title: obtaining indices of n max absolute values in dataframe row
body: <p>suppose i create a Pandas DataFrame as below</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  
  np.random.seed(0)
  x = 10*np.random.randn(5,5)
  df = pd.DataFrame(x)
  </code></pre>
  
  <p>as an example, this can generate the below:</p>
  
  <p><a href="https://i.stack.imgur.com/k1EeC.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/k1EeC.png" alt="enter image description here"></a></p>
  
  <p>for each row, i am looking for a way to readily obtain the indices corresponding to the largest n (say 3) values in absolute value terms. for example, for the first row, i would expect [0,3,4]. we can assume that the results don't need to be ordered. </p>
  
  <p>i tried searching for solutions similar to idxmax and argmax, but it seems these do not readily handle multiple values </p>