Load financial statements into pandas

Create a metadata-preserving table

Install httpx and pandas in your client environment. The example requests FY rows explicitly and reindexes display columns so a sparse response remains inspectable. The JSON response envelope is periods, not a bare list of statements.

import os
import httpx
import pandas as pd

response = httpx.get(
    "https://stockmarketapi.ai/api/v1/companies/AAPL/financials",
    params={"period_type": "FY", "limit": 5, "exchange": "NASDAQ"},
    headers={"X-API-Key": os.environ["STOCKMARKET_API_KEY"]},
    timeout=30,
)
response.raise_for_status()
payload = response.json()
frame = pd.DataFrame(payload["periods"])
if not frame.empty:
    columns = ["period_end", "period_type", "currency", "revenue",
               "primary_source", "cash_flow_basis"]
    print(frame.reindex(columns=columns).to_string(index=False))

Keep missing values and source maps

Do not run fillna(0) on statement values. Preserve primary_source and the nested sources dictionary in your stored raw response even if the display table shows only selected columns. Check currency before joining companies, and keep fiscal_year with period_end.

Change frequency deliberately

For quarterly ingestion, inspect cash_flow_basis and request cash_flow=standalone when you need de-cumulated cash flows. For period=ttm, inspect ttm_quarters rather than assuming the aggregate represents four usable quarters. Do not mix FY and quarterly rows in an unqualified sum.

• Reporting-period guide →
• Financials contract →