使用基于单索引级别的多索引从 DataFrame 中减去一个系列

Question

I have a DataFrame describing movements of multiple "objects" on a few different "tracks" on a Cartesian 2D universe. I also have their "target location" for each "track". Example data:

objs = ['car', 'bicycle', 'plane']
moves = [f'mov{i}' for i in range(1,11)]
multi = pd.MultiIndex.from_product([objs, moves, range(10)], names=['obj', 'mov', 'time'])
locations = pd.DataFrame(np.random.rand(300,2), columns=['X','Y'], index=multi)
targets = pd.DataFrame(np.random.rand(10,2), columns=['X','Y'], index=moves)

I'm interested in the euclidean-distance between the locations and the targets on each timestamp. Something like
distances = pd.Series(np.random.rand(300), index=multi)

Problem is I can't use Subtract method since both objects need to have the same index, and can't figure out how to get the 2 DataFrames' Indexes to "fit". Anyone has a nice (efficient) way for me to get those distances?

Answer 1

so apparently, unlike the subtract method that needs completely matching index-es for self and other , the sub method can take a level as an argument.
So there's a simple one-liner for calculating these euclidean distances:
movements.sub(targets, level=1).pow(2).sum(axis=1).transform(np.sqrt)

Answer 2

IICU:

targets.reset_index(inplace=True)#Reset index
targets.columns=['mov','x','y']#Rename columns
locations.reset_index(inplace=True)#Reset index
loctar = pd.merge(locations, targets, how='left', on='mov')#Merge location and target to loctar
loctar[['dXx','dYY']]=loctar[['X','Y']] - loctar[['x','y']].values#caluclate delta x and y
temp=loctar.loc[:, ~loctar.columns.isin(['obj','mov','time','X','Y','x','y'])]#create temporary datframe with the deltas
result = ((temp**2).sum(axis=1))**0.5#Calculate euclidean-distance
result = result.reset_index()#Reset index
#Can merge result with lotar if you wanted