Analyze Federal Contract Awards With Python and USAspending

A useful federal-awards notebook starts with validation, not a chart. Award searches can span long activity periods, descriptions can be missing, and a large amount can dominate every aggregate.

Export results from the USAspending Federal Awards Scraper as JSON or CSV. Keep the Actor input and run date beside the file so the analysis can be reproduced.

Validate before grouping

import pandas as pd

df = pd.read_json("dataset_items.json")
df["award_amount"] = pd.to_numeric(df["award_amount"], errors="coerce")
df["start_date"] = pd.to_datetime(df["start_date"], errors="coerce")
df["end_date"] = pd.to_datetime(df["end_date"], errors="coerce")

key = df["generated_internal_id"].fillna(df["award_id"])
duplicates = df[key.duplicated(keep=False)]
clean = df.loc[~key.duplicated()].copy()

Inspect duplicate rows before dropping them. A true repeated API row is different from two related awards that share a recognizable contract number.

Check missing recipient names, negative amounts, end dates before start dates, and unusually long descriptions. Put failed checks in a review table rather than silently repairing source values.

Summarize recipients and agencies

by_recipient = (
    clean.groupby("recipient_name", dropna=False)
    .agg(
        awards=("award_id", "nunique"),
        total_amount=("award_amount", "sum"),
        median_amount=("award_amount", "median"),
        latest_end=("end_date", "max"),
    )
    .sort_values("total_amount", ascending=False)
)

Pair totals with counts and medians. One outlier award should not be allowed to masquerade as broad market activity. Agency summaries should keep awarding and funding agencies separate because they can represent different roles.

Compare scheduled runs

Join snapshots on generated_internal_id. Record changed amounts, descriptions, and dates as field-level changes. A missing award should trigger verification, not an automatic cancellation label.

Finally, keep usaspending_url in every exception report. Public-source analysis is strongest when a reviewer can move from a surprising number to the official record in one click.