DataFrameup
$ pip install frameup
$ frameup <path-to-csvfile>
Frameup is the easiest way to get your Pandas DataFrame up into a Python-based web application. Simply import frameup and your DataFrames will become URL query parameter, and pagination aware.
Zero dependencies, except Pandas of course.
Quick look
Serve a csv as a frameup dataframe on localhost
$ python -m frameup.serve <path-to-csv-file>
Then navigate to http://localhost:8000/. Use the Pandas DataFrame query syntax in the query box.
... or, get a JSON payload:
$ curl 'http://localhost:8000/?query=&limit=10&page=1' | python -m json.tool
Use it in your web application
Flask example
Given a template similar to example.js.html
from flask import Flask, jsonify, render_template, request, url_for
import pandas as pd
import frameup
app = Flask(__name__)
df = pd.read_csv(YOUR_CSV_FILE)
@app.route('/mydataframe')
def main():
data = df.frameup.data(path=url_for('main'), **request.args)
return render_template('example.j2.html', **data)
For something ajaxy, just replace the return with:
return jsonify(**data)
On query parameter objects
Be sure the query parameter object you pass frameup does not return lists for values. classes is the only multi-valued parameter accepted, and should be passed as a comma-delimited list rather than multiple classes keys.
Python web frameworks all have their own way of dealing with the vagaries of GET parameter specification hell. Most implement some concept of a MultiDict, but the APIs for these vary from one framework to the next. Thus, the requirement of only single-valued GET params greatly simplifies things here.
Other projects
Projects to review / learn from / use instead
