Skip to main content

Convenient, but not fast

Project description

starlette-dataframe-response

Convenient, but not fast.

Resources:

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

starlette-dataframe-response-0.0.0.tar.gz (5.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

starlette_dataframe_response-0.0.0-py3-none-any.whl (6.0 kB view details)

Uploaded Python 3

File details

Details for the file starlette-dataframe-response-0.0.0.tar.gz.

File metadata

  • Download URL: starlette-dataframe-response-0.0.0.tar.gz
  • Upload date:
  • Size: 5.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/45.0.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.8.5

File hashes

Hashes for starlette-dataframe-response-0.0.0.tar.gz
Algorithm Hash digest
SHA256 ac422c3b0b611c449f191cb8cb8c5fc765be41668eb5f7394da18cf17cf57ca2
MD5 ae875ae8c5c22aaf8628bab077ecca15
BLAKE2b-256 2ed08f38d127202063caf3ed1c400cbb0c4f694cda7d0b4379ef90e5b1ead437

See more details on using hashes here.

File details

Details for the file starlette_dataframe_response-0.0.0-py3-none-any.whl.

File metadata

  • Download URL: starlette_dataframe_response-0.0.0-py3-none-any.whl
  • Upload date:
  • Size: 6.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/45.0.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.8.5

File hashes

Hashes for starlette_dataframe_response-0.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 bcfc481859728a915f1db0fe85f6624dc3c3c6e8181ba9e9df6b58107efd197d
MD5 12c7fdd686ff508db6dcf01ab131b24d
BLAKE2b-256 37e23531fdae8ae996bbf04818361ab38962d5dac4946c68a995106633f054c5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page