datacore-vn 0.2.0

Python client for the Datacore API (https://datacore.vn). Imported as `datacore`.

Datacore Python Client

Python client library for the Datacore API — supports two access modes:

• Demo: Preview datasets without an API key
• Paid: Full access with an API key

Distribution name vs. import name: install as datacore-vn, import as datacore. (Same pattern as pip install scikit-learn → import sklearn.) The shorter datacore distribution name on PyPI is a 2022 abandoned project unrelated to us; we have a request open to reclaim it.

Installation

From PyPI:

pip install datacore-vn                # core (pandas-only)
pip install "datacore-vn[polars]"      # also installs polars
pip install "datacore-vn[all]"         # all optional extras

Or with uv:

uv add datacore-vn
uv add "datacore-vn[polars]"

From GitHub directly:

pip install git+https://github.com/DataCore-VietNam/DataCore.git
pip install "datacore-vn[polars] @ git+https://github.com/DataCore-VietNam/DataCore.git"

For contributors only (you do not need this to use the library — the commands above are all an end user needs):

git clone https://github.com/DataCore-VietNam/DataCore.git
cd DataCore
pip install -e ".[dev]"

Configuration (.env, optional)

Create a .env file in your working directory (see .env.example):

X_API_KEY=your-api-key-here

Never commit your real API key. .env is git-ignored. Use .env.example as a template.

---

Usage

1. Initialize the client

from datacore import Datacore

# Demo mode (no API key required)
client = Datacore()

# Paid mode — pass the key explicitly...
client = Datacore(api_key="your-api-key")

# ...or rely on X_API_KEY from .env / environment
client = Datacore()

# Enable request/response debug logging
client = Datacore(api_key="your-api-key", debug=True)

---

2. Preview a dataset (Demo mode)

Preview data without an API key.

# All columns
df = client.preview("dataset_historical_price")
print(df.head())

# Filter specific columns
df = client.preview("dataset_historical_price", columns=["symbol", "date", "close_price"])
print(df.head())

---

3. Fetch data (Paid mode)

# All columns — returns {"data": DataFrame, "info": str}
result = client.get_data("dataset_historical_price")
print(result["data"].head())
print(result["info"])
# num: 3760607, totalPage: 37607, currentPage: 1, queried_rows: 100

# Filter specific columns
result = client.get_data(
    "dataset_historical_price",
    columns=["symbol", "date", "close_price"],
)
print(result["data"].head())

Full parameters:

result = client.get_data(
    dataset_code="dataset_historical_price",
    columns=["symbol", "date", "close_price"],   # client-side column filter (optional)
    conditions=None,         # server-side row filter -- see note below (optional)
    select_fields=None,      # server-side field selection (optional)
    page=1,
    limit=100,               # max 100 server-side (HTTP 400 if higher)
    return_type="dataframe", # "dataframe" | "polars" | "json" | "dict"
    include_info=True,       # True: returns {"data": ..., "info": ...} | False: data only
)

Page size: the gateway currently caps limit at 100 rows per request. Passing a larger value returns HTTP 400: Invalid request content. For larger downloads, paginate with download_data or paginate.

conditions format: the server-side conditions filter is forwarded to the gateway verbatim. The exact accepted JSON shape is defined by the gateway, not by this client; the shape that worked in earlier internal examples is not currently accepted by gateway.datacore.vn/data/ds/search. Contact the Datacore backend team for the documented filter schema before relying on conditions in production. As a workaround you can fetch unfiltered data and filter client-side on the returned DataFrame.

A convenience wrapper that returns the DataFrame directly (no info dict):

df = client.get_dataframe("dataset_historical_price", limit=100)

---

3b. Polars output (optional)

If you installed with pip install "datacore-vn[polars]", you can ask for a polars DataFrame instead of pandas:

# Via return_type
result = client.get_data(
    "dataset_historical_price",
    columns=["symbol", "date", "close_price"],
    limit=100,
    return_type="polars",
)
print(type(result["data"]))     # <class 'polars.DataFrame'>
print(result["data"].head())

# Convenience method (no info dict, just the polars frame)
df_pl = client.get_polars("dataset_historical_price", limit=100)

# Preview supports polars too
df_pl = client.preview("dataset_historical_price", return_type="polars")

Pandas is the default for backwards compatibility; polars is purely opt-in. The same columns= filter works for both backends.

---

4. Iterate all pages (Paid mode)

for page_df in client.paginate("dataset_historical_price", limit=100, max_pages=5):
    print(page_df.shape)

---

5. Download data to file

# Download all pages of a small dataset (76 pages, ~7.5k rows)
download_result = client.download_data(
    dataset_code="gross_domestic_product_dataset_ds",
    output_path="data.csv",
    file_format="csv",     # "csv" or "json"
    start_page=1,
    end_page=None,         # None = download until the last page
    limit=100,             # max per-request page size (see note above)
    show_progress=True,
)
print(download_result)
# {"output_path": "data.csv", "pages_downloaded": 76, "rows_downloaded": 7551, ...}

# `dataset_historical_price` is large (~3.7M rows / 37k pages at limit=100);
# expect it to take a long time and a lot of network.

# Download only first 3 pages, filtered to specific columns (CSV only)
download_result = client.download_data(
    dataset_code="dataset_historical_price",
    columns=["symbol", "date", "close_price"],
    output_path="data_page1_3.csv",
    file_format="csv",
    start_page=1,
    end_page=3,
    show_progress=True,
)

columns filtering only applies to file_format="csv". JSON output preserves the full raw API response.

---

Method Summary

Method	Description	Requires API key
preview(dataset_code, columns, return_type)	Preview a dataset (pandas or polars)	No
preview_raw(dataset_code)	Preview a dataset, raw dict response	No
get_data(dataset_code, ...)	Fetch data, returns {"data", "info"} by default	Yes
get_dataframe(dataset_code, ...)	Fetch data, returns pandas DataFrame directly	Yes
get_polars(dataset_code, ...)	Fetch data, returns polars DataFrame directly (needs [polars] extra)	Yes
get_data_info(dataset_code, ...)	Get dataset metadata summary	Yes
paginate(dataset_code, ...)	Generator yielding one pandas DataFrame per page	Yes
download_data(dataset_code, output_path, ...)	Download data to CSV/JSON file	Yes
set_api_key(api_key)	Set / replace the API key on an existing client	—
is_authenticated()	Returns True if an API key is configured	—

---

Error Handling

Error	Cause	Solution
AuthenticationError	Missing or invalid API key (HTTP 401 or httpCode:401 in body)	Pass api_key= or set X_API_KEY in .env
PermissionDeniedError	No access to dataset (HTTP 403)	Check your subscription plan
APIRequestError	Server error, invalid request, or unknown dataset	Check dataset_code and conditions
ValueError	Bad argument (e.g. unknown column, page < 1, bad file_format)	Check the error message

All exceptions inherit from DatacoreError, so you can catch them generically:

from datacore import Datacore, DatacoreError

try:
    client.get_data("dataset_historical_price")
except DatacoreError as e:
    print(f"Datacore call failed: {e}")

---

License

MIT — see LICENSE.