从基于单索引级别的MultiIndex的数据帧中减去序列

由中央一套发布于 2020-05-24 06:23:07

我有一个DataFrame，用于描述笛卡尔2D宇宙中多个“对象”在几个不同“轨迹”上的运动。对于每个“曲目”，我也都有其“目标位置”。示例数据：

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?

pnon

2020-05-24 06:23:07

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)

小鸡鸡

2020-05-24 06:23:07

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

中央一套

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