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The problem appears to be related to generating a path from a root node in a directed graph, where each edge has a certain weight. The goal is to find the shortest path or all simple paths from the root node to leaf nodes, excluding longer paths that include some intermediate nodes.

Here’s a step-by-step approach using Python and Pandas:

1. Represent the Graph: First, we’ll represent our graph as a directed graph where each edge has a weight (which is ignored in this case but could be useful for future calculations). We can use NetworkX to handle the graph structure.
2. Filter the Dataframe: We will filter out rows from our dataframe df that have children nodes equal to themselves, because these nodes are not connected to any other node and thus cannot be part of a path.
3. Initialize the “path” Column: In this step, we initialize the “path” column by concatenating the parent node with its child node.

Here is a code snippet that represents our steps in Python:

import pandas as pd
import networkx as nx

# Load the data
df = pd.read_csv('data.csv')

# Filter out nodes that are also their children
df = df[~df["parent"].isin(df["child"])]
G = nx.DiGraph()
for index, row in df.iterrows():
    G.add_edge(row['parent'], row['child'])

root = df.loc[~df["parent"].isin(df["child"]), "parent"].unique()[0]

new_df = pd.DataFrame(
    {"parent": [root], "child": [""], "path": [str(root)]},
)

new_df["depth"] = new_df["path"].str.split(", ").map(len)
new_df['level_1'] = df.loc[df.parent == root, 'child'].astype(str).fillna('-999999')
new_df['level_2'] = df.loc[df.child.isin(new_df['level_1']), 'parent'].astype(str).fillna('-999999')

# Concatenate levels to generate path
new_df['path'] = new_df.stack()
new_df['path'] = new_df['path'].groupby(level=0, sort=False).agg(','.join)
new_df['path'] = new_df['path'].str.replace(r'\,+$', '', regex=True)

# Add depth for level_1 and level_2 nodes
new_df['depth'] += 1

print(new_df.to_markdown(index=False, tablefmt="github"))

In the above code:

• We first create an empty directed graph G.
• Then we add edges to this graph based on our dataframe’s parent-child relationship.
• Next, we find the root node by filtering out nodes that are also their children from the dataframe. The root is stored in variable root.
• After that, we initialize the “path” column with the root node.
• We calculate the depth of each path based on its split length and assign it to a new ‘depth’ column.

Note: Replace 'data.csv' with your actual file name if you have one.

Last modified on 2025-03-04