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.varoqu...@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 <tmrs...@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.rasc...@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 <tmrs...@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 > >> > _______________________________________________ > >> > scikit-learn mailing list > >> > scikit-learn@python.org > >> > https://mail.python.org/mailman/listinfo/scikit-learn > >> > >> _______________________________________________ > >> scikit-learn mailing list > >> scikit-learn@python.org > >> https://mail.python.org/mailman/listinfo/scikit-learn > > > > > > > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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