Thanks Martin-- this is really great. My major question now is that I need to transition to Python for a project and I need to learn how to think in Python instead of in R. The two strategies I have used so far are: a) going through the description and exercises in <a href="http://www.openbookproject.net/thinkcs/python/english2e/">http://www.openbookproject.net/thinkcs/python/english2e/</a> and b) trying to convert my R code into Python.<div>
<br></div><div>On a high-level, do you have any other suggestions for how I could go about becoming more proficient in Python? </div><div><br></div><div>Thanks again to you and everyone else who responded. I am really very much obliged.</div>
<div><br></div><div>Benjamin<br><br><div class="gmail_quote">On Sat, May 19, 2012 at 5:32 PM, Martin A. Brown <span dir="ltr"><<a href="mailto:martin@linux-ip.net" target="_blank">martin@linux-ip.net</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><br>
Greetings Benjamin,<br>
<br>
To begin: I do not know R.<br>
<br>
: I'm trying to improve my python by translating R code that I<br>
: wrote into Python.<br>
:<br>
: *All I am trying to do is take in a specific column in<br>
<div class="im"> : "uncurated" and write that whole column as output to "curated."<br>
: It should be a pretty basic command, I'm just not clear on how to<br>
</div> : execute it.*<br>
<br>
The hardest part about translation is learning how to think in a<br>
different language. If you know any other human languages, you<br>
probably know that you can say things in some languages that do not<br>
translate particularly well (other than circumlocution) into another<br>
language. Why am I starting with this? I am starting here because<br>
you seem quite comfortable with thinking and operating in R, but you<br>
don't seem as comfortable yet with thinking and operating in Python.<br>
<br>
Naturally, that's why you are asking the Tutor list about this, so<br>
welcome to the right place! Let's see if we can get you some help.<br>
<div class="im"><br>
: As background, GSEXXXXX_full_pdata.csv has different patient<br>
: information (such as unique patient ID's, whether the tissue used<br>
: was tumor or normal, and other things. I'll just use the first<br>
: two characteristics for now). Template.csv is a template we built<br>
: that allows us to take different datasets and standardize them<br>
: for meta-analysis. So for example, "curated$alt_sample_name"<br>
: refers to the unique patient ID, and "curated$sample_type" refers<br>
: to the type of tissue used.<br>
<br>
</div>I have fabricated some data after your description that looks like<br>
this:<br>
<br>
patientID,title,sample_type<br>
V6IF0OqVu,0.5788,70<br>
GXj51ljB2,0.3449,88<br>
<br>
You, doubtless have more columns and the data here are probably<br>
nothing like yours, but consider it useful for illustrative purposes<br>
only. (Illustrating porpoises! How did they get here? Next thing<br>
you know we will have illuminating egrets and animating<br>
dromedaries!)<br>
<div class="im"><br>
: I've been reading about the python csv module and realized it was<br>
: best to get some expert input to clarify some confusion on my<br>
: part.<br>
<br>
</div>The csv module is very useful and quite powerful for reading data in<br>
different ways and iterating over data sets. Supposing you know the<br>
index of the column of interest to you...well this is quite trivial:<br>
<br>
import csv<br>
def main(f,field):<br>
for row in csv.reader(f):<br>
print row[0],row[field]<br>
<br>
# -- lists/tuples are zero-based [0,1,2], so 2 is the third column<br>
#<br>
#<br>
main(open('GSEXXXXX_full_pdata.csv'),2)<br>
<br>
OK, but if your data files have different numbers of or ordering of<br>
columns, then this can become a bit fragile. So maybe you would<br>
want to learn how to use the csv.DictReader, which will give you the<br>
same thing but uses the first (header) line to name the columns, so<br>
then you could do something more like this:<br>
<br>
import csv<br>
def main(f,id,field):<br>
for row in csv.DictReader(f):<br>
print row[id],row[field]<br>
<br>
main(open('GSEXXXXX_full_pdata.csv'),'patientID','sample_type')<br>
<br>
Would you like more detail on this? Well, have a look at this nice<br>
little summary:<br>
<br>
<a href="http://www.doughellmann.com/PyMOTW/csv/" target="_blank">http://www.doughellmann.com/PyMOTW/csv/</a><br>
<br>
Now, that really is just giving you a glimpse of the csv module.<br>
This is not really your question. Your question was more along the<br>
lines of 'How do I, in Python, accomplish this task that is quite<br>
simple in R?'<br>
<br>
You may find that list-comprehensions, generators and iterators are<br>
all helpful in mangling the data according to your nefarious will<br>
once you have used the csv module to load the data into a data<br>
structure.<br>
<br>
In point of fact, though, Python does not have this particular<br>
feature that you are seek...not in the core libraries, however.<br>
<br>
The lack of this capability has bothered a few people over the<br>
years, so there are a few different types of solutions. You have<br>
already heard a reference to RPy (about which I know nothing):<br>
<br>
<a href="http://rpy.sourceforge.net/" target="_blank">http://rpy.sourceforge.net/</a><br>
<br>
There are, however, a few other tools that you may find quite<br>
useful. One chap wanted access to some features of R that he used<br>
all the time along with many of the other convenient features of<br>
Python, so he decided to implement dataframes (an R concept?) in<br>
Python. This idea was present at the genesis of the pandas library.<br>
<br>
<a href="http://pandas.pydata.org/" target="_blank">http://pandas.pydata.org/</a><br>
<br>
So, how would you do this with pandas? Well, you could:<br>
<br>
import pandas<br>
def main(f,field):<br>
uncurated = pandas.read_csv(f)<br>
curated = uncurated[field]<br>
print curated<br>
<br>
main(open('GSEXXXXX_full_pdata.csv'),'sample_type')<br>
<br>
Note that pandas is geared to allow you to access your data by the<br>
'handles', the unique identifier for the row and the column name.<br>
This will produce a tabular output of just the single column you<br>
want. You may find that pandas affords you access to tools with<br>
which you are already intellectually familiar.<br>
<br>
Good luck,<br>
<br>
-Martin<br>
<br>
P.S. While I was writing this, you sent in some sample data that<br>
looked tab-separated (well, anyway, not comma-separated). The<br>
csv and pandas libraries allow for delimiter='\t' options to<br>
most object constructor calls. So, you could do:<br>
csv.reader(f,delimiter='\t')<br>
<span class="HOEnZb"><font color="#888888"><br>
--<br>
Martin A. Brown<br>
<a href="http://linux-ip.net/" target="_blank">http://linux-ip.net/</a><br>
</font></span></blockquote></div><br></div>