如何用Python构建多层级分类树结构并遍历至末层随机选节点

Alright, let's break this down properly. First, since nested lists won't cut it for representing hierarchical categories with clear parent-child relationships, we'll use a nested dictionary structure where each node has a name (the category label) and a children list (containing its sub-category nodes). This structure is intuitive, easy to traverse, and scales well for future CSV imports.

Step 1: Manually Build the Hierarchical Structure #

Here's how to represent your given categories with this structure:

# Manual hierarchical category structure
categories = [
    {
        "name": "A",
        "children": [
            {
                "name": "A1",
                "children": [
                    {"name": "A11", "children": []},
                    {
                        "name": "A12",
                        "children": [
                            {"name": "A121", "children": []},
                            {"name": "A122", "children": []}
                        ]
                    }
                ]
            },
            {"name": "A2", "children": []}
        ]
    },
    {"name": "B", "children": []},
    {
        "name": "C",
        "children": [
            {"name": "C1", "children": []},
            {"name": "C2", "children": []}
        ]
    }
]

Step 2: Traverse to Deepest Levels & Randomly Select a Category #

We'll use a recursive function to collect all "leaf nodes" (deepest-level categories with no children), then randomly pick one from each top-level category's leaf set.

import random

def get_leaf_nodes(node, leaves=None):
    """Recursively collect all leaf nodes (deepest categories) from a given node"""
    if leaves is None:
        leaves = []
    # If the node has no children, it's a leaf
    if not node["children"]:
        leaves.append(node["name"])
    else:
        # Recurse through all child nodes
        for child in node["children"]:
            get_leaf_nodes(child, leaves)
    return leaves

# Iterate through each top-level category and select a random leaf
for top_cat in categories:
    top_name = top_cat["name"]
    leaf_nodes = get_leaf_nodes(top_cat)
    selected_leaf = random.choice(leaf_nodes)
    print(f"Top category *{top_name}*: Randomly selected deepest category → {selected_leaf}")

Example Output #

Top category *A*: Randomly selected deepest category → A121
Top category *B*: Randomly selected deepest category → B
Top category *C*: Randomly selected deepest category → C2

Step 3: Future Plan - Import from CSV #

For CSV imports, structure your CSV with each row representing a full path to a leaf category (e.g., A,A1,A12,A121). Here's a function to convert this CSV into our nested dictionary structure:

import csv

def build_categories_from_csv(csv_file_path):
    """Build the hierarchical category structure from a CSV file"""
    categories = []
    node_map = {}  # Tracks nodes by their full path to avoid duplicates
    
    with open(csv_file_path, newline='', encoding='utf-8') as csvfile:
        reader = csv.reader(csvfile)
        for row in reader:
            current_parent = None
            for idx, cat_name in enumerate(row):
                # Create a unique key for the node using its full path
                node_key = ">".join(row[:idx+1])
                if node_key not in node_map:
                    new_node = {"name": cat_name, "children": []}
                    node_map[node_key] = new_node
                    # Add to top-level list if it's a root category
                    if current_parent is None:
                        categories.append(new_node)
                    # Add to parent's children if it's a sub-category
                    else:
                        current_parent["children"].append(new_node)
                current_parent = node_map[node_key]
    return categories

# Usage example:
# categories = build_categories_from_csv("categories.csv")

Your CSV file (categories.csv) would look like this:

A,A1,A11
A,A1,A12,A121
A,A1,A12,A122
A,A2
B
C,C1
C,C2

内容的提问来源于stack exchange，提问作者user3541631