[scikit-learn] R user trying to learn Python

[Sean Violante]

CW

you might want to read http://greenteapress.com/wp/think-python/
 (available as free pdf)
(for basics of programming and python)
and
Python for Data Analysis
 Data Wrangling with Pandas, NumPy, and IPython, O'reilly

(for data analysis libraries: pandas, numpy, ipython...)







On Sun, Jun 18, 2017 at 10:18 PM, 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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>> > scikit-learn at python.org
>> > https://mail.python.org/mailman/listinfo/scikit-learn
>>
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>
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