title: pandas dataframe group with condition
body: <p>I have a 3D dataframe with x and y and time as 3rd dimension.
  The data are 5 indizes of satellite images that were taken at different times.
  The x and y describes every pixel.</p>
  <pre><code> x        y              time       SIPI       classif
  7.620001 -77.849990     2018-04-07  1.011107    2.0
                          2018-10-14  1.023407    2.0
                          2018-12-28  0.045107    3.0
                          2020-01-10  0.351107    2.0
                          2018-06-29  0.351107    2.0
           -77.849899     2018-04-07  1.010777    8.0
                          2018-10-14  0.510562    2.0
                          2018-12-28  1.410766    4.0
                          2020-01-10  1.010666    8.0
                          2018-06-29  2.057068    8.0
           -77.849809     2018-04-07  0.986991    1.0
                          2018-10-14  0.986991    8.0
                          2018-12-28  0.986991    5.0
                          2020-01-10  0.984791    5.0
                          2018-06-29  0.986991    3.0
           -77.849718     2018-04-07  0.975965    10.0
                          2018-10-14  0.964765    7.0
                          2018-12-28  0.975965    10.0
                          2020-01-10  0.975965    10.0
                          2018-06-29  0.975965    3.0
           -77.849627     2018-04-07  1.957747    2.0
                          2018-10-14  0.132445    6.0
                          2018-12-28  0.589677    2.0
                          2020-01-10  1.982445    2.0
                          2018-06-29  3.334456    7.0
  </code></pre>
  <p>I need to group the data and as new column I need the value from column 'classif_rf', which is most frequent in 5 datasets. The values are integers between 1 and 10. I want to add an condition which add only frequency higher than 3.</p>
  <pre><code> x          y           classif
  7.620001 -77.849990     2.0
           -77.849899     8.0
           -77.849809     Na
           -77.849718     10.0
           -77.849627     2.0
  </code></pre>
  <p>So as a result I need dataframe where each pixel has a value with highest frequency and when the frequency is lower than 3 there should be a NA value.</p>
  <p>Can the pandas.groupby function do that? I thought about value_counts(), but I'm not sure how to implement that to my dataset.</p>
  <p>Thank you in advance!</p>