Lets suppose I have a list like this:

    mylist = [&quot;a&quot;,&quot;b&quot;,&quot;c&quot;,&quot;d&quot;]

To get the values printed along with their index I can use Python&#39;s `enumerate` function like this

    &gt;&gt;&gt; for i,j in enumerate(mylist):
    ...     print i,j
    ...
    0 a
    1 b
    2 c
    3 d
    &gt;&gt;&gt;

Now, when I try to use it inside a `list comprehension` it gives me this error

    &gt;&gt;&gt; [i,j for i,j in enumerate(mylist)]
      File &quot;&lt;stdin&gt;&quot;, line 1
        [i,j for i,j in enumerate(mylist)]
               ^
    SyntaxError: invalid syntax

So, my question is: what is the correct way of using enumerate inside list comprehension?

Python using enumerate inside list comprehension

I know this is bad practice. Don&#39;t write code like this if at all possible. 

Of course, we&#39;ll always find ourselves in situations where a clever snippet of inline Javascript can address an issue quickly. 

I am pursuing this query in the interest of fully understanding what happens (and the potential pitfalls) when something like this is written: 

    &lt;a href=&quot;#&quot; onclick=&quot;alert(&#39;Hi&#39;)&quot;&gt;Click Me&lt;/a&gt;

As far as I can tell this is functionally the same as 

    &lt;script type=&quot;text/javascript&quot;&gt;
       $(function(){ // I use jQuery in this example
           document.getElementById(&#39;click_me&#39;).onclick = 
               function () { alert(&#39;Hi&#39;); };
       });
    &lt;/script&gt;
    &lt;a href=&quot;#&quot; id=&quot;click_me&quot;&gt;Click Me&lt;/a&gt;

Extrapolating from this it seems that the string assigned to attribute `onclick` is inserted within an anonymous function which is assigned to the element&#39;s click handler. Is this actually the case? 

Because I&#39;m starting to do things like this: 

    &lt;a href=&quot;#&quot; onclick=&quot;$(this).next().fadeIn(); return false;&quot;&gt;Display my next sibling&lt;/a&gt; &lt;!-- Return false in handler so as not to scroll to top of page! --&gt; 

Which works. But I don&#39;t know how much of a hack this is. It looks suspicious because there is no apparent function that is being returned from!

You might ask, why are you doing this, Steve? Inline JS is bad practice! 

Well to be quite honest I&#39;m tired of editing three different sections of code just to modify one section of a page, especially when I&#39;m just prototyping something to see if it will work at all. It is so much easier and sometimes even makes sense for the code specifically related to this HTML element to be defined *right within* the element: When I decide 2 minutes later that this was a terrible, terrible idea I can nuke the entire div (or whatever) and I don&#39;t have a bunch of mysterious JS and CSS cruft hanging around in the rest of the page, slowing down rendering ever so slightly. This is similar to the concept of locality of reference but instead of cache misses we&#39;re looking at bugs and code bloat. 

How does inline Javascript (in HTML) work?

I&#39;m looking to iterate row by row through a pandas `DataFrame`.  The way I&#39;m doing it so far is as follows:

    for i in df.index:
        do_something(df.ix[i])

Is there a more performant and/or more idiomatic way to do this?  I know about apply, but sometimes it&#39;s more convenient to use a for loop.  

iterating row by row through a pandas dataframe

I'm looking to iterate row by row through a pandas <code>DataFrame</code>. The way I'm doing it so far is as follows:
<pre><code class="hljs language-css">for i in df.index:
 do_something(df.ix[i])
</code></pre>
Is there a more performant and/or more idiomatic way to do this? I know about apply, but sometimes it's more convenient to use a for loop.

I am trying to add a path to the PYTHONPATH environment variable, that would be only visible from a particular virtualenv environment. 

I tried `SET PYTHONPATH=...` under a virtualenv command prompt, but that sets the variable for the whole environment.

How do I achieve that?

How do I add a path to PYTHONPATH in virtualenv

In a block comment, I want to reference a URL that is over 80 characters long.

What is the preferred convention for displaying this URL?

I know bit.ly is an option, but the URL itself is descriptive. Shortening it and then having a nested comment describing the shortened URL seems like a crappy solution.

How should I format a long url in a python comment and still be PEP8 compliant

I&#39;m writing a Django RESTful API to back an iOS application, and I keep running into Django&#39;s CSRF protections whenever I write methods to deal with POST requests.

My understanding is that cookies managed by iOS are not shared by applications, meaning that my session cookies are safe, and no other application can ride on them. Is this true? If so, can I just mark all my API functions as CSRF-exempt?

Do CSRF attacks apply to API&#39;s?

In **`numpy`** / **`scipy`**, is there an **efficient** way to get frequency counts for unique values in an array?

Something along these lines:

    x = array( [1,1,1,2,2,2,5,25,1,1] )
    y = freq_count( x )
    print y

    &gt;&gt; [[1, 5], [2,3], [5,1], [25,1]]

( For you, R users out there, I&#39;m basically looking for the `table()` function )

numpy: most efficient frequency counts for unique values in an array

I&#39;m using a Pandas `DataFrame` to do a row-wise t-test as per this example:

    import numpy
    import pandas
  
    df = pandas.DataFrame(numpy.log2(numpy.randn(1000, 4), 
                          columns=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;, &quot;d&quot;])

    df = df.dropna()

Now, supposing I have &quot;a&quot; and &quot;b&quot; as one group, and &quot;c&quot; and &quot;d&quot; at the other, I&#39;m performing the t-test row-wise. This is fairly trivial with pandas, using `apply` with axis=1. However, I can either return a DataFrame of the same shape if my function doesn&#39;t aggregate, or a Series if it aggregates.

Normally I would just output the p-value (so, aggregation) but I would like to generate an additional value based on other calculations (in other words, return two values). I can of course do two runs, aggregating the p-values first, then doing the other work, but I was wondering if there is a more efficient way to do so as the data is reasonably large.

As an example of the calculation, a hypotethical function would be:

    from scipy.stats import ttest_ind

    def t_test_and_mean(series, first, second):
        first_group = series[first]
        second_group = series[second]
        _, pvalue = ttest_ind(first_group, second_group)

        mean_ratio = second_group.mean() / first_group.mean()
        
        return (pvalue, mean_ratio)

Then invoked with 

    df.apply(t_test_and_mean, first=[&quot;a&quot;, &quot;b&quot;], second=[&quot;c&quot;, &quot;d&quot;], axis=1)

 Of course in this case it returns a single Series with the two tuples as value.

Instead, ny expected output would be a DataFrame with two columns, one for the first result, and one for the second. Is this possible or I have to do two runs for the two calculations, then merge them together?

Returning multiple values from pandas apply on a DataFrame

I have a `dataFrame` in pandas and several of the columns have all null values. Is there a built in function which will let me remove those columns?

Remove NaN/NULL columns in a Pandas dataframe?

I&#39;m using the pandas library to read in some CSV data.  In my data, certain columns contain strings.  The string `&quot;nan&quot;` is a possible value, as is an empty string.  I managed to get pandas to read &quot;nan&quot; as a string, but I can&#39;t figure out how to get it not to read an empty value as NaN.  Here&#39;s sample data and output

	One,Two,Three
	a,1,one
	b,2,two
	,3,three
	d,4,nan
	e,5,five
	nan,6,
	g,7,seven

    &gt;&gt;&gt; pandas.read_csv(&#39;test.csv&#39;, na_values={&#39;One&#39;: [], &quot;Three&quot;: []})
        One  Two  Three
    0    a    1    one
    1    b    2    two
    2  NaN    3  three
    3    d    4    nan
    4    e    5   five
    5  nan    6    NaN
    6    g    7  seven

It correctly reads &quot;nan&quot; as the string &quot;nan&#39;, but still reads the empty cells as NaN.  I tried passing in `str` in the `converters` argument to read_csv (with `converters={&#39;One&#39;: str})`), but it still reads the empty cells as NaN.

I realize I can fill the values after reading, with fillna, but is there really no way to tell pandas that an empty cell in a particular CSV column should be read as an empty string instead of NaN?

Get pandas.read_csv to read empty values as empty string instead of nan

Is there a way to write an aggregation function as is used in `DataFrame.agg` method, that would have access to more than one column of the data that is being aggregated? Typical use cases would be weighted average, weighted standard deviation funcs.

I would like to be able to write something like

    def wAvg(c, w):
        return ((c * w).sum() / w.sum())

    df = DataFrame(....) # df has columns c and w, i want weighted average
                         # of c using w as weight.
    df.aggregate ({&quot;c&quot;: wAvg}) # and somehow tell it to use w column as weights ...


Pandas DataFrame aggregate function using multiple columns

I would like to shift a column in a Pandas `DataFrame`, but I haven&#39;t been able to find a method to do it from the documentation without rewriting the whole DF. Does anyone know how to do it? 
DataFrame:

    ##    x1   x2
    ##0  206  214
    ##1  226  234
    ##2  245  253
    ##3  265  272
    ##4  283  291

Desired output:

    ##    x1   x2
    ##0  206  nan
    ##1  226  214
    ##2  245  234
    ##3  265  253
    ##4  283  272
    ##5  nan  291

iterating row by row through a pandas dataframe

Python Problem Overview

Python Solutions

How does inline Javascript (in HTML) work?

Python using enumerate inside list comprehension

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