groups are just suggestions, feel free to check out any of them you like. I didn't make any of the logos except my Java Seal one so kudos to you other logo makers.)
Gave them freepythonhub:
Most of my current workflow involves some manner of data analysis / visualization / relatively light stats in an IPython notebook. A new source of data (Factset, if it helps) has well-developed interfaces for R and Matlab – both of which I’ve used extensively in the past, but barely at all in the last ~year.
My question is which – R or Matlab – is going to lend itself to more flexibility in terms of using data pulled through one of them in Python (at least in cases where switching back over to Python makes sense in the first place)? Would you rather have to use a combination of Python and R, or a combination of Python and Matlab?
Thanks!
submitted by josiahstevenson [link] [comment] [ link ]
Clearly R. By far more accessible. Open Source=free. R libraries grow fast in most areas of research. Reminds me what DEC did with the PDP-11. DEC gave them free to many selected Universities. Soon they became the standard. When grads got jobs eventually they opted for what they knew well. And demended it.
R API or Matlab API for integration with Python downstream? (x-post /r/pystats) [reddit]
You have just found Keras. #Keras is a minimalist, highly modular #neural #network library in the spirit of Torch, written in #Python / Keras: Theano-based Deep Learning library
Fast Deep Learning Prototyping for Python [reddit]
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