nse-xbrl 0.1.1

Parse NSE 'Integrated Filing - Financials' XBRL documents into structured Python data

nse-xbrl

Parse NSE's "Integrated Filing - Financials" XBRL documents into typed, structured Python data — income statement, balance sheet (current + prior year), and cash flow, all from a single XBRL file.

Why

Since 2024, NSE-listed companies file quarterly/annual results as a combined Integrated Filing XBRL document (the IFIndAs taxonomy) instead of the older, simpler "Financial Results" format. The existing popular NSE scraping libraries (nsepython, jugaad-data) predate this format and don't parse it — you're left writing your own XBRL-tag mapping by hand.

nse-xbrl does that mapping for you: ~100 financial line items, namespace handling (filers use in-bse-fin, in-capmkt, in-ind-as, etc. interchangeably for the same tags), and the four-context structure (OneD / FourD / OneI / PY_I) that every Integrated Filing follows.

Install

pip install nse-xbrl

# with pandas helpers
pip install nse-xbrl[pandas]

(Not yet on PyPI — for now, install from source: pip install -e .)

Quick start: parse an XBRL file you already have

from nse_xbrl import FilingResult

xml_text = open("RELIANCE_IntegratedFiling_Q3FY26.xml").read()
result = FilingResult.from_xbrl(xml_text, symbol="RELIANCE", is_consolidated=True)

print(result.period_start, "->", result.period_end)
print("Revenue:", result.q_revenue)
print("PAT:", result.q_pat)
print("EBITDA:", result.q_ebitda)            # computed: EBIT + Depreciation
print("Total assets:", result.bs_total_assets)
print("Debt/Equity:", result.debt_equity_ratio)

Every field absent from the filing is None — no exceptions, no silent zeros.

Fetching filings from NSE directly

NSE has no official, key-based API. NSEClient uses the same approach as nsepython/jugaad-data: it reuses cookies issued to a real browser session to call NSE's public (but undocumented) JSON endpoints.

from nse_xbrl import NSEClient

# reads cookies from the NSE_COOKIE env var, or pass cookie_string=...
client = NSEClient()

filings = client.fetch_financials("RELIANCE", "Reliance Industries Limited", max_filings=4)
for f in filings:
    print(f.period_end, f.q_revenue, f.q_pat)

Getting cookies

1. Open https://www.nseindia.com in Chrome and let the page finish loading.
2. DevTools → Network → click any request to nseindia.com → copy the Cookie request header.
3. export NSE_COOKIE="_ga=GA1.1...; AKA_A2=A; bm_sz=..." (or NSEClient(cookie_string="...")).

Cookies are short-lived (hours). NSEClient re-seeds the session on 401/403/500 by hitting the NSE homepage, which refreshes some cookies — but if the Akamai-issued ones expire you'll need to paste a fresh header.

See examples/fetch_reliance.py for a full example.

pandas helper

from nse_xbrl.frames import to_dataframe

df = to_dataframe(filings)   # one row per filing, one column per field

Field reference

All monetary fields are Optional[float], in absolute INR (NSE typically reports decimals="-7", i.e. precision to the nearest ₹10 million — divide by 1e5 for lakhs or 1e7 for crores). EPS fields are INR per share.

Prefix	Meaning	XBRL context
q_*	Current quarter / period	OneD
ytd_*	Year-to-date / full year	FourD
bs_*	Balance sheet, current	OneI
py_*	Balance sheet, prior year	PY_I
cf_*	Cash flow, year-to-date	FourD

Income statement (q_* / ytd_*): revenue, other_income, total_income, employee_expense, cost_of_materials, purchase_stock_trade, changes_inventories, depreciation, finance_costs, other_expenses, total_expenses, exceptional_items, ebit, pbt, current_tax, deferred_tax, total_tax, pat, pat_owners, pat_nci, oci, total_comprehensive, diluted_eps, basic_eps.

Balance sheet (bs_*, plus a subset for py_*): total_assets, noncurrent_assets, ppe, goodwill, other_intangibles, noncurrent_investments, noncurrent_fin_assets, deferred_tax_assets, other_noncurrent_assets, current_assets, inventories, trade_receivables, current_investments, current_fin_assets, other_current_assets, equity, equity_share_capital, other_equity, equity_owners, nci, total_liabilities, noncurrent_liabilities, noncurrent_fin_liab, deferred_tax_liabilities, other_noncurrent_liab, current_liabilities, trade_payables, current_fin_liab, other_current_liab, provisions_current, current_tax_liab.

Cash flow (cf_*): tax_paid, capex, dividends_paid, interest_received, net_change_in_cash, fx_effect, other_investing, other_financing.

Shared: paid_up_equity, face_value.

Anything not covered by the above is still available in result.raw_facts — a {tag_name: {context_id: value}} dict restricted to the four main contexts.

Computed properties

• q_ebitda, ytd_ebitda — EBIT + Depreciation
• shares_outstanding — paid_up_equity / face_value
• debt_equity_ratio — total financial liabilities / total equity
• book_value_per_share — total equity / shares outstanding

Limitations & disclaimer

• Unofficial. This talks to NSE's public website, not a documented API. NSE can change its bot-protection or response formats at any time, which may break NSEClient without notice. The FilingResult/parse_xbrl parsing layer has no such dependency — it works on any XBRL file you already have.
• Cookie-based auth is fragile and arguably against NSE's terms of use. Use at your own risk, for personal/research purposes, and don't hammer their servers.
• Coverage. Tag mappings come from observed Integrated Filings across a sample of companies. Some filers may use nonstandard or additional tags not yet mapped — check raw_facts if a field you expect is None.
• Not investment advice. This is a data-parsing tool, nothing more.

Development

pip install -e ".[dev]"
pytest

License

MIT