如何根据颜色值创建chord图的矩阵：

Question

假设我有一个数据框架，它具有以下格式的数据。

UID | Name | ID
----------------
1 | ABC | IM-1
2 | XYZ | IM-2
3 | XYZ | IM-2
4 | PQR | IM-3
5 | PQR | IM-4
6 | PQR | IM-5
7 | XYZ | IM-5
8 | ABC | IM-5

我需要创建一个矩阵，输入到chord图代码中。这需要以下列格式输出：

(array([[0,1,1,1],
        [1,1,1,0],
        [1,1,0,2]]),['ABC','XYZ','PQR'])

注:在本例中，“名称”在列表中是有限的(即ABC、XYZ或PQR) - " ID“在记录之间共享-第四列是独立的记录数(例如，ABC是单个记录IM-1的一部分，PQR在IM-4和IM-5中出现两次)矩阵的其他成员是基于ID的名称之间的联系(例如IM-5，增加PQR-XYZ，XYZ-PQR，PQR-ABC，ABC-PQR，ABC-PQR，-目标是为"Name“字段之间的连接创建一个chord图

我知道这是个不错的读物。提前谢谢你的帮助。

Answer 1

更新了我的答案，但方法基本上是一样的。将数据解析为数据框架，在ID上执行内连接以获得通过共享公共ID链接的名称对。然后将这个边表转换成一个邻接矩阵。最后，一些人四处走动以获得“悬空”边缘，即只有一次出现的ID (添加在更新的答案中)，并根据相应的Name对其计数进行分组。

#!/usr/bin/env python
"""
Create adjacency matrix from a dataframe, where edges are implicitly defined by shared attributes.

Answer to:
https://stackoverflow.com/questions/57849602/how-to-create-the-matrix-for-chord-diagram-based-on-coloumn-value
"""
import numpy as np
import pandas as pd
from collections import Counter

def parse_data_format(file_path):
    # read data skipping second line
    df = pd.read_csv(file_path, sep='|', skiprows=[1])

    # strip whitespace from column names
    df = df.rename(columns=lambda x: x.strip())

    # strip whitespace from values
    df_obj = df.select_dtypes(['object'])
    df[df_obj.columns] = df_obj.apply(lambda x: x.str.strip())

    return df


def get_edges(df):
    """Get all combinations of 'Name' that share a 'ID' value (using an inner join)."""
    inner_self_join = df.merge(df, how='inner', on='ID')
    excluding_self_pairs = inner_self_join[inner_self_join['UID_x']!=inner_self_join['UID_y']]
    edges = excluding_self_pairs[['Name_x', 'Name_y']].values
    return edges


def get_adjacency(edges):
    "Convert a list of 2-tuples specifying source and target of a connection into an adjacency matrix."
    order = np.unique(edges)
    total_names = len(order)
    name_to_idx = dict(list(zip(order, range(total_names))))
    adjacency = np.zeros((total_names, total_names))
    for (source, target) in edges:
        adjacency[name_to_idx[source], name_to_idx[target]] += 1
    return adjacency, order


def get_dangling_edge_counts(df):
    # get IDs with count 1
    counts = Counter(df['ID'].values)
    singles = [ID for (ID, count) in counts.items() if count == 1]
    # get corresponding names
    names = [df[df['ID']==ID]['Name'].values[0] for ID in singles]
    # convert into counts
    return Counter(names)


if __name__ == '__main__':

    # here we read in the data as a file buffer;
    # however, normally we would hand a file path to parse_data_format instead
    import sys
    if sys.version_info[0] < 3:
        from StringIO import StringIO
    else:
        from io import StringIO

    data = StringIO(
        """UID | Name | ID
        ----------------
        1 | ABC | IM-1
        2 | XYZ | IM-2
        3 | XYZ | IM-2
        4 | PQR | IM-3
        5 | PQR | IM-4
        6 | PQR | IM-5
        7 | XYZ | IM-5
        8 | ABC | IM-5
        """
    )

    df = parse_data_format(data)
    edges = get_edges(df)
    adjacency, order = get_adjacency(edges)
    print(adjacency)
    # [[0. 1. 1.]
    #  [1. 0. 1.]
    #  [1. 1. 0.]]
    print(order)
    # ['ABC' 'PQR' 'XYZ']

    dangling_edge_counts = get_dangling_edge_counts(df)
    print(dangling_edge_counts)
    # Counter({'PQR': 2, 'ABC': 1})

    last_column = np.zeros_like(order, dtype=np.int)
    for ii, name in enumerate(order):
        if name in dangling_edge_counts:
            last_column[ii] = dangling_edge_counts[name]
    combined = np.concatenate([adjacency, last_column[:, np.newaxis]], axis=-1)
    print(combined)
    #[[0. 1. 1. 1.]
    # [1. 0. 1. 2.]
    # [1. 1. 2. 0.]]