starlette-dataframe-response

Convenient, but not fast.

Resources:

• Source: https://github.com/podhmo/starlette-dataframe-response

Installation

$ pip install starlette-dataframe-response

Requirements

Python 3.7+

Dependencies

• starlette
• pandas
• (vega_datasets)
• (magicalimport)

Example

import pandas as pd
from starlette.requests import Request
from starlette_dataframe_response import DataFrameResponse, guess_media_type

async def get_dataset(request: Request):
    df: DataFrame = pd.read_csv("<some dataset>.csv")
    return DataFrameResponse(df, media_type=guess_media_type(request))

app = Starlette(
    debug=True,
    routes=[
        Route("/dataset/<some dataset>", get_dataset),
    ],
)

Then, supporting the request following.

# return dataset as json (orient=records)
GET /dataset/<some dataset>

# return dataset as csv
GET /dataset/<some dataset>?format=csv
# return dataset as markdown
GET /dataset/<some dataset>?format=markdown
# return dataset as html
GET /dataset/<some dataset>?format=html

If you want to customize the JSON response.

# use orient="columns"
DataFrameResponse(df, media_type=guess_media_type(request), to_json_orient="columns")

Or, if the response is created by DataFrameResponse.from_request(request, df), it is also OK.

$ use orient="columns"
GET /dataset/<some dataset>?to_json_orient=columns

python -m starlette_dataframe_response.distribute

And It also includes examples using vega-datasets.

$ python -m starlette_dataframe_response.distribute --port 8888

(with httpie)

$ http :8888/
$ http :8888/iris
$ http :8888/cars
$ http :8888/iris  format==csv
$ http :8888/iris/columns
$ http :8888/iris/describe
$ http :8888/iris/groupby/species/aggs/sepalWidth
$ http :8888/iris/groupby/species/aggs/sepalWidth fn==min fn==max fn==count fn==mean fn==std
$ http :8888/iris/groupby/species/aggs/sepalWidth,sepalLength

Or if you want to see an example using a custom data, in above included app examples/01data-provider/

Contribution