From time to time, I need to compute organizational stats related to manager-report relationships.
I used to do this with nested loops or a recursive walk through the org chart plus memoization (i.e., caching results to avoid recomputation). It works - but it’s verbose and easy to get wrong (recursion limits, cycle guards, cache invalidation).
Recently I came upon a much more elegant and clear solution: rely on graph libraries like NetworkX, whose built-in functions let me easily traverse manager-report relationships and compute the metrics I need.
Some quick wins with this approach:️
- Span of control: direct reports (number of outgoing links from a manager) vs. total reports (number of unique people reachable by following those links through all levels).️
- Org depth / layers: shortest-path lengths from a chosen root to its descendants.️
- Dotted-line structures: multiple managers are fine; compute totals via unique descendants (no double-counting).️
- Data quality checks: detect cycles (impossible loops) and find disconnected sub-orgs.️
- Fewer footguns: no hand-rolled recursion, cycle guards, or bespoke caches.️
- Extensibility: once it’s a graph, adding metrics (e.g., centralities, bridges) is just another function call.️
- Clarity & auditability: easier to review than nested recursion with custom memoization.
So if you’re computing org stats and your code is full of nested loops, NetworkX and other similar libraries can make it shorter, safer, and more expressive. I only wish I’d discovered this earlier - I could have saved myself a lot of headaches 🫣
P.S. Below is a minimal working example of Python code that builds edges from manager_id → employee_id, followed by a simple loop to compute direct, indirect, and total reports for every manager.
import pandas as pd
import networkx as nx
# mydata: columns ['manager_id', 'employee_id']
df = (mydata.dropna(subset=['manager_id','employee_id'])
.drop_duplicates(['manager_id','employee_id']))
df = df[df['manager_id'] != df['employee_id']] # drop self-loops just in case
G = nx.DiGraph()
G.add_edges_from(df[['manager_id','employee_id']].itertuples(index=False, name=None))
# Managers = anyone who manages at least one person
managers = set(df['manager_id'])
rows = []
for m in managers:
total = len(nx.descendants(G, m)) # all reports at any depth (unique people)
direct = G.out_degree(m) # direct reports
rows.append((m, direct, total - direct, total))
out = (pd.DataFrame(rows, columns=['manager_id','direct','indirect','total'])
.sort_values(['total','direct'], ascending=False))
Related notes
- Can flatter orgs undermine people management?
- Induced centralities
- Multilevel modeling in people analytics
- The hidden trade-offs in corporate culture?
- Org chart and collaboration
📄 Read the original post with full outputs on my blog.