Skip to content

Fund Census (N-CEN): Parse Annual Fund Reports with Python

Every registered investment company files Form N-CEN annually -- a comprehensive census of fund operational data. EdgarTools parses these filings into structured Python objects, revealing fund series, service providers, board composition, ETF mechanics, broker relationships, and securities lending programs.

from edgar import get_filings

filings = get_filings(form="N-CEN")
census = filings[0].obj()
census

Fund Census N-CEN report parsed with Python edgartools

Three lines to get a fully parsed annual census with registrant info, fund series, directors, and all operational disclosures.

Access Series Data

The series_data() method returns a DataFrame with one row per fund series, showing assets, commission totals, and service provider counts:

census.series_data()
Column What it is
name Fund series name
series_id SEC series identifier (e.g., "S000052123")
lei Legal Entity Identifier
fund_type Fund classification
avg_net_assets Average monthly net assets
aggregate_commission Total broker commissions paid
num_advisers Count of investment advisers
num_custodians Count of custodians
has_etf Whether series has ETF structure

Average net assets and commission figures are in full dollars (not thousands). An avg_net_assets of 19,574,292 means exactly $19.6 million.

Access Service Provider Network

See who manages, custodies, administers, and services the funds:

census.service_providers()

Returns a DataFrame with all service providers flattened across fund series:

Column What it is
series_name Fund series name
series_id Series identifier
role Provider role ("adviser", "custodian", "transfer agent", etc.)
provider_name Provider firm name
lei Legal Entity Identifier
affiliated Whether provider is affiliated with the fund

This includes advisers, custodians, transfer agents, administrators, pricing services, and shareholder servicing agents -- everyone touching the fund's operations.

Analyze Broker Relationships

N-CEN discloses broker-dealer and broker commission payments:

census.broker_data()
Column What it is
series_name Fund series name
series_id Series identifier
type "broker-dealer" or "broker"
broker_name Firm name
lei Legal Entity Identifier
commission Commission amount paid

Commission values are in full dollars. Use this to analyze broker concentration and trading costs across the fund complex.

Examine Board Composition

Access the board of directors with interested-person flags:

census.director_data()
Column What it is
name Director name
crd_number CRD identifier (if registered)
interested_person Whether director is an interested person under the '40 Act

Directors marked as "interested persons" have material relationships with the fund or its adviser. Independent directors are not interested persons.

Access ETF-Specific Data

For funds with ETF structures, get creation/redemption mechanics and authorized participants:

census.etf_data()

Returns a DataFrame with ETF-specific metrics:

Column What it is
series_name Fund series name
series_id Series identifier
exchange Primary exchange (e.g., "NYSE", "NASDAQ")
ticker Trading symbol
creation_unit_size Number of shares per creation unit
avg_pct_purchased_in_kind Average percentage of creations settled in-kind
avg_pct_redeemed_in_kind Average percentage of redemptions settled in-kind
is_in_kind Whether fund permits in-kind transactions
num_authorized_participants Count of authorized participants

This DataFrame is empty for fund complexes without ETFs. Always check with df.empty before analysis.

Look Up a Specific Fund Complex

Search by management company CIK or name:

from edgar import Company

company = Company("VANGUARD")
filing = company.get_filings(form="N-CEN").latest(1)
census = filing.obj()

print(census.name)                      # Registrant name
print(f"{census.num_series} series")    # Series count in this filing
print(census.report_date)               # Report period end

A single N-CEN filing may cover multiple fund series, or just one series from a larger complex. Check census.total_series to see how many series the registrant has total.

Common Analysis Patterns

Service provider concentration

providers = census.service_providers()
# Count unique providers per role
providers.groupby('role')['provider_name'].nunique()

# Find affiliated providers
affiliated = providers[providers['affiliated'] == True]

Commission analysis

brokers = census.broker_data()
if not brokers.empty:
    # Top brokers by commission
    top_brokers = brokers.groupby('broker_name')['commission'].sum().sort_values(ascending=False)
    print(top_brokers.head(10))

Board independence

directors = census.director_data()
total = len(directors)
interested = directors['interested_person'].sum()
independent = total - interested
print(f"Board composition: {independent} independent, {interested} interested")

ETF creation mechanics

etf = census.etf_data()
if not etf.empty:
    for idx, row in etf.iterrows():
        print(f"{row['ticker']}: {row['avg_pct_purchased_in_kind']:.1f}% in-kind purchases")

Access Individual Objects

The FundCensus class uses Pydantic models for structured access to all fund census data. These are useful when building custom applications or exporting to other formats.

Registrant Information

reg = census.registrant

print(reg.name)                    # Registrant name
print(reg.cik)                     # CIK
print(reg.classification_type)     # Fund classification
print(reg.total_series)            # Total series count
print(reg.accountant.name)         # Public accountant
print(reg.cco_name)                # Chief Compliance Officer

Fund Series

for series in census.series:
    print(f"{series.name} ({series.series_id})")
    print(f"  Advisers: {len(series.advisers)}")
    print(f"  Custodians: {len(series.custodians)}")
    print(f"  Securities lending: {series.is_securities_lending}")

    # ETF details
    if series.etf_info:
        etf = series.etf_info
        print(f"  ETF ticker: {etf.ticker}")
        print(f"  Exchange: {etf.exchange}")
        print(f"  Creation unit: {etf.creation_unit_size}")

Service Providers

series = census.series[0]

# Investment advisers
for adviser in series.advisers:
    print(f"{adviser.name} - {adviser.role}")
    print(f"  Affiliated: {adviser.is_affiliated}")

# Custodians
for custodian in series.custodians:
    print(f"{custodian.name}")

Broker Dealers

series = census.series[0]

for bd in series.broker_dealers:
    print(f"{bd.name}")
    print(f"  Commission: ${bd.commission:,.2f}" if bd.commission else "  No commission reported")
    print(f"  LEI: {bd.lei}")

Line of Credit

series = census.series[0]

if series.line_of_credit:
    loc = series.line_of_credit
    if loc.has_line_of_credit:
        print(f"Line of credit: ${loc.size:,.0f}")
        print(f"Committed: {loc.is_committed}")
        print(f"Institutions: {', '.join(loc.institution_names)}")

Securities Lending

series = census.series[0]

for sl in series.securities_lending:
    print(f"Agent: {sl.agent_name}")
    print(f"  Affiliated: {sl.is_affiliated}")
    print(f"  Indemnified: {sl.is_indemnified}")

Metadata Quick Reference

Property Returns Example
name Registrant name "360 Funds"
cik CIK identifier "0001319067"
report_date Report period end date "2025-11-30"
num_series Number of series in filing 1
total_series Total series registered 8
classification_type Fund classification "N-1A"
is_etf_company Has any ETF series True or False
registrant RegistrantInfo object Full registrant details
series List of FundSeriesInfo All series with full details
filing Source Filing object Filing or None

Methods Quick Reference

Method Returns What it does
series_data() DataFrame Fund series summary with assets and counts
service_providers() DataFrame All service providers flattened across series
broker_data() DataFrame Broker-dealer and broker commission data
director_data() DataFrame Board of directors with interested-person flags
etf_data() DataFrame ETF-specific metrics for ETF series

Things to Know

Values are in full dollars. Unlike 13F filings (which report in thousands), N-CEN values are in actual USD. An avg_net_assets of 19,574,292 means exactly $19.6 million.

Annual filings. N-CEN is filed once per year, unlike monthly N-PORT or N-MFP reports. Each filing covers the fund's fiscal year.

Multiple series. A single N-CEN filing may cover one series or dozens, depending on how the fund complex structures its filings. Check num_series vs total_series to understand coverage.

N/A sentinel values. The XML uses "N/A" as a sentinel. EdgarTools converts these to None automatically.

Boolean display. Pydantic booleans render as "Yes/No" in the Rich display for readability.

Service provider diversity. Funds use many service provider types: advisers, custodians, transfer agents, administrators, pricing services, shareholder servicing agents. All are captured in service_providers().

ETF mechanics. Only series with ETF structures have etf_info populated. Use etf_data() to filter to ETF series automatically.

Securities lending. Not all funds engage in securities lending. Check is_securities_lending or the length of series.securities_lending.

Line of credit. Funds may have committed or uncommitted lines of credit with one or more institutions. The line_of_credit object captures all details.

Director CRD numbers. Only registered directors have CRD numbers. Many directors won't have one -- this is normal.