Pandas dataframe: calculate metrics by year

[mcva]

Hi

I have some dataframes with a very diferent number of years , similar to the following dataframe:

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Date    obs sim 6/12/2000   22.32   14.6 8/11/2000   19.82   13.4 10/10/2000  16.63   16.7 2/14/2001   11.92   14.8 10/1/2001   19.15   13.4 10/23/2001  14.42   16.3 11/9/2001   9.97    19.9 11/27/2001  10.75   12.4 12/18/2001  8.22    10.6 1/16/2002   7.72    11.2 2/20/2002   7.92    11 3/21/2003   15.43   15.8 4/18/2003   12.69   14.6 5/20/2003   16.46   17

I need to calculate the average mean error (AME) and other metrics by year, between the obs and sim columns. How can I solve this problem. Using groupy? Splitting the dataframe? Do you have some example?

Thank you

[mcva]

This will work!

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import numpy as np import pandas as pd from sklearn.metrics import r2_score, mean_squared_error   def r2_rmse( g ):     r2 = r2_score( g['Actual'], g['Predicted'] )     rmse = np.sqrt( mean_squared_error( g['Actual'], g['Predicted'] ) )     return pd.Series( dict(  r2 = r2, rmse = rmse ) )   your_df.groupby( 'Type' ).apply( r2_rmse ).reset_index()