An Introduction to Statistics with Python
work.thaslwanter.at
work.thaslwanter.at
One other thing I hope comes along is annotated mathematical formulas/expressions. Any time I see a mathematical symbol I want to be able to hover over it and get its definition (in the context it's being used) and other relevant information. So often I see formulas in articles/books where they don't explain everything. It makes it really hard for outsiders/amateurs to catch on to the ideas being discussed.
It is a great REPL in a browser experience. Personally RStudio has done pretty amazing things and it has the ability to also run with different kernels but it is specifically focused on R.
> Syntax highlighting modes for many new languages including Clojure, CoffeeScript, C#, Graphviz, Go, Groovy, Haskell, Java, Julia, Lisp, Lua, Matlab, Perl, Ruby, Rust, Scala, and Stan. [http://www.rstudio.com/products/rstudio/download/preview-rel...]
I like the way it has a strong Python focus.
It looks like more of an ebook and it seems to cover most of the essentials in your typical undergrad course + bootstrapping and intro to bayesian. From what I scanned it might be a little slow if you're just looking for a stats refresher -- depending on your level of experience I'd recommend the numpy and pandas docs as a "next step up" from the link (and maybe a bit more practical IMO).
I don't know of good stats books focusing on Python, but I'm sure there are plenty. "An Introduction to Statistical Learning" is free online [1] but it emphasizes R and has very little overlap, so I don't know if it addresses your needs, but it's very good.
Harvard Data science class, in python: http://cs109.github.io/2014/
Python is also better than R for ad hoc statistical modeling and algorithim development (you can write python code on the order of C fast with numba) , general programming, scraping, natural language processing, agent based modeling etc.
Python is also better for GIS, optimization, symbolic math and larger datasets with blaze and dask and pyspark.
R right now is a bit better for visualization, reporting and exploratory data analysis (I think this will change soon though with Bokeh and blaze) and has many more esoteric stats packages.
With statsmodels, pymc3, pandas and scikitlearn etc you can probably do 98% of everything you need in python without needing to dip into the more esoteric packages of R (with some exceptions). For everything else, you can call R packages with Rpy2. With this you get all the advantages of working in one language (not spread too thin) and the advantages that python offers while leveraging R's wealth of packages.
The latter is a bit more difficult through python, but not as hard as trying to remember the syntax of and gluing together a two language workflow.
That is why I chose python... Also I can write excel addins with python (xlwings) instead of using VBA
As for vis, yhat wrote a python version of ggplot:http://ggplot.yhathq.com/
They both are great. I personally strongly prefer R over Python due to libraries and the flexibility. Others hate the flexibility of R and like Python's only one way to correctly do something.
It comes down to preferences. I like the R is for statistical specifically. There are people who call R an aweful horrible language and it isn't. It has grown incredibly over the last 5 years and has major funding from many corporations for a reason.
If it seems passionate for or against either language it is more an emotional response to the question. R works best for me and Python might be someone else's prefer solution.
Selecting Julia blindly is just as stupid as selecting R or python blindly. Language features are only a small part of the reason you choose a language for a large project.
There are not enough libraries (I looked 3 months ago) for me to do my work in Julia.
There isn't enough users to make the libraries for my use case.
However, I really don't agree with the "X works best for me but Y might work better for someone else so let's not fight" argument. Doesn't that imply that the question "Which is better, X or Y"? shouldn't be asked? On the contrary, I think that's often an important question when you need to practically get stuff done, choose between libraries, etc, and I want to know people's opinions.
No, it just implies that there is, in the domain in which it is applied, no objectively correct answer.
1) Python 0 based as opposed to 1 based is big to me. I don't want to always have to minus on on columns to call them.
2) I much prefer Hadley Wickham's syntax to Pandas which closely follows it, but R piping with %>% is amazing with library magritr. Using dplyr and stringr, tidyr, rvest, httr, luberdate are awesome and consistant.
3) In R I can use dplyr whcih is fast or even use data.table for even more speed that uses a sql like syntax. In Python your choices are limited in comparison to R for tools.
4) HUGE and the reason I switched to R. I can output my graphics into a editable PowerPoint slide. My managers and company doesn't know that there are anything besides MS Office and I can give them slides that they can fiddle with for their own presentations.
5) RMarkDown and knitr for outputing reports in Word (UGH but ti works great), PDF or , LaTex or HTML is fantastic. Reproducable output is great. If I want I can also use Project Jupyter (IPython's new name in next version) with a R kernel.
6) Tools - I got RStudio installed on my work computers, and my personaly server has RStudio Server installed. This is what I have settled into using because it is the best statistic programing tool out there. Works better than Jupyter/IPython for 90% of the time, though I love Love LOVE Jupyter/IPython.
7) The community. R's community is fantastic [http://www.r-bloggers.com/] just look at all the daily information, new libraries and tutorials. The books are great in R, (I really enjoyed the Python Pandas Book by Wesly) Hadley Wickham's Newest book is avilable in Physical form for purchase and free on the internet [http://adv-r.had.co.nz/]
8) Tutorials - Datacamp, Swirl in R tutorial, Coursera Courses, etc. are great and have just about anything you would like to learn.
9) The libraries and install system with Cran installs [http://cran.r-project.org/] and devtools to install GitHub libraries install and updated on your system [https://github.com/hadley/devtools]
I could go further but those are my opinions to why I like R so much.
To expand, R has every cool statistical model (outside finance) to hand. It's deadly buzz. It's finicky as hell to program in though. Classes are a bit crap. Brackets are all over the shop. Arrows? Really? Meh. Statistical models take ages to program and test, though, and the academic paper that was published last week has had the model as an R package for 6 months.
Python is a beautiful programming language, especially for anything involving transferring data from here to there and possibly back again. I particularly like it when working with a diverse team of context experts, who would think nothing of laying a faecal nugget in your code-base, to save the time it takes to drink a cup of coffee. It's a lot harder to write crap code in Python than in many other languages.
So, in our local technical landscape, R is used for prototyping, and is used in a slave fashion for running complex stats models. Python is used as the "Controller" of the program, and also for database type operations, web-scraping, calling QuantLib, ...