title: How to calculate distance for every row in a pandas dataframe from a single point efficiently?
body: <p>I have a point</p>
  <pre><code>point = np.array([0.07852388, 0.60007135, 0.92925712, 0.62700219, 0.16943809,
         0.34235233])
  </code></pre>
  <p>And a pandas dataframe</p>
  <pre><code>           a           b           c           d           e           f
  0   0.025641    0.554686    0.988809    0.176905    0.050028    0.333333
  1   0.027151    0.520914    0.985590    0.409572    0.163980    0.424242
  2   0.028788    0.478810    0.970480    0.288557    0.095053    0.939394
  3   0.018692    0.450573    0.985910    0.178048    0.118399    0.484848
  4   0.023256    0.787253    0.865287    0.217591    0.205670    0.303030
  </code></pre>
  <p>I would like to calculate the distance of every row in the pandas dataframe, to that specific point</p>
  <p>I tried</p>
  <pre><code>import numpy as np
  d_all = list()
  for index, row in df_scaled[cols_list].iterrows():
          d = np.linalg.norm(centroid-np.array(list(row[cols_list])))
          d_all += [d]
  df_scaled['distance_cluster'] = d_all
  </code></pre>
  <p>My solution is really slow though (taking into account that I want to calculate the distance from other points as well.</p>
  <p>Is there a way to do my calculations more efficiently ?</p>