title: Drop columns with low standard deviation in Pandas Dataframe
body: <p>Is there any way of doing this without writing a for loop?</p>
  
  <p>Suppose we have the following data:</p>
  
  <pre><code>d = {'A': {-1: 0.19052041339798062,
        0: -0.0052531481871952871,
        1: -0.0022017467720961644,
        2: -0.051109629013311737,
        3: 0.18569441222621336},
       'B': {-1: 0.029181417300734112,
        0: -0.0031021862533310743,
        1: -0.014358516787430284,
        2: 0.0046386615308068877,
        3: 0.056676322314857898},
       'C': {-1: 0.071883343375205785,
        0: -0.011930096520251999,
        1: -0.011836365865654104,
        2: -0.0033930358388315237,
        3: 0.11812543193496111},
       'D': {-1: 0.17670604006475121,
        0: -0.088756293654161142,
        1: -0.093383245649534194,
        2: 0.095649943383654359,
        3: 0.51030339029516592},
       'E': {-1: 0.30273513342295627,
        0: -0.30640233455497284,
        1: -0.32698263145105921,
        2: 0.60257484810641992,
        3: 0.36859978928328413},
       'F': {-1: 0.25328469046380131,
        0: -0.063890702001567143,
        1: -0.10007720832198815,
        2: 0.08153164759036724,
        3: 0.36606175240021183},
       'G': {-1: 0.28764606940509913,
        0: -0.11022209861109525,
        1: -0.1264164305949009,
        2: 0.17030074112227081,
        3: 0.30100292424380881}}
  df = pd.DataFrame(d)
  </code></pre>
  
  <p>I know I can get the std values by <code>std_vals = df.std()</code>, which gives the following result, and use these values to drop the columns one by one.</p>
  
  <pre><code>In[]:
          pd.DataFrame(d).std()
  Out[]:
          A    0.115374
          B    0.028435
          C    0.059394
          D    0.247617
          E    0.421117
          F    0.200776
          G    0.209710
          dtype: float64
  </code></pre>
  
  <p>However, I don't know how to use the Pandas indexing to drop the columns with low std values directly.</p>
  
  <p>Is there a way to do this, or I need to loop over each column?</p>