title: Assigning column based on Quantile() vs qcut
body: <p>I have a dataframe with 'Zip', 'Median Income' and I'd like to add another column based on what quantile the income is.</p>
  <p>Running <code>dfq = dfq.quantile([.1, .25, .50, .75, .9])</code> it returns the following which I believe is correct for the 'Median Income'.</p>
  <pre><code>               Zip  Median Income
  0.10  60303.80       32176.90
  0.25  60608.25       39896.00
  0.50  60627.00       49755.00
  0.75  60645.75       59829.25
  0.90  60664.70       75555.30
  </code></pre>
  <p><code>print(dfq['Median Income'].median())</code>
  49755.0</p>
  <p>So the .50 and median line up correctly.</p>
  <p>What I'd like to do is assign a column next to Median Income with the quantile it falls into, but can't seem to do that easily?</p>
  <p>I tried the qcut function.</p>
  <pre><code>dfq['quantile'] = pd.qcut(dfq['Median Income'], 5, labels=['10%','25%','50%','75%','90%'])
  </code></pre>
  <p>But the results seem different from using the quantile function since it separates the rows equally (around 70 rows)</p>
  <p>Am I missing something here, isn't the quantile calculation correct vs my qcut?</p>