🏢 Agency-Level Analytics¶
This page covers the DogeAnalytics methods used to rank agencies based on financial metrics like savings, contracts, and leases.
All methods return a ranked pandas.DataFrame with the agency name and the aggregated total value.
💰 top_agencies_by_savings(top_n=5)¶
Description¶
Returns the top N agencies by total reported savings from the /savings/grants endpoint.
Parameters¶
| Name | Type | Description |
|---|---|---|
top_n |
int | Number of agencies to return |
Returns¶
pandas.DataFrame with columns:
agencysavings
Example¶
with DogeAnalytics(fetch_all=True) as da:
top_savings = da.top_agencies_by_savings(top_n=10)
print(top_savings)
📜 top_agencies_by_contracts(top_n=5)¶
Description¶
Returns the top N agencies based on total contract value from the /savings/contracts endpoint.
Assumes the field is named contract_value in the API response.
Parameters¶
| Name | Type | Description |
|---|---|---|
top_n |
int | Number of agencies to return |
Returns¶
pandas.DataFrame with columns:
agencysavings
Example¶
with DogeAnalytics(fetch_all=True) as da:
top_contracts = da.top_agencies_by_contracts()
print(top_contracts)
🏢 top_agencies_by_leases(top_n=5)¶
Description¶
Returns the top N agencies by total lease cost from the /savings/leases endpoint.
Assumes the field is named lease_cost in the API response.
Parameters¶
| Name | Type | Description |
|---|---|---|
top_n |
int | Number of agencies to return |
Returns¶
pandas.DataFrame with columns:
agencysavings
Example¶
with DogeAnalytics(fetch_all=True) as da:
top_leases = da.top_agencies_by_leases(top_n=3)
print(top_leases)
🔁 Customizing the Aggregation¶
You can easily write your own metric-based groupings:
df = da.contracts_dataframe()
top = df.groupby("agency")["contract_type_A"].sum().sort_values(ascending=False).head(5)
print(top)
📤 Exporting the Rankings¶
da.export_dataset(top_savings, "top_savings_by_agency", format="xlsx")