[scikit-learn] R user trying to learn Python

[C W]

Thank you all for the love!

Sean,
I think your recommendation is perfect! It covers everything, very concise,
to the point.

Sebastian,
I will certainly invest time into that course when I have time.

Nelle,
I agree! And from what I read, thee head(), tail(), and data.frame() in
Python actually came from R at request. Hence, I came to think they are
similar.

For anyone else in the world reading, I think pandas doc is also good:
http://pandas.pydata.org/pandas-docs/stable/pandas.pdf

Mike

On Sun, Jun 18, 2017 at 4:37 PM, Nelle Varoquaux <nelle.varoquaux at gmail.com>
wrote:

> Hello,
>
> The concepts behind R and python are entirely different. Python is
> meant to be as explicit as possible, and uses the concepts of
> namespace which R doesn't.
> While it can seem that python code is more verbose, it is very clear
> when reading python code which functions come from which module and
> submodule (this is link to your code 1 and code 3 examples).
>
> For example 2, R indeed saves everything to a variable, while python
> does not. The advantage is that Python is much more time and memory
> efficient than R. The tradeoff is that you do not keep intermediate
> results.
>
> Hope that explains,
> N
>
> On 18 June 2017 at 13:18, C W <tmrsg11 at gmail.com> wrote:
> > Hi Sebastian,
> >
> > I looked through your book. I think it is great if you already know
> Python,
> > and looking to learn machine learning.
> >
> > For me, I have some sense of machine learning, but none of Python.
> >
> > Unlike R, which is specifically for statistics analysis. Python is broad!
> >
> > Maybe some expert here with R can tell me how to go about this. :)
> >
> > On Sun, Jun 18, 2017 at 12:53 PM, Sebastian Raschka <
> se.raschka at gmail.com>
> > wrote:
> >>
> >> Hi,
> >>
> >> > I am extremely frustrated using this thing. Everything comes after a
> >> > dot! Why would you type the sam thing at the beginning of every line.
> It's
> >> > not efficient.
> >> >
> >> > code 1:
> >> > y_sin = np.sin(x)
> >> > y_cos = np.cos(x)
> >> >
> >> > I know you can import the entire package without the "as np", but I
> see
> >> > np.something as the standard. Why?
> >>
> >> Because it makes it clear where this function is coming from. Sure, you
> >> could do
> >>
> >> from numpy import *
> >>
> >> but this is NOT!!! recommended. The reason why this is not recommended
> is
> >> that it would clutter up your main name space. For instance, numpy has
> its
> >> own sum function. If you do from numpy import *, Python's in-built `sum`
> >> will be gone from your main name space and replaced by NumPy's sum.
> This is
> >> confusing and should be avoided.
> >>
> >> > In the code above, sklearn > linear_model > Ridge, one lives inside
> the
> >> > other, it feels that there are multiple layer, how deep do I have to
> dig in?
> >> >
> >> > Can someone explain the mentality behind this setup?
> >>
> >> This is one way to organize your code and package. Sklearn contains many
> >> things, and organizing it by subpackages (linear_model, svm, ...) makes
> only
> >> sense; otherwise, you would end up with code files > 100,000 lines or
> so,
> >> which would make life really hard for package developers.
> >>
> >> Here, scikit-learn tries to follow the core principles of good object
> >> oriented program design, for instance, Abstraction, encapsulation,
> >> modularity, hierarchy, ...
> >>
> >> > What are some good ways and resources to learn Python for data
> analysis?
> >>
> >> I think baed on your questions, a good resource would be an introduction
> >> to programming book or course. I think that sections on objected
> oriented
> >> programming would make the rationale/design/API of scikit-learn and
> Python
> >> classes as a whole more accessible and address your concerns and
> questions.
> >>
> >> Best,
> >> Sebastian
> >>
> >> > On Jun 18, 2017, at 12:02 PM, C W <tmrsg11 at gmail.com> wrote:
> >> >
> >> > Dear Scikit-learn,
> >> >
> >> > What are some good ways and resources to learn Python for data
> analysis?
> >> >
> >> > I am extremely frustrated using this thing. Everything comes after a
> >> > dot! Why would you type the sam thing at the beginning of every line.
> It's
> >> > not efficient.
> >> >
> >> > code 1:
> >> > y_sin = np.sin(x)
> >> > y_cos = np.cos(x)
> >> >
> >> > I know you can import the entire package without the "as np", but I
> see
> >> > np.something as the standard. Why?
> >> >
> >> > Code 2:
> >> > model = LogisticRegression()
> >> > model.fit(X_train, y_train)
> >> > model.score(X_test, y_test)
> >> >
> >> > In R, everything is saved to a variable. In the code above, what if I
> >> > accidentally ran model.fit(), I would not know.
> >> >
> >> > Code 3:
> >> > from sklearn import linear_model
> >> > reg = linear_model.Ridge (alpha = .5)
> >> > reg.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
> >> >
> >> > In the code above, sklearn > linear_model > Ridge, one lives inside
> the
> >> > other, it feels that there are multiple layer, how deep do I have to
> dig in?
> >> >
> >> > Can someone explain the mentality behind this setup?
> >> >
> >> > Thank you very much!
> >> >
> >> > M
> >> > _______________________________________________
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> >>
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