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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