加速附近团体的计算？

Question

我有一个数据框，其中包含组ID，两个距离度量（经度/纬度类型度量）和一个值。对于给定的一组距离，我想查找附近其他组的数量以及附近其他组的平均值。

我已经写了下面的代码，但它效率太低，以至于它不能在合理的时间内完成非常大的数据集。附近零售商的计算很快。但是附近零售商的平均价值的计算是极其缓慢。有没有更好的方法来提高效率？

distances = [1,2] 

df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), 
        columns=['Group','Dist1','Dist2','Value']) 

# get one row per group, with the two distances for each row 
df_groups = df.groupby('Group')[['Dist1','Dist2']].mean() 

# create KDTree for quick searching 
tree = cKDTree(df_groups[['Dist1','Dist2']]) 

# find points within a given radius 
for i in distances: 
    closeby = tree.query_ball_tree(tree, r=i) 

    # put into density column 
    df_groups['groups_within_' + str(i) + 'miles'] = [len(x) for x in closeby] 

    # get average values of nearby groups 
    for idx, val in enumerate(df_groups.index): 
     val_idx = df_groups.iloc[closeby[idx]].index.values 
     mean = df.loc[df['Group'].isin(val_idx), 'Value'].mean() 
     df_groups.loc[val, str(i) + '_mean_values'] = mean 

    # merge back to dataframe 
    df = pd.merge(df, df_groups[['groups_within_' + str(i) + 'miles', 
           str(i) + '_mean_values']], 
        left_on='Group', 
        right_index=True) 

Answer 1

它清楚地表明问题是使用isin方法对主数据框进行索引。随着数据帧的长度增长，必须进行更大的搜索。我建议您在较小的df_groups数据框上进行相同的搜索，然后计算更新的平均值。

df = pd.DataFrame(np.random.randint(0,100,size=(100000, 4)), 
        columns=['Group','Dist1','Dist2','Value']) 
distances = [1,2] 
# get means of all values and count, the totals for each sample 
df_groups = df.groupby('Group')[['Dist1','Dist2','Value']].agg({'Dist1':'mean','Dist2':'mean', 
                    'Value':['mean','count']}) 
# remove multicolumn index 
df_groups.columns = [' '.join(col).strip() for col in df_groups.columns.values] 
#Rename columns 
df_groups.rename(columns={'Dist1 mean':'Dist1','Dist2 mean':'Dist2','Value mean':'Value','Value count': 
          'Count'},inplace=True) 


# create KDTree for quick searching 
tree = cKDTree(df_groups[['Dist1','Dist2']]) 

for i in distances: 
    closeby = tree.query_ball_tree(tree, r=i) 
    # put into density column 
    df_groups['groups_within_' + str(i) + 'miles'] = [len(x) for x in closeby] 
    #create column to look for subsets 
    df_groups['subs'] = [df_groups.index.values[idx] for idx in closeby] 
    #set this column to prep updated mean calculation 
    df_groups['ComMean'] = df_groups['Value'] * df_groups['Count'] 

    #perform updated mean 
    df_groups[str(i) + '_mean_values'] = [(df_groups.loc[df_groups.index.isin(row), 'ComMean'].sum()/
              df_groups.loc[df_groups.index.isin(row), 'Count'].sum()) for row in df_groups['subs']] 
    df = pd.merge(df, df_groups[['groups_within_' + str(i) + 'miles', 
           str(i) + '_mean_values']], 
        left_on='Group', 
        right_index=True) 

用于和更新过的平均值的公式仅仅是（M1 * N1 +平方米* N 2）/（N1 + N2）

old setup 

100000 rows 
%timeit old(df) 
1 loop, best of 3: 694 ms per loop 

1000000 rows 
%timeit old(df) 
1 loop, best of 3: 6.08 s per loop 

10000000 rows 
%timeit old(df) 
1 loop, best of 3: 6min 13s per loop 

新设置

100000 rows 
%timeit new(df) 
10 loops, best of 3: 136 ms per loop 

1000000 rows 
%timeit new(df) 
1 loop, best of 3: 525 ms per loop 

10000000 rows 
%timeit new(df) 
1 loop, best of 3: 4.53 s per loop