How to calculate the median of different elements in a dataframe in python-CodePudding

I'm trying to calculate the average location of an animal in a certain area.

I have this kind of dataframe, where each 'N° Tag' is the specific individual, Lat is the latitude of the recorded position and Lon is the longitude of the recorded position:

N° Tag       Lat        Lon                        
     1      49.05567   -67.05242  
     4      49.05517   -67.05249
     1      49.05575   -67.05247
     2      49.05584   -67.05288
     4      49.05523   -67.04214
     2      49.05698   -67.05299
     1      49.05567   -67.05246
     1      49.05587   -67.05248
     4      49.05477   -67.05211

I would calculate the median position (median Lat and Lon) of each animal and add a column in the present dataframe with such value like this :

N° Tag       Lat        Lon        Median Lat   Median Lon                
     1      49.05567   -67.05242    49.05562    67.05562     
     4      49.05517   -67.05249    49.05612    67.05515     
     1      49.05575   -67.05247    49.05562    67.05562     
     2      49.05584   -67.05288    49.05571    67.05526     
     4      49.05523   -67.04214    49.05612    67.05515     
     2      49.05698   -67.05299    49.05571    67.05526     
     1      49.05567   -67.05246    49.05562    67.05562     
     1      49.05587   -67.05248    49.05562    67.05562     
     4      49.05477   -67.05211    49.05612    67.05515

Thanks for the help!

CodePudding user response：

Try using transform():

df['median lon'] = df['lon'].groupby(df['n tag']).transform('median')

CodePudding user response：

How about

df['Median Lon'] = df['Lon'].groupby(df['Lon']).transform('median')
df['Median Lat'] = df['Lat'].groupby(df['Lat']).transform('median')
df
Out[86]: 
   N°Tag       Lat       Lon  Median Lon  Median Lat
0      1  49.05567 -67.05242   -67.05242    49.05567
1      4  49.05517 -67.05249   -67.05249    49.05517
2      1  49.05575 -67.05247   -67.05247    49.05575
3      2  49.05584 -67.05288   -67.05288    49.05584
4      4  49.05523 -67.04214   -67.04214    49.05523
5      2  49.05698 -67.05299   -67.05299    49.05698
6      1  49.05567 -67.05246   -67.05246    49.05567
7      1  49.05587 -67.05248   -67.05248    49.05587
8      4  49.05477 -67.05211   -67.05211    49.05477