<div dir="ltr"><div><div>You can also have a look at "Effective Computation in Physics" by Anthony Scopatz and Kathryn D. Huff.</div></div><div><br></div><div>It gives a very good overview of Python/numpy/pandas...</div><div><br></div><div>Albert Thomas</div><div><div><br><div class="gmail_quote"><div>On Tue, 20 Jun 2017 at 07:25, C W <<a href="mailto:tmrsg11@gmail.com" target="_blank">tmrsg11@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div>I am catching up to all the replies, apologies for the delay. (replied in reverse order)</div><div><br></div>@ Gaël,<div>Thanks for your comments. I actually started with 1) Data Camp courses and 2) Python for Data Science book.<br></div><div><br></div><div>Here's my review:</div><div>1) The course: it is fantastic! But they only give you a flavor of A FEW things.</div><div>2) The book: it is quick crash course, but not enough for you to take off. See code below.</div><div><br></div><div># Toy Python Code</div><div><div>import numpy as np</div><div>import pandas as pd</div><div><br></div><div>N = 100</div><div>df = pd.DataFrame({</div><div> 'A': pd.date_range(start='2016-01-01',periods=N,freq='D'),</div><div> 'x': np.linspace(0,stop=N-1,num=N),</div><div> 'y': np.random.rand(N),</div><div> 'C': np.random.choice(['Low','Medium','High'],N).tolist(),</div><div> 'D': np.random.normal(100, 10, size=(N)).tolist()</div><div> })</div></div><div>df.x</div><div>len(dir(df))<br></div><div># end of Python code<br></div><div><br></div><div>My confusion: </div><div>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.</div><div>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.</div><div><br></div><div>@ Gael</div><div>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.</div><div><br></div><div>@ Mail </div><div>Thanks for the details, I will try to pick these computer science terminologies up. It has been a brutal week.</div><div><br></div><div>@Massimo</div><div>Yes, I have used that. It is indeed great for one to one equivalence reference.</div><div><br></div><div>Thanks!</div><div><br></div><div><br></div><div><br></div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, Jun 20, 2017 at 12:32 AM, Gaël Pegliasco via scikit-learn <span><<a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div text="#000000" bgcolor="#FFFFFF">
<div class="m_8157703262412189234m_3528447784601577172m_-3202966796920151065moz-cite-prefix">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:<br>
<br>
<a class="m_8157703262412189234m_3528447784601577172m_-3202966796920151065moz-txt-link-freetext" href="https://github.com/jakevdp/PythonDataScienceHandbook" target="_blank">https://github.com/jakevdp/PythonDataScienceHandbook</a><div><div class="m_8157703262412189234m_3528447784601577172h5"><br>
<br>
Le 20/06/2017 à 06:28, Gaël Pegliasco via scikit-learn a écrit :<br>
</div></div></div><div><div class="m_8157703262412189234m_3528447784601577172h5">
<blockquote type="cite">
<div class="m_8157703262412189234m_3528447784601577172m_-3202966796920151065moz-cite-prefix">Hi,<br>
<br>
You may find these R/Python comparison-sheets useful in
understanding both languages syntaxes and concepts:<br>
<br>
<ul>
<li><a class="m_8157703262412189234m_3528447784601577172m_-3202966796920151065moz-txt-link-freetext" href="https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis" target="_blank">https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis</a></li>
<li><a class="m_8157703262412189234m_3528447784601577172m_-3202966796920151065moz-txt-link-freetext" href="http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html" target="_blank">http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html</a></li>
</ul>
<br>
Gaël,<br>
<br>
Le 18/06/2017 à 18:02, C W a écrit :<br>
</div>
<blockquote type="cite">
<div>Dear Scikit-learn,
<div><br>
</div>
<div>What are some good ways and resources to learn Python for
data analysis?</div>
<div><br>
</div>
<div>
<div>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.</div>
</div>
<div><br>
</div>
<div>code 1:</div>
<div>y_sin = np.sin(x)</div>
<div>y_cos = np.cos(x)</div>
<div><br>
</div>
<div>I know you can import the entire package without the "as
np", but I see np.something as the standard. Why?<br>
</div>
<div><br>
</div>
<div>
<div>
<div>Code 2:</div>
<div>model = LogisticRegression()</div>
<div>model.fit(X_train, y_train)</div>
</div>
<div>model.score(X_test, y_test)<br>
</div>
</div>
<div><br>
</div>
<div>In R, everything is saved to a variable. In the code
above, what if I accidentally ran model.fit(), I would not
know.</div>
<div><br>
</div>
<div>Code 3:</div>
from sklearn import linear_model<br>
reg = linear_model.Ridge (alpha = .5)<br>
reg.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
<div><br>
</div>
<div>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?</div>
<div><br>
</div>
<div>Can someone explain the mentality behind this setup?</div>
<div><br>
</div>
<div>Thank you very much!</div>
<div><br>
</div>
<div>M</div>
</div>
<br>
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