Python append value to a list returned from a function via for loop
<p>I have a function:</p>

<pre><code>def function(x,y):
    do something
    print a,b
    return a,b
</code></pre>

<p>Now I use a for loop like:</p>

<pre><code>for i in range(10,100,10):
    function(i,30)
</code></pre>

<p>which prints the values <code>a,b</code> for the given input values via the for loop. 
It also returns <code>a,b</code> if I say for example <code>function(10,30)</code> like:</p>

<pre><code>Out[50]: (0.25725063633960099, 0.0039189363571677958)
</code></pre>

<p><strong>I would like to append the values of <code>a,b</code> obtained for my different input parameters <code>(x,y)</code> via the for loop to two empty lists.</strong> </p>

<p>I tried</p>

<pre><code>for i in range(10,100,10):
    list_a,list_b = function(i,30)
</code></pre>

<p>but <code>list_a</code> and <code>list_b</code> are still empty. </p>

<p><strong>EDIT:</strong></p>

<p><strong>I have also tried:</strong></p>

<pre><code>list_a = []
list_b = []
for i in range(10,100,10):
    list_a.append(function(i,30)[0])
    list_b.append(function(i,30)[1])
</code></pre>

<p>But <code>list_a</code> and <code>list_b</code> are empty! </p>

<p><strong>What I don't understand is that</strong>, when I call </p>

<p><code>function(10,30)[0]</code> </p>

<p>for instance, <strong>it outputs a value! But why am I not able to append it to a list?</strong></p>

<p><strong><em>Here is the entire function as asked by a few.</em></strong> </p>

<pre><code>def function(N,bins):
    sample = np.log10(m200_1[n200_1&gt;N]) # can be any 1D array
    mean,scatter = stats.norm.fit(sample) #Gives the paramters of the fit to the histogram
    err_std = scatter/np.sqrt(len(sample))

    if N&lt;30:
        x_fit = np.linspace(sample.min(),sample.max(),100)
        pdf_fitted = stats.norm.pdf(x_fit,loc=mean,scale=scatter) #Gives the PDF, given the parameters from norm.fit
        print "scatter for N&gt;%s is %s" %(N,scatter)
        print "error on scatter for N&gt;%s is %s" %(N,err_std)
        print "mean for N&gt;%s is %s" %(N,mean)  

    else:
        x_fit = np.linspace(sample.min(),sample.max(),100)
        pdf_fitted = stats.norm.pdf(x_fit,loc=mean,scale=scatter) #Gives the PDF, given the parameters from norm.fit
        print "scatter for N&gt;%s is %s" %(N,scatter) 
        print "error on scatter for N&gt;%s is %s" %(N,err_std)
        print "mean for N&gt;%s is %s" %(N,mean)

    return scatter,err_std 
</code></pre>