You can also have a look at "Effective Computation in Physics" by Anthony
Scopatz and Kathryn D. Huff.
It gives a very good overview of Python/numpy/pandas...
Albert Thomas
On Tue, 20 Jun 2017 at 07:25, C W <tmrs...@gmail.com> wrote:
> I am catching up to all the replies, apologies for the delay. (replied in
> reverse order)
>
> @ Gaël,
> Thanks for your comments. I actually started with 1) Data Camp courses and
> 2) Python for Data Science book.
>
> Here's my review:
> 1) The course: it is fantastic! But they only give you a flavor of A FEW
> things.
> 2) The book: it is quick crash course, but not enough for you to take off.
> See code below.
>
> # Toy Python Code
> import numpy as np
> import pandas as pd
>
> N = 100
> df = pd.DataFrame({
> 'A': pd.date_range(start='2016-01-01',periods=N,freq='D'),
> 'x': np.linspace(0,stop=N-1,num=N),
> 'y': np.random.rand(N),
> 'C': np.random.choice(['Low','Medium','High'],N).tolist(),
> 'D': np.random.normal(100, 10, size=(N)).tolist()
> })
> df.x
> len(dir(df))
> # end of Python code
>
> My confusion:
> a) df.x gives you column x, but why, I thought things after dot are
> actions, or more like verbs performed on the object, namely df, in this
> case.
> b) len(dir(df)) gives 431. I only crated a dataframe, where did all these
> 431 things come from? Is there a documentation about this? It scares me
> because I only asked for a dataframe.
>
> @ Gael
> This is a pretty solid reference. It explains methods among other things,
> which is awesome! I think method is the barrier to entry for R users.
>
> @ Mail
> Thanks for the details, I will try to pick these computer science
> terminologies up. It has been a brutal week.
>
> @Massimo
> Yes, I have used that. It is indeed great for one to one equivalence
> reference.
>
> Thanks!
>
>
>
>
>
> On Tue, Jun 20, 2017 at 12:32 AM, Gaël Pegliasco via scikit-learn <
> scikit-learn@python.org> wrote:
>
>> And, answering your last question, a good way to learn Data science using
>> Python is, for I, "Python data science handbook" that you can read as
>> Jupyter notebooks:
>>
>> https://github.com/jakevdp/PythonDataScienceHandbook
>>
>>
>> Le 20/06/2017 à 06:28, Gaël Pegliasco via scikit-learn a écrit :
>>
>> Hi,
>>
>> You may find these R/Python comparison-sheets useful in understanding
>> both languages syntaxes and concepts:
>>
>>
>> -
>> https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis
>> - http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html
>>
>>
>> Gaël,
>>
>> Le 18/06/2017 à 18:02, C W a écrit :
>>
>> 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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>> Tél : 02 51 79 80 84
>> Portable : 06 41 69 16 09
>> 11 rue du Marchix FR-44000 Nantes
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