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 wrote:
> I am catching up to all the replies, apologies for the delay. (replied in
> re
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
t
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
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
Another reference that I like a lot for people who already know a
programming language and are trying to learn Python is "Python Essential
Reference" by David Beazley. It gives a good understanding of how Python
works, though it does not talk about numerical computing libraries.
Gaël
_
Hi M,
I think what you describe can be summarized as the difference of a domain
specific language (r) and a general purpose language (Python). Most of what you
describe is related to namespaces - "one honking great" feature of python.
Namespaces are less needed in r because r is domain s
Hi, along with all the great tips you received, perhaps you may find this
useful:
http://www.cert.org/flocon/2011/matlab-python-xref.pdf
I know is not on-topic with your question, but I found it very useful when I
start to use python (coming from R)
So I thought it was worth to post it here.
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 ca
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 an
Hi, C W,
yeah I'd say that Python is a programming language with lots of packages for
scientific computing, whereas R is more of a toolbox for stats. Thus, Python
may be a bit weird at first for people who come from the R/stats field and are
new to programming. Not sure if it is necessary to le
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
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 wi
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 s
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)
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