如何基于列值创建和弦图矩阵：

Question

Say I have a data-frame which has data in the following format.

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

I need to create a matrix that feeds into the chord diagram code. that requires the output in the following format:

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

Note: In this example, - the "Name" is finite in the list (ie ABC, XYZ or PQR) - "ID" is shared between records - the fourth column is the number of records that stand alone (for example ABC is part of a single record IM-1 and PQR appears twice in IM-4 and IM-5 - the other members of the matrix are the linkages between Names based on ID (for example IM-5 , increases the value of PQR-XYZ , XYZ-PQR , PQR-ABC , ABC-PQR , XYZ-ABC & ABC-XYZ ) - the goal is to create a chord diagram for the connections between the "Name" field

I know this is quite a read. Thanks in advance for your help.

Answer 1

Updated my answer but the approach is basically the same. Parse the data into a data frame, do an inner join on ID to get the pairs of names that are linked by sharing a common ID . Then convert this edge list into an adjacency matrix. Finally some faffing around to get the "dangling" edges, ie the ID with only a single occurrence (added in the updated answer), and group their counts by the corresponding Name .

#!/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.]]