nse-archives 1.2.0

Download NSE India market data as pandas DataFrames — bhavcopy, indices, F&O, equity, debt, commodity, currency derivatives. Works on AWS Lambda and Snowflake.

nse-archives

Download NSE India market data as pandas DataFrames.
91 datasets — bhavcopy, Nifty indices, F&O, debt, SLB, IRD, EGR.
Works on AWS Lambda and Snowflake.

Download NSE India market data as pandas DataFrames or raw files to local disk / S3. Works from AWS Lambda, Snowflake, or any cloud environment.

Full Documentation → NikhilSuthar.github.io/indian-market-data

Part of the indian-market-data monorepo — also see mcx-data.

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Installation

pip install nse-archives

# With S3 support
pip install nse-archives[s3]

Quick Start

from nsedata import nse

# Get any dataset as a DataFrame
df = nse.get("capital_market", "equities_sme", "sec_bhavdata_full", "2026-05-22")
df = nse.get("capital_market", "indices",      "ind_close_all",     "2026-05-22")
df = nse.get("derivatives",    "equity",        "fo_bhav_udiff",     "2026-05-22")
df = nse.get("debt",           "tri_party_repo","trm_bc",            "2026-05-22")

# Download raw file to disk
nse.download("capital_market", "equities_sme", "bhavcopy_pr", "2026-05-22",
             output_dir="./data")

# Download to S3 (uses IAM role — no credentials needed)
nse.download("capital_market", "equities_sme", "sec_bhavdata_full", "2026-05-22",
             s3_bucket="my-bucket", s3_prefix="raw/nse/")

# List all 91 supported datasets
nse.list_datasets()
nse.list_datasets(category="capital_market")

API

nse.get(category, subcategory, dataset, date, **kwargs)  → DataFrame
nse.download(category, subcategory, dataset, date, ...)  → path or s3://uri
nse.list_datasets(category=None, subcategory=None)       → DataFrame
nse.get_config_info(category, subcategory, dataset)      → dict

CLI

# Get data as CSV
nse-data get capital_market equities_sme sec_bhavdata_full 2026-05-22
nse-data get capital_market indices ind_close_all 2026-05-22
nse-data get derivatives equity fo_bhav_udiff 2026-05-22

# Download raw file
nse-data dl capital_market equities_sme bhavcopy_pr 2026-05-22 --out ./data
nse-data dl capital_market equities_sme cvar1 2026-05-22 --out ./data --snapshot 1

# List datasets
nse-data list
nse-data list --category capital_market
nse-data list --category derivatives --subcategory equity

# Dataset info
nse-data info capital_market equities_sme sec_bhavdata_full

Dataset Categories

Category	Subcategory	Datasets	Docs
capital_market	equities_sme	26	→ Capital Market
capital_market	indices	2	→ Indices
capital_market	mutual_fund	1	→ Mutual Fund
capital_market	slb	10	→ SLB
derivatives	equity	8	→ Equity F&O
derivatives	commodity	3	→ Commodity
derivatives	currency	3	→ Currency
derivatives	interest_rate	9	→ Interest Rate
debt	corporate	13	→ Debt Corporate
debt	debt_segment	4	→ Debt Segment
debt	tri_party_repo	1	→ Tri-Party Repo
egr	egr	1	→ EGR

Total: 91 datasets | 83 return DataFrame | 8 download-only

Notes

• Data source: nsearchives.nseindia.com — no Cloudflare, works from any IP
• Only available on trading days — weekends and NSE holidays return HTTP 404
• Date format: YYYY-MM-DD for daily, YYYY-MM for monthly datasets
• S3 upload uses IAM role — install pip install nse-archives[s3] for boto3

Polars output (optional)

By default every function returns a pandas DataFrame. To get polars DataFrames instead, install the extra and set one environment variable before importing — no code changes needed:

pip install nse-archives[polars]

import os
os.environ["IMD_DATAFRAME"] = "polars"   # set before importing nsedata

from nsedata import nse
df = nse.get("capital_market", "equities_sme", "sec_bhavdata_full", "2026-05-22")
type(df)   # polars.DataFrame

All internal logic stays in pandas; conversion happens only at the final return step. Leave IMD_DATAFRAME unset (or =pandas) for the default pandas output.

License

MIT — see LICENSE