Python 如何从比较函数生成ngroup

您希望对属于同一组或距离较近的行进行聚类.为此，用scipy.spatial.distance.pdist计算距离以识别接近点，并创建一个用networkx来识别连接组件的图:

import networkx as nx
import pandas as pd
from scipy.spatial.distance import pdist
from itertools import combinations

# example input
df = pandas.DataFrame({
    'id' : [0, 1, 2, 2, 3],
    'value1' : [1.1, 1.2, 1.3, 1.4, 1.1],
    'value2' : [2.1, 2.2, 2.3, 2.4, 2.1]
})

thresh = 1e-8
cols = ['value1', 'value2']

# create graph based on already connected ids
G = nx.compose_all(map(nx.path_graph, df.index.groupby(df['id']).values()))

# add pairs of values with distance below threshold as edges
G.add_edges_from(pd.Series(combinations(df.index, 2))
                 [pdist(df[cols])<thresh]
                )

# form groups based on the connected components
groups = {n: i for i, c in enumerate(nx.connected_components(G))
          for n in c}
# {0: 0, 4: 0, 1: 1, 2: 2, 3: 2}

# update index based on above dictionary
df.index = df.index.map(groups)

输出:

   id  value1  value2
0   0     1.1     2.1
1   1     1.2     2.2
2   2     1.3     2.3
2   2     1.4     2.4
0   3     1.1     2.1

图(仅基于ID；数字是原始索引):

曲线图(考虑距离后):