Use Dash to Build to Web Apps on CSV Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for CSV, the pandas module, and the Dash framework, you can build CSV-connected web applications for CSV data. This article shows how to connect to CSV with the CData Connector and use pandas and Dash to build a simple web app for visualizing CSV data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live CSV data in Python. When you issue complex SQL queries from CSV, the driver pushes supported SQL operations, like filters and aggregations, directly to CSV and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to CSV Data

Connecting to CSV data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The DataSource property must be set to a valid local folder name.

Also, specify the IncludeFiles property to work with text files having extensions that differ from .csv, .tab, or .txt. Specify multiple file extensions in a comma-separated list. You can also set Extended Properties compatible with the Microsoft Jet OLE DB 4.0 driver. Alternatively, you can provide the format of text files in a Schema.ini file.

Set UseRowNumbers to true if you are deleting or updating in CSV. This will create a new column with the name RowNumber which will be used as key for that table.

After installing the CData CSV Connector, follow the procedure below to install the other required modules and start accessing CSV through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize CSV Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.csv as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData CSV Connector to create a connection for working with CSV data.

cnxn = mod.connect("DataSource=MyCSVFilesFolder;")

Execute SQL to CSV

Use the read_sql function from pandas to execute any SQL statement and store the result set in a DataFrame.

df = pd.read_sql("SELECT City, TotalDue FROM Customer WHERE FirstName = 'Bob'", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-csvedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our CSV data and configure the app layout.

trace = go.Bar(x=df.City, y=df.TotalDue, name='City')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='CSV Customer Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the CSV data.

python csv-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for CSV to start building Python apps with connectivity to CSV data. Reach out to our Support Team if you have any questions.

Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.csv as mod
import plotly.graph_objs as go

cnxn = mod.connect("DataSource=MyCSVFilesFolder;")

df = pd.read_sql("SELECT City, TotalDue FROM Customer WHERE FirstName = 'Bob'", cnxn)
app_name = 'dash-csvdataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.City, y=df.TotalDue, name='City')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='CSV Customer Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)