How do you calculate expanding mean on time series using pandas?
- by mlo
How would you create a column(s) in the below pandas DataFrame where the new columns are the expanding mean/median of 'val' for each 'Mod_ID_x'. Imagine this as if were time series data and 'ID' 1-2 was on Day 1 and 'ID' 3-4 was on Day 2.
I have tried every way I could think of but just can't seem to get it right.
left4 = pd.DataFrame({'ID': [1,2,3,4],'val': [10000, 25000, 20000, 40000],'Mod_ID': [15, 35, 15, 42],
'car': ['ford','honda', 'ford', 'lexus']})
right4 = pd.DataFrame({'ID': [3,1,2,4],'color': ['red', 'green', 'blue', 'grey'], 'wheel': ['4wheel','4wheel', '2wheel', '2wheel'],
'Mod_ID': [15, 15, 35, 42]})
df1 = pd.merge(left4, right4, on='ID').drop('Mod_ID_y', axis=1)
